Computer-supported collaborative concept mapping (CSCCM) integrates technology and concept mapping to support students' knowledge understanding, and much research on the behavioral patterns involved in CSCCM activities has been conducted. However, there is limited understanding of the differences in knowledge understanding and behavioral patterns…

In this study, we propose a MOOC Analytic Statistical Visual model (MOOC-ASV) to explore students' engagement in MOOC courses and predict their performance on the basis of their behaviors logged as big data in MOOC platforms. The model has multifunctions, which performs on visually analyzing learners' data by state-of-the-art techniques. The model…

To better understand the self-regulated learning process in online learning environments, this research applied a data mining method, the two-layer hidden Markov model (TL-HMM), to explore the patterns of learning activities. We analyzed 25,818 entries of behavior log data from an intelligent tutoring system. Results indicated that students with…

With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students…

Online discussions have become more common as social network services have become more ubiquitous and complement various learning activities. However, studies investigating online discussions in recent years have shown that off-topic messaging has increased with the use of social network services. Thus, determining the design of a mechanism to…

Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…

Individual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners' learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin's field…

As a representative practice of the theory of connectivism, cMOOCs emphasize learners' content-based connective learning. Effectively promoting learners' content production is the focus of cMOOC research and practice. This study explores whether and how learners' online interactions affect the content production of courses. Based on 45166…

Learning analytics methods have proven useful in providing insights from the increasingly available digital data about students in a variety of learning environments, including serious games. However, such methods have not been applied to the specific context of educational escape rooms and therefore little is known about students' behavior while…

Different from the group formation approaches led by teachers, learners' generative learning objectives and the independent choice of collaborative partners are important in the Internet learning environment. This study takes the cMOOC (connectivist massive open online course) 5.0 "Internet + education: dialogue between theory and…

Predicting student performance in Massive Open Online Courses (MOOCs) is important to aid in retention efforts. Researchers have demonstrated that video watching features can be used to accurately predict student test performance on video quizzes employing neural networks to predict video test grades from viewing behavior including video searching…

Students are termed "multitaskers," and it is likely that they easily fall prey to other subjects or topics that most interest them. They occasionally took heed or gave close and thoughtful attention to the lectures they were on. In the current educational system, our young generations receive materials from their leftovers, and their…

Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the student level leads researchers to enhance PFA by…

Different e-learning technologies may offer different incentive factors, which influence behavioural intention. Moreover, when adopting a new e-learning technology for an extended period, learners' perceptions and learning behaviour may change during the learning period. Unfortunately, as formative assessments (FAs) are often continuously…

In this study, a predictive model is constructed to analyze learners' performance in programming tasks using data of programming behavioral events and behavioral sequences. First, this study identifies behavioral events from log data and applies lag sequence analysis to extract behavioral sequences that reflect learners' programming strategies.…