This systematic literature review aims to identify the recent research trend, most studied factors, and methods used to predict student academic performance from 2015 to 2021. The PRISMA framework guides the study. The study reviews 58 out of 219 research articles from Lens and Scopus databases. The findings indicate that the research focus of current studies revolves around identifying factors influencing student performance, data mining (DM) algorithms performance, and DM related to e-Learning systems. It also reveals that student academic records and demographics are primary aspects that affect student performance. The most used DM approach is classification and the Decision Tree classifier is the most employed DM algorithm.

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' performance prediction, and the relationship between these two subjects. The study employed the archival data of students who were 16 years old in 2019 and sat for the Malaysian Certificate of Examination (MCE) in 2021. The learning of English and Mathematics is a concern in many countries. Three main factors, namely students' past academic performance, demographics, and psychological attributes were scrutinized to identify their impact on the prediction. This study utilized the Orange software for the DM process. It employed Decision Tree (DT) rules to determine the characteristics of students with low, moderate, and high performance in English and Mathematics subjects. DT and Naïve Bayes (NB) techniques show the best predictive performance for English and Mathematics subjects, respectively. Such characteristics and predictions may cue appropriate interventions to improve students' performance in these subjects. This study revealed students' past academic performance as the most critical predictor, as well as a few demographics and psychological attributes. By examining top predictors derived using four different classifier types, this study found that students' past Mathematics performance predicts their MCE English performance and students' past English performance predicts their MCE Mathematics performance. This finding shows students' performances in both subjects are interrelated.

Satisfaction is the key to determine students' intention to continue using a Massive Open Online Course (MOOC) and examining these students' MOOC learning experience can provide insights into their satisfaction. Thus, this study aims to explore the MOOC learning experience of on-campus students who took up a MOOC on ICT Competency to identify aspects that are satisfying as well as dissatisfying to them. This study employed a qualitative approach in which eight students who had completed the ICT Competency MOOC were purposively chosen. The critical incident technique (CIT) was employed to collect and analyze information about significant experiences or critical occurrences of these participants during their MOOC learning. The meanings to these critical occurrences were collected to determine whether each experience inferred satisfaction towards the MOOC learning experience or otherwise. Participants were interviewed individually and the interview was guided by the eight aspects of Badrul Khan's e-learning framework. The study reveals six satisfaction factors and another six dissatisfaction factors that point to six important lessons in designing and implementing a MOOC. The six lessons learned include the importance of providing flexibility in learning; providing a user-friendly interface and appealing as well as comprehendible learning materials; providing manageable, relevant assessments with clear assessment instructions; providing adequate instructor engagement; providing essential infrastructure and stable technological affordances; and incorporating anti-plagiarism strategies.