One challenging issue in improving the teaching and learning methods in MOOCs is to construct potential knowledge graphs from massive learning resources. Therefore, this study proposes knowledge graphs driving online learning behaviour prediction and multi-learning task recommendation in MOOCs. Based on the knowledge graphs supported by…

Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In…

This study aimed to develop a prediction model to classify students based on their academic procrastination tendencies, which were measured and classified as low and high using a self-report tool developed based on the students' assignment submission behaviours logged in the learning management system's database. The students' temporal learning…

With the widespread application of online education platforms, the necessity for identifying learners' mental states from webcam videos is increasing as it can be potentially applied to artificial intelligence-based automatic identification of learner states. However, the behaviors that elementary school students frequently exhibit during online…

Existing research studying MOOC learner diversity has mainly taken unidimensional approaches, which have led to partial or inconsistent findings. This paper addresses this issue by proposing a multi-dimensional model that helps to identify and build the personas of key learner subgroups in any given MOOC course. By linking learners' behavioral…

The number of online courses that have been developed by faculty continues to increase in higher education. The purpose of this mixed-methods research study is to investigate the effects of online courses developed using the Quality Matters (QM) framework on self-regulated learning behaviors for students enrolled in online courses. The research…

Some researchers have questioned the use of dropout metrics to assess the quality of MOOCs. The main reason for this doubt is that participants register for online courses with different intentions. Therefore, it is proposed to use a learner-centred approach and to study the learner intention-fulfilment. Researchers studied the effect of…

As higher education is shifting to virtual teaching, examinations are increasingly conducted virtually, often proctored. Unfortunately, virtual proctored examinations--proctored remotely using technologies--can be prone to cheating. Some anti-cheating measures can be built into the virtual proctoring technologies, but cheating can still occur.…

Understanding how students engage with feedback is often reduced to a study of feedback messages that sheds little light on effects. Using the emerging notion of feedback encounters as an analytical lens, this study examines what characterizes productive feedback encounters when learning online. Drawing from a cross-national digital ethnographic…

Background: Learners in Massive Open Online Courses (MOOCs) are presented with great autonomy over their learning process. Learners must engage in self-regulated learning (SRL) to handle this autonomy. It is assumed that learners' SRL, through monitoring and control, influences learners' behaviour within the MOOC environment (e.g., watching…

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery (SD) approach based on MapReduce. The proposed SD approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope…

The prediction of student performance and the analysis of students' learning behaviour play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behaviour, educators can gain valuable insights into the factors that influence students' academic outcomes and identify areas of…

Combining nudge theory with learning analytics, 'nudge analytics', is a relatively recent phenomenon in the educational context. Used, for example, to address such issues as concerns with student (dis)engagement, nudging students to take certain action or to change a behaviour towards active learning, can make a difference. However, knowing who to…

This paper examines the effect of class size on student's academic and behavioral performance in synchronous online courses, utilizing student-level administrative data and website clickstream data from a research university in China. By examining variations in class sizes within students but across classes, we revealed a significant negative…

Self-regulated learning (SRL) refers to students' ability to take charge of their own learning and consists of behavioral strategies that are associated with better online academic performance. This quasi-experimental study investigated the effect of two interventions (SRL training alone and SRL training paired with a learning diary) on online…