Academic Journal
Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling
| العنوان: | Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling |
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| اللغة: | English |
| المؤلفون: | Pavlik, Philip I. (ORCID |
| المصدر: | Grantee Submission. Oct 2021 14(5):624-639. |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| تاريخ النشر: | 2021 |
| Sponsoring Agency: | National Science Foundation (NSF) Institute of Education Sciences (ED) |
| Contract Number: | 1443068 1934745 R305A190448 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Descriptors: | Technology Uses in Education, Educational Technology, Models, Computer Assisted Instruction, Integrated Learning Systems, Knowledge Level, Individual Differences, Memory |
| DOI: | 10.1109/TLT.2021.3128569 |
| تدمد: | 1939-1382 |
| مستخلص: | Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, logistic knowledge tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to six learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. We introduce features to stand in for student-level intercepts and argue that to be maximally applicable, a learner model needs to adapt to student differences. The results of our comparisons show the general importance of modeling recent learning for all datasets, with special importance for terms that model memory in datasets involving fact learning. [This article was published in "IEEE Transactions on Learning Technologies" (EJ1324365).] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2022 |
| رقم الانضمام: | ED618076 |
| قاعدة البيانات: | ERIC |
| تدمد: | 1939-1382 |
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| DOI: | 10.1109/TLT.2021.3128569 |