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Xiaona Xia; Wanxue Qi – Technology, Pedagogy and Education, 2025
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…
Descriptors: Graphs, Knowledge Level, MOOCs, Prediction
Xinhong Zhang; Xiangyu Wang; Jiayin Zhao; Boyan Zhang; Fan Zhang – IEEE Transactions on Education, 2024
Contribution: This study proposes a student dropout prediction model, named image convolutional and bi-directional temporal convolutional network (IC-BTCN), which makes dropout prediction for learners based on the learning clickstream data of students in massive open online courses (MOOCs) courses. Background: The MOOCs learning platform attracts…
Descriptors: MOOCs, Dropout Characteristics, Dropout Research, Predictor Variables
Hengtao Tang; Yeye Tang; Miao Dai; Xu Du; Jui-Long Hung; Hao Li – TechTrends: Linking Research and Practice to Improve Learning, 2024
Blended learning, integrating online and in-person components, has been increasingly adopted in higher education to enhance students' learning experience and outcomes. While the advantages of blended learning are well-evidenced, research has primarily focused on the online pre-learning component, neglecting the significance of in-class activities.…
Descriptors: Blended Learning, Behavior Patterns, Learning Processes, Learning
Shen, Yawei; Wang, Shiyu – Measurement: Interdisciplinary Research and Perspectives, 2023
This study explores various approaches to investigate participants' testing performance and learning behaviors in a computer-based spatial rotation learning program. Using multivariate learning and assessment data, including responses, response times, learning times and selected covariates, a comprehensive data analytic framework is developed that…
Descriptors: Academic Achievement, Statistical Analysis, Learning Processes, Student Behavior
Kerstin Huber; Maria Bannert – Journal of Computing in Higher Education, 2024
The empirical study investigates what log files and process mining can contribute to promoting successful learning. We want to show how monitoring and evaluation of learning processes can be implemented in the educational life by analyzing log files and navigation behavior. Thus, we questioned to what extent log file analyses and process mining…
Descriptors: Learning Processes, Data Analysis, Navigation (Information Systems), Student Behavior
Wenming Wang; Guijiang Liu; Deyang Liu; Youzhi Zhang – International Journal of Information and Communication Technology Education, 2025
With the rapid development of information technology, the internet has emerged as a pivotal driving force in reshaping higher education paradigms. This paper delves into clustering algorithms and proposes an enhanced version, exploring how this enhanced clustering algorithm can be applied to blended teaching of digital electronic technology…
Descriptors: Algorithms, Blended Learning, Educational Technology, Internet
Annika Thyberg; Konrad Schönborn; Niklas Gericke – International Journal of Science Education, 2024
The aim of this study is to investigate students' meaning-making of multiple visual representations of epigenetics at different levels of biological organisation, and to discern what visual aspects of the multiple visual representations might influence students' reasoning. Adopting an exploratory approach, we analysed how students made meaning of…
Descriptors: Foreign Countries, Grade 9, Science Education, Genetics
E. Janiuniene; M. Stonkiene; M. Šupa – Educational Research and Evaluation, 2024
Feedback is identified in the works of researchers as an essential element for improving the learning process of students. Research shows that lecturer's feedback creates value when it provides not only appraisal information but also links to further learning. This type of feedback affects the learner's information behaviour, i.e., encourages…
Descriptors: Student Behavior, Feedback (Response), Information Seeking, Universities
Takayuki Goto – Journal of Experimental Education, 2024
The present study investigated the impact of a utility-value intervention on students' behavioral, cognitive, and emotional engagement. Students assigned to the intervention condition were required to write an essay to connect the course contents with their personal hobbies, interests, or goals three times during the course. The results showed…
Descriptors: Intervention, Learner Engagement, Assignments, Student Behavior
Linyu Yu; Peter F. Halpin; Matthew L. Bernacki; Sirui Ren; Robert D. Plumley; Jeffrey A. Greene – Journal of Learning Analytics, 2025
Digital traces have been used to measure self-regulated learning (SRL), yet the validity of inferences made about these traces has often been questioned. Recently, researchers have used multiple channels of data -- including digital traces, verbalizations, and self-reports -- to validate inferences about individual SRL events. Research on the…
Descriptors: Learning Analytics, Independent Study, Learning Processes, Undergraduate Students
Yun-Qi Bai; Ya-Qian Xu; Jian-Jun Xiao – Interactive Learning Environments, 2024
This study takes the value-based adoption model and CIE model of the learning process as the theoretical basis and combines them to explore the influencing factors and mechanisms of learners' online interaction and perceived value. Based on the questionnaire survey data of 81 learners' potential factors and their 45,166 real-time behavior data on…
Descriptors: MOOCs, Interaction, Student Behavior, Learning Processes
Xia, Xiaona; Qi, Wanxue – International Journal of Educational Technology in Higher Education, 2023
The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an…
Descriptors: MOOCs, Dropouts, Prediction, Decision Making
Fengjiao Tu; Linjing Wu; Kinshuk; Junhua Ding; Haihua Chen – Education and Information Technologies, 2025
With the development of information and communication technology, project-based learning (PBL) has become an important pedagogical approach. Group leaders are critical in PBL, and prestige influences learner leadership. Regulation affects learners' prestige, but research on their relationship is lacking. Through content analysis and epistemic…
Descriptors: Learning Processes, Reputation, Active Learning, Student Projects
Osipenko, Maria – Education and Information Technologies, 2022
A data-driven model where individual learning behavior is a linear combination of certain stylized learning patterns scaled by learners' affinities is proposed. The absorption of stylized behavior through the affinities constitutes "building blocks" in the model. Non-negative matrix factorization is employed to extract common learning…
Descriptors: Behavior Patterns, Models, Undergraduate Students, Preferences
Jansen, Renée S.; Leeuwen, Anouschka; Janssen, Jeroen; Kester, Liesbeth – Journal of Computer Assisted Learning, 2022
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…
Descriptors: Student Behavior, Learning Processes, Online Courses, Personal Autonomy

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