Representation for interactive exercises
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Intelligent Computer Mathematics - 16th Symposium, Calculemus 2009 - 8th International Conference, MKM 2009 - Held as Part of CICM 2009, Proceedings. ed. / Jaque Carette; L. Dixon; Claudio Sacerdoti Coen; Stephen M. Watt. Springer, 2009. p. 294–309 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5625 LNAI).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Representation for interactive exercises
AU - Goguadze, Giorgi
N1 - Conference code: 6
PY - 2009
Y1 - 2009
N2 - Interactive exercises play a major role in an adaptive learning environment ActiveMath. They serve two major purposes: training the student and assessing his current mastery, which provides a basis for further adaptivity. We present the current state of the knowledge representation format for interactive exercises in ActiveMath. This format allows for representing multi-step exercises, that contain different interactive elements. The answer of the learner can be evaluated semantically. Various types of feedback and hint hierarchies can be represented. Exercise language possesses a construction for specifying additional components generating (parts of) the exercise. One example of such component is a Randomizer, which allows for authoring parametrized exercises. Another example is so-called Domain Reasoner Generator, that automatically generates exercise steps and refined diagnosis upon the learner's answer. This turns ActiveMath system into an ITS as soon as some Domain Reasoner is connected to it. Finally, several tutorial strategies can be applied to the same exercise. This strategies control feedback and the way the exercise is navigated by the learner, and can adapt to the learner.
AB - Interactive exercises play a major role in an adaptive learning environment ActiveMath. They serve two major purposes: training the student and assessing his current mastery, which provides a basis for further adaptivity. We present the current state of the knowledge representation format for interactive exercises in ActiveMath. This format allows for representing multi-step exercises, that contain different interactive elements. The answer of the learner can be evaluated semantically. Various types of feedback and hint hierarchies can be represented. Exercise language possesses a construction for specifying additional components generating (parts of) the exercise. One example of such component is a Randomizer, which allows for authoring parametrized exercises. Another example is so-called Domain Reasoner Generator, that automatically generates exercise steps and refined diagnosis upon the learner's answer. This turns ActiveMath system into an ITS as soon as some Domain Reasoner is connected to it. Finally, several tutorial strategies can be applied to the same exercise. This strategies control feedback and the way the exercise is navigated by the learner, and can adapt to the learner.
KW - Didactics of Mathematics
UR - http://www.scopus.com/inward/record.url?scp=69049103191&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02614-0_25
DO - 10.1007/978-3-642-02614-0_25
M3 - Article in conference proceedings
SN - 3-642-02613-3
SN - 978-3-642-02613-3
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 294
EP - 309
BT - Intelligent Computer Mathematics - 16th Symposium, Calculemus 2009 - 8th International Conference, MKM 2009 - Held as Part of CICM 2009, Proceedings
A2 - Carette, Jaque
A2 - Dixon, L.
A2 - Sacerdoti Coen, Claudio
A2 - Watt, Stephen M.
PB - Springer
T2 - 8th International Conferences on Intelligent Computer Mathematics - 2009
Y2 - 6 July 2009 through 12 July 2009
ER -