Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling
نویسندگان
چکیده
Adaptive learning technology solutions often use a learner model to trace and make pedagogical decisions. The present research introduces formalized methodology for specifying models, logistic knowledge tracing (LKT), that consolidates many extant modeling methods. strength of LKT is the specification symbolic notation system alternative regression models powerful enough specify in literature new models. To demonstrate generality LKT, we fit 12 some variants well-known newly devised, six datasets. results indicated no single was best all cases, further justifying broad approach considers multiple features context. We introduce stand student-level intercepts argue be maximally applicable, needs adapt student differences. our comparisons show general importance recent datasets, with special terms memory datasets involving fact learning.
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ژورنال
عنوان ژورنال: IEEE Transactions on Learning Technologies
سال: 2021
ISSN: ['2372-0050', '1939-1382']
DOI: https://doi.org/10.1109/tlt.2021.3128569