Najihah Binti Nasir

Department of Convergence Education Software, Jeju National University, South Korea // najihahnasir28@gmail.com

Shivani Devi

Department of Faculty of Science and Education, Jeju National University, South Korea // iamshivanidevi@gmail.com

Seong Baeg Kim

Department of Computer Education, Jeju National University, South Korea // sbkim@jejunu.ac.kr

ABSTRACT: 

Metaverse and pair programming are effective approaches that can be used in programming education. Combining these methods could potentially enhance both the learning experience and outcomes; however, research on their integration is currently extremely limited. Therefore, this study aims to expand the existing knowledge by proposing an educational model that integrates metaverse concepts with pair programming. The primary goal of this study is to develop and validate a metaverse-based pair programming model that focuses on collaboration between students and between students and AI. The model includes several components, such as the roles of driver and navigator, a general procedure for metaverse-based pair programming, and specific flows for forming pairs and conducting student-to-student and student-to-AI pair programming sessions. Based on this model, sample lesson plans and evaluation rubrics were developed. These were validated by experts through two rounds of a Delphi survey. The results were analyzed using content validity analysis, specifically the Content Validity Ratio (CVR), which showed that most items in the first round achieved the required minimum CVR values, and by the second round, all items met these values. This confirms that the metaverse-integrated pair programming model is both appropriate and valid, as verified by the experts. The findings from this study could aid in the development of a metaverse-pair programming platform for educational purposes.

Keywords: 

Metaverse, Pair programming, Educational technology, Programming education  

Khoula Al. Abri

Sohar University, Oman // Kobaid@su.edu.om

ABSTRACT: 

This study presents an ensemble-based integration of existing unsupervised learning models to detect unreliable responses in student evaluations of teaching (SETs) within higher education. Although SETs are used widely to judge teaching quality, they are often affected by careless, biased, or fake feedback. To address this problem, six unsupervised machine learning algorithms—Local Outlier Factor, Isolation Forest, One-Class SVM, k-Nearest Neighbors, Mahalanobis Distance, and Autoencoder—were used on real data taken from a private university’s e-learning platform. Grid search was used to optimize model parameters, and a consensus-based voting strategy flagged responses identified as anomalous by at least three models. After filtering, approximately 46.15% of student records were removed. This significantly altered instructor rankings, indicating that unreliable responses can distort teaching evaluations. The findings emphasize the value of anomaly detection in educational quality assurance and demonstrate how artificial intelligence can enhance the credibility of institutional feedback systems.

Keywords: 

Student evaluation of teaching (SET), Anomaly detection, Ensemble learning, Unsupervised algorithms, Educational data quality