Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…

Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing…

The dropout of students is one of the major obstacles that ruin the improvement of higher education quality. To facilitate the study of students' dropout in Moroccan universities, this paper aims to establish a clustering approach model based on machine learning algorithms to determine Moroccan universities categories. Our objective in this…

Purpose: The goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners. Design/methodology/approach: The authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the…

Monitoring students' academic progress is vital for ensuring timely completion of their studies and supporting at-risk students. Educational Data Mining (EDM) utilizes machine learning and feature selection to gain insights into student performance. However, many feature selection algorithms lack performance forecasting systems, limiting their…

Design science research (DSR) is one of important research paradigms in information systems (IS) that focus on addressing business problems by building and implementing design artifacts. Recently, predictive analytics has become one major stream of DSR thanks to the improvement of computational power and methods and the increase in available…

Both educational data mining and learning analytics aim to understand learners and optimise learning processes of educational settings like Moodle, a learning management system (LMS). Analytics in an LMS covers many different aspects: finding students at risk of abandoning a course or identifying students with difficulties before the assessments.…

Video clickstream behaviors such as pause, forward, and backward offer great potential for educational data mining and learning analytics since students exhibit a significant amount of these behaviors in online courses. The purpose of this study is to investigate the predictive relationship between video clickstream behaviors and students' test…

Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…

Using multimodal data fusion techniques, we built and tested prediction models to track middle-school student distress states during educational gameplay. We collected and analyzed 1,145 data instances, sampled from a total of 31 middle-school students' audio- and video-recorded gameplay sessions. We conducted data wrangling with student gameplay…

Early warning systems (EWSs) have proven to be useful in identifying students at risk of failing both online and conventional courses. Although some general systems have reported acceptable ability to work in modules with different characteristics, those designed from a course-specific perspective have recently provided better outcomes. Hence, the…

Predictive analytics has taken higher education by storm, with its promise of closing equity gaps, raising student retention rates, and increasing tuition revenue by keeping students enrolled. Many colleges and universities have made an investment in predictive analytics for student success initiatives, and even more are looking into implementing,…

Over the last decade, there has been great research interest in the application of artificial intelligence (AI) in various fields, such as medicine, finance, and law. Recently, there has been a research focus on the application of AI in education, where it has great potential. Therefore, a systematic review of the literature on AI in education is…

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…

In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…