Student Modeling and Analysis in Adaptive Instructional Systems
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...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9784853/ |
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| author | Jing Liang Ryan Hare Tianyu Chang Fangli Xu Ying Tang Fei-Yue Wang Shimeng Peng Mingyu Lei |
| author_facet | Jing Liang Ryan Hare Tianyu Chang Fangli Xu Ying Tang Fei-Yue Wang Shimeng Peng Mingyu Lei |
| author_sort | Jing Liang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8970afeae11e48a0b32f9a0bdb4e7f20 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8970afeae11e48a0b32f9a0bdb4e7f202025-08-25T23:00:27ZengIEEEIEEE Access2169-35362022-01-0110593595937210.1109/ACCESS.2022.31787449784853Student Modeling and Analysis in Adaptive Instructional SystemsJing Liang0https://orcid.org/0000-0001-9145-8276Ryan Hare1Tianyu Chang2Fangli Xu3Ying Tang4https://orcid.org/0000-0001-6064-1908Fei-Yue Wang5https://orcid.org/0000-0001-9185-3989Shimeng Peng6Mingyu Lei7Macao Institute of Systems Engineering, Macau University of Science and Technology, Macao, ChinaDepartment of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USATeachers College, Columbia University, New York, NY, USASquirrel AI Learning by Yixue Education Inc., Highland Park, NJ, USADepartment of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USAState Key Laboratory of Management and Control for Complex Systems, Inst. of Automation, Chinese Academy of Sciences, Beijing, ChinaDepartment of Intelligent Systems, Graduate School of Informatics, Nagoya University, Nagoya, JapanChina Academy of Information and Communications Technology, Beijing, ChinaThere 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.https://ieeexplore.ieee.org/document/9784853/Adaptive instructional systemsstudent modelingmultimodal learning analytics |
| spellingShingle | Jing Liang Ryan Hare Tianyu Chang Fangli Xu Ying Tang Fei-Yue Wang Shimeng Peng Mingyu Lei Student Modeling and Analysis in Adaptive Instructional Systems IEEE Access Adaptive instructional systems student modeling multimodal learning analytics |
| title | Student Modeling and Analysis in Adaptive Instructional Systems |
| title_full | Student Modeling and Analysis in Adaptive Instructional Systems |
| title_fullStr | Student Modeling and Analysis in Adaptive Instructional Systems |
| title_full_unstemmed | Student Modeling and Analysis in Adaptive Instructional Systems |
| title_short | Student Modeling and Analysis in Adaptive Instructional Systems |
| title_sort | student modeling and analysis in adaptive instructional systems |
| topic | Adaptive instructional systems student modeling multimodal learning analytics |
| url | https://ieeexplore.ieee.org/document/9784853/ |
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