論文

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.

Purpose: The availability of daily assessment data in a centralized monitoring system at school provides the opportunity to detect unusual scores soon after the assessment is carried out. This paper introduces a model for the detection of unusual scores of individual students to immediately improve performances that deviate from a normal state. Design/methodology/approach: A student's ability, a subject's difficulty level, a student's specific ability in a subject, and the difficulty level of an assessment in a subject are selected as factor effects of a linear ANOVA model. Through analysis of variance, a case study is conducted based on 330 data points of assessment scores of primary grade students retrieved from an international school in Japan. Findings: The actual score is below the lower control limit, which is recognized as an unusual score, and the score can be detected immediately after sitting for an assessment and is beneficial for students to take immediate remedies based on daily assessment. This is demonstrated through a case study. Originality/value: The detection of unusual scores based on a linear model of individual students soon after each assessment benefits from immediate remedy aligns with a daily management concept. The daily assessment data in a school system enable detection based on individual students, subject-wise and assessment-wise to improve student performances in the same academic year.