Diagnosing Knowledge using Learning Activity Traces Generated by Various Problems Solving Modalities

Résumé : Learners work increasingly with a large panel of learning environments that involve them in various activities thanks to a number of tools. These tools generate activity traces, which must then be taken into consideration and combined so as to establish the most accurate diagnosis about the learner’s activity. This paper presents a diagnosis model, called DiagElec. This model considers traces generated by various independent tools. DiagElec integrates a notion of uncertainty in the diagnoses that it generates thanks to the notion of ‘degree of belief’ which is defined by the rules of the diagnosis. To evaluate our model, we carried out a two-phase experiment, first with learners and then with teachers. From the corpus of diagnoses compiled by the teachers, we look for the emergence of a model of human behaviour in order to ascertain whether or not it is necessary to readjust the degree of belief defined by the rules of the diagnosis.
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Article dans une revue
International Journal of Learning Technologies, not yet available, 2014, 9 (1), pp.67-90. 〈10.1504/IJLT.2014.062449〉
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https://hal.inria.fr/hal-00948733
Contributeur : Denis Bouhineau <>
Soumis le : mardi 18 février 2014 - 16:20:51
Dernière modification le : jeudi 7 février 2019 - 14:59:50

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Sandra Michelet, Vanda Luengo, Jean-Michel Adam, Nadine Mandran. Diagnosing Knowledge using Learning Activity Traces Generated by Various Problems Solving Modalities. International Journal of Learning Technologies, not yet available, 2014, 9 (1), pp.67-90. 〈10.1504/IJLT.2014.062449〉. 〈hal-00948733〉

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