Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that…

The rapid growth of online education, especially since the pandemic, is presenting educators with numerous challenges. Chief among these is concern about academic dishonesty, especially on unproctored online exams. Students cheating on exams is not a new phenomenon. The topic has been discussed and debated within institutions of higher learning,…

Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India.…

Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…

Dual enrollment (DE)--where students earn college credits during high school--is expanding rapidly. To facilitate DE, institutional actors across K-12 schools and colleges must build or repurpose structures across separate organizations to determine course offerings, assignments, modality, and composition. Yet the organization and implications of…

This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students'…

In light of the dramatic growth of Chinese learners worldwide and a need for a cross-linguistic research on Chinese literacy development, this study investigated (a) the effects of character properties (i.e., orthographic consistency and transparency) on character acquisition, and (b) the effects of individual learner differences (i.e.,…

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting…

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic…