Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective
A student's learning system is a system that guides the student's knowledge acquisition process using available learning resources to produce certain learning outcomes that can be evaluated based on the scores of questions in an assessment. Such a learning system is analogous to a control...
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Elsevier
2024-12-01
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| Series: | Computers and Education: Artificial Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X2400095X |
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| author | Cheng Ning Loong Chih-Chen Chang |
| author_facet | Cheng Ning Loong Chih-Chen Chang |
| author_sort | Cheng Ning Loong |
| collection | DOAJ |
| description | A student's learning system is a system that guides the student's knowledge acquisition process using available learning resources to produce certain learning outcomes that can be evaluated based on the scores of questions in an assessment. Such a learning system is analogous to a control system, which regulates the process of a plant through a controller in order to generate a desired response that can be inferred from sensor measurements. Inspired by this analogy, this study proposes to model the monitoring of students' knowledge acquisition process from a control-theory viewpoint, which is referred to as control knowledge tracing (CtrKT). The proposed CtrKT comprises a dynamic equation that characterizes the temporal variation of students' knowledge states in response to the effects of learning resources and an observation equation that maps their knowledge states to question scores. With this formulation, CtrKT enables tracking students' knowledge states, predicting their assessment performance, and teaching planning. The insights and accuracy of CtrKT in postulating the knowledge acquisition process are analyzed and validated using experimental data from psychology literature and two naturalistic datasets collected from a civil engineering undergraduate course. Results verify the feasibility of using CtrKT to estimate the overall assessment performance of the participants in the psychology experiments and the students in the naturalistic datasets. Lastly, this study explores the use of CtrKT for teaching scheduling and optimization, discusses its modeling issues, and compares it with other knowledge-tracing approaches. |
| format | Article |
| id | doaj-art-84f9cd2d95da45469f37d508abad1e2b |
| institution | OA Journals |
| issn | 2666-920X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj-art-84f9cd2d95da45469f37d508abad1e2b2025-08-20T02:34:40ZengElsevierComputers and Education: Artificial Intelligence2666-920X2024-12-01710029210.1016/j.caeai.2024.100292Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspectiveCheng Ning Loong0Chih-Chen Chang1Corresponding author.; Department of Civil and Environmental Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, ChinaDepartment of Civil and Environmental Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, ChinaA student's learning system is a system that guides the student's knowledge acquisition process using available learning resources to produce certain learning outcomes that can be evaluated based on the scores of questions in an assessment. Such a learning system is analogous to a control system, which regulates the process of a plant through a controller in order to generate a desired response that can be inferred from sensor measurements. Inspired by this analogy, this study proposes to model the monitoring of students' knowledge acquisition process from a control-theory viewpoint, which is referred to as control knowledge tracing (CtrKT). The proposed CtrKT comprises a dynamic equation that characterizes the temporal variation of students' knowledge states in response to the effects of learning resources and an observation equation that maps their knowledge states to question scores. With this formulation, CtrKT enables tracking students' knowledge states, predicting their assessment performance, and teaching planning. The insights and accuracy of CtrKT in postulating the knowledge acquisition process are analyzed and validated using experimental data from psychology literature and two naturalistic datasets collected from a civil engineering undergraduate course. Results verify the feasibility of using CtrKT to estimate the overall assessment performance of the participants in the psychology experiments and the students in the naturalistic datasets. Lastly, this study explores the use of CtrKT for teaching scheduling and optimization, discusses its modeling issues, and compares it with other knowledge-tracing approaches.http://www.sciencedirect.com/science/article/pii/S2666920X2400095XKnowledge tracingItem response theoryMemory retentionTeachingControl theory |
| spellingShingle | Cheng Ning Loong Chih-Chen Chang Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective Computers and Education: Artificial Intelligence Knowledge tracing Item response theory Memory retention Teaching Control theory |
| title | Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective |
| title_full | Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective |
| title_fullStr | Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective |
| title_full_unstemmed | Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective |
| title_short | Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective |
| title_sort | control knowledge tracing modeling students learning dynamics from a control theory perspective |
| topic | Knowledge tracing Item response theory Memory retention Teaching Control theory |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X2400095X |
| work_keys_str_mv | AT chengningloong controlknowledgetracingmodelingstudentslearningdynamicsfromacontroltheoryperspective AT chihchenchang controlknowledgetracingmodelingstudentslearningdynamicsfromacontroltheoryperspective |