Learning has a temporal characteristic in nature, which means that it occurs over the passage of time. The research on the temporal aspects of learning faces several challenges, one of which is utilizing appropriate analytical techniques to exploit the temporal data. There is no coherent guide to selecting certain temporal techniques to lead to…

Multimodal learning analysis emphasizes using diverse data from various sources and forms for precise examination of learning patterns. Despite recent rapid advancements in this field, conventional learning analysis remains predominantly cross-sectional and group-focused, which is insufficient for understanding continuous and personalized learning…

Behavioural engagement as a predictor of academic success hinges on the interplay between effort and time. Exploring the longitudinal development of engagement is vital for understanding adaptations in learning behaviour and informing educational interventions. However, person-oriented longitudinal studies on student engagement are scarce.…

As technology advances, learning analytics is expanding to include students' collaboration settings. Despite their increasing application in practice, some types of analytics might not fully capture the comprehensive educational contexts in which students' collaboration takes place (e.g., when data is collected and processed without predefined…

Learning analytics needs to pay more attention to the temporal aspect of learning processes, especially in self-regulated learning (SRL) research. In doing so, learning analytics models should incorporate both the duration and frequency of learning activities, the passage of time, and the temporal order of learning activities. However, where this…

The main goal of learning analytics and early detection systems is to extract knowledge from student data to understand students' trends of activities towards success and risk and design intervention methods to improve learning performance and experience. However, many factors contribute to the challenge of designing and building effective…

Recent advances in smart devices and online technologies have facilitated the emergence of ubiquitous learning environments for participating in different learning activities. This poses an interesting question about modality access, i.e., what students are using each platform for and at what time of day. In this paper, we present a log-based…

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…

The study of the relationships between self-regulated learning and formative assessment is an active line of research in the educational community. A recent review of the literature highlights that the study of these connections has been mainly unidirectional, focusing on how formative assessment helps students to self-regulate their learning,…

Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time-investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course…

There exist numerous resources online to gain the desired level of knowledge on any topic. However, this complicates the process of selecting the most appropriate resources. Every learner differs in terms of their learning speed, proficiency, and preferred mode of learning. This paper develops an adaptive learning management system to tackle this…

This study explores the potential of using screen time data in learning management systems (LMS) to estimate student learning time (SLT) and validate the credit value of courses. Gathering comprehensive data on actual student learning time is difficult, so this study uses LMS Moodle logs from a computer programming course with 490 students over 16…

E-tutorial learning aids as worked examples and hints have been established as effective instructional formats in problem-solving practices. However, less is known about variations in the use of learning aids across individuals at different stages in their learning process in student-centred learning contexts. This study investigates different…

The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student's actions on a prior portion of the assessment. In…

Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student…