Lactic acid bacteria derived probiotics and human health
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In the human and animal bodies, LAB are part of the normal microbiota or microflora, the ecosystem that naturally inhabits the gastrointestinal and genitourinary tracts, which is comprised by a large number of different bacterial species with a diverse amount of strains [1,2].
Phylogenetically the LAB belong to the
Many strains of LAB are among the most important groups of microorganisms used in the food and feed industries, although some of the genus Pediococcus cause deterioration of foods, which results in their spoilage [4]. LAB have been used in food preservation and for the modification of the organoleptic characteristics of foods, for example flavors and texture [2]. Various strains of LAB [8] can be found in dairy products (yoghurt, cheese), fermented meats (salami), fermented vegetables (olives, sauerkraut), sourdough bread, etc [9]. The European Food Safety Authority (EFSA) has stated that several LAB strains can be considered to have “Qualified Presumption of Safety” QPS-status [9].
Moreover, nowadays, LAB play an important role in the industry for the synthesis of chemicals, pharmaceuticals, or other useful products (Figure 1). Also, the biotechnological production of lactic acid has recently reported that offers a solution to the environmental pollution by the petrochemical industry [10].
Uses and Functional Ingredients of Lactic Acid Bacteria
This chapter will discuss recent applications of LAB as source of probiotics, starter cultures, antimicrobial agents, vitamins, enzymes and exopolysaccharides, especially those that can satisfy the increasing consumer’s demands for natural products and functional foods in relation with human health.
Etymologically the term probiotics is derived from the Greek “probios” which means “for life”. In 1974 Parker [11] defined as probiotics “organisms and substances which contribute to intestinal microbial balance”. Fuller in 1989 [12] defined as probiotic “a live microbial feed supplement which beneficially affects the host animal by improving its intestinal microbial balance”. Later the Food and Agriculture Organization / World Health Organization defined probiotic bacteria as “live microorganisms which when administered in adequate amounts confer a health benefit on the host” [13]. Since probiotics can colonize the gastrointestinal tract and exert their beneficial effect long term, without requiring continuous medical intervention, they have been used for a century to treat a variety of mucosal surface infections (gut, vagina), but their use decreased after the appearance of antibiotics. However, today, probiotics are considered as an alternative solution to antibiotics due to the increasing spread of antibiotic resistance and the need for treatment cost reduction [14].
Microorganisms considered as commercial probiotics are mainly of the Lactobacillus genus with over one hundred species recognized, for example:
Probiotic bacteria are very sensitive to many environmental stresses, such as acidity, oxygen and temperature [20,21] and they must fulfill some functional and physiological aspects such as [21,22]: a) Adherence to the intestinal epithelium and colonization of the lumen of the tract. b) Ability to stabilize the intestinal microbiota. c) Counteracting the action of harmful microorganisms. d) Production of antimicrobial substances. e) Stimulation of the immune response.
There are subcategories of the general term probiotic [23,24] which are: a) Probiotic drugs: intended to cue, treat and prevent disease. b) Probiotic foods: food ingredients and dietary supplements. c) Direct-fed microbials: probiotics for animal use. d) Designer probiotics: genetically modified. Generally, foods containing probiotic bacteria fall in the category of functional foods [25].
Probiotics have multiple and diverse effects on the host. The main mechanisms of action of probiotic bacteria by which they improve mucosal defenses of the gastrointestinal tract include:
Antimicrobial activity: The probiotics block the colonization of pathogenic bacteria by decreasing luminal pH, inhibiting bacterial invasion and adhesion to epithelial cells and producing antimicrobial compounds such as bacteriocins and defensins, organic acids and hydrogen peroxide. The interaction of LAB with the mucosal epithelial cells of the gastrointestinal tract and the lymphoid cells in the gut enhance the gut immune response against ingested pathogens [26,27].
Enhancement of mucosal barrier function against ingested pathogens: It is achieved with the increasing mucus production through modulation of cytoskeletal and tight junctional protein phosphorylation. The probiotic bacteria compete with pathogenic bacteria for epithelial binding sites, inhibiting the colonization of strains like
Immunomodulation: Specific strains of probiotics might influence the innate and the acquired immune system, thus playing an important role in human diseases. Probiotic bacteria may affect the epithelial cells, the dendritic cells, the monocytes / macrophages and the various types of lymphocytes (Natural killer cells, T-cells and T-cell redistribution) directly or secondarily [33,34]. This action of probiotics could be important for the elimination of neoplastic host cells [22]. Moreover, the effects of probiotics on B-lymphocytes and antibody production resulted in an increase in IgA secretion and the enhancement of response to vaccination [34]. Recently, it was also reported that probiotics can have positive effects on the respiratory system by preventing and reducing the severity of respiratory infections, because of an increase of IgA in the bronchial mucosa [35].
Functional properties of probiotics have been demonstrated for various therapeutic applications. Nevertheless, the health benefits provided by probiotics are strain-specific, therefore no probiotic strain will have all proposes benefits, not even strains of the same species [36]. Among the LAB probiotic strains
Moreover, in animal nutrition dietary probiotics or direct fed microbials, term which is preferred in the USA, are able to help the maintenance of a healthy intestinal microflora. This microflora may serve to improve performance and health status of the animals, but also to suppress food born pathogens such as
Some of the beneficial effects of probiotics are well established as shown in Table 1.
Lactic Acid Bacteria | Effects on human health | References |
May shorten the course of rotavirus causing diarrhea. Helps to alleviate the symptoms of ulcerative colitis and atopic dermatitis. | 41-45 | |
Reduces the severity and duration of diarrhea. It can stimulate the immune system of the gut and alleviates the symptoms of Crohn’s disease | 17, 43, 46 | |
Secretes lactic acid which reduces the pH of the gut and inhibits the development of pathogens ( Reduces blood cholesterol. | 17, 46, 47 | |
Effective in inhibition of | 17, 36 | |
Produces short-chain fatty acids that block the generation of carcinogenic agents by reducing enzyme activities | 17, 36 | |
Effective in restoration of a normal microflora. Effective against bacterial vaginosis flora | 48 | |
Reduces the duration of diarrhea | 49 | |
Can reduce blood cholesterol leading to decreased blood pressure | 50-52 |
Lactic acid bacteria derived probiotics and human health
LAB derived probiotics have potential health benefits in the following situations:
Diarrheal diseases:
Infective diarrhea. The most studied gastrointestinal condition treated by probiotics is acute infectious diarrhea in infants. Children represent a main target of studies due to the importance of limiting the spread of diseases and decreasing the need of antibiotics (Aureli et al. 2011). Clinical trials with LAB derived probiotics (
Antibiotic associated diarrhea. A variety of probiotic bacteria, mainly lactobacilli have been used in the treatment and prevention of antibiotic associated diarrhea [55,56]. In a recent study
Clostridium difficile associated diarrhea.
Travelers diarrhea. Probiotics with Lactobacilli did not seem to be effective on such diarrhea, which is caused by bacteria, in particular enterotoxigenic E. coli [14,53].
Radiation induced diarrhea. Although there is little research on this subject, probiotics seem to be promising in decreasing radiation diarrhea [53].
Inflammatory bowel disease
LAB may affect positively the intestinal mobility and relieve constipation, possibly through a reduction of the intestinal pH [58].
Pouchitis. It is a chronic inflammation of the ileal pouch. The treatment with probiotics such as
Crohn’s disease. This disease can involve the whole gastrointestinal tract and is characterized by inflammatory processes occurring deeper in the tissues. Among other typical treatments
Ulcerative colitis. It is an acute or chronic disease only affecting the large bowel. LAB probiotics (
Irritable bowel syndrome
This term is used to describe a heterogenous group of gastrointestinal symptoms, like diarrhea, constipation, bloating and abdominal pain.
Prevention of colon cancer
The anticarcinogenic effect of probiotiocs may be attributable to a combination of mechanisms like the induction of pro- or anti-inflammatory and secretary responses that could inhibit carcinogenesis [22]. In vitro studies with lactobacillus strains have shown anti-mutagenic activities. However, there is no evidence yet that probiotics can protect against the development of colon cancer in humans [64,65]. Although, it is hypothesized that the strains tested may have anti-carcinogenic effects by reducing the activity of the enzyme β-glucuronidase.
Helicobacter pylori
It is a common chronic bacterial infection in humans, which causes many problems, such as chronic gastritis, septic ulcers and gastric cancer. Probiotics
Lactose intolerance
It is the most common disorder of the intestinal carbohydrate digestion. In both adults and children it has been shown that probiotics can improve the lactose digestion by reducing the intolerance symptoms and slowing orocecal transit [69,70].
Blood cholesterol
Recently it has been suggested that some strains of probiotic bacteria,
Other disorders
The majority of probiotics use has focused on diseases related to the gut, but there are studies that evaluated probiotics, in allergic conditions, including atopic dermatitis, rhinitis, bacterial vaginosis and food allergies [53,71].
Atopic Dermatitis. It is the most common of the chronic skin disorders, known as eczema. Investigations have shown that probiotics like
Bacterial Vaginosis. Probiotics (
Other ailments. Probiotics such as
LAB for a long time have been applied as starter cultures in fermented foods and beverages, because they can improve nutritional, organoleptic, technological and shelf-life characteristics [83,84]. LAB initiate rapid and adequate acidification in the raw materials, through the production of various organic acids from carbohydrates. Lactic acid is the most abundant, followed by acetic acid, whilst LAB can also produce ethanol, bacteriocins, aroma compounds, exopolysaccharides and some enzymes [85]. Earlier the production of fermented foods and beverages was obtained on a spontaneous fermentation, due to the microflora naturally present on the raw materials. Later on, the direct addition of selected starter cultures to the food matrix was preferred by the food industry. The advantages were the high degree of control over the fermentation process and the standardization of the final product [84].
As starter culture can be defined a microbial preparation of a large number of one or more microorganisms which is introduced to a raw material aiming to produce a fermented food by accelerating and steering its fermentation process [86,87].
The industries of fermented foods mainly utilize commercial starter cultures for the direct inoculation to the food matrix, which are available as frozen and freeze dried concentrates or lyophilized preparations [88].
Recently the use of functional starter cultures in food and beverage fermentation is being explored. These cultures have at least one functional property, contributing in the improvement of the fermentation process, enhancing the quality and of the end safety product and conferring health benefits [84]. Nevertheless, the selection of starter cultures must also eliminate undesirable side effects like the formation of D-lactic acid or a racemate of lactic acid (DL) or the formation of biogenic amines [84,89].
A category of successful starter cultures are LAB produced probiotic cultures. Firstly, Metchnikoff [90] discovered the beneficial effects of LAB on human health, through the consumption of yoghurts and fermented milks. Currently probiotic cultures are used for a number of products such as yoghurt, yoghurt drinks, infant formulas, dietary supplements, etc [91]. Yoghurt is manufactured using
A manufacture in order to choose any probiotic microbial strain to be used as starter culture or better as a blend with a traditionally used starter culture (co-culture), must check the following aspects [92]: 1) The ability of the probiotics to grow in a medium to increase the cells counts. 2) The robustness of the organism to withstand the freezing and drying stages of preparation. 3) The tolerance to acidity of the gastric acid and the bile salts during their passage in the gastrointestinal tract. Thus, the probiotic strains must be stable in order to claim the health benefits [92].
LAB are used as starter cultures either in dairy or non-dairy products (Table 2).
Genus | Application in dairy foods | Application in non-dairy foods |
Cultured dairy products, cheese, yoghurt, kefir | Sausage, sourdough bread, fermented vegetables | |
Cheese, butter milk sour cream, cultured dairy products | - | |
Cheese, cultured dairy products, sour cream, buttermilk | Fermented vegetables | |
Cheese, yoghurt | - | |
- | Sausage, fermented vegetables | |
- | Soy sauce | |
- | Wine |
Lactic acid bacteria used as starter cultures in fermented foods
Traditionally, LAB have been used in the fermentation of dairy products, as a simple and safe way of preserving such foods. The main species of LAB that can potentially be used as probiotic cultures in dairy products belong to the
Dairy products are considered as ideal vehicles for delivering probiotics to the human gut. Yoghurt is considered the most important, followed by cultured buttermilk, kefir, cheeses, ice-cream [17,22,92] or frozen desserts like chocolate mousse [93]. Moreover proteolytic strains of LAB produced probiotics are used to release bioactive peptites called angiotensin I-converting enzyme inhibitors, which are examined for their hypotensive role [94]. Furthermore interaction between probiotics and starter cultures are possible, either as synergism (e.g. yoghurt) or antagonism (e.g. bacteriocins which exhibit antibiotic properties) [20].
A minimum viable LAB count of 106 CFU/g in fermented dairy food is recommended for the claimed health benefits [95].
The preservation of meat and meat products by fermentation has been used from ancient times and it was based mainly on natural meat microorganisms. Recently, researchers begun to develop starter cultures for meat products, in order to ensure standard quality for the fermentation process [87]. In 1995 the first LAB meat starter culture used by Niven et al. [96] in the USA was a pure culture of
As commercial meat LAB starter cultures the species more used belong to the Lactobacillus and Pediococcus strains, which can be isolated from dry sausages [97], sauerkraut [20], or smoked salmon [99]. Strains of the above LAB were found to have the best survival activity under acidic conditions and high levels of bile salts [98]. The role of the starter culture as aforementioned is for the safety of foods by inactivating pathogens and spoilage microorganisms via the acid and bacteriosin production. Therefore, the production of biogenic amines is inhibited and microbial growth is suppressed, without the use of antibiotics [97].
Several studies have reported that LAB from meat and meat products can have antibiotic resistance [100]. Thus, before using novel starter cultures or probiotic cultures it is important to check that they do not contain transferable resistance genes [97]. In addition, the selection of LAB starter cultures for sausage production must not have amino decarboxylase activity. Otherwise, biogenic amines will be produced in foods, such as histamine, tryptamine, tyramine, cadaverine, putrescine and phenylethylamine which have toxic effects [101].
LAB fermentation of vegetables can be achieved due to the presence of carbohydrates. Usually fermented vegetable juices are produced from cabbage, red beet, carrot, celery and tomato [18,102]. Also, LAB play an important role in pickles and table olives fermentation, affecting the final flavour and shelf-life [103,104].
Ensiling is a traditional method of preserving forages and is widely used all over the world. It is based on natural fermentation, where LAB ferment water-soluble carbohydrates into organic acids, mainly lactic acid or acetic and formic acids, under anaerobic conditions. Inoculation of LAB is often used as silage additive to enhance lactic acid fermentation [18]. This results in decreasing pH, inhibiting detrimental anaerobes and preserving the nutritional value and palatability of the forage [105,106].
Among the LAB genera frequently used are
LAB derived probiotic bacteria display a wide range of antimicrobial activities. Some strains of LAB produce non specific antimicrobial substances (short chain fatty acids, hydrogen peroxide) while others produce toxins (bacteriosins, bacteriosin-like components) [109]. Short chain fatty acids (formic, acetic, propionic, butyric and lactic acids) which are produced during the anaerobic metabolism of carbohydrates, decrease the pH. It has been considered that these acids are responsible for the domination of mucosal ecosystems by LAB [110]. Also, hydrogen peroxide inhibits the growth of pathogens [111].
Most of bacteriocins originating by Gram positive bacteria are produced from LAB. They are proteins that have bacteriocidal activity against species closely related to the bacteriocin producing strains, which could be applied in food preservation and health care [112,113]. Traditionally bacteriocin production has been considered an important characteristic in the selection of probiotic strains, while nowadays it is considered that they may function within the gastrointestinal tract [114], perhaps as alternatives to antibiotics for medical and veterinary use [115]. Generally bacteriocins are cationic peptides which display hydrophobic or amphilitic properties and usually the bacterial membrane is the target for their action [116].
The majority of bacteriocin produced by LAB are distinguished from classical antibiotics because: a) They are ribosomally synthesized and have a relatively narrow killing spectrum. b) They can be divided into two main groups, produced by Gram-negative and Gram-positive bacteria [117,118].
Bacteriocins according to their structure and characteristics can be classified mainly in the following classes:
Class I (lantibiotics), small peptides [119].
Class II, small heat-stable proteins which are further divided into subclasses such as IIa (pediocin-like bacteriocins) and IIb (two peptite bacteriocins) [119].
Class III (helveticin) [120].
Bacteriocins mainly produced by
LAB derived bacteriocins are suitable to use as food preservatives due to their characteristics: a) protein nature – they are inactivated by proteases in the gastrointestinal tract. b) Non-toxic and generally non-immunogenic. c) Thermoresistant thus the antimicrobial activity remains after pasteurization and sterilization. d) Affect most of the Gram-positive bacteria. e) Genetic determinants generally located in plasmid facilitating genetic manipulation to increase the variety of natural peptides. f) Usually act on the bacterial cytoplasmic membrane having no cross resistance with antibiotics [122,123].
Some benefits of the use of bacteriocins as food preservatives are: a) extended shelf-life of foods. b) reduction of the risk of transmission of food born pathogenic bacteria. c) Amelioration of economic losses due to food spoilage. d) No addition of chemical preservatives. e) Decrease of the intensity of heat treatments resulting in better preservation of food nutrients and sensory properties of the food. f) Marketing of “novel” foods, less acidic, less salty and with higher water content [123].
Nicin (lantibiotic – class I) is the first bacteriosin produced by LAB (
There are at least three ways in which bacteriocins can be incorporated into a food to ameliorate its safety: a) By using a purified or semi-purified bacteriocin preparation as food ingredient. b) By introducing an ingredient that has earlier been fermented with a bacteriocin producing strain. c) By using a bacteriocin-producing culture in fermented products to produce the bacteriocin in situ [125].
Additionally, bacteriocin production can contribute to the probiotic functionality of intestinal LAB, while in certain cases may be directly responsible for it, with respect to either beneficially modulating the gut microbiota or inhibiting some gastrointestinal pathogenic bacteria [30].
Consequently bacteriocins, derived from LAB can cover a broad field of applications, including the food industry and the medical sector, mainly in combination with other treatments to increase their effectiveness in humans and animals [126]. In the latter, bacteriocins can be used as growth promoters, instead of antibiotics, which have been banned in the European Union since 2006 [127].
Human life cannot exist without vitamins, because they are involved in essential functions e.g. cell metabolism and antioxidant activities. Humans cannot synthesize most of these vitamins, although it is well known that some intestinal bacteria like LAB can produce some vitamins (folate, vitamin B12 or cobalamin, vitamin K2 or menaguino, riboflavin and thiamine) [128,129]. The gut microbiota has been recognized as a source of some water-soluble vitamins, while such vitamins have also been reported as results of the LAB fermentation in yogurt, cheeses and other fermented foods.
Folate is the term used to describe the folic acid derivatives, such as the folyl glutamates which are naturally present in foods and folic acid that is the chemically synthesized form of folate, commonly used for food fortification and nutritional supplements. Folate belongs to the B-group of vitamins and participates in many metabolic pathways like the biosynthesis of DNA and RNA and the inter-conversions of amino acids. Moreover, folate possesses antioxidant capacity that protects the genome by preventing free radical hack of DNA [130].
Dietary folate is essential for humans, since it cannot be synthesized by mammalian cells. Folate can be found in legumes, leafy greens, some fruits and vegetables, in liver and fermented dairy products [131], especially in yogurts, where it may be increased depending on the starter cultures used and the storage condition, to values above 200 μg / lt [132]. Epidemiological studies indicated that folate deficiency is associated with a variety of disorders like Alzheinmer’s disease, coronary heart diseases, osteoporosis and increased risk of breast and colorectal cancer [130,133].
LAB having the ability to produce folate belong to the
Vitamin B12 or cobalamin is required for the metabolism of fatty acids, amino acids, nucleic acids and carbohydrates [134]. Vitamin B12 cannot be synthesized by mammals and must be obtained from exogenous sources like foods or the intestinal microbiota [128]. It has been reported that among the microorganisms some members of the
Vitamin B12 deficiency can cause various pathological disorders that affect the haematopoietic (pernicious anaemia), nervous and cardiovascular system. Furthermore, this deficiency in male animal models influenced the number of offspring which showed growth retardation and decrease in some blood parameters [136].
Vitamin K is involved in blood clotting, tissue calcification, atherosclerotic plaque and bones and kidneys function [137]. Vitamin K is present as phylloquinone (Vitamin K1) in green plants and as menaquinone (K2) produced by some intestinal bacteria, like LAB and especially strains of the genera
Riboflavin or vitamin B2 is necessary in cellular metabolism, being the precursor of coenzymes acting as hydrogen carriers in biological redox reactions [129]. Although, riboflavin is present in many foods such as dairy products, meat, eggs, green vegetables, its deficiency occurs with damages in the liver, skin and changes in the brain glucose metabolism [128,129], with symptoms like hyperaemia, sore throat, odema of oral and mucous membranes, cheilosis and glossitis [139].
Currently, riboflavin-producing LAB strains were isolated and used as a convenient biotechnological application for the preparation of bread (fermented sourdough) and pasta to enrich them with vitamin B2 [140].
LAB possess an extensive collection of enzymes many of which have the potential to influence the composition and the processing, organoleptic properties and quality of foods and feeds. LAB release various enzymes into the gastrointestinal tract and exert potential synergistic effects on digestion and alleviate symptoms of intestinal malabsorption [141]. In other cases these organisms may serve as a source for the preparation of enzyme extracts that are able to function under the environmental conditions of fermentation [142]. The enzymatic activity has been studied mainly in LAB isolated from wine or other fermented foods like cheeses and yoghurt [143,144]. Species of
Also, enzymes play an important role in winemaking. Wine flavor and aroma apart from aromas originating in grapes and alcoholic fermentation, is derived mainly from the activity of the LAB, through the action of their enzymes. These bacteria grow in wine during malolactic fermentation, following alcoholic fermentation, while a broad range of secondary modifications improve the taste and flavor of wine [144].
A number of LAB can produce a variety of long chain sugar polymers, called exopolysaccharides (EPS) which are mainly employed for the production of fermented dairy products. They are synthesized either extracellularly from sucrose by glycansucrases or intracellularly by glycosyltransferases from sugar nucleotide precursors [148]. These EPS can be classified according to their chemical composition and biosynthesis mechanism as homopolysaccharides, consisting of a single type of monosaccharide and heteropolysaccharides consisting of repeating units of two or more types of monosaccharides, substituted monosaccharides and other units like phosphate, acetyl and glycerol [149-151].
Homopolysaccharides are further divided into fructans including levan and inuline-type and glucans including dextran, mutan, alteran and b-1, 3 glucan [152]. On the other hand, heteropolysaccharides demonstrate little structural similarity to one another. Their production is influenced by the bacterial growth, phase, medium composition (carbon and nitrogen source), pH and temperature [153]. They can be produced by
In the last years, EPS derived from LAB have received increasing interest because of their GRAS status and their properties. EPS can improve the rheology of fermented foods (viscosity and elasticity) as natural biothickeners, emulsifiers, gelling agents and physical stabilizers to bind water and limit syneresis [153,156]. In particular commercial products like LAB dextran could be utilized apart from foods in gel filtration products, in the pharmaceutical industry, as blood volume expander and flow improver, in chemistry as paper and metal plating processes, in enhanced oil recovery and in chromographic media. Furthermore, levan can find use in the food industry as biothickener, while alteran as low-viscocity factor, extender, etc [128,151,157]. Additionally, EPS may produce oligosaccharides having prebiotic properties that could find important applications in functional foods [158]. The successful application of EPS in the manufacture of fermented milks is determined by the ability to bind water, interact with proteins and increase the viscosity of the milk serum phase [159]. Although, many LAB strains are able to produce EPS, their yield is low [149] and their industrial applications for the improvement of the properties of food products are limited [155].
Apart from the technological benefits some EPS derived from LAB are claimed to have beneficial physiological effects on consumer’s health. These benefits are detectable at very low concentrations [153]. The EPS due to their increased viscosity in foods may remain for longer time in the gastrointestinal tract and therefore be beneficial to the transient colonization by probiotic bacteria [153,160]. Another health benefit is the generation of short chain fatty acids by colonic microflora degradation in the gut. Several of these fatty acids are possibly involved in the prevention of colon cancer [153,159]. In addition, LAB synthesized EPS appear to have anti-tumor, anti-ulcer, immuno-modulating and cholesterol-lowering activity [155].
The EPS used in the industry represent only a small fraction of the biopolymers used. The reasons are their economical production which needs a global knowledge of their biosynthesis and an adapted bioprocess technology [151]. Moreover, large scale production of LAB derived EPS is low, since LAB being anaerobes, are relatively inefficient in converting energy from carbohydrates, compared to aerobes [149,161]. So this technological barrier must be overcome for cost effective production of EPS. Furthermore, the genetic instability of EPS production is a problem to industrial applications, resulting in loss or reduction of production or change in the composition of EPS [162].
Increasing the knowledge on EPS structure may lead to the production of the “designer” EPS, including the modification primary in structure by altering their physical properties, their function and their production levels. However, for such production of EPS legal approval and the acceptance by the consumers and the food industries are required [151,163]. However, if these biomolecules are to be developed commercially, they must be cost effective.
In some circumstances EPS cause food spoilage. For instance during the fermentation of wine or cider the final products receive undesirable properties. The EPS synthesis is responsible for dental plaque that results in dental caries. Moreover, the accumulation of EPS cause many technical and hygienic problems in the cheese and milk industries [156,164].
Recently, low calorie sugars produced from LAB have attracted the interest of researchers, industries and consumers, since they can find application as vital food ingredients mainly in foods marketed as “diabetic foods”, like sugar-free candies, cookies and chewing gums [165,166]. Manitol, sorbitol, xylitol, tagatose and thehalose are sweeteners produced by LAB. These substances are polyols, i.e. sugar alcohols and can be produced in food fermentation processes. They can be incorporated directly to foods or be produced in the food by LAB, leading to the production of foods containing such sweeteners [165].
A number of health benefits have been attributed to these LAB produced low calories sweeteners, like low glycemic index, osmotic diuretics, weight control, antiplaque, prebiotic. These GRAS substances could be used especially by children, diabetic patients and weight watchers [165,166].
Lactic acid bacteria are very promising sources for novel products and applications, especially those that can satisfy the increasing consumer’s demands for natural products and functional foods. They can be used in the diet of humans and animals, with particular role in their health status. Despite recent advances, the study of LAB and their functional ingredients is still an emerging field of research that has yet to realize its full potential.
The courses taught by the authors of this chapter (ICT-oriented, undergraduate university level courses) use a type of accumulative model of tracking students’ activities, where multiple traditional written mid-term assessments grant most of the points required to pass the course. A more specific feature of this tracking model is that the units of learning contents are assessed multiple times. In other words, every subsequent mid-term assessment includes the re-assessment of previous content too, but with diminishing contributions – for example, 2nd mid-term assessment might include 40% of the content from the 1st mid-term assessment and 60% of new content, 3rd mid-term might include 10% of the oldest content (1st mid-term), 30% of the older content (2nd mid-term) and 60% of brand new content, etc.
Although we were generally satisfied (in terms of overall course grades) with the results of our traditional non-adaptive pen-and-paper assessment approach, we wanted to explore the possibilities of including Information and Communications Technology (ICT) support and adaptive assessment into the accumulative tracking model, to achieve following improvements:
To adapt the re-assessment portion of the mid-term to each individual student, based on the results he/she obtained for that content during previous mid-term:
Students that have shown higher levels of mastery of particular content during previous mid-term need not be re-assessed about that content in detail – i.e. they may receive less questions or less complex questions (to demonstrate that they have not forgotten what they had known before).
Students that have shown lower levels of mastery of old content should be re-assessed about that content more thoroughly (to demonstrate that currently they have more knowledge than before).
To include ICT support into adaptive knowledge assessment process, because manual adaptation of subsequent pen-and-paper mid-terms for each individual student, as suggested above, would be too complex to manage.
Such assessment model should be
With that respect, our primary goal was to explore the possibilities of improving the in-house knowledge assessment process by making it adaptive, but without introducing the complexity of complete adaptive learning systems (which will be mentioned briefly in the opening paragraphs of Chapter 3.
To achieve the desired goal (as mentioned in Introduction - inclusion of ICT support and adaptive assessment into our existing accumulative tracking model), we used Design and Development Research (DDR) Method that allows researchers to establish new procedures, techniques and tools based on specific needs analysis [1] and that consists of seven iterative phases [2]. Within the first, “Focus” phase we bounded the scope of the project to ensure that the project pursues an important goal that can be achieved with current resources, which is presented in the introduction section. Within the “Understand” phase we analyzed research literature to investigate the problem (section “The Context of the Study”). Research objectives and hypothesis were then identified within the “Define” phase. The initial solution was designed under the “Conceive” phase (section “Rationale Behind the Proposed Model”). The “Build” phase aimed at developing the model and building a test platform (section “Development of the Model”). We evaluated the efficacy and behavior of the solution in a real context within the “Test” phase (section “Testing the Model”). This chapter, in overall, is part of the last phase in the DDR methodology (“Present”), where we elaborate how the developed solution contributed to solving the problem.
The adaptive online education is highly represented in current scientific and professional research, especially the studies focused on adaptive learning and adaptive learning systems (ALS). Here we refer to adaptive learning as a process which creates unique learning experiences for every learner by taking into consideration many learner’s traits, such as his/her interests, performance, personality, etc. [3]. Most research efforts in the ALS field are focused on full e-learning systems, which are driven by two main principles: (a) selection and delivery of the appropriate learning contents to each participant, so that (b) each participant can improve the effects of his/her education [4, 5].
Although this chapter follows a similar principle, it does not focus on adaptive education in its broader and general sense. Instead, it puts an emphasis on the process of adaptive online knowledge assessment [6, 7, 8], i.e. on the process of selection and application of different types of questions within written online knowledge assessment, in order to improve each student’s achievement levels of learning goals. In the context of this chapter, like the approach taken by the Stanford University, we consider learning goals as the statements of “… what we want our students to be able to demonstrate at the end of our class.” [9]. Examples of such learning goals can be found in Table 1. Achievement of such learning goals can be measured by standard knowledge assessment grading techniques.
Online tests | Learning goal codes | Learning goal descriptions |
---|---|---|
Define decision support systems and expert systems | ||
Describe the elements, components, objectives and functions of IS | ||
Describe the structure of decision support systems and expert systems | ||
Describe the decision-making process and the role of DSS and ES in the decision-making process | ||
Describe the types of IS | ||
Distinguish the life and development cycle of IS | ||
Define types of content search on the Internet | ||
Define common Internet services | ||
Describe the elements of a computer network | ||
Describe the elements, functions and structure of the Internet, intranets and extranets | ||
Describe and compare the types of content search on the Internet | ||
Describe the ISO/OSI model and TCP/IP model | ||
Describe common Internet services | ||
Define concepts of multimedia and virtual reality | ||
Describe multimedia systems and virtual reality | ||
Define concepts of the safety and security of IS | ||
Describe and explain the safety and security of IS |
Learning goals used in Adaptivity application to test the model.
To be able to consider users’ individual differences, ALSs rely on user models [10] that keep track of many elements, including learning styles, learners’ personal preferences, prior knowledge, skills and competences. Many studies stress the importance of learning styles during adaptation process. As shown by Soflano, Connoly and Hainey [11], the adaptation based on learning styles in games-based learning (GBL) environment allowed learners to complete the tasks faster, compared to both non-adaptive GBL and to classic textbook learning. Tseng, Chu, Hwang and Tsai [12] report that the approach based upon multiple sources of personalization (learning behavior and personal learning style) is helpful in improving both the learning achievements and learning efficiency of individual students.
Although adaptations based on learning styles might have a role in improving learning achievements when applied on an entire ALS level, it should be noted that learning styles would not be that useful if they were used as a foundation for adaptations within narrower field of knowledge assessment only. Hartley [13] claims that individual learning styles are mostly static in time and not easily changed, unlike learning strategies which are primarily dynamic, conditioned by current tasks and can be manipulated with during shorter periods. Hartley defines learning strategies as “… the different combinations of activities (i.e. ‘strategies’) students use while learning.”. Similarly, Mayer [14] defines them as “… behaviors of a learner that are intended to influence how the learner processes information”. For this chapter, we consider these two basic learning strategies - deep and surface learning, which can be briefly described as follows [15, 16]:
Surface learning – any combination of the activities used by the students while learning, that lead to the learning aimed at mere reproduction of the contents. Understanding of learning contents is very low or non-existent.
Deep learning – any combination of the activities used by the students while learning, that lead to the learning aimed at understanding of the contents, i.e. questioning of alternatives, raising additional questions, etc.
We aim to use the feedback part of proposed adaptive system to steer the students towards behaviors and activities which would preferably lead to deep learning while preparing for the re-assessment of earlier learning contents.
Learning strategies, as described above, can be measured by various instruments, such as Study Process Questionnaire [17], although their direct measurement is not in the scope of this chapter. Here we refer to additional study which has shown that learning strategies can be facilitated (stimulated) and have important influence on achievement levels of learning goals [18] – it has been shown that an announcement of any type (form) of online knowledge assessment is not suitable for the facilitation of the more desirable deep learning and that all learning strategies facilitated by such announcements do not equally contribute to the achievement levels of the required learning goals. Zlatović, Balaban and Kermek [18] have demonstrated that a deep learning strategy has a positive effect on results in both essay and multiple-choice types of online assessment, while surface learning strategy has a negative impact on results in online essay, and no impact on results in online multiple-choice question assessment. When it comes to the levels of knowledge, the study has demonstrated that achievements of lower levels of knowledge (rote memorizing, reproduction, understanding) have been primarily stipulated by surface learning strategies which were facilitated by using online assessments containing multiple-choice questions. Achievements of higher levels of knowledge (analysis, synthesis and evaluation) were better when essay-based online assessments were used to facilitate deep learning strategies. Due to all these findings, we decided to incorporate the effects of learning strategies facilitation in proposed model, as an important supportive element in the adaptation of the re-assessment of the old learning contents.
Another major aspect of our model involves feedback which is a major element of quality in teaching and assessment [19, 20]. Students also appreciate the value of feedback and are aware of its importance in achieving learning goals [21]. Maier, Wolf and Randler [22] have examined feedback effects with computer-assisted multiple-tier tests and it was revealed that feedback is more effective when it is designed as elaborated (specific) feedback and that the elaborated feedback is effective when it is perceived as helpful.
By using feedback based on the results of individual’s current assessment, the adaptive system we propose will announce to each individual student the following instructions related to the re-assessment part of the next assessment:
What type of questions is predominantly going to be used in the following iteration (e.g. essay-oriented questions, matching the terms, fill-in the blanks, multiple choice) and
What is the expected difficulty of those questions (i.e. easy, medium or hard)?
Such announcements are supposed to facilitate the appropriate learning strategies (preferably deep learning) during preparations for re-assessment.
The central aspects of the model we are proposing are the
Review in the field of the adaptive online knowledge assessment reveals that historically most efforts are focused on studying various aspects of adaptability within a single knowledge assessment, usually within a self-assessment and/or formative assessment [23, 24].
However, to continuously monitor students’ progress, a continuous knowledge assessment was proposed. McAlpine [25] defines it as “… the more modern form of modular assessment, where judgments are made at the end of each field of study”. Continuous knowledge assessment belongs to the group of formative assessment techniques, since it provides plethora of individuals’ learning progress indicators while students are still committed to the learning process. Therefore, such indicators can be used to carry-on corrective actions while the teaching process is still ongoing – e.g. to adapt teaching process to the specific needs of participants.
Continuous formative evaluation using ALS system Amrita Learning [26] uses multiple assessments in adaptive manner, but each assessment covers different learning contents and old contents are never re-assessed. Therefore, such adaptation process does not consider the results of earlier assessment(s).
Grundspenkis [8] and Grundspenkis and Anohina [27] have described an adaptive learning and assessment system where concept maps are used as a more machine-friendly replacement for essays. Course contents are introduced gradually in time, through multiple stages. Every subsequent stage can only upgrade existing content from previous stage with new concepts. Adaptive knowledge assessments take place between stages, but although these assessments encompass contents from all available stages (similarity with our approach), the adaptivity is still limited to a single assessment. Adaptivity is reflected via two properties: (i) student can request a task with reduced difficulty, if initial version is too difficult and (ii) system can automatically increase the difficulty of the following task if the student has achieved required score without any reductions. Still, there is no evidence that e.g. an assessment that takes place between stages 2 and 3 takes into consideration the results from the assessment conducted between stages 1 and 2. There are also examples of adaptive and continuous assessment within commercial e-learning platforms – e.g. Khan Academy, whose approaches towards assessing students’ mastery of a particular topic is described in [28]. Historically, the Khan Academy used the streak concept, where student had to solve correctly at least 10 problems in a row. Then the system assumes that required proficiency level has been achieved and student can progress further to new topics. More advanced proficiency model replaced the streak approach – next task was selected using logistic regression techniques, considering both previously solved tasks and current proficiency level of a student. While the element of adaptivity over the series of assessments is present, it still lacks the systematic inclusion of older content into upcoming assessments.
Within the area of ALSs we often encounter distinctions mentioning micro- and macro-adaptation. It is suggested by Van Lehn [29] that primary focus of macro-adaptation is application of adaptivity on a global task selection process within entire ITS, while primary focus of micro-adaptations are lower-level in-task interactions. Knowledge assessment is usually considered to belong to the micro-level of an ITS. Results of assessments are then used to update learner models, which are then used in subsequent macro-adaptation activities [24, 30]. Since we propose the adaptive model of continual assessment that is designed primarily to be used standalone, without being part of a larger ALS or ITS, macro-level of adaptation will be represented by adapting the re-assessment part of the next assessment. Results of micro-activities (individual assessments) would update simplified user model (user’s achievement levels per topic/learning goal), which is later used to perform macro-adaptation between two assessments.
Review of the available research suggests that sufficient investigation effort has not yet been put into assessment systems which implement adaptivity within series of interconnected assessments, specifically into systems using adaptivity to re-assess previous learning contents. Additional insights about such systems is one of the scientific contributions of this study.
In respect to the issues noted from the research literature, the objectives of this study are as follows:
To develop and test a model of the adaptive online knowledge assessment system that facilitates those learning strategies that lead towards better achievement of the required learning goals.
To provide feedback to students based on individual results of their online assessments, containing suggestions about the assessment types that are going to be used for the re-assessment of particular learning goals in the next iteration of an adaptive assessment.
In line with the research objectives, the following hypothesis is formulated:
Based on the findings and the experience from previous research regarding the learning strategies, as well as the other relevant work indicated in previous section, we propose the model of an adaptive online knowledge assessment system, which supports series of assessments connected in a linear way, in a chain-like structure. It is designed to guide the individual towards continuous improvements in achievement levels of required learning goals within traditional higher education class-based courses by focusing on several key aspects:
Assessment process is carried out continually during longer period (e.g. one semester), throughout a series of assessments following the principles generally common for ALS described in previous section. Necessity for having longer period is also supported by findings from Dembo and Praks-Seli [31], stating that changes in students’ learning strategies cannot appear instantaneously, due to them (strategies) being either part of individual’s automated behavior patterns or being carried over from other courses.
Personalized feedback per assessed learning goal (for example, see Maier, Wolf and Randler [22]) will be presented at the end of each assessment, based on individuals’ achievement levels per topic/learning goal, suggesting what type of questions will be used next time, to re-assess those learning goals.
Given the application of above-mentioned feedback and having enough time between two assessments, individuals have enough time to adjust their learning strategies [18, 31] – preferably towards deep learning, so that they are more likely to improve their achievement levels in re-assessed topics.
Inclusion of the following aspects into the proposed assessment model is part of the original contribution of this chapter:
Every subsequent assessment includes re-assessment of the topics from previous assessments (continual assessment of topics, to stimulate improvements of learning goals’ achievement levels – here we build upon findings from the field of cognitive psychology, where it was shown “… that repeated testing of information produces superior retention relative to repeated study, especially when testing is spaced out over time.” [32]).
Adaptive re-assessment of old topics based on individuals’ previous achievement levels per old topic/learning goal.
The knowledge assessment model proposed in this chapter represents a type of
Inclusion of adaptivity elements within the above-described type of assessment, as well as modeling and development of a system which selects the types of questions to facilitate learning strategies, which in turn lead to a better achievement of the required learning goals, is an important contribution of this chapter.
Following general practices from the field of adaptive knowledge assessment are integrated within the proposed model (references to the numberings 1 to 3 will be used later in the text as “general practice 1”, “general practice 2” and “general practice 3”):
Quantitative expression of individuals’ success in achieving particular learning goal [34], e.g., using percentage scale that mimics grading system.
Multi-level qualitative marking of questions difficulty, in context of the assessment of associated learning goal, e.g. easy/medium/difficult [33, 34].
Besides those elements, continual and cumulation properties are paired with adaptivity features are also built into the model. Cumulation property enables the inclusion of desired elements of adaptivity in the assessment system, in a sense that re-assessment of the earlier learning content may become individualized and in accordance with the achievements examinees have demonstrated during previous iterations:
Individual goal achievements from the previous iterations can be used to formulate the announcement of the type and the difficulty of the assessment that will be used to re-assess these goals in a new iteration.
The system informs each examinee what type of the assessment will be used in re-assessment of various portions of earlier learning content, so that (i)
Such announcements provide
Effects of the facilitated learning strategies lead to the improvements of students’ performance.
The basic structure of the proposed assessment model is shown in Figure 1. The
Basic elements of the proposed model of the continual adaptive online knowledge assessment system.
The
A
The
All the above-mentioned elements (cognitive levels, learning objects, learning goals, question difficulty levels) are defined manually by the teacher within the proposed system – it is solely their responsibility to set-up the database of interrelated learning goals, objects and questions.
The
The
Fail (F or 1): 0–49,99%
Sufficient (D or 2): 50–62,49%
Good (C or 3): 62,5-74,99%
Very good (B or 4): 75–87,49%
Excellent (A or 5): 87,5–100%
The
(1) Results per Learning Goal (LGs) | (2) Announcement of question types and difficulties to be used for a learning goal re-assessment: |
---|---|
- > it will be re-tested using difficult questions (predominantly using more demanding essay-type questions) - > it will be re-tested using medium and difficult questions (predominantly using essays and matching terms/statements questions; less likely by fill-in-the-blanks and multiple-choice questions) - > it will be re-tested using easy and medium difficulty questions (predominantly using multiple choice, fill-in-the-blanks and matching questions; less likely by short and less demanding essay questions) | |
Excerpt from an automated feedback presented to student at the end of each assessment.
The first assessment iteration in the assessment cycle is always non-adaptive, as illustrated in Figure 2. In this iteration, since it is the first time that all topics are being assessed, all students will have identical structure of the test. Only teacher (without intervention of the built-in adaptivity mechanics) decides (a) which learning objects and goals to include, (b) what difficulty levels of the questions will be required to assess particular learning goal and (c) how many questions (of required difficulty and type) will be included in the test. Besides already mentioned criteria (objects/goals, difficulty and number of questions), teacher can also define that in the initial phase of the assessment all student will be given either: (i) fully identical set of questions, or (ii) randomly selected questions, or (iii) a mixture of fixed and randomly selected questions.
Flow of the continual and cumulative adaptive knowledge assessment.
Based on individual results from the first iteration, it is possible to adaptively automate and personalize each student’s questions structure for the re-assessment of old learning goals in the following iteration. Therefore, the second (and each subsequent) iteration of the assessment implements the cumulation property and it is comprised of the:
First assessment of
Repeated assessment of
Likewise, the N-th iteration is also cumulative in nature – it includes the first assessment of new learning objects (initial phase with identical assessment structure for all students, teacher defines all parameters for question selection) and the repeated assessment of learning objects which were included in all the previous iterations (without teacher’s influence, governed only by built-in adaptivity rules, General practice 3).
Automated process of selecting the questions for learning goals that have entered the adaptive phase relies on five adaptive rules, which will be briefly summarized in following section, for the completeness and clarity of the chapter. More elaborate descriptions and case studies of those rules can be found in [37].
There are three categories of adaptive rules used to select questions for learning goals which have reached the adaptive phase of the assessment. Rules are built around general practices (general practices 1 and 2) and the properties of continuality and cumulation:
Regarding rule R1, at first it may seem pedagogically wrong to use only the difficult questions during the re-assessment of failed learning goals. It may very well be perceived as a punishment, but only if those difficult questions
Adaptivity, the web application for continual adaptive online knowledge assessment, was developed based on the proposed model and built upon Microsoft ASP.NET platform (MS Windows Server, MS SQL Server and ASP.NET) in order to test the model. However, detailed description of the web application is not in the scope of this chapter. More elaborate description of Adaptivity’s architecture can be found in Zlatović and Balaban [38].
The procedure of testing the effectiveness of the model involved approximately half of the students who regularly attended classes at the “Informatics 2” (convenience sample, N = 78), which is held at authors’ university as a part of the undergraduate curriculum for the bachelor’s degree in the field of information systems and technology. All students enrolled in “Informatics 2″ were divided into two groups (alphabetically, by Faculty administration). We selected randomly one of those groups to participate in experiment. The course is elective and is being taught at the bachelor university level, with first-year students being enrolled predominantly (more than 90% of the population). It is also available for students who attend 2nd and 3rd year of the bachelor program.
Formal curriculum of the course prescribed four written assessments (hereinafter tests) during the semester. The first test was used to verify the functionality of the proposed system in a real environment and under the workload generated by the actual number of users. Therefore, the three remaining tests were included in the research. The type of assessment was cumulative, meaning that each subsequent test included new learning materials along with the old one (as illustrated in Figure 2). With respect to the terminology used in previous Section, the individual test in the experimental group matches one iteration within the proposed model of the assessment. All the tests were conducted in strictly controlled environment (in Faculty’s computer labs, under teachers’ supervision).
In this section we analyze the results achieved by using Adaptivity to explore whether its usage increased levels of achievement of learning goals that had not been considered satisfactory in previous iterations. Table 1 shows all the learning goals (LGs) that were examined during the three tests cycle (tests t1, t2 and t3).
Upon completion of all three tests, average achievement scores per learning goals were compared. Prior to any comparisons, all individual achievement scores were converted from absolute points into relative percentages. Absolute points would not make sense here, because each student’s assessment in adaptive phase will have different amount of questions used (due to built-in adaptive rules R4 and R5 in particular) and consequentially, absolute points maximum would differ from student to student. Although the distribution of the achievement scores of learning goals in all three iterations did not follow normal distribution (both Kolmogorov–Smirnov and Shapiro–Wilk tests were used), the size of the experimental group (N = 78) is large enough to warrant the usage of parametric t-tests [39]. Specifically, two-tailed paired samples t-tests were conducted, because pre- and post-test scores produced by the same students were compared.
Table 3 shows the results of the comparisons made at the end of each iteration and Table 4 the results of the comparisons made between the first and the final test. Only the learning goals which elicited significant increase or decrease in the average achievements score were kept in those tables. In the first cycle, learning goals LG14 and LG15 were not calculated, because in the second test (t2) those goals were assessed for the first time, so there were no results for them from the previous iteration. Likewise, when displaying the results of the second cycle, LG16 and LG17 are not shown either. Item pairs in tables are encoded using simple LGx_ty scheme, where LGx stands for Learning Goal X (1 < =x < =17) and ty stands for particular test iteration y (1 < =y < =3) – e.g. LG6_t2 represents the score of Learning Goal 6 in test iteration 2.
Differences after the 1st test cycle (test t2 vs. test t1) | Paired Diff. Mean | t | Sig. (2-tailed) |
---|---|---|---|
LG1_t2 - LG1_t1 | −1.28205 | −0.217 | 0.829 |
LG6_t2 - LG6_t1 | 6.19692 | 1.462 | 0.148 |
LG7_t2 - LG7_t1 | 1.06410 | 0.183 | 0.855 |
LG8_t2 - LG8_t1 | 6.51282 | 2.501 | |
LG9_t2 - LG9_t1 | 9.38013 | 3.267 | |
LG10_t2 - LG10_t1 | −17.37179 | −3.479 | |
LG11_t2 - LG11_t1 | 3.57859 | 1.035 | 0.304 |
LG13_t2 - LG13_t1 | 12.17949 | 2.838 | |
LG1_t3 - LG1_t2 | 10.25641 | 2.432 | |
LG6_t3 - LG6_t2 | 9.88244 | 2.232 | |
LG7_t3 - LG7_t2 | 10.89744 | 2.194 | |
LG8_t3 - LG8_t2 | 1.91026 | 0.716 | 0.476 |
LG9_t3 - LG9_t2 | 6.97538 | 2.074 | |
LG10_t3 - LG10_t2 | 12.94872 | 2.406 | |
LG11_t3 - LG11_t2 | 6.10051 | 1.750 | 0.084 |
LG13_t3 - LG13_t2 | 7.13141 | 2.236 | |
LG14_t3 - LG14_t2 | 6.72962 | 1.808 | 0.075 |
LG15_t3 - LG15_t2 | −9.13500 | −2.462 |
Comparison of the achievements of learning goals between consecutive tests - comparisons after the 1st cycle (test t2 vs. test t1) and the 2nd cycle (test t3 vs. test t2) of the continual assessment, paired samples t-test (N = 78, df = 77, p < 0.05).
Differences between the first and the final test (test t3 vs test t1) | Paired Diff. Mean | t | Sig. (2-tailed) |
---|---|---|---|
LG1_t3 - LG1_t1 | 8.97436 | 1.867 | 0.066 |
LG6_t3 - LG6_t1 | 16.07936 | 4.090 | |
LG7_t3 - LG7_t1 | 11.96154 | 2.256 | |
LG8_t3 - LG8_t1 | 8.42308 | 2.443 | |
LG9_t3 - LG9_t1 | 16.35551 | 5.341 | |
LG10_t3 - LG10_t1 | −4.42308 | −0.991 | 0.325 |
LG11_t3 - LG11_t1 | 9.67910 | 2.468 | |
LG13_t3 - LG13_t1 | 19.31090 | 4.437 |
Comparison of the achievements of learning goals between the final and the first test - comparisons between the final test (t3) and the first test (t1) of the continual assessment, paired samples t-test (N = 78, df = 77, p < 0.05).
Paired-samples t-test statistics from Table 3 show that at the end of the 1st cycle of assessment, only 4 learning goals displayed significant changes in average achievement scores – for three of them (LG8, LG9 and LG13) there is significant increase of the average scores (ranging from 6.51% to 12.18% higher score on the average), while one learning goal (LG10) displayed significant decrease of the average score (17.37% lower score on the average). After the 2nd cycle, statistically significant increases of the average scores were noted for 6 learning goals in total (LG1, LG6, LG7, LG9, LG10 and LG13, ranging from 6.98% to 12.95% higher score on the average) and one learning goal (LG15) has shown statistically significant decrease of the average score (9.14% lower score on the average).
After the 2nd cycle, LGs from 1 to 13 have been adaptively re-tested for the second time, while LGs 14 and 15 have been adaptively re-tested for the first time. Lack of the statistically significant difference in score for LG8 after the 2nd cycle can be interpreted as the stagnation (compared to the significant increase LG8 has had after the 1st cycle) – slight average increase of 1.91% cannot be taken as statistically significant at p < 0.05. Differences in achievement levels for learning goal LG11 show stagnation after both 1st and 2nd cycle of the assessment. Interestingly, Table 4 suggests that LG11 has significantly higher average score when entire chain of the assessments is taken into consideration.
Results shown in Table 4 (final test t3 vs. first test t1) include only those learning goals that have been used throughout entire chain of assessments, i.e. only LGs from 1 to 13 (LGs 14 and 15 were introduced in test t2 for the first time, while LGs 16 and 17 were introduced in test t3 for the first time). At the end of the series of assessments, 6 learning goals in total (LG6, LG7, LG8, LG9, LG11 and LG13) have shown statistically significant increase of the average achievement score (ranging from 8.42% to 19.31% on the average).
The results of one learning goal (LG10) have effectively canceled themselves out during the repeated assessments – data from Table 3 shows that LG10 recorded significant decrease of the score after the 1st cycle and significant increase of the score after 2nd cycle – the results for LG10 after test t3 have become similar to the initial results after test t1. This is shown as statistically insignificant decrease of 4.42% on the average in Table 4. Although final results for LG10 indicate stagnation, initial significant decrease of students’ score after LG10’s first re-assessment has been compensated by significant increase after the second re-assessment of LG10. Similar reasoning can be applied to LG1 too – the decrease of the score after the 1st cycle was not large enough to be considered significant and the increase of the score after the 2nd cycle was significant (Table 3). But the final results for LG1 (in Table 4) suggest that observed increase for LG1 between the last (3rd test) and the first assessment (1st test) is borderline insignificant at p < 0.05, because students had achieved slightly lower score at LG1 during 3rd assessment than during 2nd.
In addition to the already discussed LG10 and LG1, for 5 more learning goals in total (LG2, LG3, LG4, LG5 and LG12) repeated assessment did not cause statistically significant changes in average scores and those LG’s were omitted from Tables 3 and 4. These results can also be interpreted as the stagnation in the achievement levels.
Based on those indicators, it is shown that the use of the proposed model encourages improvements in the level of achievement for almost 50% of the evaluated learning goals (6 out of 13 goals which have been included in the assessment from the beginning), or at least it enables the retention of the existing levels of the achievement (7 out of 13 goals which have been included in the assessment from the beginning). Constant decrease of the achievement levels has not been noticed at any of the learning goals which have been re-assessed at least twice.
It has been demonstrated that the application of the Model has positive influence on improving achieved levels of knowledge per individual learning goals being assessed. During the three-test assessment cycle, it was shown that for 6 learning goals there was a global tendency of improving the achievement (i.e constantly increased achievement levels during re-assessments of those learning goals) - predominantly for the more complex goals, which required the ability to describe and understand concepts, not just to recall the facts. For 5 learning goals, there was a global tendency to maintain previous level of achievement. Only one learning goal showed negative initial result, although, as already described, after 2nd iteration that learning goal recorded significant improvement in scores, but not adequate to globally overcome the low score after the 1st iteration. And the improvements for one more learning goal were borderline insignificant.
It has been mentioned in Section 7 that only half of the student population enrolled in course “Informatics 2” were used to test the model (i.e. “experimental group”). One could ask why the results obtained during model testing have not been compared with the results of the other half of the class (i.e. “control group”). Main reason is that there have been too many differences in the overall knowledge assessment process between two groups, for the comparisons to be valid and meaningful. While the “experimental” half of the class used online Adaptivity system, which had provided mixture of various types of questions (multi-choice, fill-in, match, essay), between-assessment adaptation and individualized post-assessment feedback per learning goal, students in so-called “control” half of the class were given only pen-and-paper tests using essay-type questions exclusively, without detailed feedback and without any form of adaptation (i.e. the traditional way of administering the summative assessments within the course).
It must be mentioned that number of re-assessments per LO and LG used in this research (one initial assessment and at most two adaptive re-assessments) may not be enough in terms of proper continual knowledge assessment. Since the assessment results of the experimental group had to be used as a formally valid substitute for the final summative results of the “Informatics 2”, the assessment process design for the experimental group could not have diverged too far from the assessment process used for the rest of the class. E.g. fixed and relatively small number of assessments per semester was one of the constraints that had to be adhered to. It would be highly recommended to use more frequent (re)assessments in future research. Nevertheless, despite relatively low number of re-assessments, the proposed model did yield at least the retention of the previously reached levels of achievements (for 7 of 13 LGs), if not slight improvements in levels of achievements during re-assessments (for 6 of 13 LGs).
Another valid question is what type of knowledge has been taught and the type of teaching used. Content of the “Informatics 2” course is related to purely theoretical knowledge, within the area of expertise in ICT belonging to both social and technical sciences. Teaching process had consisted of purely ex-cathedra lectures with supplementary slides and lectures available within learning management system (LMS). Because of the assessed knowledge nature, success percentages in the Adaptivity have been set to mimic traditional grading system, requiring at least 50% success for a positive grade. If necessary, grading scales in Adaptivity can be re-adjusted to fit other areas of expertise, where higher cut-off points may be required for positive grades.
Most of the LGs (see Table 1) used in this study are focused on lower levels of knowledge. While not ideal, it is consistent with findings in [8] that even the most sophisticated automated assessment systems do not allow for testing of knowledge which is higher than level 3 or 4 in Bloom’s taxonomy. Adaptivity as a system does support usage of essay-type of questions, which must be graded manually by teachers. Therefore, higher levels of knowledge could also be re-assessed in the continual adaptive manner, at the expense of re-introducing increased teachers’ workload.
Overall, those findings are in-line with traditional features of continuous assessment, i.e. the ability to apply corrective actions while the education is still ongoing [25, 40] and the superior retention of information due to repeated testing spaced-out over time [32]. These are also in-line with several observations given in [41]: (i) assessment should not encourage surface learning and (ii) adaptive assessment provides benefits to both summative and formative assessment.
Application of the proposed system also helps alleviate one of the biggest practical disadvantages of manual continuous assessment reported in literature – vastly increased teachers’ workload, due having to spend more time to prepare and carry out frequent activities to track their learners [30, 42]. Proposed system is fully automating the adaptive portion of the continual re-assessment of old topics, leaving the teacher with task to manually create only the content related to the new topics, which are being assessed for the first-time.
Thus, it is shown that the Model, which employs continual and cumulative approach towards knowledge assessment and which: (a) individually adjusts amount, difficulty and type of questions per learning goal, based on previously demonstrated levels of achievement of learning goals, and (b) announces what types of the assessment will be used to test particular learning goals in the upcoming iteration, has predominantly positive effects on individual’s success at the level of particular learning goals, therefore supporting research objectives and hypothesis.
This research was conducted among ICT-oriented higher education students, which have already been using online education before. Therefore the sample used may not represent well the population from other fields of higher education (natural, technical, biomedical, humanistic, etc.) or outside of the higher education (e.g. secondary education, workplace education and/or life-long learning, etc.). Inclusion of respondents from other areas would ensure more varied population of respondents. Also, research was conducted within a course that uses blended education model (mixture of traditional class-based education and elements of online education), therefore it is advised to exercise caution when trying to generalize the results of this study to institutions and environments that practice either self-paced education, full online education, or traditional class-based education. The specifics of the assessment process itself represent another limitation – the assessment was adjusted to fit the continuous monitoring of students’ activities in the context of high education that adheres to Bologna Process.
The course was taught by the authors themselves and the authors have also designed the assessments, so a methodological bias needs to be considered when analyzing the results of this study. Further research should include both courses taught by and assessments designed by other teachers too.
We have also included only learner’s cognitive abilities. Affective characteristics of students (e.g., motivation, mastery goal orientation), which can also be important when designing adaptive assessment system, were not included. Further research should include broader student modeling. In line with [40], further research could also expand onto teacher responsiveness, which builds upon continuous results provided by the proposed assessment system.
On a different note, the current implementation of the Model could be a worthy contribution to further development of the Adaptive Learning Management systems that consider various users’ individual differences. Integration of the proposed Model in such adaptive environment as a complementary to the adaptive lessons could present a significant step forward in the design and implementation of Adaptive Learning Management systems.
This study describes original approach related to the modeling and implementation of the continual adaptive online knowledge assessment within class-based courses, where the adaptive aspects of assessment are used to re-assess old topics and are:
Applied within the series (or chains) of assessments, and
Based on the results that students have achieved in the previous assessments, rather than being based on the results achieved in the current, isolated assessment.
The Model introduces adaptation throughout a series of assessments in order to continuously monitor students and uses immediate feedback (mostly based on recommendations from Rowe and Wood [21] and Maier, Wolf and Randler [22]) as a major element of quality in teaching and assessment, which is given to students at the end of each assessment to facilitate the appropriate learning strategies.
The empirical study of the Model’s efficiency has shown that it is possible to design the system for adaptive online knowledge assessment, which can facilitate desirable learning strategies, which in turn lead to the achievement of required learning goals by announcing and using the appropriate types of questions in assessments.
Since it was shown that continual and cumulative adaptive online assessment is an efficient tool for facilitation of the appropriate learning strategies, the results of this chapter can be useful to the educational institutions when designing and implementing online knowledge assessments within class-based courses. The proposed Model also fits particularly well in continual monitoring and evaluation of students’ activities which is in line with Bologna Process, and in the same time relieves teachers from heavier workload.
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This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"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",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",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"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/107956",hash:"",query:{},params:{id:"107956"},fullPath:"/profiles/107956",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()