This paper proposes a model for detecting unexpected examination scores based on past scores, current daily efforts and trend in the current score of individual students. The detection is performed soon after the current examination is completed, which helps take immediate action to improve the ability of students before the commencement of daily assessments during the next semester.
The scores of past examinations and current daily assessments are analyzed using a combination of an ANOVA, a principal component analysis and a multiple regression analysis. A case study is conducted using the assessment scores of secondary-level students of an international school in Japan.
The score for the current examination is predicted based on past scores, current daily efforts and trend in the current score. A lower control limit for detecting unexpected scores is derived based on the predicted score. The actual score, which is below the lower control limit, is recognized as an unexpected score. This case study verifies the effectiveness of the combinatorial usage of data in detecting unexpected scores.
Unlike previous studies that utilize attribute and background data to predict student scores, this study utilizes a combination of past examination scores, current daily efforts for related subjects and trend in the current score.
The authors thank the Global Indian International School (GIIS) for their cooperation in conducting the case study. In addition, the authors acknowledge the financial support from the Keio Leading-Edge Laboratory of Science and Technology (KLL Ph.D. Program Research Grant).
Alauddin, N., Tanaka, S. and Yamada, S. (2023), "Detecting unexpected scores of individual students in an examination based on past scores and current daily efforts", The TQM Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/TQM-07-2022-0226
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