\r\n\tThere are a variety of approaches to reversing biodiversity loss, ranging from economic, to ecological and ethical. The utilitarian approach to conservation, bolstered by the concept of ecosystem services, can be utilized to improve the conservation case by supplementing the burgeoning biodiversity rhetoric. To address this issue, a pluralistic approach to biodiversity is required for conservation and sustainability.
",isbn:"978-1-80356-339-8",printIsbn:"978-1-80356-338-1",pdfIsbn:"978-1-80356-340-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"ab014f8ed1669757335225786833e9a9",bookSignature:"Dr. Gopal Shukla, Dr. Jahangeer Bhat and Dr. Sumit Chakravarty",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11460.jpg",keywords:"Ecosystem Services, Intrinsic Value, Global Trends in Biodiversity Loss, Convention on Biological Diversity, Utilitarian Value, Biodiversity Conservation, Perception, In Situ and Ex Situ Conservation, Nature Conservation, Sustainable Development Goals, Drivers of Degradation, Prioritizing Biodiversity",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 17th 2022",dateEndSecondStepPublish:"April 22nd 2022",dateEndThirdStepPublish:"June 21st 2022",dateEndFourthStepPublish:"September 9th 2022",dateEndFifthStepPublish:"November 8th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Gopal Shukla, prior to becoming an assistant professor, has worked under NAIP (National Agricultural Innovation Project), NICRA ( National Innovations on Climate Resilient Agriculture), and SERB (Science and Engineering Research Board) projects. The focus of his research and development work is forest conservation. He has authored 75 research papers, 10 book chapters and has edited 5 books.",coeditorOneBiosketch:"Dr. Jahangeer is a Guest Associate Editor in Frontiers in the Environmental Science journal and is the first researcher to report the first time growing of Acacia dealbata Link. (Silver Wattle), an invasive species in the high altitudes of the Himalayas. He has 11 years of research and 8 years of teaching experience with a publication record of more than 60, including research articles, review papers, conference papers, and books of national and international repute.",coeditorTwoBiosketch:"Dr. Chakravarty, Ph. D., has a wide experience in forestry training, research, and development. He is currently working as a Professor in Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India. He has conducted research on several aspects of forestry, agroforestry, medicinal plants, and climate change. He has trained many students in these fields. The focus of his research and development work is on forest ecology and conservation.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"101105",title:"Dr.",name:"Gopal",middleName:null,surname:"Shukla",slug:"gopal-shukla",fullName:"Gopal Shukla",profilePictureURL:"https://mts.intechopen.com/storage/users/101105/images/system/101105.jpg",biography:"Dr. Gopal Shukla, Ph.D., is currently an assistant professor of Forestry in Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India. He holds an MSc and Ph.D. in Forestry from Uttar Banga Krishi Viswavidyalaya. Before joining the university, he worked under NAIP (National Agricultural Innovation Project), NICRA ( National Innovations on Climate Resilient Agriculture), and SERB (Science and Engineering Research Board) projects. The focus of his research and development work is forest ecology and conservation.",institutionString:"North Bengal Agricultural University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"North Bengal Agricultural University",institutionURL:null,country:{name:"India"}}}],coeditorOne:{id:"329967",title:"Dr.",name:"Jahangeer",middleName:null,surname:"Bhat",slug:"jahangeer-bhat",fullName:"Jahangeer Bhat",profilePictureURL:"https://mts.intechopen.com/storage/users/329967/images/system/329967.png",biography:"Jahangeer A. Bhat, Ph.D., is a former head of the Department of Forestry, College of Agriculture, Fisheries and Forestry, Fiji National University, Republic of Fiji Islands. Dr. Jahangeer has worked as a counsellor, mentor, and coordinator for forestry academic programmes. He has been instrumental in developing HE and TVET streams of forestry and allied programmes and worked closely in accreditation with the Fiji Higher Education Commission and forestry stakeholders. Before joining Fiji National University, he worked for HNB Garhwal University, Srinagar, India, and has 11 years of research and 8 years of teaching experience with a publication record of more than 60, including research articles, review papers, conference papers, and books of national and international repute. Dr. Jahangeer reviews research articles for several scientific journals and has handled research projects in his capacity as Principal Investigator and Co-Principal Investigator. His major interests lie in emerging issues in forestry including conservation of biodiversity, traditional knowledge of plants, and sustainable management of forest resources. His focus of research is vegetation ecology, ethnobotany, and evaluation of ecosystem services, forest plant biodiversity, climate change, and socio-cultural issues in forestry. Dr. Jahangeer is currently working at the College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.",institutionString:"Central Agricultural University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Central Agricultural University",institutionURL:null,country:{name:"India"}}},coeditorTwo:{id:"94999",title:"Dr.",name:"Sumit",middleName:null,surname:"Chakravarty",slug:"sumit-chakravarty",fullName:"Sumit Chakravarty",profilePictureURL:"https://mts.intechopen.com/storage/users/94999/images/system/94999.jpg",biography:"Dr. Sumit Chakravarty, Ph.D., has wide experience in forestry training, research, and development. He is currently a professor at Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India. He holds an MSc in Forestry and a Ph.D. in Agronomy from Punjab Agricultural University, Ludhiana. He has conducted research on several aspects of forestry, agroforestry, medicinal plants, and climate change. He has trained many students in these fields. 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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5539",title:"Forest Ecology and Conservation",subtitle:null,isOpenForSubmission:!1,hash:"6bd160f6d1da73fc253dfe6c4df7c095",slug:"forest-ecology-and-conservation",bookSignature:"Sumit Chakravarty and Gopal Shukla",coverURL:"https://cdn.intechopen.com/books/images_new/5539.jpg",editedByType:"Edited by",editors:[{id:"101105",title:"Dr.",name:"Gopal",surname:"Shukla",slug:"gopal-shukla",fullName:"Gopal Shukla"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6264",title:"Forest Biomass and Carbon",subtitle:null,isOpenForSubmission:!1,hash:"964f96c9209ff2a3eaf3c5c6a54d81c3",slug:"forest-biomass-and-carbon",bookSignature:"Gopal Shukla and Sumit Chakravarty",coverURL:"https://cdn.intechopen.com/books/images_new/6264.jpg",editedByType:"Edited by",editors:[{id:"101105",title:"Dr.",name:"Gopal",surname:"Shukla",slug:"gopal-shukla",fullName:"Gopal Shukla"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9841",title:"Agroforestry",subtitle:"Small Landholder’s Tool for Climate Change Resiliency and Mitigation",isOpenForSubmission:!1,hash:"ec5444e2a12dcd63ab9e7246d93a63ab",slug:"agroforestry-small-landholder-s-tool-for-climate-change-resiliency-and-mitigation",bookSignature:"Gopal Shukla, Sumit Chakravarty, Pankaj Panwar and Jahangeer A. 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1. Introduction
Climate signals from 10Be records in marine environments have been studied for last two decades (Aldahan et al., 1997, Bourlès et al., 1989, Christl et al., 2003, Eisenhauer et al., 1994, Horiuchi et al., 2000, 2001, Kim and Nam, 2010, Knudsen et al., 2008, McHargue et al., 2010, McHargue and Donahue, 2005). Understanding of regional climate signals is feasible through not only 10Be but also 9Be from the sediment. This is because 9Be is terrigenous origin while 10Be signal is affected by climatic condition and production at the top of atmosphere. Recent study from the East Sea of Korea (05-GCRP-21) indicated that climate signals from 10Be records of Korean marine sediment are generally representing global climate variations during warm and cold periods from Last Glacial Maximum to Holocene and also MIS (marine isotope stage) 6 to Eemian. The 10Be records of the East Sea are well compared with those from the oxygen isotopic record of this marginal sea (Kim and Nam, 2010). During the warm interglacial periods the 10Be concentrations per sediment mass have significantly increased while during the cold glacial periods those have decreased (Aldahan et al., 1997, Eisenhauer et al., 1994). This result also shows that a vivid record of 10Be/9Be indicates a significant increase of 10Be at a time of 120 kyr, which might be an indication of the paleomagentic excursion.
Interestingly, it was found that the 10Be concentrations per 1g sediment of this region were about 30% lower than other 10Be records of largely open marine environment. We also found that 10Be concentrations of the Blake Outer Ridge were similar to those from Korean marine sediments (McHargue et al., 2000). Two study areas are located nearby large continents: the East Asia (the East Sea) vs North America (the Atlantic). This could be caused by sediment inflow to the marine environment which is close enough to the continent. Therefore, local marine environmental influence is revealed through the beryllium isotopes. Both cases would have similar climatic signals due to their geographical locations nearby continent. The lower 10Be concentrations for these regions could be also involved in ocean current and circulation. Relatively deep sea water of these regions may not be well mixed rapidly with the surface water and old sea water with relatively lower 10Be concentration remains in the sediment records. For this chapter, we investigated climatic signals from Be isotope records of the East Sea of Korea and the Mendeleev Ridge of the Arctic Ocean and compared with the records from the Blake Outer Ridge studied by McHargue et al., 2000. In addition, global 10Be records of marine sediments for various regions will be briefly discussed. This chapter would provide a new insight guide into understanding climate signals through 10Be records of various marine environments.
2. Beryllium isotopes in terrestrial environments
2.1. Cosmic ray induced 10Be production rate
Production rates of cosmogenic isotopes depend on geomagnetic latitude, altitude, and flux of incoming cosmic rays to the earth (Lal, 1988). The Earth’s geomagnetic field deflects incoming cosmic rays and has an effect on the production rate of in situ cosmogenic isotopes. This deflection affects the incident angle and the rigidity of cosmic rays. The rigidity is defined as r = pc/q, where p is the momentum, q is the charge of the particle, and c is the velocity of light (O’Brien, 1979). The vertical cutoff rigidity is the lowest at the geomagnetic poles and highest at the equator. Therefore, greater cosmic rays reach to the poles and attenuation length at low latitude is greater than at high latitude (Simpson and Uretz, 1953). Since geomagnetic latitude affects the production rate of cosmogenic isotopes, understanding of the secular variation of the Earth’s geomagnetic field is important. It is known that variations of geomagnetic field intensity cause changes in the flux of cosmic rays, in solar activity, and in shielding by the Earth’s magnetosphere. Laj et al., 1996 describes geomagnetic intensity and 14C abundance in the atmosphere and ocean during the past 50 kyr. This paper shows geomagnetic change effects on the 14C production, which is increased by the decrease of the Earth’s dipole moment. Similarly, the relationship between 10Be production rate and geomagnetic field intensity was studied using deep-sea sediments. These results also demonstrate the importance of the relationship between cosmogenic nuclide production and intensity of geomagnetic dipole moment variation (Frank et al., 1997). Thus, production of cosmogenic isotope should be corrected with the variation of geomagnetic dipole moment variation. It has been found that the Earth’s geomagnetic pole is essentially the geographic pole for periods greater than about 2 kyr (Champion, 1980, Ohno and Hamano, 1992). Therefore, a correction from geographic reading to geomagnetic reading is required. For most cases of 10Be or 26Al surface exposure dating samples, the working range of age is from several thousand years to a few million years. Thus, in this case, the correction for geomagnetic reading may not be required, but the correction of production related to secular variation of geomagnetic dipole moment intensity is required. Masarik et al., 2001 demonstrated the correction of in situ cosmogenic nuclide production rates for geomagnetic field intensity variations during the past 800 kyr. This paper indicated that at the equator integrated production rates for exposure ages between ~40 and 800 kyr are 10 to 12% higher than the present day value, whereas at latitudes greater than 40 degree, geomagnetic field intensity variations have hardly influenced in situ cosmogenic nuclide production.
Production rates as a function of both latitude and altitude have been studied in the past. For a few decades, models from Lal and Peters, 1967 and Lal, 1988, have been widely used for the scaling factors for production rates of in situ produced cosmogenic nuclides. A third degree polynomial equation found in Lal, 1991, enables one to calculate the production rate of 10Be and 26Al with respect to geomagnetic latitude and altitude. A reevaluation of scaling factors for these production rates has been attempted recently using non-dipole contributions of the geomagnetic field to the cosmic ray flux and observed attenuation lengths (Dunai, 2000). The scaling factors for the nuclear disintegration with respect to geomagnetic latitude and altitude from Lal’s work are involved in the geocentric axial dipole hypothesis and this is appropriate for time scales exceeding 200 kyr. The non-dipole components of the Earth’s magnetic field contribute up to 20% to the total field; therefore, they must be considered for short-term effects of cosmic rays. It is known that the new scaling factors and those of Lal are significantly different, by up to about 30%, especially at high altitude and at low latitude. Currently, a few other research groups have been involved in studying production rates of cosmogenic nuclide or measurement of neutron flux as a function of geomagnetic latitude and altitude. This additional research on this field may provide a confirmation of these scaling factors for the production of in situ cosmogenic isotopes. (Stone, 2000, Graham et al., 2005a,b,c, 2000, Lifton et al., 2001, Desilets et al., 2006).
2.1.1. Production rate in the atmosphere
10Be is produced in the atmosphere by nuclear interactions with oxygen and nitrogen (Peters, 1955, Goess and Phillips, 2001). The intensity of the cosmic ray flux depends on galactic and solar sources, and modulation by the heliomagnetic and geomagnetic fields. Both 10Be is produced by spallation reactions in the atmosphere, and then 10Be is well mixed up (Ueikkila et al., 2009) and removed from the atmosphere by precipitation scavenging of aerosol particles to land and sea. Eventually, these nuclides are deposited within ocean sediment. The 10Be concentration at 10.7 km of stratosphere and at 19.2 km in the tropospheric concentration are known to be 7 x 106 atoms/m3, and 1.3 x 107 atoms/m3, respectively. The global average 10Be production rate is found to be (1.21±70) x 106 atoms/cm2/yr (Monaghan et al., 1985). Estimates of the 10Be production rate derived from measurements on ice cores, lake sediments, and deep-sea sediments range from 0.35 x 106 atoms/cm2/yr to 1.89 x 106 atoms/cm2/yr. (Monaghan et al., 1985). Castagnolie et al., 2003 demonstrated reconstruction of the modulation parameter M from the open solar magnetic flux proposed by Solanki et al., 2000, and experimental values calculated from the GCR spectra measured with balloons and spacecraft are compared well with 10Be concentration measured at the Dye3 ice core, assuming constant accumulation rate during the period of 1810-1997. The production rate of 10Be ranged from 0.015 to 0.025 atoms/cm2/s (Castagnolie et al., 2003).
The precipitation onto the surface of the Earth and the deposition of 10Be in soils is influenced by climate. In turn, the production of 10Be in the atmosphere is influenced by the magnetic dipole field of the Earth to which it is inversely related. This relationship between the production of 10Be and the geomagnetic field has been shown by the correlation between the variations of 10Be and those of the me paleo-asuredinclination data of the dipole field in sediments (Frank et al., 1997, Frank,2000, Masarik et al., 2001, Laj et al., 2000), and the concentrations of 10Be in marine sediments and the measured paleointensity (Carcaillet et al., 2004, McHargue and Donahue, 2005).
The influence of climate on the deposition of 10Be, otherwise is problematic for interpretations of the cosmic-ray flux, in itself is a worthy subject for study. For example, variations in the deposition rates of 10Be and sediments affect the 10Be/9Be ratio due to the uneven mixing of the two isotopes in the hydrological cycle. That, 10Be, produced largely in the atmosphere, is transported to the surface of the earth by rain and dry precipitation to the sea. In contrast, 9Be, derived from terrigenous materials, is transported to the sea largely by rivers, and to a lesser extent by atmospheric deposition.
Location
10Be
Source
Reference
USA continent
(1.38±0.36) ~(3.96 ± 0.35) x 106 atoms/cm2/yr
rain
Monagahan et al., 1985
USA Hawaii
1.9 x 103 ~ 8.94 x 104 atoms/g
rain
Monagahan et al., 1985
Tropical region
Avg. 1.53 x 104 atoms/g
rain
Monagahan et al., 1985
Illinois, USA
2 ~7 x 107 atoms/g
soil
Brown et al., 1989
Japan, Kikari Is
(0.80 ~ 7.17) x 109 atoms/g
soil
Maejima et al., 2005
Japan, Kikari Is
(2.0 ~ 3.5) x 106 atoms/cm/yr
rain
Maejima et al., 2005
New Zealand
(2.1 ~ 2.9) x 104 atoms/g
rain
Graham et al., 2003
Korea, Masanri
(0.67 ~ 1.47) x 108 atom/g
soil
Kim et al., 2011a
India
(0.43~3.34) x 107 atoms/l
rain
Somayajulu et al., 1984
Global
(1.21 ±0.70) x 106 atoms/cm2/yr
rain
Monagahan et al., 1985
Table 1.
Production rate and concentration of 10Be in the rain and soil.
2.1.2. 10Be in land surface
Precipitation was collected for approximately one year during 1980 at seven localities in the continental U.S.A. (Monagahan et al., 1985). The 10Be flux ranged from (1.38±0.36) x 106 to (3.96±0.35) x 106 atoms/cm2/yr (Monagahan et al., 1985). In the case of Hawaii, 10Be concentration ranges from 1.9 x 103 to 8.94 x 104 atoms/g in rain water. The mean 10Be deposition rate in temperate latitude is determined to be 1.53 x 104 atoms/g in rain water (20% error). Monagahan et al., 1985 indicated that the concentration of 10Be in surface soils and river sediments varies between 107 and 109\n\t\t\t\t\t\t10Be atoms/g soil with the modal concentration of 10Be lying between 4 x 108 and 6 x 108 atom/g. The relationship between annual rainfall and 10Be deposition rate is plotted to be linearly proportional to each other (Maejima et al., 2005). This study shows that 10Be fluxes (cm/day) for the two rain collection sites are relatively higher during a collection period of January 22 to April 22 than other collection period (Maejima et al., 2005).
Seasonal variations for 7Be and 10Be concentration in Tokyo and Hachijo-Island during a period of 2002 to 2003 were similar to each other. The peak value for 7Be and 10Be concentration appeared in April and October, respectively. Especially, in April when stratosphere-troposphere exchange occurs, peak values for the atomic ratio 10Be/7Be appeared. Low 7Be and 10Be concentrations and the atomic ratio of 10Be/7Be appeared in summer, July to August. Because the composition of the aerosol of Tokyo was almost same to the nearby soil, it is considered that Tokyo was strongly influenced by re-suspended soil contamination. Yamagata et al., 2005 indicated that using Al concentration in the aerosols, the enrichment of 10Be concentration by re-suspended soil contamination was estimated to be about 30%.
In the case of Southern Hemisphere, Graham et al., 2003 demonstrated 7Be and 10Be fluxes at 36 to 45ºS were determined to be (1.2~14) x 107 atoms/kg and (2.1~2.9) x 107 atoms/kg, respectively. These results are similar to those for rain sampled at mid-latitude sites across the USA from 1986 to 1994. The annual 7Be and 10Be flux rates are ~15 and ~27 x 109 atoms/m2, respectively, at the northern sites of Leigh and Gracefield, and are significantly lower at ~9 and ~19 x 109 atoms/m2, respectively, at the southern site of Denidin, because of the lower average rainfall there. Graham et al., 2003 indicated that 7Be/10Be in New Zealand ranged 0.47 to 0.61 and this is significantly lower than the ratio in USA (0.69~0.78). This is due to re-suspended dust to the primary atmospheric 10Be in the rain sample in New Zealand. Interestingly, the ratio of 7Be/10Be at three sites are 0.70 (Leigh), 0.65 (Gracefield) and 0.50 (Dunedin). These results suggest an overall reduction in the 7Be/10Be ratio from north to south, due to increasing residence time for Be isotopes in the atmosphere above New Zealand. The mean residence time for 7Be and 10Be in the atmosphere above New Zealand range from 77 to 109 days and are lower in the summer than the winter due to transfer of older stratospheric air to the tropopause in late spring-early summer (Graham et al., 2003).
Maejiam et al., 2005 demonstrated that 10Be concentrations of six soil samples on the raised coral reef terraces of Kikari Island, southwest Japan ranged from 0.80 to 7.17 x 109 atoms/g. The annual deposition rate of 10Be from the atmosphere to Kikari Island from 2000 and 2002 ranged from 2.0 to 3.5 atoms/cm2/y. The minimum absolute age was calculated from the inventory of meteoric 10Be in the soil, and the annual deposition rates of 10Be are ranged from 8 to 136 kyr (Maejima et al., 2005). A 36 cm of soil depth profile from the Roberts Massif, Antarctica was studied to obtain the age of soil by Graham et al., 1997. The sampling site is located in the edge of the nearby East Antarctic Ice Sheet at an altitude of 2700 m. This site is considered to have been ice-free for an extremely long period of time, of the order of several million years. The results of Graham et al., 1997 determined its minimum soil age of 12 million years which is much older than other 40Ar/38Ar dating result of 8 million years for volcanic deposit, Scoria associated with soils on the tills laid down by the Meserve Glacier, Antarctica.
2.1.3. 10Be in the ocean
Using radiocarbon, the sedimentation rates during glacial periods and deglacial periods for the western Arctic Ocean were found to be 0.5 cm/kyr and 1-2 cm/kyr, respectively (Darby et al., 1997). A recent study shows that the concentration of 10Be in the authigenic fraction of the sediment normalized to the total sediment mass is indirectly correlated to the oxygen isotope curve (McHargue and Donahue, 2005). For example, a low 10Be/9Be ratio in sediments would imply that terrestrial source of 9Be has increased compared to the more oceanic 10Be. Correlation of 10Be with δ18O recorded in marine sediment from the Blake Outer Ridge (DSDP site 72) shows a climatic effect on the 10Be record in addition to cosmogenic effects. Age-corrected 10Be variability in the sediment cores studied in Aldahan et al., 1997 and the oxygen isotope stratigraphy with the climatic stages numbered from 1 to 10, is generated from Aldahan et al., 1997. 10Be from sediments of the Arctic Ocean covering the past 350 kyr shows well defined trends of Be isotopes coincident with interglacial/glacial climatic cycles and demonstrates that the sedimentation rates are higher during glacial periods and lower generally due to low sedimentation/accumulation rate during interglacial periods (Aldahan et al., 1997).
The 10Be records of four sediment cores, taken along a transect from the Norwegian Sea via the Fram Strait to the Arctic Ocean, demonstrate that high 10Be concentration are related to interglacial stages and that sediment sequences with low 10Be concentration are related to glacial stages. This study confirms that the sharp contrast of high and low 10Be concentrations at climatic stage boundaries are an independent proxy for climatic and sedimentary change, and can be applied for 10Be stratigraphic dating of sediment cores (Eisenhauer, 1994).
Figure 1.
Age corrected 10Be as a function of age (Aldahan et al., 1997).
Also, Carcaillet et al., 2004 produced high resolution authigenic 10Be/9Be records over the last 300 kyr from sedimentary cores off the Portuguese coast. Comparison of 10Be/9Be and benthic δ18O records from the two cores suggested that dipole moment lows may be associated with the end of interglacial episodes, and have a quasi-period of 100 kyr (Carcaillet et al., 2004). In a recent study, McHargue and Donahue, 2005 showed a strong correlation between 10Be and oxygen isotope stages from the Blake Outer Ridge in the Atlantic Ocean. This relationship between climate and 10Be deposition suggest that 10Be could be used, in addition to, or as proxy for δ18O in the studies of the climatic influences on marine sedimentation.
Other considerations are the carbonate flux in sediment which is strongly correlated to 10Be flux, and carbonate-free sediments from which δ18O is difficult to obtain from foraminifera. In addition, the two isotopes of beryllium, as stated above, are source dependent, thus the relationship of 10Be to 9Be in the sediments is a function of the relative contributions from atmospheric and terrestrial sources, and their mixing time in the sea.
Paleomagnetic intensity obtained from deep sea cores is well described in recent publications (Valet, 2001, Guyodo et al., 2000, Guyodo and Richter, 1999). However, it was found that climatic influence on 10Be deposition can be significant, obscuring those variations from its production in the atmosphere, and thus must be addressed (Frank et al, 1997, Kok, 1999). Scavenging corrected 10Be records compared to calculated variation of the global 10Be production based on paleomagnetic intensity records (Christl et al., 2003) (Figure 2) show the variation of 10Be flux in each location with general agreement of inversely proposal to the paleomagentic intensity from Mazaud et al., 1994, Yamazaki and Iokyr et al., 1994, and Guyodo and Valet, 1999.
Figure 2.
Scavenging corrected 10Be records compared to calculated variation of the global 10Be production based on paleomagnetic intensity records (Christl et al., 2003).
2.2. 10Be chemistry
Generally, to extract authigenic beryllium isotopes from sediments, the procedure of Bourles et al., 1989 is used. About one gram of sediment is leached in a solution of 25% acetic acid and hydroxlyamine-HCl to separate the “authigenic” fraction of the sediment from the “terrigenous” fraction. Most samples had more than 1 g of dry sediment; however, in the case of less than 1 g, two or three neighbouring samples were combined for the analysis. When 10Be is normalized to the mass of the authigenic fraction, it should more accurately reflect its concentration in ocean water than 10Be normalized to the total mass of the sediment (McHargue and Donahue, 2005, McHargue et al., 2010). This fraction is mostly composed of exchangeable ions, carbonates, and Fe-Mn hydroxides. Two aliquots of the leachate are prepared, one for the elemental analysis with ICP-MS/ICP-AES, and one for the preparation of AMS samples.
Figure 3 shows the flow chart of 10Be chemistry to extract authigenic beryllium from sediment. This chemistry includes two steps of purification procedures using perchloric acid and nitric/hydrochloric acid. These steps are important to extract authigenic brylllium isotopes. Sometimes, this step is repeated to remove unwanted organic materials. When the unwanted organic materials are not completely removed, the residue sample is often difficult to dissolve in weak acidic solution for ICP analysis. This also causes a further problem in the step of Be separation using Na-EDTA. The concentration level for Be is mostly in ppb range; therefore, Be analysis was performed using ICP-MS. For AMS, the Be fraction is precipitated as Be(OH)2 and combusted to BeO. 10Be/9Be ratios for chemical blank are found to be less than ~ 1 x 10-14 with 2 mg of 9Be carrier. 9Be and other elements can be measured by ICP-MS and ICP-AES.
Figure 3.
Description of authigenic Be isotope extraction method.
2.3. The climate signal of 10Be from nearby a continent
The signal of 10Be from the East Sea in the Pacific Ocean and the Black Outer Ridge in the North Atlantic Ocean may give similar climatic influence because of its proximity to continents (Kim and Nam, 2010, McHargue et al., 2000). The depths of basins where sediment core collected were about 3,700 m and 3,818 m in the East Sea and Blake Outer Ridge, respectively. Because both cores were collected in the basin of the ocean, we might expect water circulation could be weaker. This could allow rather older waters can remain at the bottom of the basin. In this case, we may expect lower concentration of 10Be in sediment compared to samples collected from other open seas (Bourlès et al., 1989, Knudsen et al., 2008). Also, the influence from the continent would be similar in both regions. The terrestrial origin of 10Be over glacial/deglacial time period may similarly appear.
As shown in Figure 4, three locations were examined with respect to 10Be concentration as a function of time and any related proxies for each site. Mostly, maximum 10Be concentrations in various marine sediment samples appear to be above 1 x 109 atoms/g sediment (Bourlès et al., 1989, Knudsen et al., 2008). Both the East Sea and Blake Outer Ridge, 10Be concentrations are reached at 8 x 108 atoms/g. This value is at least 30 percent lower than the most maximum value of 10Be in each marine sediment core. Also, when 9Be is investigated with 10Be, 9Be signal may be another indicator as a signal of sediment input from the land to the offshore. In the case of the study of the East Sea, the 9Be values also show similar trend to those of 10Be. his shows that both warmer periods of the Holocene and the Eemian,
Figure 4.
Locations of the coring sites of the East Sea, Black Outer Ridge, and Mendeleev Ridge in the western Arctic Ocean.
wetter and warmer climate influenced 9Be to be transported from the land to the ocean. 10Be is transported from the both land and atmosphere. The signal of 10Be/9Be especially stands for lack of either lack of 9Be transport or higher production rate of 10Be, possibly associated with paleomagnetic intensity. In Figure 5, A, B, and C are associated with relatively higher 10Be/9Be compared to neighbouring 10Be/9Be values. A could be partially due to lowered value of 9Be; B and C could be due to production rate of 10Be. This study shows high production rate of Be at 15.5 and near 120 kyr. Also, lower 10Be production rate is shown at 130.6 kyr during MIS 6 (Figure 5) (Kim and Nam, 2010). These three points can be identified easily with 10Be/9Be rations. In the Figure 5, the regions associated with climatic influence are clearly shown as the 10Be/9B ratios to be within the value between 2 and 3. This observation could be useful in future analysis. Based on Wagner et al., 2000, lower paleomagnetic intensities are associated with the ages at 1.5, 2.5, 4.0, and 6.5 kyr (Christl et al., 2003). Therefore, B could be likely involved in higher production rate of 10Be at 1.5 kyr (Kim and Nam, 2010) (Figure 5).
Figure 6 shows the 10Be concentration and M/Mo as determined from measured NRM/ARM of core CH88-10P with respect to depth and time. This figure shows that 10Be concentration is inversely proportional to the relative paleomagnetic intensity. The production rate of 10Be occurred at about 40 and 65 kyr. The peak values of 10Be/9Be reached at maximum at about 40 kyr. This time period is named as Laschamp paleomagnetic excursion where the expected 10Be reaches at a maximum value. These paleomagnetic excursions are well compared with GRIP records.
A recent investigation on 10Be and 9Be from the Mendeleev ridge in the Arctic Ocean shows that 10Be record at 75 kyr reveals production rate decrease evidently for at least 35 kyr of duration (Kim et al., 2011b). At this time the paleomagnetic intensity is found to be at maximum (Figure 2) (Christl et al., 2003; Flank et al., 1997). Interestingly, the values of magnetic susceptibility (Guyodo and Valet, 1996) obtained from a lake (Lac du Bouchet, France) are high as well as δ18O. Also, 9Be is relatively high which stands for a warm climate. The results of this study confirm that 10Be reveals predominantly paleomagnetic features over the δ18O at the extreme point of paleomagnetic intensity. This situation brings us to have precaution in a misuse of 10Be as a climatic tracer. This study confirms the fact that the 10Be record for climatic tracer, comparison with 9Be is essential. When both beryllium isotopes behaves similarly, the pattern of Be can be used to determine whether the record of Beryllium isotopes is associated with colder or warmer climate based on their consistent concentration trend. The total authigenic 10Be and 9Be (9Be>>10Be) can be referred as 9Be because of their amount ratio in terrestrial environment. The 9Be can be used as another climatic indicator like Sr, Ca, opal, TOC. The study at The Mendeleev Ridge confirms that 9Be generally has a positive correlation with opal, TOC, δ13Corg and negative correlation with CaCO3 (Nam unpublished) (Figure 7). General trend of 9Be clearly show the anti-correlation between 9Be and Ca or Sr (Boulrès et al., 1989, Kim et al., 2011b). Therefore, we can conclude that 10Be has a positive correlation with δ18O which gives 10Be to be used as a climate indicator, however, this is only true when 9Be reveals similar climatic pattern with 10Be. Both 10Be and 9Be show lower concentration at a cold/dry climate and higher concentration at a warm/wet climate period (Kim and Nam, 2010).
Figure 5.
Authigenic beryllium isotope records from the East Sea, Korea (Kim and Nam, 2010).
Figure 6.
The 10Be concentration and M/Mo as determined from measured NRM/ARM (Schwartz et al., 1998) of core CH88-10P versus depth and time (McHargue et al., 2000).
2.4. Current problem and future research
A number of investigations show that there has been positive correlation between oxygen isotope and 10Be concentration (Aldahan et al., 1997). Also, a positive correlation between oxygen isotopes and paleomagnetic intensity and also magnetic susceptibility is investigated (Carcaillet et al., 2004). During the Holocene, paleomagnetic intensity was gradually increased since the time of Laschamp excursion. This confirmed that production rate of 10Be at present is the lowest value since Laschamp excursion. However, the 10Be values recent years are higher than the 10Be concentration and the trend of 10Be is similar to that of δ18O value. This implies that 10Be is closely related to climatic and temperature variation. Because of this contradictory fact, confining the cause of climate change using nuclides which sun’s activity related became important.
Although there have been a number of investigation on climate study using above parameters, obscurity in finding the cause of climate change is still remain. The relationship among production rate of 10Be, paleomagenetic intensity, and Sun-climate connection was studied (Sharma, 2002). This study estimated changes in 10Be production rate and the geomagnetic field intensity, variations in solar activity were calculated for the last 200 kyr., and confirms that the production of 10Be in the Earth’s atmosphere depends on the galactic cosmic ray influx that is affected by the solar surface magnetic activity and the geomagnetic dipole strength. However, large variations in the solar activity are evident. The marine δ18O record and solar modulation are strongly correlated at the 100 kyr timescale. This proposes that variation in solar activity control the 100 kyr glacial-interglacial cycles. Sharma, 2002 suggested that the 10Be production rate variations may have been under-estimated during the interval between 115 kyr and 125 kyr, and may have biased the results (Sharma, 2002).
Figure 7.
Multi-proxy record from the core (PS72/396-3) from Mendeleev Ridge, the Arctic Ocean (Kim et al., 2011b).
Usoskin et al., 2004 indicated that the reconstructed sunspot record exhibits a prominent period of about 600 years, in agreement with earlier observations based on cosmogenic isotopes. Also, there is evidence for the century scale Gleissberg cycle and a number of shorter quasi-periodicities whose periods seem to fluctuate on millennium time scale. This invalidates the earlier extrapolation of multi-harmonic representation of sunspot activity over extended time intervals and the present high level of sunspot activity is unprecedented on the millennium time scale (Usoskin et al., 2004). Accepting solar forcing of Holocene and galacial climatic shift implies that the climate system is far more sensitive to small variation in solar activity than generally believed. In order to fully understand how sensitive climate really is for variations in solar activity, we need to look for additional evidence and to quantify such evidence, both in paleorecords and in observations of present climate with models to estimate climate change in the future (Geel et al., 1999).
3. Conclusions
As a climate indicator, 10Be has been frequently investigated because of its property associated with rainfall, dust fallout and its production mechanism in the atmosphere by cosmic-rays. Similar patterns of 10Be and δ18O, magnetic susceptibility records show climatic influence, however, 10Be is incorporated with the production rate which is inversely proportional to the paleomagenetic intensity. The cyclic orbital forcing effect toward 10Be, δ18O, and paleomagenetic intensity are connected, 10Be signal is clearly mixed with climatic component and earth’s paleo magnetic strength. Scrutinizing authigenic 10Be with 9Be, a region either climatic or production related zone can be evidently identified by looking at the 10Be/9Be ratios. Investigation of 9Be is another useful tracer to examine climatic influence of marine environments with other multi-proxies which have positive or anti-correlated with 9Be. Marine environments like the East Sea of Korea, Blake Outer Ridge, and Mendeleev Ridge are associated with significant terrigeous input during deglacial periods. Examining both beryllium isotopes together with other multi-proxy stratigraphy will provide understanding the pattern of environmental change at various glacial/interglacial events much more evidently.
Acknowledgments
This study is partially supported by Korean-IODP at Korea Institute of Geoscience and Mineral Resources and also funded by K-Polar (PP11070) at Korea Polar Research Institute.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/19841.pdf",chapterXML:"https://mts.intechopen.com/source/xml/19841.xml",downloadPdfUrl:"/chapter/pdf-download/19841",previewPdfUrl:"/chapter/pdf-preview/19841",totalDownloads:2660,totalViews:211,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:2,impactScore:0,impactScorePercentile:4,impactScoreQuartile:1,hasAltmetrics:1,dateSubmitted:"November 27th 2010",dateReviewed:"April 28th 2011",datePrePublished:null,datePublished:"September 12th 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/19841",risUrl:"/chapter/ris/19841",book:{id:"396",slug:"climate-change-geophysical-foundations-and-ecological-effects"},signatures:"Kyeong Ja Kim and Seung-Il Nam",authors:[{id:"52446",title:"Dr.",name:"Kyeong Ja",middleName:null,surname:"Kim",fullName:"Kyeong Ja Kim",slug:"kyeong-ja-kim",email:"kjkim@kigam.re.kr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"61408",title:"Dr.",name:"Seung-Il",middleName:null,surname:"Nam",fullName:"Seung-Il Nam",slug:"seung-il-nam",email:"sinam@kopri.re.kr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction ",level:"1"},{id:"sec_2",title:"2. Beryllium isotopes in terrestrial environments",level:"1"},{id:"sec_2_2",title:"2.1. Cosmic ray induced 10Be production rate",level:"2"},{id:"sec_2_3",title:"Table 1.",level:"3"},{id:"sec_3_3",title:"2.1.2. 10Be in land surface",level:"3"},{id:"sec_4_3",title:"2.1.3. 10Be in the ocean",level:"3"},{id:"sec_6_2",title:"2.2. 10Be chemistry ",level:"2"},{id:"sec_7_2",title:"2.3. The climate signal of 10Be from nearby a continent ",level:"2"},{id:"sec_8_2",title:"2.4. Current problem and future research",level:"2"},{id:"sec_10",title:"3. Conclusions ",level:"1"},{id:"sec_11",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'AldahanA.NingS.PossnertG.BackmanJ.BostromK.1997Be records from sediments of the Arctic Ocean covering the past 350 kyr. 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K.1985/86The global-average production rate of 10Be, Earth and Planetary Science Letters, 76 (1985/86) 279-287.\n\t\t\t'},{id:"B50",body:'O’BrienK. A.1979Secular variations in the production of cosmogenic isotopes in the earth’s atmosphere. Journal, of Geophysical Research,\n\t\t\t\t\t8442331'},{id:"B51",body:'OhnoM.HamanoY.1992Geomagnetic poles over the past 10,000 years. Geophysical Reseach Letters,\n\t\t\t\t\t19171518'},{id:"B52",body:'PetersB.1955Radioactive beryllium in the atmosphere and on the earth, Proceedings of Indian Academy of Sciences,\n\t\t\t\t\t416771\n\t\t\t'},{id:"B53",body:'SharmaP.MiddletonR.1989Radiogenic production of 10Be and 26Al in uranium and thorium ores: Implications for studying terrestrial samples containing low levels of 10Be and 26Al, Geochimica et Cosmochimica Acta,\n\t\t\t\t\t53709716'},{id:"B54",body:'SharmaP.SomayajuluB. L. 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J.KorshinekG.NolerE.1994Determination of erosion rates with cosmogenic 26Al, Nuclear Instruments and Methods, B 92317320'},{id:"B60",body:'UsoskinI. G.MursularK.SolankiS.SchusslerM.AlankoK.2004Reconstruction of solar activity for the last millennium using 10Be data, Astronomy and Astrophysics,\n\t\t\t\t\t413745751'},{id:"B61",body:'ValetJ.P.2001Time Variations in Geomagnetic Intensity, Reviews of Geophysics, 41/1444'},{id:"B62",body:'YamagataT.SaitoT.NagaiH.MatsuzakiH.2005Seasonal variation for 7Be and 10Be concentrations in the atmosphere at Tokyo and Hachijo-Island during the period of 2002 and 2003, The 10th International Conference on Accelerator Mass Spectrometery, Sept. 510Berkeley, CA. USA.\n\t\t\t'},{id:"B63",body:'YamazakiT.IokyrN.1994Long-term secular variation of the geomagnetic field during the last 200 kyr recorded in sediment cores from the western equatorial Pacific. Earth and Planetary Science Letters, 128527544'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Kyeong Ja Kim",address:"",affiliation:'
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1. Introduction
In this chapter, I discuss primary classroom pedagogy and assert that common portrayals of teaching and learning, particularly in media, policy, and some academic discourse, fall well short of capturing the complexity of what goes on in many classrooms. Traditional depictions of learning as linear, mechanistic, and in direct causal connection to teaching “input” remain dominant across education sectors, despite their failure to explain the unpredictable and uneven topography of pupil learning outcomes. Against this background, I argue that complex adaptive systems (CAS) theory may offer a useful non-linear alternative to the dominant linear, mechanistic thinking which dominates in many conceptions of school learning. In Section 2, I present the challenges and consequences of oversimplified conceptions of teaching and learning. In Section 3, I explain CAS theory and associated ideas in the context of education and explore arguments for and against applications of CAS thinking to classroom learning. In Section 4, I explore some pedagogical implications of conceptualizing classrooms and classroom learning through a CAS lens and discuss what these might mean for teachers.
2. The over-simplification of classroom teaching and learning
There is widespread support in the literature for the view that primary classroom learning and teaching are not straightforward processes. Davis and Sumara [1] note that most teachers will attest to the unpredictability of learners’ responses to teaching. Eisner [2] described teaching as “an inordinately complex affair” and others, [3, 4, 5] have framed teachers’ roles in terms of managing uncertainty and problematizing unpredictability. Shulman [6] is unequivocal in describing teaching as “perhaps the most complex, most challenging, and most demanding, subtle, nuanced and frightening activity that our species ever invented”. Descriptions of the facilitation and elicitation of learning by teachers themselves [7, 8] also acknowledge the unpredictable, dynamic and often messy, uneven nature of learning, and Alexander et al. sum up the argument with the assertion that “one cannot begin to understand the true nature of human learning without embracing its interactional complexity [9].” The case for learning and teaching being far from straightforward is also captured succinctly by Schon, who described teachers’ work as operating in the “swampy lowlands” of everyday life. For him, “the problems of real-world practice do not present themselves to practitioners as well-formed structures. Indeed, they tend not to present themselves as problems at all but as messy indeterminate situations [10].” Considering this, it seems reasonable to suggest that what pupils learn in the context of school classrooms does not flow mechanistically from teachers’ input and is not entirely within the conscious control of either teachers or pupils. This is not to argue that learning is not a function of teaching, but that pupil learning must be driven by more than merely the influence of teachers and teaching. It seems reasonable to hypothesize that teaching input is filtered through a range of networked factors resulting in learning occurring sometimes because of, sometimes despite, and sometimes irrespective of what teachers do.
Notwithstanding these depictions of the complex nature of learning and teaching, including from teachers themselves, a popular portrayal in policy and public discourse runs counter to this, presenting teaching and learning as simple, linear, causal, and mechanistic activities [11] reflecting a technical rationalist view of the profession [12, 13]. In this conception, teachers simply apply instrumental “teaching” solutions to address well-formed “learning” problems. Discourse and national agendas concerning teaching, learning, pupil progress, curriculum, standards, and teacher professional development are typically driven by this input-output conception. In the dominant policy discourse, “outstanding” teaching is often narrowly defined as the meticulous planning of lessons to meet specific, predetermined objectives [14] and despite years of reform in the UK and comparable education systems, the language of policy [15] still partially depicts a transmission and absorption notion of teaching and learning. This leads to the popular notion that if teaching is “outstanding” learning will (or should) be too. Tessellating policies of national testing, league tables, school inspection, and teacher competency descriptors firmly position teachers as the lynchpins [16] of pupil progress with the consequence that they are routinely held accountable for a phenomenon (learning) which appears to be only partially within their control.
This technical rationalist positioning of teaching alludes to an absorption and output conception of learning and learners. However, the pupil end of any learning and teaching relationship is no less complex than the teaching end. In any given school classroom “the persistence of inequities in student achievement [17]” speaks to a range of factors influencing learning. These include inherited and environmental predispositions such as cognitive ability, personality, confidence, task commitment, and risk-taking tendencies along with influences from home and school ecosystems. More ephemeral factors influencing volition such as social dynamics, nutrition, mood, and even weather may also play a part. Research from several paradigms offers insights into learner factors and their effects on learning outcomes. Bronfenbrenner’s [18] Ecological systems theory, for example, presents a framework for describing community and environmental influences on individuals. According to the model, interaction with other individuals and institutions across five levels of systems (from micro to chrono) shapes the individual’s growth, learning, and development. These include family, peer group, school, media, and health care policies for example. Research into the emergence of gifts and talents in school-age learners [19, 20] has highlighted common elements, which typically correlate with high performance, including general cognitive ability, environment, personality, self-confidence, and chance. Numerous studies from the field of psychology [21, 22, 23, 24] illustrate how personality influences readiness to learn and learning outcomes. Common findings suggest that the long-established “Big Five” personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) interact in statistically significant ways with learning. Studies of class emotion and mood [25, 26, 27] reveal how pupil interactions, on/off-task behaviors, and learning can be influenced by interpersonal features including regulation, negotiation, and resistance. Despite wide-ranging acknowledgment across multiple disciplines that learning is influenced by a complex array of converging and mutually interacting factors (those mentioned above and many not mentioned e.g. working memory, self-efficacy, parents’ education, personal health, and cultural expectations to name a few), its depiction remains largely characterized in public discourse by the receiving and remembering of information and the mastery of a set of skills. This is evidenced most clearly in the way that policy developments in the United Kingdom, and comparable education systems, over the last 20 years have striven to routinize teaching.
It is against this landscape, in which teaching and learning are characterized quite differently in policy and media to how teachers and pupils experience them, and how research frames them, that this discussion chapter sits. I have previously suggested [28] there is a need for more accurate depictions of teachers’ work and its relationship to pupils’ learning. As Hardman [29] points out, there has been a failure of simple causal explanations to adequately account for the complexities of school and classroom learning, because, according to Davis & Sumara learning tends to “defy simplistic analyses and cause-effect explanations [1]”. Complex adaptive systems (CAS) theories may offer valuable insights about ways that learning emerges in classrooms and my hope is that advancing this discussion will contribute to unpicking current over-simplified thinking about teaching and learning and offer new perspectives on pedagogy.
3. Complex adaptive system (CAS) thinking
A less reductionist, less mechanistic, more accurate depiction of classroom teaching and learning will necessarily acknowledge their complexity. The framework of complexity theory (an umbrella term applied to the analysis of a range of dynamic, non-linear systems) is a transdisciplinary theoretical framework presenting a non-linear, non-mechanistic scheme through which to view change within systems. Emerging originally from disciplines such as computer science, cybernetics, chaos theory, and the natural sciences [30, 31, 32], complexity theory has been applied to the natural sciences since the 1950s, and to the social sciences for approximately the last 30 years, as a tool for understanding systems containing multiple agents (in the case of classrooms: pupils, teachers, ideas, environment) whose adaptation, development or change (classroom system learning) is resistant to explanation using the traditional scientific method, or as Newell puts it, “phenomena resistant to reductionist analysis [33].” Complexity theory breaks with linear, causal, or deterministic explanatory frameworks [34], rejecting a version of reality in which “a knowledge of inputs is adequate to predict outputs [1]”. Complexity theory distinguishes between systems that are merely complicated and systems that are complex. Complicated systems, such as clocks or engines, have many moving, interacting parts that behave in centralized, repetitive, predictable ways. They remain consistent over time. In contrast, complex systems display less predictable, bottom-up, emergent, and non-linear behaviors, because the elements constantly and mutually affect one another [35]. Central to the behavior of complex systems (and therefore to this discussion) are the concepts of self-organization and emergence. Complex systems are said to have self-organizing properties, meaning that they are not centrally governed or controlled, instead of individual agents in the system act with degrees of autonomy, through local decision-making. From these autonomous actions patterns of coherent, aggregate behaviors form across the system from the bottom-up; this is referred to as emergence. My contention in this chapter is that to some extent learning can be said to have emergent qualities and that complexity may provide a framework for depicting and explaining elements of classroom learning which are routinely omitted by mechanistic portrayals of classroom teaching and learning.
Complexity theory has been employed as a lens through which to analyze systems in and of education for a little under three decades now exploring a range of aspects including curriculum [36, 37, 38, 39], educational research [1, 40, 41, 42], purposes of schooling [43], educational change [44, 45] and the philosophy of education [46]. A limited range of empirical studies have been undertaken into areas including school interventions [47], non-linear modeling for education systems [48, 49], and agent-based studies at system, school and classroom levels [50, 51, 52]. Since complexity theory is still a novel framework in education, support for the application of a complexity lens to classroom learning is currently limited but growing. A number of studies have examined classrooms, focusing on links between classroom systems and complexity characteristics, analoging pupil interactivity with the non-linear, ensemble agent behavior characteristic of complex systems. My justification for framing the primary classroom as complex draws on these accounts which suggest that complexity has useful applications in the analysis of classrooms and classroom learning.
Systems that adapt themselves through complex emergence are described as complex adaptive systems (CAS). Typical examples from the natural sciences include ant colonies, insect swarms, or clouds, and city traffic is an example often cited from the human social sciences. In each case, patterns of complex aggregate behaviors emerge through the mutually self-interested actions of individual agents following simple rules. The system “learns” and adapts itself through the network of agent interactions without top-down control from any central authority. CAS is said to function more bottom-up than top-down. Whilst descriptions of CAS properties in the literature across multiple domains overlap considerably, the lack of any unified CAS field of study, a single body of literature, or agreed nomenclature has proved an impediment to achieving a universally applicable framing in the social sciences. As Sullivan points out, “it seems every theorist has his or her own list of characteristics, qualifying properties, or optimal conditions for complex adaptive systems, each slightly different from the next [53]”. Some have attempted to consolidate divergent definitions into more generalizable interpretations for CAS [53, 54, 55, 56, 57, 58], however, even in synthesized forms, there is a considerable divergence from one framing to the next. Some theorists [1, 29, 33, 40, 44, 46, 53, 59, 60] have drawn on framings from complexity sciences to describe and discuss features of CAS in the field of education, though here too, no consensus exists about how to frame CAS.
The question of whether a school classroom is a CAS has been studied and discussed by some [29, 53, 59] including me [61] with mixed, but indefinite, conclusions, which depend largely on the CAS definition used and the organizing principles at work in the classroom. I have previously acknowledged [61, 62] that a primary classroom is not a CAS as originally conceived in the natural or computational sciences. However, along with others, I maintain that classrooms have sufficient CAS-like characteristics to warrant using a CAS framing to seek otherwise tacit insights about the nature of classroom learning. The most significant voices concerned with CAS thinking and education (Davis [1, 63, 64], Sumara [1], Mason [44, 45], Morrison [46], Sullivan [53] to name some) agree that caution should be exercised when attempting to conceive of the school classroom as a CAS, or equating emergence with learning. Analogies were taken from complexity science on radically emergent systems, for example, insect swarms, suggests that a classroom organized along similar principles would maximize knowledge sharing between pupils, have little by way of top-down leadership, prioritize individual self-interest and investigate questions to which neither pupils nor teachers know the answer. This is contrary to how most classrooms operate, whereby teachers exercise central executive control, pupil to pupil knowledge sharing may be considered cheating and the teacher tends to know “the answer”. Whilst complexity offers “intriguing and generative metaphor(s)” for the classroom system [33], there are obvious limitations to such analogies. Despite some reservations, however, there is agreement among those who have examined learning through a complexity lens (Davis [1, 63, 64], Newell [33], Sullivan [53], Hardman [29] in particular) that instruction alone does not cause learning and that there are, as yet unearthed insights about learning which a CAS framing may elicit.
Whilst no two definitions of CAS in the literature align exactly, there are certain characteristics I deem to be most relevant to school classrooms that appear repeatedly in CAS definitions, shown in Table 1. These form the core framework for this discussion of emergent learning. Based on these criteria, a complex adaptive classroom system is one containing multiple autonomous, interacting pupils, whose inter-relationships create networked, self-organized, non-linear behaviors from which change (learning) emerges at different levels (individual, small groups, whole class).
CAS criteria
Definition
Self-organization
Individual agents acting with sufficient autonomy to determine their own actions/interactions/behaviors
Pupil autonomy
Well-networked interactions
Local decision-making
Impulsive/instinctive behaviors
Emergence
Organized patterns of synergistic behaviors which aggregate bottom-up across the system as a result of agent self-organization
Bottom-up behaviors
Bifurcations
Perturbations/injection of novelty
Unpredictability
Evidence of non-additive learning
Non-linearity
Information moves between agents via feedback loops and signals, therefore causality is not linear but networked and recursive
Causation networked
Information moves back and forth between pupils
Pupils send and receive signals
Transcend their components
Exhibit properties not manifest in individual agents; systems that learn; learning is more than the sum of the system parts
Produce learning beyond the knowledge/capabilities of each individual.
Table 1.
CAS characteristics salient to classrooms and classroom learning.
3.1 The idea of a complex adaptive classroom
Complexivist educational researchers have explored ways in which characteristics of school classrooms overlap with descriptions of CAS, pointing out strengths and weaknesses in the comparison. Burns and Knox [65] compared De Bot et al.’s [66] descriptions of the development of complex systems over time, with their own analysis of classrooms. They found a number of correlations, including that both consists of sets of interacting variables (pupils, teachers, resources, environment), both had unpredictable outcomes (learning outcomes, critical incidents), both are part of and connected to other systems (family, institutional, community), both are sensitive to initial conditions, meaning that small changes or incidents can result in large differences over time and both develop through interaction and through internal self-organization. These qualities produce the inherent instability which predisposes classroom systems to emergent change over time. Davis and Sumara [1] posit that to really understand the dynamics of the classroom it is necessary to stop thinking linearly, a point which is supported and explained, with reference to how the social world behaves, by Byrne [67] who asserts that outcomes are determined by multiple causes moving in non-linear ways. Typical classroom examples of this are the multiple factors that might determine whether a pupil contributes verbally or not to a class discussion. These might include (though are not limited to) peer pressure, personal ambition, knowledge of an answer, fear of failure, confidence level, social status, degree of interest, or desire to go to lunch. If the classroom is a CAS, one would also expect these factors to interact with one another and exert influence over other pupils indirectly (“if my neighbor keeps quiet I will speak up/keep quiet; if my neighbor speaks up I will compete to speak first/keep quiet”) making causality non-linear, an argument which most teachers would not find it difficult to make. The point here is that classrooms consist of more than simply 30 separate linear interactions between teachers and pupils. Pupils influence one another in multiple visible and invisible ways making it difficult to trace the antecedent(s) of any given event or outcome.
Arguing that classrooms display CAS behaviors, Guanglu [68] points to the non-linear, recursive nature of teaching and learning, in which pupils’ and teachers’ interconnections produce continuous recursions of understanding, interpretation, re-understanding, and reinterpretation. Teaching and learning do not always follow this pattern, in fact, the linear transmission of information remains common in many classrooms and arguments for more direct instruction are currently strengthening [69, 70, 71]. However, some degree of openness and randomness are characteristic of even in the most tightly controlled classrooms and at times learning can take on forms more akin to “mutual fertilization, pollination [and] active catalytic(s) [38]”. Guanglu suggests that this mutuality is seen in the experience, commonly reported by teachers, of gaining a new or better understanding of the subject matter they are teaching, through the act of teaching it [68]. Support for conceptualizing school classrooms as CAS also comes from Hardman [29] who asserts that sudden or unanticipated emergence of novel outputs in classroom activity is inevitable, partly due to the internal diversity of classrooms, including the uniqueness of individual pupils’ (and teachers’) brains. Novelties might include sudden realizations, moments of inspiration, original ideas or solutions derived from collaborative experimentation. Diversity is a theme which Davis and Sumara [1] pick up. They suggest that differences, counterpoints, and asymmetries between agents within a system cause the very perturbations from which self-organization and emergence originate. Diversity in this sense does not refer to demographic identity differences (race, gender, etc), but to the myriad tangible, intangible, perceptible, and imperceptible differences which exist between human beings which in the classroom may cause differences in perspective, motivation, intent, action or utterance. These might include, personality traits, personal histories, family environment, inherited traits, self-esteem, self-confidence, or mood. In a CAS, internal diversity is one factor that helps maintain a system’s vibrancy and promote adaptation, keeping it far from equilibrium. In the context of a primary classroom this is seen in the way that given sufficient autonomy, pupil interactions rarely follow prescribed pathways or result in predictable outcomes. Collisions between individual diversities create collisions between ideas and perspectives which in turn creates the pluriform, entangled messiness so evident to teachers. From the mess, however, novelty and innovation often emerge.
A few suggestions are evident in these descriptions which lend support to the framing of classrooms as CAS. Firstly, that classrooms, like other CAS, have many moving parts which, given sufficient opportunity to interact, will produce productive instability. Secondly, that instability is causally connected to learning insomuch as randomness changes interactive behaviors and injects novelty, which can qualitatively change learning states. Thirdly, there is an implication that even in classrooms characterized by linear transmission and high degrees of centralized teacher control, openness is inevitable to some degree. Described in these accounts of classrooms and adding some legitimacy to comparisons with descriptions of CAS, are factors beyond, or resistant to, control. Despite the structure of organized schooling, the structure of the curriculum, and the necessary order imposed by teachers, diversities reveal themselves when pupils enjoy sufficient autonomy and openness in the classroom system and this creates opportunities for unpredictability and non-linear change. An example of non-linear emergent learning is evident in the common understanding that alongside the top-down influence of the teacher, pupils also influence and change one another through mutual self-influence [63]. The flow of content, explanation, and questioning does not only travel unidirectionally from teacher to pupils and result in the development of neat predictable knowledge, understanding, and skills. Alexander et al. point out that “change that happens in the learner, be it dramatic or imperceptible, or immediate, or gradual exerts a reciprocal effect on the learner’s surroundings [9]”. This depiction offers a strong positive comparison between classrooms and CAS, implying that there is also a flow of information and influence between pupils, towards the classroom environment and climate and, presumably, back towards the teacher as well. This suggests that as pupils change, they also change one another, the teacher and their surroundings, including the environment, through their mutual interconnectedness, much like the behavior of a CAS. Davis and Sumara refer to this phenomenon when stating that complex systems, such as classrooms, are systems that learn. Within such systems, they suggest
“one cannot reliably predict how a student or a classroom collective will act based on responses in an earlier lesson, or sometimes a few minutes previous. In other words, strict predictability and reliability of results are unreasonable criteria when dealing with systems that learn.” [1]
This means that in a classroom, change (learning) is unlikely to only unfold entirely as intended or directed by the structures of organized schooling, the curriculum, or the teacher. The system and its constituent agents will also adapt and change in ways not predicted or intended by those governing structures. This is evident in the common occurrence of classroom ethos, culture, and atmosphere changing over the course of a week, month, term, semester, or academic year. Such changes are behavioral, relational, environmental, and knowledge-based and can be felt by pupils and teachers in the dynamics of the classroom system. The system adapts because the collective adapts. Groups adapt because individuals adapt. Haggis suggests that emergence is always unpredictable to some extent, stating that “what emerges will depend on what interacts, which is at least partly determined by chance encounters and changes in environments [41].” This supports Biesta’s point [72] that learning cannot be reliably predicted but is a retrospective judgment. A principal learning characteristic of classrooms according to complexivists is their tendency towards self-organization and self-maintenance, what Sullivan [53] refers to as “adapt[ing] of their own accord.” Some degree of self-organization is inevitable in any system which is not entirely mechanistic and deterministic and since wholly determining the opinions, predilections, desires, impulses, thoughts, and behaviors of groups of pupils is impossible (not to mention undesirable), the tendency for self-organization to exert an influence on classrooms is understandable.
3.2 CAS classroom framing: Some cautions and discussions
It is necessary to ask, however, to what extent this phenomenon can be said to positively influence learning. In a CAS such as an ant colony, immune system, or decentralized finance block-chain, the self-organization and its concomitant adaptation is the learning. The fluctuation and interaction of many agents (be they ants or genes) all influencing one another, all influencing the system and being influenced by it, produces change that exceeds the individual possibilities of the agents. However, this analogy does not translate perfectly into school classrooms because, as Biesta [72] points out, education is not a morally neutral activity, but a purposeful, values-orientated one, and because of this, what is learned matters. He argues that describing learning as whatever emerges as a result of classroom interactions ignores the fact that education exists so that people learn something, not just anything. This argument fits with assertions from others [42, 73] that a CAS framework has considerable limitations when analyzing classroom learning because classroom learning is goal-orientated and has prescribed directions in which teachers must steer all pupils. As Kuhn puts it
“It may be argued that there is a fundamental mismatch between complexity and educational enterprise as in essence complexity is descriptive whereas education is normative, or goal-orientated. {…} complexity offers organizational principles for describing how the world and humans function. Education, however, is orientated towards achieving certain goals [42].”
These descriptions of the purposes of education are demonstrably incompatible with depictions of CAS, in which higher complexities may emerge as a consequence of agents operating individually out of mutual self-interest. Kuhn goes on to state “complexity merely describes, whereas education aims to make a difference [42]”. A consequence of this purposefulness that characterizes education (and which distinguishes it from learning in the general sense) is the centralized control of the teacher. Teachers impose expectations and structures on classroom activity and do so in the interest of curricular aims and purposes. Biesta [72] describes how this introduces “an asymmetrical element into the educational process” which is “one of the main reasons why educational learning is radically different from collective, interactive, explorative learning”. Without the imposition of purposive structures, the likelihood of emergent learning aligning with curriculum aims is low and the risk that nothing of curricular value will be learned, potentially high. Individuals in a classroom system are not all equal and teachers do not permit pupils to behave out of self-interest, for good reason. Ramussen agrees that educational learning has “special intentions in mind” [74], describing teaching as a “social arrangement and organization aimed at intensifying possibilities for learning and the results of learning”. The absence of any overarching “special intentions” in a CAS found in nature or in human systems at a great scale, such as cities or economies, weakens the case for classrooms being viewed as CAS. Sullivan’s study [53] illustrates this. Examining three different lessons (a music class, a mathematics class, and an English class) through a CAS lens, he noted that not all the classrooms displayed complex adaptive behaviors. He suggests that a key factor in whether a classroom can usefully be classified as a CAS is whether adaptations within the system are triggered by the teacher or by the collective. If the teacher orchestrates all or most responses to daily events (snow days, timetable changes, pupil absence) with little involvement from the pupils, then adaptations cannot be described as bottom-up. In concluding he states
“One may say that classrooms are inevitably complicated, and I would certainly agree. One may even say that all classrooms exhibit some measure of complexity, and I might agree. To assume, however, that a class will network itself in such a way that it adapts in any meaningful way is too much to assume [53].”
Radford [40] bridges arguments for and against comparisons between classrooms and CAS using a metaphoric continuum between what he refers to as “clockishness” and “cloudishness”. He draws on Popper’s assertion [75] that all systems can be viewed on a continuum between deterministic, reducible, and predictable (clockish) on one hand, and indeterminate, unpredictable, and open (cloudish) on the other. Radford’s contention is that even the most deterministic systems, such as clocks, have degrees of unpredictability, and that likewise, the most open and unpredictable systems, such as clouds, have some degree of predictability. Viewed at sufficient resolution, a clock will reveal its lack of mechanistic causality and a cloud will reveal its causalities. All phenomena, according to Radford, can be thought of as having degrees of both “clockishness” and “cloudishness”. The question is, which is the most useful or accurate explanatory framework for depicting a given system. Some researchers have attempted to describe the “cloudish” features of classrooms and how exploring them might lead to new insights about classrooms and classroom teaching and learning. Semetsky for example presents a radical vision of a self-organized classroom, characterized by decentralized control, pupil autonomy, and an absence of direct instruction. She posits that this would “naturalize the concept of learning [76]” through the introduction of greater choice for pupils. She envisages a classroom in which there are no right or wrong responses or answers, just an array of choices for pupils, creating an environment with an “inherent incapacity for students to experience failure at any point within the process” because there is no “special educative aim”. This vision of classroom learning is considerably more cloud-like than clock-like and would require a radical overhaul of curriculum structures, not to mention the very purposes of education. Semetsky acknowledges that this radical vision has the potential to be counter-productive, however. She draws on Cillier’s warning about chaotic system behaviors or “catatonic shutdown [77]” and suggests that a multiplicity of pupil options may contribute to complete disorganization rather than self-organization. This is similar to Waldrup’s assertion that whilst frozen (clockish) systems can benefit from “loosening up a bit”, turbulent (cloudish) systems “can always do better by getting themselves a little more organized [78]”.
Morrison presents a similar critique and asks
“whether self-organization is such a good thing, or whether it will lead to diversity, inefficiency, time-wasting, mob rule, and a risk of people going off in so many different directions that the necessary connectivity between parts of an organization, its values and direction will be lost or suffocated [46].”
This is a valid question. Judging when sources of novelty and disruption risk undermining sources of coherence within a system is crucial to maintaining a productive edge of chaos states and is a crucial aspect of teacher professional judgment. In a CAS such equilibrium is maintained through self-organization. In a classroom, it is largely due to the influence of the teacher. A key illustration of why the conception of classrooms as CAS both is, and is not, accurate and useful.
Others present visions of classrooms as self-organizing adaptive systems, which are less adversarial to the purposes of education than Semetsky’s. Fong for example, suggests that the concept of self-emergent order is well suited to early learning environments because of their natural tendency to balance the “dual worlds of emergent order and imposed control [79]” and the challenges teachers face in managing the latter in busy nursery or kindergarten classrooms. Sullivan [53] also takes a positive view of the classroom as a CAS and posits that in classrooms where the features of CAS such as self-organization, distributed control, and agent-interaction (the more cloudish characteristics) are maximized, novel learning emerges. Defining emergent learning as the “acquisition of new knowledge by an entire group when no individual member possessed it before [53]” Sullivan suggests that some curriculum subjects lend themselves more than others to the conditions in which such learning might emerge (literacy more so than mathematics in his example). One such feature of CAS which might be emphasized and capitalized upon in the interest of classroom learning is neighbor interactions. In their study of Canadian mathematics teachers Davis and Simmt noted that with sufficient density of short-range pupil interactions and networking, the emergence of novelty was likely. Their concept of neighbor interactions includes, but also stretches beyond, pupils sitting on the same table. They emphasize that “neighbors in a knowledge-producing community are not physical bodies or social groupings. Rather, the neighbors that must “bump” against one another are ideas [63]”. They recommend maximizing conditions in which pupils’ ideas can collide, not just between neighbors on tables, but across the topography of the classroom system, because “agents within a complex system must be able to affect one another’s activities [1]”.
It is clear that school classrooms share several characteristics with CAS, however, the extent to which any classroom can usefully be described as complex depends on how it is organized. Classrooms that operate under strictly centralized control (“clockish”) will share fewer features of CAS, whereas classrooms which function in more decentralized or distributed ways (“cloudish”) are likely to create space for the sort of autonomy which invites more CAS-like behaviors. Under such organizational principles, pupils may interact in networked and non-linear ways, becoming self-organizing, and inviting learning to emerge bottom-up, rather than always traveling top-down from the teacher. Figure 1 depicts three broad typical organizing principles common to many classrooms: centralized, decentralized, and distributed. When centrally organized [A], communication flows linearly from the teacher to the pupils but there is little or no interaction between pupils. When organized in a distributed manner [B], groups of pupils interact, including with the teacher, however, there is little or no mutual interaction between pupil groups. In a more distributed organization [C], interactions occur between any individuals with no central organization from the teacher. Newell [33] points out that all three organizational principles may be enacted at different times in any given classroom (even within a single lesson). In classrooms where decentralized or distributed forms are common, ideas are more likely to collide, pupils are more likely to become mutually influential, novelty and innovation may be more apparent, and learning may emerge which exceeds what any individual pupil previously knew or understood.
Figure 1.
Centralised [A], decentralized [B], and distributed [C] classroom organizing principles. Adapted from Davis and Sumara [1].
However, as Semetsky [76] has noted and my own research [62] attests, events, occurrences, and interactions that challenge or obstruct learning are also more likely in decentralized and distributed classrooms structures. With greater pupil autonomy comes greater unpredictability, greater likelihood of social conflict, and greater unevenness in pupil participation. Many theorists [80, 81, 82, 83, 84, 85, 86, 87] have also noted that pupil interaction alone rarely results in elaborated learning and that the organizing and structuring influence of the teacher is essential in transforming pupils’ hunches and intuitions into knowledge and understanding. With this in mind, and notwithstanding the evident CAS-like qualities of classroom learning, it is relevant to ask what the potential pedagogical benefits of a CAS classroom framing might be.
4. Implications for teachers and teaching
Notwithstanding its limitations, there is sufficient merit in the arguments for CAS-classroom comparisons to conclude that viewing the classroom through a CAS lens can illuminate a range of teaching and learning behaviors that might otherwise go unnoticed, and therefore unattended to. Among the drawbacks to an over-simplified, linear view of teaching and learning mentioned in part one of this chapter, is the risk that despite knowing better, teachers may teach in ways that presume a linear relationship between teaching and learning, missing opportunities to set conditions conducive to non-linear emergence. A CAS lens may encourage teachers to think non-linearly about learning processes, to become more attuned to collective, networked effects on learning, to see the critical potential in moments they might otherwise ignore, dismiss or want to prevent, and gain a more thorough appreciation of why pupil learning does not appear to augment in a steady trajectory. Arguments have been made that the extent to which classrooms reflect CAS-like qualities depends on how they are organized. More centralized organizational structures are less likely to encourage self-organized behaviors, whereas decentralized or distributed structures are more conducive to self-organization and emergence. In this final section I argue that teachers can occasion emergence through the organizational principles they employ and the degrees of autonomy they give to pupils, in order to capitalize on useful CAS-like classroom characteristics in the interest of learning. Part four draws on preliminary findings from my own recent research into emergent learning in a British year 4 classroom to explore how this might be achieved and what the benefits might be.
4.1 Thinking non-linearly
Linear thinking tends to result in linear expectations, which for teachers means the assumption that tangible evidence of pupil learning will follow shortly after teaching input. This mechanistic reckoning about learning is a key part of the simplification problem presented at the start of this chapter and is ubiquitous in classrooms across the world. However, it does not reflect how many teachers experience classroom learning on a day-to-day basis. Thinking non-linearly about teaching and learning means understanding that learning is a consequence of multiple, networked factors which may not covary linearly or occur in the same moments or places. Learning, when it emerges tangibly, is the result of inputs from multiple sources, including direct teaching, autonomous activity, peer influence, social dynamics, personality traits, prior conceptions, understandings, misunderstandings, environment, and chance, from a range of temporally diverse events. In short, learning (including individual learning) results from system-wide factors, not simply the linear mechanisms of teaching. Instruction may be the dominant influence on pupil learning, but is far from the only influence and its effects are filtered through multiple influences both within, and outside of, the classroom system. Awareness of the networked and recursive nature of learning enables teachers to notice, attend to and even plan for the pluriform influences which may assert themselves in, and beyond, the classroom. For example, awareness that pupils’ mental models, their understandings, and the connections they make are substantially influenced by experiences outside of school may lead a teacher to elicit information about pupils’ wider experiences of a phenomenon before planning to teach it. Similarly, knowing that new knowledge may emerge through decentralized and distributed interactive structures may lead teachers to facilitate episodes of autonomous, or semi-autonomous group activity expecting pupils to create or evolve ideas which they themselves had not planned for. Thinking non-linearly can encourage teachers to not only ask conditional “if-then” questions about teaching and learning but also consider “what if …” questions which can produce sensitivity to teachable moments. Thinking non-linearly changes expectations about how, when, and from where learning might emerge and can result in teachers noticing potential and future learning in its infancy. Sensitivity and responsiveness to “soon-to-be” learning as it appears are at the heart of effective pedagogy. Understanding that pupils, their ideas, utterances, moods, similarities, differences, and personal histories interact in non-linear ways can help teachers to look for potential learning in places and at times not typically explored when expectations follow singular, linear logic. This includes finding learning, or the conditions for learning in unlikely places, including classroom disruption, in social conflict, or in incidental, unintended moments.
Thinking non-linearly is not a typical habit for most teachers, however, trained as they often are to view teaching and learning in a unidirectional causal relationship in which learning is singularly the product of teaching. This is essentially the central presumption of the “what works” educational paradigm [88] and there are understandable justifications for this habit of mind; not least because it is accurate to some extent. Learning is a consequence of teaching. The problem is not that teaching has no part to play in learning, it plays a significant part, of course. The problem is the failure to understand teaching and learning as existing in a recursive, mutually influencing relationship and failure to see learning as an emergent phenomenon that surfaces because of bottom-up dynamics as much as from top-down instruction. Non-linear thinking, which attempts to accommodate these concepts, will require some effortful reimagining on the part of teachers well-versed in the “what works” paradigm.
4.2 Attunement to networked influences on learning
Non-linear thinking about teaching and learning has the potential to unlock awareness of the different sources which influence learning and how they interact with one another. Once aware that instruction is only one of several influences and that the multiple influencing factors are mutually interactive, teachers may view common classroom scenarios and occurrences differently. For example, when pupils articulate their understanding of a concept or process it is likely that the new understanding was constructed in an uneven trajectory involving bursts, plateaus, and stops over time. In addition to teaching input, the conceptual understanding will be a result of their interactions with other pupils, the collision of ideas those interactions permit, interaction with the environment and with aspects of their own and others’ personalities, learning habits, social status, intellectual and non-intellectual pre-requisites. Sensitivity to these factors can support teacher judgments about pupil grouping, sequencing, and duration of classroom activities, whether and when to intervene, scaffolding questions to ask, and the importance of helping pupils to link experiences. My research with 8 and 9-year-old pupils in a British Year 4 classroom [62] suggests that learning is always a process of organizing, reorganizing, constructing, reconstructing, and refining existing knowledge and understanding. This was illustrated in instances where pupil learning was the consequence of errors and misunderstandings, off-task as well as on-task behaviors, and because of, rather than in spite of, social conflict. There was also evidence that asymmetries in pupil social hierarchies produced discussion and knowledge sharing which more symmetrical dynamics almost certainly would not have. In one such example, the disruptive influence of one pupil in a practical small group activity and the frustration it caused other group members actually drove reconfigurations in pupil roles and articulation of perspectives, arguments, and explanations. The atmosphere was not calm and cooperative, but ideas, demonstrations, and later articulation of learning almost certainly resulted in part because of this. Behaviors which a teacher might instinctively want to prevent unlocked learning for some pupils, to some extent. Attunement to these factors and possibilities, particularly the knowledge that learning can emerge out of social conflict (which teachers routinely, and understandably, aim to suppress) has the potential to shift teachers away from expecting evidence of learning during or shortly after instruction, towards a more authentic appreciation of and attention to the range of antecedents, including unexpected ones.
4.3 The unexpected emergence of learning
Attunement to the CAS-like features of classrooms and non-linear thinking about learning processes opens the door for teachers to re-evaluate their intuitions about where and how learning can, or might, emerge. Acknowledging that some antecedents of visible learning originate from outside of the linear mechanisms of direct instruction means acknowledging that learning is caused and supported by more than simply teacher explanation and demonstration or planned classroom activities. It may also emerge incidentally during the non-lesson time or out of instances of social disruption, which teachers typically aim to discourage. Evidence from numerous classroom studies [89, 90, 91, 92, 93] suggests that when pupils collaborate autonomously, or semi-autonomously, on shared activities some degree of social conflict is inevitable. This may range from minor disagreements about turn-taking or resources that do not interrupt the flow of activity, to larger arguments that disrupt purposeful activity, cooperation, and group productivity. What these studies do not acknowledge, however, is that whilst there are sound reasons to discourage an atmosphere of conflict in the classroom, clashes of personality, asymmetries of social dominance, disagreements about pupil roles, and the socially engendered necessity to resolve these can actually produce moments of novelty, innovation, knowledge sharing or motivation which lead to learning. It is no surprise that nowhere in the literature is there any support for the idea that social conflict might also support curriculum learning in unexpected ways, however, some of the data in my recent research [62] suggests that learning does not just survive episodes of social conflict, but can be occasioned by it. I am not here advocating a laissez-faire approach to classroom management or an “anything goes” attitude to pupil behaviors. Calmness, cooperation, and mutual respect are necessary and desirable qualities to encourage in classrooms. However, I am (somewhat tentatively) suggesting that when teachers suspend, even temporarily, their linear expectations about what produces learning (and perhaps even stop thinking about learning as a product altogether), they may notice teachable moments worth capitalizing upon in some unexpected places.
According to Davis and Simmt [63] the network of mutual self-influence between pupils, environment, and classroom climate, and the resulting randomness, opens the door to novel moments of teaching and learning. An example of this in my recent research included instances in which arguments between two pupils prompted periods of increased productivity and “on-task” behaviors from others. In another example the repeated ignoring of a low social status pupil by his small group caused him out of frustration to share his ideas with the teacher, resulting in him eventually explaining it to the whole class, whereafter most groups adopted his ideas. The learning in these incidents, or the conditions for it, emerged bottom-up rather than top-down. What emerged was a consequence of the situated, unique dynamic choreography of multiple pupils’ wills, personalities, social standing, and knowledge states, among other factors. Sensitivity to the ways these and other factors can interact to produce conditions for learning where one might not expect to find it can be advantageous to teachers. How then might teachers use this knowledge and capitalize on it in the interest of learning? The knowledge that whilst excessive pupil autonomy, interactive license, or social conflict is likely to obstruct learning, appropriate degrees of freedom from centralized teacher control can invite novelty, could lead teachers to re-evaluate their centralizing instincts and look for a balance between Radford’s “clockish” and “cloudish” organization [40] in their classrooms. Such re-evaluations can lead to teachers pausing, observing, and assessing for potential teachable moments. If there are potential benefits in allowing social conflict to play out, enabling pupils to find their own resolutions, this may lead teachers to re-assess how and when they intervene. Judging the line between tolerable and intolerable degrees of social conflict or noticing signs of potential learning benefits is unlikely to be easy, however there is evidence, albeit tentative, that there may be payoffs for teachers brave enough to try. The “edge of chaos” [1] will always be a double-edged sword, inviting innovation but also risking havoc. Semetsky [76] acknowledges that radical visions of open and unrestrained classroom systems, whilst opening doors to possibility, might also be counterproductive. Accepting that interactive learning is by nature open and generative (and therefore susceptible to disorganization), Biesta [72] has argued in favor of enabling constraints, structures which facilitate autonomous pupil interaction, encourage novelty and originality but without courting havoc. A useful question for teachers therefore might be, how can I implement limits on interactive learning which unlock and enable, rather than dampen, novelty and originality?
4.4 Occasioning emergent learning
CAS-framed classroom research and discussion [33, 61, 62, 64] suggests that CAS-like behaviors do not necessarily occur naturally in all classrooms, but depend to a large extent on balance between sources of coherence and disruption; between centralized control and decentralized autonomy. Having looked at some of the opportunities which CAS-like behaviors might offer teachers and pupils in pursuit of learning, it is worth considering ways teachers might occasion emergence by locating and exploiting “sweet spots” between rigid order and all-out chaos; in which pupils benefit from what autonomous interaction and teacher scaffolding have to offer. This is what Radford [40] refers to when describing systems, which are a balance between “clockish” and “cloudish”. This would include knowing how, and judging when, to centralize or decentralize the organization and autonomy, and understanding the consequences. Much like a jazz leader, knowing when to allow improvisation and when to return the ensemble to the main theme. “Sweet spots” exploit the most useful products of centralized, decentralized, and distributed classroom structures, whilst avoiding the unproductive excesses of each.
In tightly controlled, centralized classroom structures ideas tend to flow linearly from the teacher to the pupils and back from individual pupils towards the teacher. Under this organizing principle, ideas do not collide, they simply travel along straight lines, often in just one direction emanating from the teacher. Those ideas become known by everyone but are not interrogated, trialed, experienced, regurgitated, or challenged. The principal casualty of this tendency in highly centralized classrooms is the cross-fertilization of pupil perspectives and emergent learning. Newly emerging understandings, partially articulated thinking, and challenge to pupil assertions tend to arise more freely in distributed peer-to-peer interaction than in centralized, didactic scenarios. Too much centralized communication is likely to stifle this “soon-to-be” learning. At the opposite extreme, in classrooms principally characterized by distributed structure, ideas tend to collide but are rarely refined, sustained, or linked coherently. The main casualty here is the spread of ideas since ideas tend to remain local, petering out without dissemination, and coherence, because thinking remains atomized. Somewhere between these extremes lie sweet spots where pupils experience sufficient freedom and autonomy to encourage elements of self-organization and bottom-up novelty, accompanied by sufficient central teacher coordination for emerging ideas to be shared, digested, shaped, and sustained. Factors that teachers can manipulate to locate such sweet spots include (among other things) physical space, time periods, activity types, and groupings. Activities with sufficient openness undertaken for appropriate durations by autonomous or semi-autonomous pupil groupings with considered classroom organization and sensitive teacher intervention have the potential to create space for novelty, creativity, and innovation to emerge. Timely shifts between distributed, decentralized, and centralized structures may enable learning to emerge and progress from emergent states towards elaborated, more secure states from which it can be more easily redistributed.
In order to create conditions in which partially formed ideas may grow into articulated explanations or productive applications, a range of pedagogical pre-requisites are necessary, beginning with an understanding of what centralized, decentralized, and distributed modes of classroom organization offer the pursuit of learning. Confidence and competence in noticing signs that classroom structures need to be loosened or tightened follow logically from this, along with knowing how and judging when to do this. Creating open learning activities and opportunities that are fertile ground for ideas to collide, which are encouraging autonomous interaction and structured sufficiently to elicit novelty and originality, whilst avoiding excessive teacher control takes courage and practice. The starting point, surely, is sensitivity to potential teachable moments, attunement to the networked interacting influences at work in the classroom, and openness to the possibility of learning emerging from unexpected places.
5. Concluding thoughts
In response to the widespread tendency for teaching and learning to be depicted in over-simplified and linear-causal terms, this chapter has discussed the potential usefulness of applying a CAS framing to the primary classroom. I have sought to begin addressing the question of whether viewing classrooms as complex systems may illuminate inherent complexities of learning and ask whether doing so reveals any useful lessons for teachers. Descriptions of the behavior of CAS and consideration of the typical characteristics of many classrooms leave little doubt that classrooms have CAS-like features, though the extent to which such features may prevail in any given classroom depends to a large extent on how teaching and learning are organized. The most salient CAS qualities inherent to primary classrooms are occasioned by pupil autonomy, interconnectedness, and interaction through which there is some evidence that learning can reveal itself in emergent forms, more as a consequence of bottom-up than top-down initiative. Under the right conditions, including sufficient enabling constraints and pupil autonomy, a focus on CAS characteristics can draw teachers’ attention to the possibility of learning emerging from unexpected sources and in unanticipated times and ways. These include pupil errors, social conflict, and apparently off-task behaviors. However, more research is needed into the forms and value of such emergent learning, including how and to what extent it can be encouraged by thoughtful, imaginative, and sensitive teaching.
A possible implication for teachers is a recommendation to introduce subtle shifts in thinking about classroom practice and pupil learning. This would include considering the potential benefits of loosening central control and allowing pupils sufficient autonomy for self-organization to materialize, holding less tightly to evidence of pupils meeting learning objectives in unitary packages, and developing sensitivity to what else might be learned, when and how. I would urge my fellow teachers away from framings that locate learning as merely the linear product of teaching and instead invite them to adopt a more open and speculative mindset, exploring and investigating pluriform and interconnected antecedents of learning, rather than expecting it to appear as a product following teacher input.
\n',keywords:"complexity theory, complex adaptive systems, emergence, teaching & learning",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/79753.pdf",chapterXML:"https://mts.intechopen.com/source/xml/79753.xml",downloadPdfUrl:"/chapter/pdf-download/79753",previewPdfUrl:"/chapter/pdf-preview/79753",totalDownloads:105,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 5th 2021",dateReviewed:"November 19th 2021",datePrePublished:"December 26th 2021",datePublished:null,dateFinished:"December 22nd 2021",readingETA:"0",abstract:"Complex adaptive systems (CAS) theory is offering new perspectives on the nature of learning in school classrooms. In CAS such as social networks, city traffic systems and insect colonies, innovation, and change are occasioned through non-linear, bottom-up emergence rather than linear, top-down control. There is a growing body of evidence and discourse suggesting that learning in school classrooms, particularly in the early years and primary phases, has non-linear, emergent qualities and that teachers, school leaders, and educational researchers can gain valuable insights about the nature of interactive group learning by analyzing classrooms through a CAS lens. This chapter discusses the usefulness of a CAS framing for conceptualizing learning in primary school classrooms. It will explore key arguments, discuss relevant objections and draw on my own research to make the case for a measured application of CAS theory to primary classroom teaching and learning, explaining how it can support the development of innovative pedagogies.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/79753",risUrl:"/chapter/ris/79753",signatures:"Ben Knight",book:{id:"10662",type:"book",title:"Pedagogy - Challenges, Recent Advances, New Perspectives, and Applications",subtitle:null,fullTitle:"Pedagogy - Challenges, Recent Advances, New Perspectives, and Applications",slug:null,publishedDate:null,bookSignature:"Dr. Hülya Şenol",coverURL:"https://cdn.intechopen.com/books/images_new/10662.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-088-6",printIsbn:"978-1-80355-087-9",pdfIsbn:"978-1-80355-089-3",isAvailableForWebshopOrdering:!0,editors:[{id:"240203",title:"Dr.",name:"Hülya",middleName:null,surname:"Şenol",slug:"hulya-senol",fullName:"Hülya Şenol"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The over-simplification of classroom teaching and learning",level:"1"},{id:"sec_3",title:"3. Complex adaptive system (CAS) thinking",level:"1"},{id:"sec_3_2",title:"3.1 The idea of a complex adaptive classroom",level:"2"},{id:"sec_4_2",title:"3.2 CAS classroom framing: Some cautions and discussions",level:"2"},{id:"sec_6",title:"4. Implications for teachers and teaching",level:"1"},{id:"sec_6_2",title:"4.1 Thinking non-linearly",level:"2"},{id:"sec_7_2",title:"4.2 Attunement to networked influences on learning",level:"2"},{id:"sec_8_2",title:"4.3 The unexpected emergence of learning",level:"2"},{id:"sec_9_2",title:"4.4 Occasioning emergent learning",level:"2"},{id:"sec_11",title:"5. Concluding thoughts",level:"1"}],chapterReferences:[{id:"B1",body:'Davis B, Sumara D. Complexity Theory and Education: Inquiries into Learning, Teaching and Research. Abingdon: Routledge; 2006. p. 202'},{id:"B2",body:'Eisner E. 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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
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He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. 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Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,series:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983"},editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",slug:"anca-pantea-stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",slug:"attilio-rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",slug:"yanfei-(jacob)-qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},onlineFirstChapters:{paginationCount:17,paginationItems:[{id:"81647",title:"Diabetes and Epigenetics",doi:"10.5772/intechopen.104653",signatures:"Rasha A. 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