App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Lightweight spatio-temporal model for predicting learning behavior indicators

Chen Yifei1
Jiang Zhongkun2
Ye Xiaoling2
1. Wuxi University, School of Automation, Wuxi Jiangsu 214105, China
2. Nanjing University of Information Science & Technology, School of Automation, Nanjing Jiangsu 210044, China
DOI: 10.19734/j.issn.1001-3695.2026.01.0007

Abstract

To address the limitations of existing academic performance prediction studies, such as insufficient prediction timeliness and continuity and the neglect of changes in learning states, this paper proposes a lightweight Spatio-Temporal model for learning behavior indicator prediction, termed LST-LBIP, in line with recent developments in time series forecasting, and enables prediction at a weekly temporal granularity. The model employs a lightweight Spatio-Temporal attention mechanism to assign weights to feature data and integrates MinGRU to achieve efficient multivariate time-series modeling under limited-sample conditions. In addition, the model designs a correction mechanism that combines heteroscedastic regression and Kalman filtering to enhance prediction stability, and introduces an adaptive sliding-window length component to dynamically optimize the range of historical information used. Experiments on blended learning behavior data collected from three student cohorts over 18 instructional weeks show that the model achieves an average R² of 0.805, outperforms several commonly used time-series modeling methods, and effectively supports continuous tracking and accurate prediction of students’ learning states.

Foundation Support

国家自然科学基金资助项目(42275156)
2022年江苏省高校"智慧教育与教学数字化转型研究"专项课题(2022ZHSZ72)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0007
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-05-20] Accepted Paper

Cite This Article

陈逸菲, 姜忠堃, 叶小岭. 融合轻量化时空建模的学习行为指标预测模型 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0007. (Chen Yifei, Jiang Zhongkun, Ye Xiaoling. Lightweight spatio-temporal model for predicting learning behavior indicators [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0007. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)