Journal of Interactive Learning Research

Volume 11, Number 3/4 2000

Special Issue on Intelligent Systems/Tools in Training andLifelong Learning

LifelongLearning—More Than Training

CollaborativeInnovation as a Process for Cognitive Development

Learning at theMental Gym: How to Get Mentally Fit for the Task You Have atHand

Towards Adaptivity andAgility A Fractal view on Learning Environments

Learning-by-Judging: ANetwork Learning Environment Based on Peer Evaluation

SmexWeb: An AdaptiveWeb-Based Hypermedia Teaching System

Formalization toImprove Lifelong Learning

The GET-BITS Model ofIntelligent Tutoring Systems

Micro-Robots BasedLearning Environments for Continued Education in Small and MediumEnterprises (SMEs)

Arithmeticus: ADPS-Based Model for Arithmetical Competence

A Concept Model ForLearning

IntelligentSystems/Tools In Training and Lifelong Learning

Piet Kommers

kommers@edte.utwente.nl

Riichiro Mizzoguchi

http://www.ei.sanken.osaka-u.ac.jp/

Learning is an active process clearly distinguished from simplybeing taught. Active involvement in learning helps learners buildknowledge in their heads, which is one of the key issues advocated byconstructivists. However, learners still need other kinds of helpthat instructivists might suggest. Not all learners can always beactive. They sometimes meet an impasse they cannot resolve bythemselves. What they need is not the extreme paradigms but a mildand friendly paradigm that enables them to be active when they wantto and help and guide them when necessary. Such a paradigm needssophisticated intelligence.

Media Technology for the Further Evolution of LearningParadigms

Learning is a lifelong process where learners cannot always expectteachers around them. People need tools/systems to facilitate thelearning. One of the problems there is adaptation to the changes ofthe environment, which requires evolutionary tools. In industrialsettings, people are expected to catch up with the rapidtechnological development—that is the reason why training is soimportant in industries. Billions of dollars have been invested inemployee training in industries each year. One of the seriousproblems is lack of trainers. Advanced training systems of highperformance also require intelligence to partially mimic suchtrainers’ performance.

We have learned a lot about technology-based learning paradigms todate. They include:

• Computer Aided Instruction: CAI,
• Intelligent CAI: ICAI,
• Micro Worlds,
• Intelligent Tutoring System: ITS,
• Discovery Learning,
• Interactive Learning Environment: ILE,
• Intelligent Learning Environment: ILE,
• Simulation-Based Training,
• Computer-Supported Collaborative Learning: CSCL,
• Web-Based Learning,
• Life-Long Learning: LLL,
• and so forth.

In the evolution of the paradigms, we could see a direction thatwe were headed. It was a learner-centered, continued,network-supported, collaborative, and intelligent learningenvironment. This direction enjoys many of the characteristics of theparadigms we have seen thus far and compatible with most of them.

To learn more about such a new direction, we planned to have thespecial issue on

Intelligent Systems/Tools in Training and LifelongLearning

Fifteen papers were submitted and ten of them were acceptedtogether with one invited paper, namely the one by GerhardFisher.

The invited paper authored by Gerhard Fisher, Center for LifelongLearning & Design (L3D), University of Colorado, USA, providescomprehensive views of lifelong learning based on thoroughinvestigation of the current situation of training/learning and theconventional learning support paradigms. The paper providestheoretical foundation for learner-centered, authentic, situated,continued, domain-oriented, collaborative and open environment forlearning/training based on constructivism and technology support. Itdeclares a departure from institutional education/training. He alsopresents his activities along with his theories

The ten papers are classified into the following threecategories:

An Architecture/System Based on Constructivism

1. Collaborative Innovation as a Process for Cognitive Developmentby Madhumita Bhattacharya, and Ranajit Chatterjee

2. Learning at the Mental Gym—How to Get Mentally Fit for theTask you Have at Hand by Eshaa M. Alkhalifa and Helen Pain

3. Towards Adaptivity and Agility—A Fractal View on LearningEnvironment by Sylvain Giroux, Richard Hotte, and Kim Dao

4. Learning by Judging: A Network Learning Environment Based onPeer Evaluation by Chuen-Tsai Sun

Innovative AI Technology Applications to Educational Systems

5. SmexWeb: An Adaptive Web-Based Hypermedia Teaching System byFlorian Albrecht, Nora Koch, and Thomas Tiller

6. Pedagogical Ontology and Teaching Strategies: A NewFormalization to Improve Lifelong Learning by Sylvie Ranwez, TorstenLeidig, and Michel Crampes

7. The GET-BITS Model of Intelligent Tutoring Systems by VladanDevedzic, Ljubomir Jerinic, and Danijela Radovic

Report on Practical Experiences of Learning Environments

8. Micro-Robots Based Learning Environments for ContinuedEducation in SMEs by Pascal Leroux and Martial Vivet

9. Arithmeticus: A DPS-based Model for Arithmetical Competence byJoseph Klep

A (Retro)Prospective Study of the Elementary Conceptual Model forLearning

10. An Attempt to Define a Proper Relations Scheme BetweenInstruction, Learning and to Establish the Dynamics of Learning inRelation to Modern Political Concepts as Study-Fairness by Rik Min,Piet Kommers, Hans Vos, and Cor van Dijkum

Those in the first category share a lot besidesconstructivism-based approach. All of them deal with collaborativelearning and three of them assume Internet environments. Givinglearners the initiative in the learning process, thesystems/architectures exploit the capabilities of the Internet toenable collaboration beyond the limitation of distance. The commonattitude towards learning support includes efforts in finding a bestmixture of free exploration with highest initiative of a learner andan appropriate guidance from the environment. They have come up withtwo types of solutions to this problem: one is to put the learner incollaborative settings and the other is the introduction of guidancecapability to the system.

After comprehensive investigation of the role of collaborativeinnovation, Madhumita Bhattacharya and Ranajit Chatterjee introduceseveral useful tools for goal-oriented collaborative discussion forinnovation and designed a unified system. The unique features oftheir research include that they base the design rationale of thesystem on learning theories and that they come up with practicaltools justified by the very basic consideration. Eshaa M. Alkhalifaand Helen Pain try to combine virtual reality environment withsimulated players and intelligent tutoring to give a learnerappropriate guidance keeping a situated learning environment.Chuen-Tsai Sun also employs group learning with peer evaluationthrough the Internet to realize the Delphi method. Collaboration withhuman and/or simulated agents give the learners motivation and activeinvolvement in their learning process. These ideas also contribute toguiding learners and preventing them from being lost. To attain thesame goal, Sylvain Giroux, Richard Hotte, and Kim Dao discuss aninnovative idea of hypermedia-based navigation to realize highadaptivity and agility. A key idea here is to prepare a sophisticatedmultidimensional hyper-link mechanism that is adaptive to the contextas well as to learner’s learning and cognitive styles. Althoughthe system assumes a single learner paradigm, it succeeds inrealizing a learning environment of a good mixture of freeexploration and guided learning.

Articles in the second category are different from those discussedpreviously in that they are more enabling and technology-oriented.The common claim of the three includes artificial intelligencetechnology works well for adaptive tutoring/teaching to realizelearning environments of stronger guidance that could compensate somenegative aspects of free exploration in the huge information space.Two of them deal with web-based learning environment and the two ofthem discuss ontology as an innovative enabling technology. FlorianAlbrecht, Nora Koch, and Thomas Tiller discuss an Internet-basedapproach and propose an adaptive web-based tutoring system. Theyintroduce Java applet to build learner model-based adaptive teachingin a web-based teaching. Sylvie Ranwez, Torsten Leidig, and MichelCrampes discuss a pedagogical ontology for dynamically composingweb-based learning material. They put the adaptive navigation ofhypermedia one step further to adaptive building of hypermedia as alearning material. Thus these two articles try to find a goodsolution of active behavior of the learning environment. VladanDevedzic, Ljubomir Jerinic, and Danijela Radovic discuss asophisticated model for building an ITS. Their main interests includereusable component-based architecture that is deeply related toontology. Their contribution is based on amalgamation of softwareengineering and AI.

In the third section Pascal Leroux and Martial Vivet present areport on substantial activities in the real world, Small and MediumEnterprises: SMEs. It is about a year long practice of well-situatedand continued training using micro-robots, which has a lot to learnabout how the ideas have evolved.

Joost Klep describes the rationale and interactional consequencesof his learning program Arithmeticus. MathMirror is its front endthat allows a student to express calculations by manipulatingmathematical objects. The manipulations are interpreted as partialsolutions and are recorded with time annotations. Comparing thesestudents’ expressions with solutions produced by Arithmeticusgives abundant information about the students’ work so it ispossible to qualify solutions in terms of effectiveness, speed,degree of automation, and rote knowledge used . Those qualificationsare based on students’ individual learning history. Arithmeticusand MathMirror offer a learning environment in which a child candevelop his own strategies. There are no predefined expert solutions.A solution can be good or nicely related to a student’s learninghistory. The more strategies or facts he/she learns, the smarterArithmeticus will be and the more smart solutions Arithmeticus willexpect from the student.

In the fourth section Rik Min and his colleagues go back to themore traditional reductionism to find the most elementary mechanismin learning, regardless of the control aspects around the learner.This article may be seen as a provocation to the various learningparadigms such as cognitivism and environmentalism, not to speak ofconstructivism. In the era of “zapping” and“staccato” browsing through the information ocean on theWorld Wide Web (WWW or Web) however, this notion about short exposuretime and Short Term Memory (STM) may need this almost forgottenanalogon in order to understand the mechanism of the feedbackregulation system.

If the completely free exploration is the primary key condition toimplement constructivist’s ideas, then a set of books is thebest solution. If the complete support of the learning process isnecessary, on the other hand, ITS with stronger AI technology givesthe best solution. No one believes, however, that either of bothextremes is what we need. Appropriate stimuli should be given atappropriate timing to the learners to maintain motivation, to helpthem when they reach a deadlock, to let them reflect, and so forth.All learners cannot behave actively to explore the huge spacecontinuously. They will definitely come across difficulties that needto be overcome by help. These are areas where AI technology mightcontribute.

One of the main issues here, however, is that all AI-based methodsneed modeling which is the key to enabling sophisticated and adaptivebehaviors such as suggestion generation. Without modeling, what asystem can do is to show hard-wired behavior (canned suggestiongeneration), which cannot be adaptive to the situation. The problemwith modeling is that it does impose something negative on aconstructivism approach, since it requires limitations of the targetto model and such limitations might include learner’s cognitivestate, learner’s behaviors, interactions among co-learners, andso forth. This has been one of the main reasons why many of theAI-based systems have not been completely compliant to theconstructivist approach. Articles we have in this special issue tryto overcome these difficulties. Although the evaluation is up to thereaders, the article should be informative and thought provoking.

As we have browsed all the articles, what you will find in thisspecial issue are comprehensive and theoretical articles,sophisticated techniques to realize the theories, and practicalexperiences in the industrial settings. The co-editors would like thereaders to enjoy them.

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LifelongLearning—More Than Training

Gerhard Fischer

Wisdom is not a product of schooling, but the lifelong attemptto acquire it.—Einstein

Learning can no longer be dichotomized into a place and time toacquire knowledge (school) and a place and time to apply knowledge(the workplace). Today’s citizens are flooded with moreinformation than they can handle, and tomorrow’s workers willneed to know far more than any individual can retain.

Lifelong learning is an essential challenge for inventing thefuture of our societies; it is a necessity rather than a possibilityor a luxury to be considered. Lifelong learning is more than adulteducation and/or training—it is a mindset and a habit for peopleto acquire. Lifelong learning creates the challenge to understand,explore, and support new essential dimensions of learning such asself-directed learning, learning on demand, collaborative learning,and organizational learning. These approaches need new media andinnovative technologies to be adequately supported.

A theory of lifelong learning must investigate new frameworks tolearning required by the profound and accelerating changes in thenature of work and education. These changes include: a) an increasingprevalence of “high-technology” jobs requiring support forlearning on demand because coverage of all concepts is impossible;(b) the inevitability of change in the course of a professionallifetime, which necessitates lifelong learning; and (c) the deepening(and disquieting) division between the opportunities offered to theeducated and to the uneducated.

This article explores conceptual frameworks and innovativecomputational environments to support lifelong learning. It alsoanalyzes why training approaches need to be transcended and how thiscan be done.

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CollaborativeInnovation as a Process for Cognitive Development

Madhumita Bhattacharya

mitab@nie.edu.sg 

Ranajit Chatterjee

ranajit@cs.dis.titech.ac.jp

In this article we propose a methodology for collaborativeinnovation, which leads to cognitive development. Motivation forinnovation could be an effective means for efficient and meaningfullearning. Meaningful learning contributes to cognitive development.Therefore, in professional education the aim is to maintain thecontinuous cognitive development through lifelong learning.

Distributed cognition of people and artifacts are integratedduring the process of collaborative innovation. This, in turn,enriches the individuals’ cognition, metacognitive abilities,techniques of interpersonal communication, and reflective thinkingskills. A mechanism for the process of innovation is discussed toidentify the possible stages of interaction for collaboration. Theguidelines for interaction are identified to develop an intelligentsupport system. This support system guides participants in carryingout innovative activities. Based on participants’ personalportfolios, the network can be used to perform an intelligentcompatibility search on the World Wide Web (Web) for locatingcompanions for collaboration. The same tool can also be incorporatedfor other ontological searches in connection with the process ofinnovation such as suggesting possible problems to be explored andproviding relevant information on available resources.

In this methodology, the main intention is to continue thecognitive development process where the innovative activities areused to sustain the motivation of the learner. Any other outcome fromthese innovative activities were considered incidental. In futurework, we intend to develop a measure for assessing cognitive changesfor providing periodic feedback to participants about their progress.An intelligent tool would be developed for periodic assessment andreporting cognitive changes to the participants to help in sustainingtheir motivation.

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Eshaa M. Alkhalifa and Helen Pain

eshaaa&helen@dai.ed.ac.uk

Learning is as necessary a process for the psyche as is exercisefor the body. It is an ongoing activity that takes place with everymovement or word said. But at times we can control it by submittingourselves to a controlled environment, which would bombard us withinformation that restructures our world model and how we deal withthat particular aspect of life.

The theories that exist in learning are based on two alternativeapproaches: Constructionism versus Instructionism (Papert, 1990b).However, why should we have to choose between them when, in fact, wecould take the best of both worlds by interleaving them (Lawler &Yazdani, 1997)? Or better still, why don’t we have bothapproaches simultaneously where the student is surrounded by aconstructive Virtual Reality environment with a specific trainingtarget at hand, coupled with enough abstraction to enable him toreact to positive as well as negative expertise (Minksy, 1994)through adaptation of thinking processes rather than just memorizing.This paper proposes an interactive Virtual Reality environment thatallows learning to occur through virtual experience andexperimentation.

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Sylvain Giroux

giroux@mediatech.didael.it

Richard Hotte and Kim Dao

rhotte@teluq.uquebec.ca

Continuous education helps people cope with an ever changing labormarket, while distance education reaches them where they are, keepingthem at work. This article tackles the production of learningenvironments (LEs) on the World Wide Web (Web). The produced LEs canbe adapted to a wide variety of learners. They are also agile andable to sustain the fast evolution of contents and technology. Theresulting LEs are germane to fractals. Fractals are usuallycharacterized by a self-similar structure which remains independentof changes in scale. First, we liken changes in scale to levels inLEs. Each level expresses a given viewpoint on knowledge. Second,self-similarity establishes a classification from which to derive agrammar. Third, texts and activities are highly fragmented. Fourth,the interfaces rely on the fractal structure to provide for“spatial” landmarks. The LEs are adaptive with respect tolearners’ objectives, background and cognitive style and areagile with respect to their design, implementation, and maintenance.The fractal design and the underlying grammar set up the formalgrounds required to code procedures that generate LEs, extend them,manage updates, and maintain the site.

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Chuen-Tsai Sun

ctsun@cis.nctu.edu.tw

The paradigms of science and engineering education have beenevolving along the dimensions of theory, pedagogy, and technology. Asnetwork-based, computer-assisted learning is drawing more and moreattention the past few years, promising learning theories such asconstructive learning and collaborative learning have found newmeaning and new ways of implementation in the new, network-basedenvironment. In this article, we propose a learning environment inwhich a learning strategy, called Learning-by-Judging, can berealized to help constructive learning.

The proposed environment is a World Wide Web (Web) based systemthat supports learning strategies through design and peer evaluation.Samples designed by students are demonstrated in screen windows forothers to evaluate by way of a network. A learning procedure includesseveral rounds of sample-design and mutual evaluation. The learningprocess is recorded and analyzed, and then the result is fed back tothe students to achieve the goal of meta-cognition. We expect thisapproach to encourage students participating in instructionalactivities early and effectively.

We conducted a small-scale experiment in which color-matching waschosen as the design subject. We collected and analyzed users’behavior patterns in a preliminary manner. We are improving thefunctionality and interface of the learning environment. We expect toconduct formal instructional experiments in the near future.

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Florian Albrecht, Nora Koch, and Thomas Tiller

florian@fast.de

{albrechf, kochn, tiller}@informatik.uni-muenchen.de

http://www.pst.informatik.uni-muenchen.de/~kochn

A computer learning system that aims at being efficient andgratifying has to adapt itself to the learner’s needs. SmexWeb(Student modelled exercising on the Web) (http://pst1.pst.informatik.uni-muenchen.de:8080/) as an adaptive hypermedia system offersadvantageous features of the hypermedia paradigm. To take intoaccount spatial and mental abilities of learners, various techniqueshave been developed in the field of adaptive hypermedia. All of them,adaptations of contents as well as all kinds of adaptive navigationalsupport, are integrated into SmexWeb. While those techniques move thelocus of control towards the system, all action still has to be takenby the user. SmexWeb extends the means of navigation further bytaking navigation actions for the user based on its student model. Byemploying a technique called Passive Navigation, the level of thesystem’s activity can vary subtly and can be adjusted to theuser’s learning preferenceses.

SmexWeb guarantees easy accessibility, as it is a web-basedapplication that solely requires a standard web-browser. Partiallybypassing HTTP as a stateless protocol, SmexWeb enables a higheramount of user-system interactivity than most web-based learningenvironments. The Java server resembles a classical ITS architecture;the modularity of the framework allows an easy instantiation forfuture SmexWeb applications. First tests of a SmexWeb applicationwith students have shown that it is an easy-to-use and effectivelearning environment.

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Sylvie Ranwez1, Torsten Leidig, and Michel Crampes

mcrampes@ensm-ales.fr, ranwezs@eerie.fr

http://www.eerie.fr/LGI2P/lgi2p.htm

Torsten.Leidig@sap-ag.de

 Utilizing the World Wide Web (Web) for delivering lifelongtraining services to individuals or communities raises great interestin intelligent methods for adaptive course curriculum building.People notice that it is insufficient to make training materialsaccessible by way of the browser. To satisfy learner requirements,the learning service has to give a highly customized and up-to-dateview. Moreover, training is an interactive and collaborative process.The flow of the curriculum has to follow some didactic principles.Learners need to be guided through the training material according tothese principles and their own preferences. A conventional hypertextsystem cannot provide this, mainly because the pedagogic semantics ofthe material are not “known” by the system.

This article presents a new approach for adaptive,computer-supported learning services within our Lifelong Learningproject. This approach is based on a formalization of pedagogicalknowledge, about learning material and learning processes, as well asthe domain knowledge and a learner model. The goal of our research isto integrate efficient and reusable pedagogical knowledgerepresentations into an adaptive learning system (and authoringtools). The article concentrates on the Didactic Planning obtained byvarious Pedagogical Strategies. Therefore, we focus on the strategyformalization using an expressive knowledge representation based on adedicated learning ontology using conceptual graphs. The power of ourapproach for designing a new kind of adaptive learning system isdemonstrated through examples of pedagogical strategies applied tocourse material.

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Vladan Devedzic

devedzic@galeb.etf.bg.ac.yu

Ljubomir Jerinic

jerinic@uns.ns.ac.yu

Danijela Radovic

danijela@emi.yu

This article describes an object-oriented model of intelligenttutoring systems (ITS), called GET-BITS. The article concentrates onclass hierarchies and design of classes for knowledge representationin the GET-BITS model. Other models of intelligent tutoring systemsused today, as well as the corresponding knowledge models, differonly to an extent. However, the design methodologies employed vary alot and, sometimes, even remain blurred for the sake of the systemfunctionality alone. Although using a shell or an authoring tool fordeveloping intelligent tutoring systems brings more systematicdesign, it can also become a limiting factor if the shell/authoringtool doesn’t support a certain knowledge representationtechnique or design strategy that may be needed in a particularsystem. The GET-BITS model makes it possible to develop more flexibleintelligent tutoring systems and the corresponding softwaredevelopment environments, significantly increasing their modularityand reusability. It is based on a number of design patterns and classlibraries developed in order to support building of intelligentsystems. Important parts of any ITS design process, like domainknowledge, pedagogical knowledge, student model, and explanationstrategies, are all covered in the GET-BITS model. The advantages ofthe model are shown in the article by: (a) explicit discussion ofseveral different aspects of the model, and (b) description of aGET-BITS-based intelligent tutoring system for teaching formallanguages. The processes of computer-based tutoring and learningbased on the GET-BITS model are much closer to human-basedinstruction. The model can be easily extended to cover the needs ofspecific tutoring systems. In addition, two extremely importantissues are discussed from the GET-BITS perspective: the issue ofontologies in the area of intelligent tutoring systems, and the issueof software components in that area.

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Pascal Leroux AND Martial Vivet1

Pascal.Leroux@lium.univ-lemans.fr

Martial.Vivet@lium.univ-lemans.fr

One of the main problems in the Small and Medium Enterprises(SMEs) is the adaptation of their production process to keep theirmarkets. The improvement of the process is often realized by theintroduction of new technologies such as more fully automatedmachinery and computers. Therefore, training has been designed toprepare workers for the future by helping them use these newtechnologies.

 Since 1989, for training people in this context, we havedeveloped learning environments and defined a pedagogical approach,called ATRIUM, which is based on the use of pedagogical micro-robotsdriven by computers. The micro-robotics activities aim at helpinglearners discover technology by handling, building, designing, anddriving micro-robots through a project-based pedagogical approach.The role of the trainer is to set up and manage the activities ofseveral learner groups (each group is composed of 2-3 persons). Wehave experimented with a first generation of learning environmentsusing LOGO as the software to drive the micro-robots. Beyond theeffectiveness of the approach, we observed that our environmentscreated an overload for the trainers (we say over-appeal) becausethey had different roles to play. We chose to decrease thetrainers’ over-appeal and improve their work by increasing theautonomy of the learner groups and by having a better interactionbetween the computer and the human actors. We have designed anddeveloped pedagogical assistant software, called ROBOTEACH, whichcooperates with learners during the micro-robotics activities andhelps the trainers manage the sessions. ROBOTEACH is actuallydiffused and used in professional training in SMEs.

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Joseph Klep

J.Klep@slo.nl

http://www.slo.nl/network/engels.html

The Position of Arithmeticus Between other StudentModels

Petrushin and Sinitsa (1990) offer a short classification oflearner models. They distinguish between fixing and simulatingmodels. Both kinds of models are based on a set of expert-rules. Thelearner model is a description of students behaviour related to thatset of expert-rules.

Arithmeticus does not contain expert-rules in the field ofmathematics, but meta-mathematical rules, telling which transitionsare permitted in calulations or in argumentations. Using those rules,Arithmeticus can construct lines of arguments.

If a student has expressed his solution of a problem, Arithmeticustries to realise (for itself) this expressed solution by constructinga fitting line of arguments (or algorithm). So, Arithmeticus offers ageneric description of mathematical knowledge based onmeta-mathematical rules. The kernel of Arithmeticus is not apsychological, but an epistemological, generic description ofmathematics (arithmetics).

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An attempt to define a proper relations scheme betweeninstruction and learning, and to establish the dynamics of learningin relation to modern political concepts as study-fairness

R. Min, P. Kommers, H. Vos, and C. van Dijkum1

min@edte.utente.nl,

kommers@edte.utwente.nl

vos@edte.utwente.nl

c.vandijkum@fsw.ruu.nl

For years, it has been attempted within educational science toestablish the process of learning. A lot is known about instruction,but as to learning and acquiring knowledge and insight, we still knowvery little. A lot of research is conducted on methods ofinstruction, but very little on learning with learning tools. In thisarticle, an attempt is made to do so. Discussions should then be: howcan a learning process be turned into a model? And, how can learningbe modeled? First, there should be a conceptual model with the basicentities and the relations between them. Recently, a committee of theMinistery of Education and Science introduced the political concept“study-fairness,” an extra reason to lay down the points ofapplication of these concepts in a larger, meaningful context and anunderstandable model so that each concept can be applied in theoverall learning process. In this article, we want to construct aclear and irrefutable model, an analogon, to describe the phenomenonof learning. This might, in the long run, help us identify thefundamentals of learning. We consider learning as a student activity,and instruction, on the other hand, as a relatively static condition.(Not to be confused with the traditional teacher role.)

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