- 著者
- Stephen J.H. Yang Owen H.T. Lu Anna Y.Q. Huang Jeff C.H. Huang Hiroaki Ogata Albert J.Q. Lin
- 出版者
- Information Processing Society of Japan
- 雑誌
- Journal of Information Processing (ISSN:18826652)
- 巻号頁・発行日
- vol.26, pp.170-176, 2018 (Released:2018-02-15)
- 参考文献数
- 29
- 被引用文献数
- 34
With the rise of big data analytics, learning analytics has become a major trend for improving the quality of education. Learning analytics is a methodology for helping students to succeed in the classroom; the principle is to predict student's academic performance at an early stage and thus provide them with timely assistance. Accordingly, this study used multiple linear regression (MLR), a popular method of predicting students' academic performance, to establish a prediction model. Moreover, we combined MLR with principal component analysis (PCA) to improve the predictive accuracy of the model. Traditional MLR has certain drawbacks; specifically, the coefficient of determination (R2) and mean square error (MSE) measures and the quantile-quantile plot (Q-Q plot) technique cannot evaluate the predictive performance and accuracy of MLR. Therefore, we propose predictive MSE (pMSE) and predictive mean absolute percentage correction (pMAPC) measures for determining the predictive performance and accuracy of the regression model, respectively. Analysis results revealed that the proposed model for predicting students' academic performance could obtain optimal pMSE and pMAPC values by using six components obtained from PCA.
- 著者
- Hiroyuki Kuromiya Taro Nakanishi Izumi Horikoshi Rwitajit Majumdar Hiroaki Ogata
- 出版者
- Japan Society for Educational Technology & Japanese Society for Information and Systems in Education
- 雑誌
- Information and Technology in Education and Learning (ISSN:24361712)
- 巻号頁・発行日
- vol.3, no.1, pp.Reg-p003, 2023 (Released:2023-12-20)
- 参考文献数
- 35
- 被引用文献数
- 1
Evidence-based practice stems from medicine. It involves the concept of Real-World Evidence (RWE), which involves the analysis of routinely collected patient data to extract evidence of practice. In this paper, we propose a learning-analytics (LA) based reflective teaching workflow that analyzes data from daily teaching and learning environments. This reflective teaching workflow uses a Learning and Evidence Analytics Framework (LEAF), a specific learning platform that is currently used in multiple Japanese and international educational institutions. This workflow provides a bottom-up approach for producing evidence from routinely collected data on the LA platform. We illustrate a case study in a Japanese high school math class to demonstrate the practical use of this reflective teaching workflow. We draw implications from this reflective workflow towards producing rich and robust evidence in the LEAF system, which should lead to the democratization of evidence-based practice in the classroom.