Literaturnachweis - Detailanzeige
| Autor/inn/en | Tran, Tuan M.; Hasegawa, Shinobu |
|---|---|
| Institution | International Association for Development of the Information Society (IADIS) |
| Titel | Using Markov Chain on Online Learning History Data to Develop Learner Model for Measuring Strength of Learning Habits [Konferenzbericht] Paper presented at the International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) (19th, 2022). |
| Quelle | (2022), (4 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Markov Processes; Learning Processes; Models; Scores; Outcomes of Education; Accuracy; Prediction; Learning Analytics; Student Characteristics; Distance Education; Student Evaluation; Learning Management Systems; Profiles; Online Courses |
| Abstract | A learner model reflects learning patterns and characteristics of a learner. A learner model with learning history and its effectiveness plays a significant role in supporting a learner's understanding of their strengths and weaknesses of their way of learning in order to make proper adjustments for improvement. Nowadays, learners have been engaging in online learning frequently and intensely, leaving behind tremendous learning history data that contain informative insights about the learners' learning patterns. This paper proposed a method for developing learner models by applying the Markov chain to learning history data. Our method transforms individual learners' resource use data in a learning course into a large amount of resource use sequences, then develops a Markov learner model, and generates the resource use steady state for each learner. The resource use density, the resource steady state, and the assessment scores of individual learners tell their learning patterns and the effectiveness of the learning patterns. From the Markov learner model, we generate a learner profile for describing learning patterns and an index for measuring the strength of learning habits of the learner. We verified our method by applying it to each course in the OULAD dataset to predict the learning performance using the index. The preliminary results gain up to 97% accuracy on the pass/fail prediction problem. (As Provided). |
| Anmerkungen | International Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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