TY - GEN
T1 - Knowledgeable Feedback via a Cast of Virtual Characters with Different Competences
AU - Beek, W.
AU - Liem, J.
AU - Linnebank, F.
AU - Bühling, R.
AU - Wißner, M.
AU - Lozano, E.
AU - Gracia del Río, J.
AU - Bredeweg, B.
N1 - 15th International Conference on Artificial Intelligence in Education, AIED 2011 ; Conference date: 28-06-2011 Through 01-07-2011
PY - 2011
Y1 - 2011
N2 - DynaLearn (http://www.DynaLearn.eu) develops a cognitive artefact that engages learners in an active learning by modelling process to develop conceptual system knowledge. Learners create external representations using diagrams. The diagrams capture conceptual knowledge using the Garp3 Qualitative Reasoning (QR) formalism [2]. The expressions can be simulated, confronting learners with the logical consequences thereof. To further aid learners, DynaLearn employs a sequence of knowledge representations (Learning Spaces, LS), with increasing complexity in terms of the modelling ingredients a learner can use [1]. An online repository contains QR models created by experts/teachers and learners. The server runs semantic services [4] to generate feedback at the request of learners via the workbench. The feedback is communicated to the learner via a set of virtual characters, each having its own competence [3]. A specific feedback thus incorporates three aspects: content, character appearance, and a didactic setting (e.g. Quiz mode). In the interactive event we will demonstrate the latest achievements of the DynaLearn project. First, the 6 learning spaces for learners to work with. Second, the generation of feedback relevant to the individual needs of a learner using Semantic Web technology. Third, the verbalization of the feedback via different animated virtual characters, notably: Basic help, Critic, Recommender, Quizmaster & Teachable agent.
AB - DynaLearn (http://www.DynaLearn.eu) develops a cognitive artefact that engages learners in an active learning by modelling process to develop conceptual system knowledge. Learners create external representations using diagrams. The diagrams capture conceptual knowledge using the Garp3 Qualitative Reasoning (QR) formalism [2]. The expressions can be simulated, confronting learners with the logical consequences thereof. To further aid learners, DynaLearn employs a sequence of knowledge representations (Learning Spaces, LS), with increasing complexity in terms of the modelling ingredients a learner can use [1]. An online repository contains QR models created by experts/teachers and learners. The server runs semantic services [4] to generate feedback at the request of learners via the workbench. The feedback is communicated to the learner via a set of virtual characters, each having its own competence [3]. A specific feedback thus incorporates three aspects: content, character appearance, and a didactic setting (e.g. Quiz mode). In the interactive event we will demonstrate the latest achievements of the DynaLearn project. First, the 6 learning spaces for learners to work with. Second, the generation of feedback relevant to the individual needs of a learner using Semantic Web technology. Third, the verbalization of the feedback via different animated virtual characters, notably: Basic help, Critic, Recommender, Quizmaster & Teachable agent.
U2 - 10.1007/978-3-642-21869-9_121
DO - 10.1007/978-3-642-21869-9_121
M3 - Conference contribution
SN - 9783642218682
T3 - Lecture Notes in Computer Science
SP - 620
BT - Artificial Intelligence in Education
A2 - Biswas, G.
A2 - Bull, S.
A2 - Kay, J.
A2 - Mitrovic, A.
PB - Springer
ER -