Four-C’s of creativity.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"3829",leadTitle:null,fullTitle:"Antioxidant-Antidiabetic Agents and Human Health",title:"Antioxidant-Antidiabetic Agents and Human Health",subtitle:null,reviewType:"peer-reviewed",abstract:"The human system employs the use of endogenous enzymatic as well as non-enzymatic antioxidant defence systems against the onslaught of free radicals and oxidative stress. Enzymatic antioxidants and non-enzymatic antioxidants work synergistically with each other, using different mechanisms against different free radicals and stages of oxidative stress. Dietary and lifestyle modifications are seen as the mainstay of treatment and management of chronic diseases such as diabetes mellitus. The major aims of dietary and lifestyle changes are to reduce weight, improve glycaemic control and reduce the risk of coronary heart disease, which accounts for 70- 80% of deaths among those with diabetes. It is also important to note that medicinal plants have been used as medicines since ancient time, and continue to play significant role even in modern medicine in management and treatment of chronic diseases. Impressive numbers of modern therapeutic agents have been developed from plants. Phytochemicals have been isolated and characterised from fruits such as grapes and apples, vegetables such as broccoli and onion, spices such as turmeric, beverages such as green tea and red wine, as well as many other sources. The WHO estimates that approximately 80% of the worlds inhabitants rely on traditional medicine for their primary health care and many medicinal plants have ethno-medical claims of usefulness in the treatment of diabetes and other chronic diseases globally, and have been employed empirically in antidiabetic, antihyperlipidemic, antihypertensive, antinflammatory and antiparasitic remedies. This book examines the role of antioxidant-rich natural products in management and treatment of diabetes and other chronic diseases.",isbn:null,printIsbn:"978-953-51-1215-0",pdfIsbn:"978-953-51-7190-4",doi:"10.5772/57029",price:139,priceEur:155,priceUsd:179,slug:"antioxidant-antidiabetic-agents-and-human-health",numberOfPages:382,isOpenForSubmission:!1,isInWos:1,hash:"148f7976e4249aa1f0180cca370e36ce",bookSignature:"Oluwafemi Oguntibeju",publishedDate:"February 5th 2014",coverURL:"https://cdn.intechopen.com/books/images_new/3829.jpg",numberOfDownloads:45676,numberOfWosCitations:61,numberOfCrossrefCitations:16,numberOfDimensionsCitations:69,hasAltmetrics:1,numberOfTotalCitations:146,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 26th 2013",dateEndSecondStepPublish:"May 17th 2013",dateEndThirdStepPublish:"August 21st 2013",dateEndFourthStepPublish:"November 19th 2013",dateEndFifthStepPublish:"March 24th 2014",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,editors:[{id:"32112",title:"Prof.",name:"Oluwafemi",middleName:"Omoniyi",surname:"Oguntibeju",slug:"oluwafemi-oguntibeju",fullName:"Oluwafemi Oguntibeju",profilePictureURL:"https://mts.intechopen.com/storage/users/32112/images/3371_n.jpg",biography:"Prof Oluwafemi.O Oguntibeju is an Associate Professor and Group Leader (Nutrition and Chronic Disease Research Unit) at the Oxidative Stress Research Centre in the Department of Biomedical Sciences, Faculty of Health & Wellness, Cape Peninsula University of Technology Bellville, South Africa. He lectures and supervises postgraduate students and collaborates with national and international scientists. Over the years, he has been involved in the field of nutrition and HIV/AIDS and related-public health issues but more recently on diabetes. He has published over 90 scientific papers in peer-reviewed journals, presented over 30 papers at national and international conferences and reviewed manuscripts for over 30 international scientific journals. He has received various awards such as the Gold Research Excellence Award at his current university. Prof O.O Oguntibeju is a National Research Foundation (NRF) C-rated researcher and holds a master degree in Biochemistry from the University of Ibadan, Nigeria and a doctoral degree in Biomedical Science at the Central University of Technology, Bloemfontein, South Africa. He is a Chartered Scientist (CSci, UK) and Fellow of the Institute of Biomedical Science, London. He enjoys reading and music and he is married to Faustina and has four children.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"3",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1013",title:"Pediatric Endocrinology",slug:"pediatric-endocrinology"}],chapters:[{id:"45944",title:"Lipid Profile, Antidiabetic and Antioxidant Activity of Acacia ataxacantha Bark Extract in Streptozotocin-Induced Diabetic Rats",doi:"10.5772/57151",slug:"lipid-profile-antidiabetic-and-antioxidant-activity-of-acacia-ataxacantha-bark-extract-in-streptozot",totalDownloads:2529,totalCrossrefCites:0,totalDimensionsCites:4,signatures:"Rotimi O. Arise, Aderounmu I. 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\r\n\tThe study of populations and plant communities in their different aspects; ecological, structural, functional and dynamic, it is essential to establish a posteriori models of forest and agricultural management.
\r\n\tFor this, the methodological approaches on the type of sampling are considered essential, since there are differences between the purely ecological and the phytosociological methods, despite the fact that both pursue the same objective.
\r\n\tAlthough the ecological method for the knowledge of the vegetation is widely extended, the phytosociological one is no less so, since in the European Union it has been developed as a consequence of policies on sustainability, through which regulations have been issued, such as the habitats directive.
\r\n\tOn the other hand, research on plant dynamics and knowledge of the landscape in an integral way, have multiplied in the last 30 years, which has favored a deep knowledge of the floristic and phytocenotic wealth, which is fundamental for agricultural management, livestock and forestry.
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It presents more than one hundred works published in various national and international journals, as well as books and book chapters; and has presented a hundred papers to national and international congresses.",coeditorThreeBiosketch:"Carmelo Maria Musarella is a biologist, specialized in Plant Biology. He is a member of the permanent scientific committee of the International Seminar on “Biodiversity Conservation and Management” guested by several European universities. 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His current\nresearch projects focus on Botany, Vegetation Science (Geobotany), Biogeography,\nPlant Ecology and Biology Conservation, aiming to support Nature Conservation.\nDr. Quinto Canas has co-authored many cited journal publication, conference articles and book chapters in above-mentioned topics.",institutionString:"University of Algarve",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"203697",title:"Dr.",name:"Ana",middleName:null,surname:"Cano Ortiz",slug:"ana-cano-ortiz",fullName:"Ana Cano Ortiz",profilePictureURL:"https://mts.intechopen.com/storage/users/203697/images/system/203697.png",biography:"Ana Cano Ortiz holds a PhD in Botany from the University of\nJaén, Spain. She has worked in private enterprise, in university\nand in secondary education. She is co-director of four doctoral\ntheses. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"39029",title:'Improving Decision Support Systems with Data Mining Techniques"',doi:"10.5772/47788",slug:"improving-decision-support-systems-with-data-mining-techniques-",body:'The Decision Support System concept goes back a long time, the definition varies depending on the evolution of information technologies and, of course, on the point of view of those who issues such a definition.
Looking through several definition we can find that Moore and Chang defined the DSS as “an extensible system, capable of ad-hoc analysis and decision modeling, focused on future planning and used at unplanned and irregular timestamps” [10]. Also Carlson and Sprague cited by [3] define decision support systems as being “interactive systems that help decedent makers use data and models in resolving unstructured and semi-structured economical problems”.
In 1998 Turban defines a decision support system as “an interactive, flexible and adaptable system, exclusively designed to offer support in solving unstructured or semi-structured managerial problems, aiming to improve the decisional process. The system uses data (internal and external) and models, providing a simple and easy-to-use interface, thus, allowing the decision maker control over the decision process. The DDS offers support in all decision process’s stages”. [9]
In this context, studies show that the process of defining a Decision Support System has started from the idea of how the objectives of a DDS can be achieved, how a DDS’s components can be identified, the features that are provided to the end user and from the perception of what such a system is capable of doing (offering support in decision making processes, in solving structured and unstructured problems).
Holsapple and Whinston, in the [5], specify five characteristics of a decision support system: contains a knowledge base that describes certain facets of the decision maker’s universe (for example, the way certain activities of the decision-making process); has the ability of purchasing and managing descriptive knowledge, as well as other types of knowledge (procedures, rules etc.); has the ability of presenting ad-hoc knowledge in a periodic report format; has the ability of selecting a subset of knowledge for viewing purposes or for deriving other knowledge, mandatory in the decision making process; is able to interact directly with the decision maker, allowing choosing flexible solution and knowledge management.
In conclusion, considering all the definitions mentioned above, some of the most important characteristics of the DDSs are: uses data and models; enhances the learning process; grows the efficiency of the decision making process; offers support in the decision making process and allows the decision maker control over the entire process; offers support in all stages of the decision making process; offers support for decision makers in solving structured or un-structured problems; offers support for a user or for a group of users etc.
In order to make a decision, the managers need knowledge. In case of massive data amounts, issues may occur because of data analysis and necessary knowledge extract. Data is analyzed through an automated process, known as Knowledge Discovery in data mining techniques.
Data mining can be defined as a process of exploring and analysis for large amounts of data with a specific target on discovering significantly important patterns and rules. Data mining helps finding knowledge from raw, unprocessed data. Using data mining techniques allows extracting knowledge from the data mart, data warehouse and, in particular cases, even from operational databases.
In this context, data mining gets an important role in helping organizations to understand their customers and their behavior, keeping clients, stocks anticipation, sale policies optimization as well as other benefits which bring a considerable competitive advantage to the organization.
The main purpose of these techniques is to find patterns and hidden (but relevant) relations that might lead to revenue increase. The essential difference between data mining techniques and the conventional database operation techniques is that, for the second ones, the database becomes passive and is only being used for large amounts of data population, therefore helping in future finding of that specific data. Alternatively, the database is not passive anymore, being able to serve useful information regarding the business plans put in discussion.
Regarding data mining studies, two major types of them exists. One of them is represented by the hypothesis testing, which assumes exposing a theory regarding the relation between actions and their results. The second type of study is represented by the knowledge discovery. For this type of analysis, relations between data warehouse existing data are tracked. This can be done by using data viewing tools or by using fundamental statistical analysis, such as correlation analysis.
Data mining techniques reside from classic statistical calculation, from database administration and from artificial intelligence. They are not a substitute for traditional statistical techniques, but an extension of graphical and statistical techniques.
Data mining uses a large variety of statistical algorithms, shape recognition, classification, fuzzy logic, machine learning, genetic algorithms, neural networks, data viewing etc., from which we can mention regression algorithms, decision algorithms, neural networks, clustering analysis.
Regression algorithms. Regression represents a basic statistical method. In the case of data mining, it is also an important analysis tool, used in classification applications through logical regressions as well as forecasted reports measured using the least square or other methods. Non-linear data can be transformed into useful linear data and analyzed using linear regressions. The universal test for data mining classification is the coincidence index matrix. It is primarily focused on data classification abilities of the model. For continuous regressions, class inflection points must be identified. The applications of the methods into solving business problems are multiple.
Decision trees. In data mining technology, decision trees represent rules tree-view structures, also known as joining rules. The trees’ creation mechanism of the trees consists in collecting all the variables the analyst assumes might help the decision making and analyzing them considering their influence into result estimation.
The algorithm automatically determines which of the variables are the most relevant, based on the ease of data sorting. The decision tree algorithms are applied in Business data mining in areas like: loan request classification, applicants ranking for various positions.
Neural networks. This is one of the most commonly used data mining method. It consists of taking sets of observations and placing them in a relational system through arc-connected nodes. This idea derives from the way neurons act inside the human brain. Neural networks are usually structured in at least three layers, having a constant structure allowing reflection of complex non-linear relations. Each entry data has a node in the first layer, while the last layer represents the output data – the result. In order to classify the neural network model, the last layer (containing the output) has a corresponding node for each category. In most of the cases, this type of networks also have a mid node layer (hidden) which adds complexity to the model. The obtained results are compared to the targeted ones, and the difference is re-entered in the system for node’s cost adjustments. The process keeps looping until the network correctly classifies the input data (at a tolerance level).
Clustering analysis. One of the most general forms of this type of analysis allows the algorithm to determine the number of subsets. Partitioning is mainly used for defining new variable categories, which divide raw data in a precise number of regions (k-means clustering). Considering a random number of centers (k), data is associated to the center which is the closest to it. The basic principle of this analysis is to identify the average characteristic for different indicators in sets of data. Thus, new observations can be measured by reporting the deviation from the average. This analysis is often the base technique applied in a data mining study, being used in client segmentation and, implicitly, taking a segment-oriented action.
Developing Decision Support Systems involves time, high-costs and human resources efforts and the success of the system can be affected by many risks like: system design, data quality, and technology obsolescence. The decision support systems objective is to assist the managers and executives to make decision regarding the benefit of investment, budgeting cash flows and financial planning, especially in the case of public funds.
Presently, many institutions invest in building organizational data warehouses and data marts in order to increase the performance and the efficiency of the analytical reporting activity. Also, there are several expensive tools and software that can be used to analyze the trends and to predict some future characteristics and evolution of the business. Some of these tools analyze data from the statistic perspective or by using neural networks. In our opinion, in order to build an efficient decision support system there must be combined several techniques and methods that can improve the performance and the accuracy of the analysis from two major perspectives: historical data and forecasts. This requirement can be obtain by combining data warehousing, OLAP, data mining and business intelligence tools for analyzing and reporting into a flexible architecture that must contains: A data model’s level where an ETL process must be apply to clean and load data into a data warehouse or data marts; An application level with analytical models where multidimensional reporting like OLAP and data mining techniques can be combined to for historical and forecast analysis; An interface level where dashboards and reports can be build with business intelligence tools.
In the chapter it will be presented the consideration regarding to design the DSS’s architecture and there will be described the methods and ways for data mining integration into a data warehouse environment. In the paper [6], the authors propose a series of development stages for business intelligence systems: feasibility study, project planning, analysis, design, development and release into production.\n\t\t\t\t
These stages can be adapted and applied in decision support systems, but during the development cycle it is mandatory that differences between general system modeling and decision support systems modeling must be treated separately, in order to obtain a successful business requirements of implementing the specifications.Stage 1. The feasibility study consists of identifying the requirements and business opportunities and proposing solutions of improving the decision making process. Each of the proposed solutions must be justified by the implied costs and benefits.
Stage 2. Project planning consists of evaluating project sustainability possibilities, indentifying existent infrastructure components and future needs. The result of these activities concludes with the project plan. After its validation and approval, the effective start of the project can begin.
Stage 3. Business requirements analysis.\n\t\t\t\t\tThis stage focuses on detailing and analyzing on priority the initial requirements of the organizational management team. Usually, the requirements are indentified based on interviews conducted by managers and the project staff. These requirements might suffer slight changes during the project, but the development team must make the managers aware of the capabilities and limitations of a DSS, therefore reducing the risk of un-feasible business requirements to occur.
Data analysis – the biggest challenge of a decision support system development project – consists of identifying necessary data, analyzing its content and the way it relates to other data. Data analysis is focused on business analysis rather than system analysis performed in traditional methodologies. It is preceded by a data cleaning activity.
Data cleaning implies transforming and filtering data sources in order to be used in building the destination module – the analysis module. This process is done by: identifying necessary data from the functional modules; analyzing the content of the selected data sources; selecting the appropriate data for the project; implementation of data filtering related specifications; selecting the tools to be used in the filtering / cleaning process. During the source selection process, a few key aspects must be taken into consideration: data integrity, precision, accuracy and data format. These facets are critical in regards to the success of the new ETL process.
Metadata analysis is an important activity in which all the identified requirements would be transformed depending on the metadata structure, and stored in a metadata dictionary. A metadata dictionary contains contextual information on the data implied in the project. The system analysis phase can end by building a prototype which will be presented to the managers and project staff for functional specifications’ validation. The existence of quick development tools allow building new interfaces based on the analysis model.
An important step in this stage is choosing the technologies used in the prototype’s development and, later on, in the final system. Based on a comparative analysis over advantages and disadvantages brought by each of the technologies on the project, different approaches might be taken into consideration: usage of data warehouses, including OLAP (Online Analytical Processing) functionalities, usage of knowledge extract algorithms, data source integration tools or, on a final phase and assuming a parallel approach on building the system has been taken, usage of applications integration tools.
Stage 4. System design.\n\t\t\t\t\tDatabase / data warehouse design. According to the system’s requirements, the necessary data will be stored both on a detailed level as well as on aggregate level, therefore relational, object-oriented or multi-dimensional data storage approaches might be taken. During this sub-phase, the logical data model is refined and detailed and the physical model of the new system is developed in order to satisfy the reporting and analysis requirements of the managers.
While on “Data analysis”, the process has been oriented to data sources (data-in or data-entry) coming from operational modules, in this phase the targets or data destinations (data-out) are set aiming on reports, analysis and queries. Therefore, a list of best practices must be taken into consideration:
Due to the above mentioned aspects, we recommend that the storage, management and data processing solution to consist of a centralized data warehouse on an organizational level. Following logical and physical criteria, the data warehouse can be divided into data marts on departmental level, thus being easier to maintain and developed by separate teams, following the same set of specifications.
The ETL (extract / transform / load) process design – this phase is the most complex one in the project’s lifecycle and is directly dependant on the data sources’ quality.
We recommend the integration of all the destination databases in a single environment and building the ETL process on it, avoiding a separation of each destination module, thus mitigating the risk of distinct data marts. The strategy of building data marts in the same environment is also viable, but only on the condition that these are already integrated. The important fact here is that the ETL process must remain the same for all levels (the share one coordinated process principle).
The design of the ETL process needs a series of pre-requisite stages: preliminary processing of data sources, in order to have a standardized format, data reconciliation and redundancy and inconsistency elimination of data.
The steps to be taken in creating an ETL process are the following:
Creation of transformation specifications (mapping) of the sources in regards to the specific destinations. This may be done as a matrix or as transformation diagrams.
Choosing and testing the ETL tools to be used. At the moment, a series of ETL process modeling and implementation tools exist on market, but choosing one of them would depend on the features they provide and on the support of data source integration inside the same transformation process.
The ETL process design – several extract and transform operators are used, depending on the data model (sorting, aggregation, joining, dividing operators, etc.). The process can be split into sub-processes that would run separately in order to minimize the execution time. The execution flow of the process will be modeled using flow diagrams.
ETL programs design. Depending on the program in which the data is loaded, three phases of data loading are applied:
initial load – the initial load of destinations with current operational data
historical load – the initial load of destinations with archived historical data
incremental load – regular loading of destinations with current data coming from operational systems
Choosing the environment for running the ETL process – represents the decision over using a dedicated server / machine or the process would be divided and run decentralized. The decision depends on the available resources and on the processing time, as well as on the timelines that the process is scheduled to run.
The results of these activities is materialized in the data mapping documentation, the flow diagram / diagrams of the ETL process, the transformation programs documentation and the process execution specifications.
Metadata repository design – if the repository is acquired and a predefined template is used, then, in this sub-stage, slight changes may occur according to the requirements identified in the metadata analysis sub-stage, but, if the option has been to build a proprietary repository, then the metadata logical model will be implemented for the new system, based on the data storage options: a relational, object-oriented or multidimensional model will be implemented.
If the option was for building a proprietary warehouse, we consider that centralization and standardization of it would represent a good strategy into a more facile administration. The activities performed in this stage are materialized in the detailed logical model and the metadata physical model.
Stage 5. Building the system. The technologies that are used for decision support systems’ development are part of the business intelligence technologies category and consist of: technologies for data warehouse data organization, OLAP (On-Line Analytical Processing) analysis systems, data mining algorithms, extract, transform and load (ETL) tools, CASE (Computer-Aided Software Engineering) modeling tools and web technologies.
Stage 6. System implementation. Represents the stage when the system is being delivered, training sessions are held for implied managers / business owners, the necessary technical support is provided, data loading procedures are run, the application is installed and the performance is being tracked.
The stage ends with the release of the system into production (commercial go-live) and with the delivery of the utilities and final project documentation, the user guides and presentation manuals for the application.
Depending on the requirements indentified in the analysis phase, all of these technologies can be merged and combined, creating reliable decision support system architecture. The field literature (for example in [2], [3], [4]) proposes a typical decision support system architecture that contains distinct levels which use the above mentioned technologies in order to be created.
In [3], the definition provided by Bonczek and Holsapple, the main components of a decisional system are emphasized: a DSS is described as being “a system composed of three interacting modules: the user interface (Dialog Management), the data management component (Data Management), the model management component (Model Management)”. In [4] it is identified four core components that form a decision support system: the interface, often considered to be the most important component, the database system which includes all the databases and the database management systems (DBMS) of the organization, the model system containing the analytical, mathematical and statistical models and the communication component, composed of the core network and the mobile devices.
The DSS architecture can also be seen from a development level point of view, from bottom to top, pyramidal, having three layers: bottom-tier middle-tier and top-tier, the connection of all these layers being made on the telecom layer. Thus, DSS architecture might be composed of the following levels:
Level I (bottom-tier) – data management. It is composed of data, metadata, DBMS (database management systems), data warehouses, data dictionaries and metadata dictionaries. At this level, data coming from several different systems must be integrated and the main techniques used for this process are replication, federalization or data migration, together with data warehouse loading. Data coming from operational databases and external sources are extracted using interface-type applications, known as gateways, running on DBMS and allowing client-applications to generate server-side executable SQL code. During the extract and data processing, different tools can be used – filtering, predefined procedures etc. Data cleaning and transformations is strongly dependent on the data sources and on the quality of it. Several sources may be put in discussion: files, databases, e-mails, internet and unconventional sources.
This sub-stage focuses on implementing the ETL designed requirements, running and testing them.
At this architectural level, in order to load data in a data warehouse, a series of tasks is mandatory:
Collecting and extracting data from the data sources that have been identified during the analysis phase, according to the management’s business requirements. A source data warehouse (staging area) can be created in order to load all the necessary data which then must be processed and loaded into another destination warehouse. This process, most often, transforms raw data for compliance with the internal format of the warehouse;
Data cleaning and transformation to assure data accuracy and to confirm data can be used for analysis;
Loading data in the destination warehouse.
This process is extremely important for the success of the future decision support system, thus a faulty design could lead to the failure of the DSS. Subsequently, a method for data refresh must be taken into consideration as time passes. Therefore, the ETL process must be automatically run on accurate timestamps defined during the design and analysis stages. The destination warehouse data will be used at higher levels of the system. If the chosen approach has been to build a new data warehouse (custom development) instead of purchasing a predefined solution, the metadata dictionaries used by the components and utilities of the system must be build and adapted to the solution, integrated and the connection interfaces between the user and the metadata centralized dictionary and the dictionaries used by each component separately must also be developed. Thus, the structure of the dictionary is created and the data will be loaded according to the logical and physical models already designed.
Level II (middle-tier) – model management or analysis level. This is the level where data is processed and the necessary information for decision making is extracted. This level contains data analysis, simulation and forecast models, in order to respond to the high level business requirements. On this level, the core components are: the model base, the model database management system, the meta-models, the model management and execution server.
For creating this level, OLAP technology can be used. It is based on multidimensional data representation and allows quick and interactive data analysis by using roll-up, drill-down, slice or dice operations.
If the business requirements requires, at this level, knowledge extract algorithms using data mining algorithms can be designed and implemented. These algorithms assure data transformation into knowledge using statistical analysis or artificial intelligence techniques and allowing the identification of correlations, rules and knowledge in order to support the decision making process. In order to integrate the analysis and models resulted from different sub-system types, several application integration technologies could be used: application servers that implement middleware models, service-oriented architecture (SOA), Java platforms.
Extracting knowledge from data (Data Mining) – very often, the success of a DSS is determined by the discovery of new facts and data correlations and not by building reports that just presents data. In order to fulfill these requirements, data mining techniques must be applied, together with knowledge extract from the organizational data, such as: clustering, forecasting, predictive modeling and classification.
In this context, an analysis regarding the data mining applicability domains, the specific algorithms to be used and the teams that would develop these initiatives must be done as a mandatory task.
The tasks to be performed are:
Domain and applicability objective setting for data mining techniques – specific requirements that cannot be resolved through other methods are analyzed, as well as the opportunity of applying data mining techniques and the way they would solve the problem
Data collecting – a validation is preformed in order to check the existence of the loaded data; if not, new data sources are set and previous steps regarding data design need re-validation
Data consolidation and cleaning – when data is not loaded in the correct format, cleaning processes are applied. The ETL processes implemented in Stage II may change due to these additional requirements.
Data setup – data mining algorithms use setup steps by formatting and loading data, in order to be compliant to the agreed techniques.
Building the analytical model – the step focuses on implementing the data mining algorithms and the specifications for the learning and testing stages
Result interpretation – according to the agreed requirements and objectives, a validation of the results must be performed. The measured values are interpreted and it is decided whether they can be used by managers.
Result validation – the measured values are compared to the expected ones, accepted deviations and errors are set based on statistics or comparative analysis and why these deviations have occurred. If the result can be used, they are presented to the managers (business owners) for the final validation.
Analytical model monitoring – the performance of the model is tracked in a timeline already defined in the requirements phase.
After applying data mining techniques, a complete database is obtained and will be used by specific programs, as well as the analysis model specifications.
Level III (top-tier) – The interface or the presentation layer. Represents the level where the interaction with the users takes place, where the managers and the persons involved in the decision making process can communicate with the system and can analyze the presented results. The user interface must be specially designed so that this type of users will easily interact with the system. This level is composed of queries and reports generating tools, dynamic analysis tools (data viewing using different perspectives, post-implementation evolution analysis, forecasting, data correlations), data publishing and data presenting tools in a simple, intuitive and flexible way for the end-users. On this level, the human resource can be found, represented by decision makers which interact with the system through its interfaces. In the last few years, a growing share in the development of decision support system interfaces is taken by the portal-based web technologies. Business Intelligence portals hold the most important position in creating specialized, flexible, user-friendly and accessible interfaces, allowing users a good end-to-end experience, nice graphical appearance, report integration options and graphic tools, obtained in the previous stages.
The DSS architecture used for WPP systems
Level IV – Telecommunications. Represents the level that allows interconnecting all the previous levels and may contain web servers, computer networks, communication devices, distributed platforms, GRID technologies and mobile communication platforms.
Based on these steps and DSS’ architecture, in the following sections we propose a conceptual model that can be implemented in the case study in which the National Power System’s (NPS) activity was analyzed, especially the wind power pants’ production and wind energy integration into the NPS.
The architecture is explained in [1] where all the components are presented in detail. The entire case study consider the characteristics of wind power plants, the ways to integrate the energy produced and the impact on decision-making system from the technical point of view (the characteristics and impact on reserve power), financial, commercial, environmental, legal (by analyzing the issues raised by access to the electric wind power). The next section present only the methods applied to predict and determine the wind energy based on data mining techniques and the results obtained for some of the algorithms.
Setting a wind farm is particularly important because its energy production depends to a several meteorological factors of that site. The main criteria for determining the location of wind generating units are wind speed and direction, orography and terrain conditions, environmental conditions, distance from the electricity grid, substations and connection conditions, access to equipment and personal interference human activities (tourism, proximity to settlements, roads, railways, airports), electromagnetic interference, conditions on land use and relationships with local authorities the power of its consumers.
But the main natural factor, wind speed, records significant fluctuations even within hours. Optimal range of wind speeds that produce wind turbines’ energy ranges from 3 to 25 m / s If the wind speed falls below this limit or exceeding 27 m / s, the turbines stop. Even for an area such as Dobrogea (where the area is windy) the wind speed in certain areas of land varies significantly. Therefore, to determine if the location is suitable it is necessary to measure the meteorological factors, such as wind speed and direction, temperature and pressure, etc.
From the above mentioned in the investment phase is particularly important to determine the energy produced by wind sources in order to choose the type of wind generator and location of each production station and also in the operational phase to achieve good production forecasts. But forecasts in wind power plants (WPP) still records significant deviations from the real values of energy products due to the inability of present systems to correctly estimate the wind speed. The problem becomes more complex because the forecasts obtained are used to establish the energy resources necessary to cover any gaps in the energy system. It is well known that rapid availability of power is questionable or expensive. If the forecast of wind energy from wind sources is more accurate the more it reduces the power system reserves. The role of a good prognosis is particularly important because it reduces the costs of ensuring safe operation National Power System (NPS) and therefore has no significant increases in energy prices as a result of these reserves.
But the most important problem, which depends on the amount of energy reserves, and return on investment in wind power, is the component on which these wind power plants are based, namely the wind. From Figure 2 one can see large fluctuations of the wind recorded by an anemometer within 24 h at a height of 50 m
Wind speeds measured within 24 h in a location in Dobrogea area
Wind energy production is conditioned by other factors, some of which are characterized by low predictability, such as the effect of shading, soil orography, the power, losses to the point of connection, etc. Currently, there are several informational systems used for the prediction of energy but the accuracy of these systems is still quite low. This can be seen analyzing the notifications forwarded by the wind power energy production within 7 days (Figure 3).
Comparison of energy prediction and actual wind production (source [8])
These problems regarding the low prediction accuracy and of data integration from various equipment and local systems, energy efficiency analysis, lead to the need to develop solutions for a better predictive power as products, but also to support decision making in this area.
A better prediction cannot be achieved by classical statistical methods, and this is the reason for requiring the use of modern techniques like data mining. In these considerations we have been analyzed in the following section, in detail, the main algorithms that can be applied to predict the wind.
For building and testing the data mining algorithms we’ll use Oracle Data Miner (ODM) software tool developed by Oracle Corporation that provides a friendly interface for data analysis and validation results. Oracle Data Miner provides several tools (wizards) for the data processing and for the stages of preparation, training, testing and evaluation required in data mining technology.
Oracle Data Miner implements the following types of algorithms [7]:
Predictive models or supervised training: classification algorithms, regression algorithms and selection of important attributes;
Descriptive models or unsupervised learning: clustering, association rules, extraction algorithms for new attributes based on existing ones;
Models for multimedia (TEXT) and bio-informatics (BLAST).
For all the algorithms the data preparation is required. In our case study the data were measured and recorded from 10 to 10 minutes between 09.11.2009 - 28.02.2010. The values recorded at height of 50 m count 16,037 records. The minimum value recorded in this period is 0 m / s, maximum value of 24.8 m / s and average 6.9 m / s.
From the set consisting of the 16,037 records of wind speed at 50 m height, about 2500 were lower or equal to 3.5 m / s - start speed of a wind generator (GGE). In approx. 1100 cases wind recorded a speed exceeding 12 m / s Approximately 8,800 measured values were lower than average speed, and about 7200 values above average wind speed (6.9 m / s).
For the algorithms, the source table entries were imported and divided into three sets for each stage that will be completed. Thus these sets of records will be inserted in three tables, namely: wind_build, wind_test, wind_apply (Figure 4).
Each table contains information on different time intervals in which measurements are made as follows: for table for learning are considered records of the period 09.11 - 15.01 (about 10,000 records), the table for the testing process are considered records of the period 16.01 - 15.02 (about 4600 records), and for evaluation the table contains records in period 16.02 - 23.02 (about 1100 records). Tables can be viewed directly in Oracle Data Miner interface by accessing data sources.
The records prepared for data mining
After the data preparation step, we applied the following algorithms: Naïve Bayes with an error rate of about 8%, decision tree with a 1% error rate, regression with error rate of 43% and after analyzing the results we modified the regression model and obtained a significant increase in prediction accuracy from 57.68% to 93.72%. The steps and the results are presented in the next sections.
We’ll further present how to apply the Naїve Bayes (NB) algorithm on the measured data to analyze the target attribute E-01. Attribute E-01 has two values: 1 – the turbine produces energy when wind speed is within the range 3.5 to 25 m / s and 0 otherwise.
By applying the NB algorithm will forecast whether or not the turbine will produce energy depending on weather conditions.
It will go through three distinct phases:
The learning stage consists in applying the algorithms NB on the wind_build table data set and build the analysis model;
The test phase, the model built in the previous step is tested on the table wind_test;
The validation phase, the model built on data set is applied on wind_apply table to check the results obtained from the algorithm.
The learning stage. In this stage we applied the templates of analysis to build the model on the table wind_build.
Setting the parameters for the implementation of Naive Bayes algorithm
For this scope a number of steps are required to configure the Oracle Data Miner algorithm parameters, such as setting the model name (NB_wind_build), source table, and minimum thresholds for the interpretation of outliers (Figure 5). In our case, is considered the minimum threshold of 5% - only data above this threshold will be considered on the basis of learning (for example, large fluctuations of wind speed are observed at high variations of temperatures whose incidence is rare).
We obtained a 88.6% accuracy of predictions with the NB algorithm.
The test phase. After building the NB model for our data set, we applied the testing algorithm. The results are shown in Figure 6.
The validation phase. To validate the results we considered three sets of validation data (figure 7): table wind_apply with data during 16-23.02.2010 period, wind_apply1 table with data from 24 h (from 02/24/2010) and wind_apply2 table with data from 24h (02/25/2010).
The accuracy of predictions obtained for the three sets is: 91% for table wind_apply (104 erroneous predictions of 1152 records), 99% for table wind_apply1 (1 prediction error of 144 records), 91% for table wind_apply2 (13 of erroneous predictions of 144 records).
The results of NB algorithm on the test set
The results of NB algorithm
In conclusion, the error rate resulting from the application of NB algorithm is less than 8% which is considered to be satisfactory.
Another prediction algorithm applied to E_01 variable is the Decision Tree. After building and testing the model on the data sets, following the same steps presented in the previous section, we obtain an accuracy of 99.48% (Figure 8), higher than that obtained by applying the NB algorithm.
The results obtained by applying the decision tree algorithm
The results detailed on the three tables: the set from table wind_apply recorded an error of only 0.6%, for table wind_apply1 no errors were detected and the table wind_apply2 recorded an error of 0.7%.
In conclusion the results obtained by applying the Decision Tree algorithm are better than those obtained with the NB algorithm. But to get a real energy prediction of turbine’s actual values is necessary to apply other algorithms where the target attribute has discrete values, not only values 0 or 1.
On the initial data set we introduced column E which is the amount of power produced by wind speed (S2) measured at 50 m cubed. The values in this column will be the target attribute for the regression algorithm. We applied the regression on the data sets, following the same steps (preparation, learning, testing and applying) and the results obtained from the algorithm have an accuracy of only 57.68% (Figure 9) which gives no confidence to achieve rigorous forecasts.
We observed significant errors between actual values and the predicted values ranges from ± 250 kW (figure 9), which would mean that if the current value of the power produced is 50 kW then the algorithm will predict a value of 300 kW, the difference between them being unacceptable.
Deviations registered for the Regression model
Consequently, the regression model should be applied to an attribute with a low degree of scattering depending on meteorological factors. Thus, we introduced E_PRAG attribute for grouping values into intervals depending on power produced by wind velocity of 0.5 m / s. For example, we found that at wind speeds between 0 and 3.5 m / s there is 0 kW power output, at speeds ranging from 3.5 to 4 m / s power output is 43 kW, etc.. These thresholds are defined in accordance with the power characteristics of the turbines.
After building the regression model on these thresholds it shows a significant increase in prediction accuracy from 57.68% to 93.72%. Applying the model on the test set (the table wind_test) and observing the diagram in Figure 10 we found that the variation of residual value y, the deviation between the actual value and predicted value lies within ± 50 kW, which is an acceptable deviation. By placing the cursor on any point on the diagram (capture in figure 10) one can view the following information: the current value (in our case 1500 kW), the prediction (1492 kW) and the deviation Y (7.5 kW).
The regression results on E_Prag attribute with thresholds values
The deviations recorded for the E_PRAG attribute forecast for one day
At the evaluation process for the regression model for E_PRAG attribute, the results are presented in Figure 11. It is noted that deviations are located within ± 20 kW which means that at the current value of 1000 kW the results in a forecast is between 980 kW and 1020 kW, which is an acceptable error.
Summarizing the results of the evaluation phase in Table 1 (only for the schedule 0:00 to 5:00), it shows the actual and predicted values for E_PRAG attribute every 10 minutes.
Applying data mining algorithms for the prediction power of WPP, notable results were achieved in particular by setting the thresholds for E_PRAG. Thus the data mining algorithm was able to learn and to establish better dependence between variables and the prognosis is much closer to actually measured values.
Finally was done the forecasting model for wind power plants produced energy, which can be applied successfully in a DSS prototype according to the architecture presented in Part I.
This paper is a result of the research project PN II, TE Program, Code 332: “Informatics Solutions for decision making support in the uncertain and unpredictable environments in order to integrate them within a grid network”, financed within the framework of People research program.
The belief that creativity is too difficult to measure is still a dominant myth [1] and can be considered as a byproduct of definitional issues. Researchers from various cultures and disciplines attempted to define creativity and offer a valid way to assess it. As creativity is a multifaceted phenomenon, it is a complicated task to define and operationalize it. For the sake of the discussion, one should start with defining “creativity”. The usefulness of higher order cognitive constructs is related to their definitions’ degree of clarity [2]. Unfortunately, most creativity research oversees the importance of this point. In a content analysis done for the articles published in two major creativity research journals, Creativity Research Journal and Journal of Creative Behavior respectively, researchers found that only 34% of the selected articles provided and explicit definition of creativity [3]. In order to examine a concept scientifically, we should rely on operationalized definitions and the relatively low rates of explicit definitions on creativity, constitutes a major problem for the field. As a result, I will use the following definition provided in Ref. [3] to clarify my perspective for this chapter. Creativity is “the interaction among aptitude, process, and environment by which an individual or group produces a perceptible product that is both novel and useful as defined within a social context”.
Starting with a definition would help but not provide the answer to our question at hand, why assess creativity? Although this question may have hundreds of answers the most basic and extensive answer would be: because creativity is the apex of human evolution and it is the most desirable skill in the information age. Creative thinking was the main ability that helped humans to move forward towards using a hand ax to complicated machines or produce complex language algorithms. Furthermore, creativity has become one of the most popular skills that schools and organizations search for. World Economic Forum, in its Future of Jobs report, ranked creativity in number three out of ten most important skills for the fourth industrial revolution [4], and also creativity is listed in the competencies part of 21st century skills. As of now, supporting creativity is the common goal of a kindergarten, a research institute or the biggest corporations in the world. The importance of creativity is anticipated to increase in the future due to various societal and economic trends as explained in Ref. [5].
Globalized markets require more competition.
Product development cycles shortened due to the information and communication technologies (For example, contemporarily any product that has been manufactured is redesigned within 5–10 years and this time period decreases to 6–12 months if the product is a technological device).
More and more jobs get automatized if it does not require creativity.
As job market demanded creativity more, the schools started to restructure their goals and curriculum to meet those need too. In the educational context, assessment of creativity is mostly about recognizing creativity and creating ideal conditions to nurture it, not about categorizing the students as “creative” or “not creative”. In Ref. [6] possible purposes of creativity assessment have been discussed; these can be summarized as follows:
Guide the individuals recognize their own strengths and support them in nourishing them.
Develop a better understanding about human abilities like intelligence and creativity. By maintaining that we will gain insight into the working structures of these complicated concepts.
Restructure the curriculum and learning experiences in accordance with the needs of the students. If educators understand their students’ strengths and weaknesses regarding creativity, they can tailor the educational opportunities for supporting creativity.
Imply creativity assessment as a program evaluation tool. Educators typically implement programs to enhance creativity, without pre and post assessments it would be impossible to know which approach worked best.
Utilization of standard measures will provide a common language for professionals to discuss various aspects of creativity.
Despite its importance, creativity did not become a major research area in psychology. Till the midst of 20th century creativity was seen as a marginal research topic and only 0,2% of the references in Psychological Abstracts indexes were about creativity [7]. Even the term “creativity” was not widely used before 50’s, however there were some influential works and essays written by philosophers and scientists (e.g. Bergson, Einstein, Kekulé, Poincaré) or early models proposed by researchers (see [8]). Modern creativity research began in 1950s and J. P. Guilford’s famous presidential address in American Psychological Association ignited the wick [9]. After Guilford’s call various researchers began to work on the field of creativity. Before that, assessment of creativity was not even a concern, especially for the young people or in the educational context. Because previous studies were solely focused on extraordinary creative achievements or eminent creative people. However, Binet’s pioneering intelligence test constituted an exception, it included some items to measure “creative imagination” [10]. Historically, some intelligence test developers considered creativity to be a part of intelligence or a totally independent construct [11]. In Ref. [12], authors categorized the approach towards the relations between creativity and intelligence under five groups. These are; creativity is a subset of intelligence, intelligence is a subset of creativity, creativity and intelligence are overlapping sets, creativity and intelligence are coincident sets and creativity and intelligence are disjoint sets. In the light of recent research, it can be claimed that the relation between intelligence and creativity depends on how each construct is defined and measured. Contemporary research widens these horizons. Creativity is now seen as a psychological trait distributed in the general population, that can be developed and measured [13].
The growing mindset which sees creativity as a flexible trait, increased the attention about the levels of creative magnitude. Creative accomplishments were categorized as everyday (little c) and historical (Big C) creativity. Imagine a 14-year-old math fan solving problems enthusiastically and compare it with the work of Fields Medal winner Andrew Wiles. She will not be as creative as Wiles and she does not need to be. Everyday creativity is certainly different from world changing efforts. The little-c, Big-C dichotomy was so sharp that one cannot distinguish the creative levels ranging in between. Kaufman and Beghetto [14, 15] proposed a “Four-C Model of Creativity” (mini-c, little-c, Pro-c and Big-C) to present a new perspective to this problem (see Table 1).
Mini-c | Learning is closely related to creativity, when we learn a new thing or try to solve a new problem some degree of creativity will be involved. At the mini-c level the creative act or product is new and original for the individual himself. For example, after several trials Sasha baked her first ceramic, although it was just in beginner’s level, it was new and meaningful to her. |
Little-c | The little-c level is one step further of the mini-c. The product or idea might be valuable to others. Sasha brought her ceramic to her house and her family loved it and put it on top of the dresser so that they use and enjoy seeing it. |
Pro-c | In the Pro-c level individual is at a professional level with years of experience and deliberate practice. Sasha majored in art in college and her artwork is now exhibited in galleries. Her work is followed by art experts and she is considered to be a creative artist. |
Big-C | People who achieve Big-C level are eminent ones and will be remembered in history books. One’s whole career and work is evaluated for this level. Sasha’s ceramics have been bought by art collectors and exhibited in art galleries regularly. |
Four-C’s of creativity.
Thus, it can easily be seen that every level of “c” requires a different approach and technique for assessing creativity. Over the years, researchers and theorists have proposed several different methods and theories for assessing creativity (e.g., Amabile, Csikszentmihalyi, Kaufman and Baer, Sternberg and Lubart, Torrance) (see [16, 17, 18, 19, 20]). These few examples constitute just the tip of the iceberg, there exist dozens of definitions, methods and theories in the field of creativity. As an illustration, in Ref. [21] Treffinger presented more than 100 different creativity definitions and as your definitions guide your assessment approaches, there are at least as many techniques to assess it. The reader can find information on more 70 different creativity assessments on Center for Creative Learning’s web page (see reference [22]). However, the variety of definitions and assessment techniques does not mean that creativity research has no consensus at all. Researchers tried to identify psychological factors that best predict creative outcomes and proposed several assessment techniques that imply these factors as a means of measurement [13]. Indeed, we can even argue that the field of creativity assessment has never been so prosperous before.
Today it is accepted that creativity is a combination of cognitive, conative and emotional factors which interact with the environment dynamically. As all of these factors are present in human beings and all these variables affects us to a certain degree, it can be argued that a specific combination of them results in creativity. In the historical research of creativity, several researchers tried to investigate the nature of creativity through the eyes of the aforementioned factors. The 4P framework (process, person, product, press) proposed by Rhodes [23] is a widely accepted categorization in psychometric study of creativity.
Process: Mental processes involved in creative thought or creative work.
Person: Personality traits or personality types associated with creativity.
Product: Products which are judged to be creative by a relevant social group.
Press (Environment): The external forces that effects creative person or process (e.g. sociocultural context, trauma)
In this section, historical and recent research in the field of creativity assessment will be presented. Although, every single creativity test, scale or rating will not be discussed, instead the focus will be on the historical milestones and contemporary methods of creativity assessment. This chapter embraced the integrative review approach with the aim of assessing, critiquing and synthesizing the literature on assessment of creativity.
Psychometric measures of creative process and potential has been extensively implied in the field. These processes involve cognitive factors that lead to creative production like finding and solving problems, selective encoding (i.e. selecting info that is relevant to problem and ignoring distractions), evaluation of ideas, associative thinking, flexibility and divergent thinking. Nevertheless, from this long list of cognitive factors the assessment of creative process mostly relied on divergent thinking in the creativity assessment tests. Even researchers in Ref. [24] underlined the irony in the study of creativity, although creativity itself requires novel and original solutions to a problem, researchers mostly focused on divergent thinking (DT) tasks. Not only major efforts were put on developing DT tests, even the earliest DT tests are still widely used in creativity research and educational areas. Divergent thinking can be explained as a thought process used to generate creative ideas via searching for many possible solutions. Whereas, convergent thinking is the ability to arrive the “correct” solution. Guilford [25] who came up with these concepts clearly underlined the difference between them.
In convergent thinking tests, the examinee must arrive at one right answer. The information given generally is sufficiently structured so that there is only one right answer… An example with verbal material would be: “What is the opposite of hard?” In divergent thinking, the thinker must do much searching around, and often a number of answers will do or are wanted. If you ask the examinee to name all the things, he can think of that are hard, also edible, also white, he has a whole class of things that might do. It is in the divergent thinking category that we find the abilities that are most significant in creative thinking and invention (p. 8)
In divergent thinking it is important to produce as many responses to verbal or figural stimuli as possible such that, more is better in DT. After the examinee come up with various answers, testers score them. The scoring is based on the concepts of originality (uniqueness of responses to a given stimuli), fluency (number of responses produced to a given stimuli), flexibility (number and/or uniqueness of categories of responses to a given stimuli) and elaboration (to add details to the ideas produced for a given stimuli) [25, 26]. As Guilford pioneered the research on creativity, initial efforts to assess it came from him and his colleagues too. Though, there were others who developed test batteries to measure creative thinking abilities and focused mostly on process components (e.g., Kogan and Wallach, Torrance, Mednick).
Structure of Intellect Divergent Thinking Test: Guilford’s famous Structure of Intellect Model (SOI) was mainly about defining and analyzing the factors constitute intelligence and he proposed 24 distinct types of DT [27]. His model covers 180 (6x5x5) intellectual abilities organized along three dimensions namely; operations (evaluation, convergent production, divergent production, memory, cognition), contents (visual, auditory, symbolic, semantic, behavioral) and products (units, classes, relations, systems, transformation, implications). Guilford’s SOI battery included several DT tasks like; in figural implications examinees were required to add lines to simple figures to create a new figure or in semantic units, listing commonly mentioned consequences of an impossible event, such as people not needing to sleep. Other examples include the Making Objects task (fluency with figural systems); in which participants make a new object from the provided four and by using alt least two of them or the Name Grouping task (flexibility with symbolic classes) which requires participants, given a set of names, forming subgroups based on different rules.
“Guilfordian” Tests: Guilford’s work was so influential that it was followed, replicated and reinterpreted by different researchers in 60s. Wallach and Kogan [28] argued that creativity tests should be administered in a game-like environment and should not apply time limitations. With this in mind, they focused on assessing creativity in children and developed the Instances Test (list as many things that move wheels, things that make noise) and the Uses Test (tell me the different ways you can use knife, tire or like in Ref. [29] toothpicks, chair or bricks). Wallach and Kogan proposed a different perspective than Guilford, not in the content of the test but for the target age group and way of administration (for a detailed discussion on the effects of different testing environments see reference [30]). Testing the divergent thinking ability of children would allow the educators and educational institutions to recognize their creatively able children and provide the necessary support and enrichment in their education.
Torrance Tests of Creative Thinking (TTCT): If we were to make a hits list for creativity assessment tests, TTCT most probably would be the number one. Torrance’s name was equated with assessment of creativity but it was not his major goal. TTCT was developed for research and to provide a tool that can be used to individualize the instruction [31, 32]. The TTCT, which are mainly based on SOI battery, are the most widely used and studied creativity tests [33, 34] and continue to attract attention in international level [35, 36]. Over the course of years, TTCT was refined in terms of scoring and administration and re-normed, which can account for its popularity. The TTCT consist of two different tests, the TTCT-Verbal and the TTCT- Figural, and each test has two parallel forms allowing it to be used as pre-posttests in experimental settings. The TTCT scores were expressed by four factors: fluency, originality, flexibility and elaboration. After the streamlined system introduced, Figural tests scored for resistance to premature closure and abstractness of titles in addition to originality, fluency and elaboration. Flexibility was removed because of the close correlation between fluency and flexibility scores [37]. The TTCT recommend an administration of game-like environment like Wallach and Kogan but apply time limitations.
The TTCT-Verbal is entitled as “Thinking Creatively with Words” and the Figural form entitled as “Thinking Creatively with Pictures”. Verbal form consists of six activities each whereas figural form consists of three (see Table 2).
TTCT-Figural | |
---|---|
Picture Construction | Participant uses a basic shape and expands on it to create a picture. |
Picture Completion | Participant is asked to finish and title incomplete drawings. |
Lines/Circles | Participant is asked to modify many different series of lines and circles. |
TTCT-Verbal | |
Asking | Participant asks as many questions as possible about the picture. |
Guessing Causes | Participant lists possible causes for the pictured action. |
Guessing Consequences | Participant lists possible consequences for the pictured action. |
Product Improvement | Participant is asked to make changes to improve a toy. |
Unusual Uses | Participant is asked to think of many different possible uses for an ordinary item. |
Unusual Questions | Participant asks as many questions as possible about an ordinary item (this item does not appear in later editions). |
Just Suppose | Participant is asked to “just suppose” that an improbable situation has happened then list possible ramifications |
TTCT- figural and TTCT-verbal subtests (adapted from reference [38]).
Remote Associates Test: Mednick [39], proposed a different perspective to creativity assessment and instead of solely focusing on divergent thinking he argued that convergent thinking should be taken into consideration too. Mednick believed that creative people are able to produce original ideas because they have the ability to form associations in their minds. Mednick analyzed the creative process through stimulus-response (S-R) perspective, he thought producing unusual or original responses to a stimulus required creativity and defined creativity based on this point of view.
….define the creative thinking process as the forming of associative elements into new combinations which either meet specified requirements or are in some way useful. The more mutually remote the elements of the new combination, the more creative the process or solution ([39], p. 221).
Mednick argued that people can achieve a creative solution through serendipity, similarity and mediation. His analysis showed that people’s associative hierarchies or set of responses to stimulus situations differ. Noncreative people have steep hierarchies, with a strong or dominant response to a given situation. As an example, if someone says pros, and if I cannot think anything else besides cons, that will be my dominant response to that stimulus and I will display a steep associative hierarchy. Whereas, the creative person has a flat associative hierarchy with multiple responses to a given stimulus. For example, for the stimulus word “table” a creative person might come up associations like chair, class, wood, leg, food whereas a non-creative person might come up with strongest associative links like chair, class and wood and stuck there.
For the operational definition of his theory, Mednick developed the Remote Associates Test (the RAT). RAT consisted of 30 items originally, each item included three stimulus words and the participant was required to find a fourth word that links them all. As an example; given stimulus set is; ‘book/shelf/telephone’ and the fourth word that link them all will be ‘book’. Some argued that, as test requires a single correct answer, it does not seem to require creative thinking [40]. However, one should note that the RAT itself is not aimed to measure creative thinking directly; it is measuring the capacity to think creatively and also in order to reach a single answer one should think divergently in RAT. Weisberg [41] joined this discussion by giving the example of a marathon runner, if one wants to identify a runner who has the potential to be a good marathon runner, he should measure lung capacity instead of running speed.
The Test for Creative Thinking – Drawing Production (TCT-DP): The discussion on TTC-DP should start with an annotation that it is not solely based on measuring creative processes (especially traditional divergent thinking tests) instead designed to mirror a more holistic concept of creativity. Though, as the theoretical basis of the test reflects mostly the cognitive processes involved in creative production, I preferred to discuss it under this heading. Urban [42] explained the approach in developing TCT-DP as a more holistic and gestalt-oriented one and aimed to consider not only divergent thinking but also aspects like content, gestalt, composition, elaboration, mental risk taking, breaking of boundaries, unconventionality and humor. The TCT-DP was developed by Jellen and Urban [43] and the test consist from a ‘big square frame’ with five fragments in the square and one fragment out of it. The participants are required to complete the drawing as they wish. TCT-DP has two parallel forms and although participants are not informed about the time limit during administration, it has a fifteen-minute duration for each form. TCT-DP is both an individual and group-oriented test and can be used with test-takers of most ages, from 4 to 95 years. The evaluation manual for TCT-DP includes a set of 14 key criteria ([42, 43], see Table 3).
Continuations (Cn) | Any use, continuation or extension of the six given figural fragments. |
Completion (Cm) | Any additions, completions, complements, supplements made to the used, continued or extended figural fragments. |
New elements (Ne) | Any new figure, symbol or element. |
Connections made with a line (Cl) | Between one figural fragment or figure or another. |
Connections made to produce a theme (Cth): | Any figure contributing to a compositional theme or “gestalt”. |
Boundary breaking that is fragment dependent (Bfd) | Any use, continuation or extension of the “small open square” located outside the square frame. |
Boundary breaking that is fragment independent (Bfi) | Any use or extension located outside the square frame independent of “small open square”. |
Perspective (Pe) | Any breaking away from two-dimensionality. |
Humor and affectivity (Hu) | Any drawing which elicits a humorous response, shows affection, emotion, or strong expressive power. |
Unconventionality, (Uc, a) | Any manipulation of the material. |
Unconventionality, b (Uc, b) | Any surrealistic, fictional and/or abstract elements or drawings. |
Unconventionality, c (Uc, c) | Any usage of symbols or signs. |
Unconventionality, d (Uc, d) | Unconventional use of given fragments. |
Speed (Sp) | A breakdown of points, beyond a certain score-limit, according to the time spent on the drawing production. |
Evaluation of Potential Creativity (EPoC): EPoC, similar to TCT-DP is not solely a process assessment, although it has strong cognitive factors it synthesized several traditions of measurement. The developers [44] embraced the multivariate approach proposed by researchers [45], which is, the combination of the cognitive, conative-affective and environmental factors influences creative capacity. EPoC was developed for children aged between 5 to 12 years old and aims to evaluate the creative potential of school-aged children. The test has two parallel forms and measurement relates to two fields of expression, graphic and verbal, and implies divergent-exploratory (find numerous original responses based on a given stimulus) and convergent-integrative (produce an original work integrating several elements in a creative synthesis) ways of thinking [13, 44]. EPoC’s forms are composed of eight subtests, administered individually and it is considered to be a modular domain-specific tool (see Table 4). EPoC is the most up to date creativity assessment instrument and the team is working on the extension of the test battery for new domains of creativity like music and science.
Field of expression | Exploratory-divergent thinking | Integrative-convergent thinking |
---|---|---|
Graphic | Abstract form | Abstract forms |
Concrete object | Concrete objects | |
Verbal | Story endings | Story with given title |
Story beginnings | Story with characters |
Distribution of the tests by field of expression and the mode of thinking evaluated for each parallel form (source [44]).
For convenience TCT-DP and EPoC has been presented under assessing the creative process and the discussion regarding their psychometric evidence is included in the next part along with other process assessment tools. As the reader may guess, there exist numerous tools for creativity assessment. Furthermore, there is a growing interest for domain-specific creativity assessment but domain-specific measures of creative potential are beyond the scope of this chapter, interested readers may check the suggested sources (i.e., For example, see [46, 47, 48]).
The most important question regarding any measurement instrument, whether it is a thermometer or test of creative thinking would be; is it reliable, does it produce consistent outcomes? To ensure reliability psychometric instruments must show consistent results in tests of reliability like test-retest reliability and split-half reliability. Research studies have showed that divergent thinking tests are reliable [30]. However, there are important points for further consideration, for example, some studies found that performance on DT tasks is affected by instructions (if you instruct people to be creative, they score higher). Weisberg [41], highlighted this situation by asking the question ‘If you instruct the examinee to be smart in the IQ test, will he be smarter?’. Weisberg himself gives the answer to this question; as children are used to answer questions exists in IQ tests, their score will not change with the instruction to be smart. However, questions in creativity tests are different in nature, most of them do not have a single correct answer and children are not familiar with this kind of questions. Thus, additional instruction might not be flaw for tests of creativity.
Once the reliability of a testing instrument is maintained, questions about validity arouse. Validity is a complex concept that can be ensured in a testing instrument via different analyses like discriminant, face, criterion and predictive validity. Tests of creative potential are reliable yet major discussions and suspicions exists about their predictive and discriminant validity.
To start with the Guilford SOI model, it is known that there exist enormous amount of assessment data and the archives are still available. SOI data was analyzed extensively within the years and the results generally supported the model [49, 50], or some researchers said that revisions needed [51] or concluded that the model has serious problems [52]. The results are pretty much same for Wallach and Kogan, although tests are reliable there are mixed results about its validity.
TTCT has been the most widely used and researched test of creativity, thus having extensive data to support its reliability and validity. Research about TTCT report good reliability scores for scoring and test-retest reliability [53, 54]. The majority of predictive validity studies for TTCT was run by Torrance himself, beginning in 1958 they included all grades 1 to 6 in two Minnesota elementary schools and in 1959 all students in grades 7–12 took TTCT. They followed up these students in four time periods (7-12-22-40 years) and collected data about their creative achievements. The longitudinal studies have shown that [20, 37, 55, 56] TTCT results correlate to adult creative achievement thus having predictive validity (for a detailed discussion see [57]). Though, Baer [58] raised some questions about the relevance of criterion variables (subscribing to a professional journal, learning a foreign language), do questions asked for the creative achievements in adult life are solely related to creativity? One can justifiably argue that, these criterion variables are strongly related to intelligence too. In addition, Torrance tests also correlate with intelligence then the predictability of creative achievements might be based on intelligence not on divergent thinking ability [41]. On the other hand, Plucker [59] presented more positive results concerning the predictive validity of the divergent thinking tests. He used multiple-regression analysis to reanalyze the Torrance data and examined its predictive power and provided support for the tests’ usefulness. Weisberg and Baer make other criticisms including the design of the study and interested readers should refer to these sources (see [41, 58]).
Mednick ‘s Remote Associates Test enjoy mixed support in terms reliability and validity too. Although RAT showed to be reliable [60], validity of the test is problematic [61]. It is important to note that the criterion/predictive validity of RAT, TCT-DP or EPoC have been subject to less investigation compared to divergent thinking tests like SOI or TTCT. TCT-DP has been normed in several countries like Germany, Korea, Poland and Australia for different age groups. The reliability studies showed fair to very good scores in terms of parallel test, scoring and differential reliability [42, 43]. Urban stated that the question of validity is hard to answer for TCT-DP as there are no instruments directly comparable to it [42]. So, they examined correlations with intelligence and verbally oriented divergent thinking tests and expected low or slightly positive correlations to ensure the instruments validity and attained supportive findings for the validity of the test [42]. As a modern creativity assessment instrument, EPoC was initially developed and validated in France with French sample. Internal validity was acceptable and for external validity researchers reached satisfactory results by proving that EPoC scores are independent from intelligence scores, moderately correlated with personality-relevant dimension like openness to experience and highly correlated with classic divergent tests [13, 44]. Although, EPoC shows promising validity results, extensive research is needed to support its criterion and predictive validity.
Extensive discussion regarding the reliability and validity of creativity assessment is mostly based on the divergent thinking tasks and tests. One major problem is about the scoring systems and several researches showed that fluency can act as a contaminating factor on originality scores [62]. To resolve fluency problem a new calculation named Creativity Quotient (CQ) was proposed by researchers [63]. CQ formula rewards response pools that are highly fluent and flexible at the same time. The discussion on fluency scoring is ongoing and some researchers advocate that fluency is a more complex construct than it is originally thought.
The debate on the predictive validity of divergent thinking tests is still ongoing, it seems like there exist two camps of researchers, one supporting the predictive power of DT [59, 64] and the other opposes [41, 58]. In an extensive review Kaufman and his colleagues [24] summarized the methodological issues in studies of DT tests’ predictive validity and pointed out that scores may be susceptible to intervention effects, administration procedures can affect the originality and fluency scores, statistical procedures may be inadequate, score distributions often violate the statistical assumption of normal distribution and creative achievement in adulthood may be domain specific and the DT tests used are almost always domain general. Runco [65] with all these criticism in mind, advocated for DT tests by saying;
Theorists who dismiss divergent thinking as entirely unimportant have ignored recent empirical research. . . . Additionally, some critics seem to expect too much from divergent thinking. Again, divergent thinking is not synonymous with creativity. Divergent thinking tests are, however, very useful estimates of the potential for creative thought. Although a high score on a divergent thinking test does not guarantee outstanding performance in the natural environment, these tests do lead to useful predictions about who is capable of such performances. . . . Divergent thinking is a predictor of original thought, not a criterion of creative ability. (p. 16)
In the early 60s and 70s creativity assessment was pretty much equal to DT tests however after several years and hundreds of research, the field should embrace a wider perspective. We now have more complex systems theories of creativity and it would be more prosperous for the field, if the upcoming research focus on developing and testing contemporary instruments more.
Autonomous, self-confident, open to new experiences, independent and original are some of the character traits that creative persons possess and the assessment of creative person deals with it. Measures that focus on the characteristics of creative person are self-reports or external ratings of past behavior or personality traits and they have been reviewed extensively in the literature [66]. Creative personality traits are diverse and can be perceived to be both positive and negative. Such as; perseverance, tolerance for ambiguity risk taking, psychoticism, dominance or non-conformity. One of the leading theories of personality is the five-factor theory. These five factors are neuroticism, extraversion, openness to experience, conscientiousness and agreeableness. Openness to experience is highly associated with creativity measures such as self-reports [67], verbal creativity [68], and psychometric tests [69].
Researchers study the common personality characteristics and past behaviors of people who are accepted as creative and develop instruments to measure personality correlates of creative behavior. There exist numerous instruments of personality scales and attitude checklist such as; The Khatena-Torrance Creative Perception Inventory, Group Inventory for Finding Talent, Creativity Achievement Questionnaire or Runco Ideational Behavior Scale.
The Khatena-Torrance Creative Perception Inventory: This inventory consists of two self-rating scales called What Kind of Person Are You? (WKOPAY) and Something About Myself (SAM). It is designed to identify creative people 10 years or older [70]. There are 50 forced-choice items in each inventory and asks test takers for example, if they have courage for what they believe or select true or false options for the sentences like; I have made a new dance or song. The inventory has satisfactory reliability data and validity data was moderate.
Group Inventory for Finding Creative Talent (GIFT): GIFT is a self-report for 1–6 grader to assess their creative potential [71]. Students give yes/no answers to a series of questions aiming to assess flexibility, curiosity, perseverance or hobbies such as; I like to take things apart to see how they work. Later in 1982, Davis and Rimm developed a new personality scale called Group Inventories for Finding Interests (I and II), known as GIFFI. These instruments were designed for junior and senior high school students and are very similar to GIFT [72]. Reliability and validity data for GIFT and GIFFI were moderate and researchers stressed that additional data is needed to support their psychometric structure.
The NEO Personality Inventory - NEO-Five Factor Inventory: Costa and McCrae’s [73, 74] inventories are one of the most popular five-factor measures of personality theory. For openness to experience part, they used down to earth-imaginative, uncreative-creative, conventional-original, prefer routine-prefer variety as adjective definers and fantasy, esthetics, feelings, actions, ideas and values as scale definers [73]. This type of items has been used in numerous studies and most of the studies did not find any personality differences among cultures except in some studies it has been shown that European-American cultures tended to be more open to experience than Asian-African cultures (for a detailed discussion see [24]).
Creativity Achievement Questionnaire (CAQ): Self-reports of activities and attainments can be used to measure creativity. CAQ developed by researchers in Ref. [75] and assesses achievement across 10 domains of creativity. It is a self-report checklist consisting 96 items that load on to an Arts (Drama, Writing, Humor, Music, Visual Arts and Dance) and a Science factor (Invention, Science and Culinary). The respondent indicates to which extent the phrases in the items represent him/her. For example, within Scientific Discovery scale items range from “I do not have training or recognized ability in this field” to “I have won a prize at a science fair or other local competition”, to “My work has been cited by other scientists in national publications.” The CAQ possess high levels of evidence of reliability and acceptable evidence of validity [75] and has been used in several studies (see [76, 77]).
Runco Ideational Behavior Scale (RIBS): In everyday life, generating creative ideas is a sign of creative performance and RIBS’s purpose is to measure this idea generation. Ideation involves idea generation and attribution of value to it; thus, it can be an adequate creativity criterion. Runco and his colleagues developed a set 100 items and reduced it to 23 to measure ideational behavior [78]. Sample items include, “I am able to think about things intensely for many hours” or, “I often find that one of my ideas has led me to other ideas that have led me to other ideas, and I end up with an idea and do not know where it came from”. Psychometric integrity of RIBS in terms of reliability and validity has been proven to be adequate [78] and RIBS has been used in several studies and adapted to other languages as well (see [79, 80]).
“Person” perspective or conative factors in creativity assessment mainly take into account that significant personal characteristics and existing creative behavior are best predictors of future creative behavior. Feist, an influential personality researcher, for example investigated the personality characteristics of scientists versus scientists, more creative versus less creative nonscientists and artists versus nonartists. In general, he showed that creative people are more open to new experiences, less conventional and less conscientious, more self-confident, self-accepting, ambitious, dominant, hostile and impulsive [81, 82]. In sum, self-reported creativity has attracted considerable attention in the field because it is fast and easy to score. Although, researchers willing to use these instruments should take into account the validity issues and the possibility that respondents may not be telling the truth. All kinds of self-assessments generally correlate to each other but the correlation data with performance assessments are contradictory [83, 84, 85]. Thus, citing from reference [24] “although self-assessments have a function and purpose, they are not useful in any type of high-stakes assessment”.
Think about the Nobel, Oscar or Grammy prizes, how the winners are designated? For example, do the Nobel committee requires the nominees to take TTCT or fill the creativity questionnaires or a taxi driver’s opinion will be count as an expert opinion in determining the nominees for chemistry? As explained in theories of Csikszentmihalyi and Amabile any idea or product to be seen as creative it should be valued by others or recognized experts in that field [86, 87]. Measuring the creativity of a product can be the most important aspect of creativity assessment yet it did not receive as much attention as process or personality variables. Some researchers even believe that product assessment is probably the most appropriate assessment of creativity and referred as the “gold standard” of it [88]. Researchers developed several instruments to evaluate creative products, such as Creative Product Semantic Scale or Student Product Assessment Form. These instruments ask educators to rate the specific features of students’ products. Though, above all Consensual Assessment Technique is the most popular way of assessing products. A brief explanation of each is provided below.
Creative Product Semantic Scale (CPSS): The CPSS is based on a theoretical model that conceptualizes three dimension of product attributes: novelty (the product is original, surprising and germinal), resolution (the product is valuable, logical, useful, understandable) and elaboration and synthesis (the product is organic, elegant, complex and well-crafted) [89]. The instrument relies on the idea that untrained judges can evaluate the creativity of a product by using a validated and reliable instrument [90]. The CPSS is scored on 7-point Likert-type scale, ranging from 1 to 7 between bipolar adjectives such as old-new. CPSS has shown to have adequate reliability values.
Student Product Assessment Form (SPAF): SPAF was developed by Renzulli and Reis [91], and aimed to assess the various types of products developed by students in enrichment programs. SPAF is designed for use with gifted learners and provides ratings of nine creative product traits (e.g. problem focusing, appropriateness of resources, originality, action orientation, audience) [92]. SPAF again, like CPSS have evidence of reliability although validity issues remained to be addressed.
Consensual Assessment Technique (CAT): Researchers need external criteria in creativity research to reach evidence of validity but an absolute criterion of creativity is not readily available (criterion problem) [24]. In CAT, the creativity of a product is judged by the experts in that field. These experts can be a group of mathematics professors to a group of kindergarten teachers depending on the product at hand. CAT was formulated by Amabile [87, 93] and since then has been applied in the creativity research extensively. When using CAT, the participants are asked to produce something (an actual product like haiku, collage, poem etc.) and experts rate the creativity of these products according to their perception of a creative product. CAT’s procedure is working similar to the real world and it does not provide standard scores, only comparative scoring is possible.
CAT has been proven to be reliable in several studies [58, 85, 88, 93, 94], inter-rater reliabilities ranged between .70 to .90. The average number of judges involved in the CAT studies run by Amabile [93] was just over ten. Using expert judges ranging between 5 to 10 is recommended, fewer than 5 experts may results in low inter-reliability levels and using more than 10 (although desirable) can be expensive and hard. Although, CAT steadily shows high reliability in various studies, using experts in creativity assessment is not without controversy. For example, Amabile states that determining the necessary level of expertise for judges is important and it is recommended that the experts should have formal training and experience in the target domain. Furthermore, researchers reported mixed results about the expert and novice ratings. For example, Kaufman and his colleagues showed low correlations among novice and expert raters [95], whereas in another study higher correlations reported [96], in more recent work researchers approached the expertise problem from a different perspective and argued that it should be understand as a continuum [88]. CAT also possess strong face validity yet, face validity (an instruments capability to measure what it looks like to measure) is not sufficient enough. For example, experts can agree a product is not creative and still be wrong (e.g. van Gogh was not valued as a creative artist by the experts in his time). Predictive validity discussion is even more complicating, it has been shown that CAT scores do predict later CAT scores, meaning they are stable across time in the same domain. However, does this mean CAT scores can predict later creative achievement? Historiometric research data supports this argument, for example analysis of Mozart’s music pieces in his early life predicted his later creative achievement [97].
Various environmental factors contribute to creative potential and have deep effects on it. Parental practices, trauma, birth order, culture, teaching practices and group interactions may affect creativity. Following the previous example of Mozart, we know that he was born in Salzburg and to a musical family (his father was a music teacher, composer, conductor and violinist). Imagine what would happen to the same Mozart if he would have born in small village in the Alps as son of a shepherd, would he be able to develop as a musical prodigy? Although creativity is highly related to cognitive factors, it is impossible to disregard the impact of environment.
As environmental factors are identified as important contributors to creative potential, studies aiming to determine the presence or absence of these factors in an individual’s environment become really important. There are instruments for assessing classroom and learning environment like Classroom Activities Questionnaire-CAQ (cited in [13]). However, the majority of the instruments for assessing environmental effects on creativity are mostly about the organizational structures, such as KEYS: Assessing the Climate for Creativity [98]. CAQ has not been widely applied in research studies therefore lacking the psychometric data, KEYS on the other hand, which was designed to “assess individuals perceptions and influence of those perceptions on the creativity of their work” ([98], p. 1157) possess evidence of reliability and validity and is widely applied in the organizational creativity field.
Creativity has various definitions, theories and also understood therefore assessed in many ways. Enhancing students’ creative thinking skills has become one of the major goals of education. Unfortunately, Kim’s comprehensive research on TTCT is disquieting. The normative data of TTCT 1974, 1984, 1990, 1998 and 2008 (272,599 participants) were re-analyzed and it was found that creative thinking scores either remained static or decreased, starting at the sixth grade [99]. There can be millions of reasons behind this failure. The inability to embed creativity in classroom practices can be one reason whereas the development and implication of up to date creativity assessment is the other. The field should move forward to using comprehensive theories as the basis of assessment, renew the norms of existing creativity tests such as TTCT and pay more attention to the validity studies of the creativity assessment instruments.
This chapter introduced a brief overview of existing tools of creativity assessment and to reach a “perfect” measure, researchers should take these approaches’ and instruments’ strengths and weaknesses into account (a brief overview is provided in Table 5).
Type of Assessment | Examples | Advantages | Disadvantages |
---|---|---|---|
Process based assessment (e.g. divergent thinking tests) | Torrance Tests of Creative Thinking | Well researched having years of research data available | May only tap limited aspects of creativity |
Person based assessment (e.g. Assessment by others) | Group Inventory for Finding Creative Talent or other instruments | Creativity is rated by a teacher, peer, or parent who knows the individual. | Questions about validity and reliability |
Person based assessment (e.g. Self-assessment) | Asking someone to rate his or her own creativity | Quick, cheap, and has high face validity | People can be subjective about their level of creativity |
Product based assessment (e.g. Consensual assessment technique) | Having experts rate a creative product | Allows for very domain-specific information about creativity, | Time consuming and expensive |
Brief overview of creativity assessment (adapted from [24]).
Furthermore, the argument that Sternberg [100] made by claiming that the evaluation of creativity is always local has to be kept in mind. Judging any thought or product is relative to some set of norms and this perspective raises questions for tests like TTCT or Unusual Uses, because these tests assume that some sort universal creativity exists and they measure it. Sternberg believes that creativity should be assessed locally because it has culture dependent elements just like intelligence and he suggests that “we should agree that our evaluations of what usually is viewed as constituting creativity – novel, surprising, and compelling ideas or products – represent local norms” ([100], p. 399).
The laypeople, the philosophers, the artists, and the creativity researchers all agree that creativity is a complex phenomenon and we know less about its scope and measurement than we wish to know. However, from a historical perspective in recent years more research has been conducted on creativity and the field of creativity can said to be at its prime. Hence, upcoming efforts of understanding and assessing creativity has the potential to produce more reliable, valid and comprehensive methods and theories. As discussed in this chapter, creativity assessment has its own limitations but it is recommended for future efforts to focus more on building a theoretical basis and providing multifaceted, multimodal assessment systems to measure creativity in order to overcome the aforementioned limitations.
The author declares no conflict of interest.
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\n\nOpenness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
\n\nDisruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
\n\nIntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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