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Title:
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USING MARKOV CHAIN ON ONLINE LEARNING HISTORY DATA TO DEVELOP LEARNER MODEL FOR MEASURING STRENGTH OF LEARNING HABITS |
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Author(s):
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Tuan M. Tran and Shinobu Hasegawa |
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ISBN:
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978-989-8704-43-6 |
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Editors:
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Demetrios G. Sampson, Dirk Ifenthaler and Pedro Isaías |
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Year:
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2022 |
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Edition:
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Single |
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Keywords:
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Clickstream Data, Online Learning Environment, Markov Chain, Learner Model, SRL Profile, SRL Index |
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Type:
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Short Paper |
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First Page:
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335 |
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Last Page:
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338 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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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. |
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