Student Modeling and Analysis in Adaptive Instructional Systems
文献类型：期刊论文
| 作者 | Liang, Jing7 ; Hare, Ryan6; Chang, Tianyu5; Xu, Fangli7; Tang, Ying3,4,6; Wang, Fei-Yue3,4; Peng, Shimeng2; Lei, Mingyu1
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| 刊名 | IEEE ACCESS
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| 出版日期 | 2022 |
| 卷号 | 10页码:59359-59372 |
| 关键词 | Adaptation models Artificial intelligence Predictive models Analytical models Adaptive systems Ontologies Mathematical models Adaptive instructional systems student modeling multimodal learning analytics |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2022.3178744 |
| 通讯作者 | Tang, Ying(tang@rowan.edu) |
| 英文摘要 | There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability. |
| WOS关键词 | MULTIMODAL DATA ; FUZZY-LOGIC |
| 资助项目 | National Science Foundation[1913809] ; Division of Undergraduate Education (DUE) |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000809361700001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Science Foundation ; Division of Undergraduate Education (DUE) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49617]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Tang, Ying |
| 作者单位 | 1.China Acad Informat & Commun Technol, Beijing 100191, Peoples R China 2.Nagoya Univ, Grad Sch Informat, Dept Intelligent Syst, Nagoya, Aichi 4648601, Japan 3.Qingdao Acad Intelligent Ind, Inst Smart Educ, Qingdao 266000, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Columbia Univ, Teachers Coll, New York, NY 10027 USA 6.Rowan Univ, Dept Elect & Comp Engn, Glassboro, NJ 08028 USA 7.Squirrel AI Learning Yixue Educ Inc, Highland Pk, NJ 08904 USA |
| 推荐引用方式 GB/T 7714 | Liang, Jing,Hare, Ryan,Chang, Tianyu,et al. Student Modeling and Analysis in Adaptive Instructional Systems[J]. IEEE ACCESS,2022,10:59359-59372. |
| APA | Liang, Jing.,Hare, Ryan.,Chang, Tianyu.,Xu, Fangli.,Tang, Ying.,...&Lei, Mingyu.(2022).Student Modeling and Analysis in Adaptive Instructional Systems.IEEE ACCESS,10,59359-59372. |
| MLA | Liang, Jing,et al."Student Modeling and Analysis in Adaptive Instructional Systems".IEEE ACCESS 10(2022):59359-59372. |
入库方式： OAI收割
来源：自动化研究所
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