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This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE)
Connie Shin
Universiti Malaysia Sabah
Education

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Academic engagement and artificial intelligence platform behaviors in grammar achievement Wang Yadan; Soon Singh Bikar Singh; Connie Shin; Zheng Juncai; Zhang Qianqian
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i3.37822

Abstract

This study is among the first to use archival institutional records to test the incremental validity of artificial intelligence platform behaviors (AI_index) in predicting grammar achievement (GA). Using data from 405 non–English-major freshmen enrolled in a compulsory grammar course at a private Chinese university, we examined whether AI_index predicts end-of-semester grammar exam performance beyond course-embedded behavioral academic engagement (AE_index). AE_index was derived from grade-book quizzes and class interactions, whereas AI_index was constructed from institutional platform logs capturing coursework completion and assigned video viewing. Indices were scaled to a 0–100 range, and GA was measured by a unified final exam. Descriptive statistics, correlations, and hierarchical regression analyses showed that AE_index was a small but significant predictor of exam performance, whereas AI_index was weak and non-significant and added no incremental predictive value beyond AE_index. Together, the two indices explained a modest proportion of variance in GA. These findings suggest that completion-based platform metrics are unlikely to reflect effortful learning unless platform tasks align with summative assessment demands (e.g., translation and proofreading). The findings caution against using completion-based AI metrics as high-stakes indicators without demonstrated task–assessment alignment.
Exploring the relationship between teacher work engagement and organizational citizenship behavior in early childhood education Yixuan Zhao; Connie Shin
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i4.39029

Abstract

The role of teacher work engagement (TWE) is very important in creating positive organizational citizenship behavior (OCB) within the early childhood education (ECE) context. This study hypothesizes the use of TWE structural equation modeling (SEM) with advanced logistic regression (ALR) (TWE-SEM-ALR) model and predict the occurrence of OCB among teachers. When 250 early childhood educators in both public and privates collected a teacher-based questionnaire data, it was observed that the information was gathered in questionnaires. Evaluation was done through experimental means, where an 80:20 train-test splitting was used. The model with the suggested accuracy of 92.4 and precision of 0.91, recall as 0.93, F1-score as 0.92, and area under the curve (AUC) of 0.95 was better than models based on logistic regression, support vectors machines, and SEM-only models. The error analysis and the ablation studies also served to validate the role of every model component, as it was shown that the combination of SEM and ALR greatly minimizes the error in predictions and enhances classification stability. The findings of the effectiveness of the proposed framework in explaining complex engagements-behavior relationships and it has rich implications to educational administrators who wish to improve teacher performance and organizational performance.
Co-Authors Soon Singh Bikar Singh Wang Yadan Yixuan Zhao Zhang Qianqian Zheng Juncai