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Ryan S. Baker; J. Elizabeth Richey; Jiayi Zhang; Shamya Karumbaiah; Juan Miguel Andres-Bray; Huy Anh Nguyen; Juliana Maria Alexandra L. Andres; Bruce M. McLaren – Instructional Science: An International Journal of the Learning Sciences, 2025
Digital learning games have been increasingly adopted in classrooms to facilitate learning and to promote learning outcomes. Contrary to common beliefs, many digital learning games can be more effective for female students than male students in terms of learning and affective outcomes. However, the in-game learning mechanisms that explain these…
Descriptors: Elementary School Students, Middle School Students, Sex, Gender Differences
Ya-Wen Cheng; Cheng-Huan Chen; Nian-Shing Chen – International Journal of Science and Mathematics Education, 2025
This study aims to investigate whether there are differences in the intentions to adopt maker education between in-service and pre-service teachers. It also seeks to identify the key factors affecting their intentions, providing insights for tailoring professional development (PD) training programs to meet the needs of teachers at different career…
Descriptors: Preservice Teachers, Intention, Shared Resources and Services, Teacher Attitudes
Jerry Chih-Yuan Sun; Che-Tsun Lin; Wen-Li Chang – Australasian Journal of Educational Technology, 2025
This study aimed to investigate the predicted relationship among online behavioural characteristics, intrinsic motivation and user engagement. An online learning platform was used to collect data on the online reading time and the number of test attempts of 161 graduate students, as well as their post-learning motivation and user engagement…
Descriptors: Foreign Countries, Graduate Students, Student Behavior, Learning Motivation
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
Tabares, Marta S.; Vallejo, Paola; Montoya, Alex; Correa, Daniel – Journal of Computing in Higher Education, 2022
Understanding learners' behavior is the key to the success of any learning process. The more we know about them, the more likely we can personalize learning experiences and provide successful feedback. This paper presents a feedback model implemented in a ubiquitous microlearning environment based on contextual and behavioral information and…
Descriptors: Feedback (Response), Models, Student Behavior, Educational Environment
Chambers, Brittany; Lowe, Jaylen; Muldrow, Lycurgus – Journal of STEM Education: Innovations and Research, 2022
There remains a need for more diverse STEM students that will be equipped with the necessary skills and growth mindset principles to pursue STEM positions and careers. While previous research has examined broadening participation through interventions geared toward a specific group, the intervention method used in this research case study utilized…
Descriptors: Student Attitudes, STEM Education, College Students, Intervention
Humida, Thasnim; Al Mamun, Md Habib; Keikhosrokiani, Pantea – Education and Information Technologies, 2022
Digital transformation and emerging technologies open a horizon to a new method of teaching and learning and revolutionizes the e-learning industry. The goal of this study is to scrutinize a proposed research model for predicting factors that influence student's behavioral intention to use e-learning system at Begum Rokeya University, Bangladesh.…
Descriptors: Student Behavior, Intention, Electronic Learning, College Students
Gillett-Swan, Jenna K.; Lundy, Laura – Oxford Review of Education, 2022
Schools present a unique context for the generation and resolution of conflicts of human rights. While the conflicts that arise are many and various, a default response appears to be the prioritisation of the rights of the majority. Hence the rights of the many then trump the rights of the few. However, the intersection of multiple stakeholders,…
Descriptors: Civil Rights, Conflict Resolution, Student Behavior, Conflict
Luna, J. M.; Fardoun, H. M.; Padillo, F.; Romero, C.; Ventura, S. – Interactive Learning Environments, 2022
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…
Descriptors: Online Courses, Student Characteristics, Classification, Student Behavior
Gao, Zhikai; Erickson, Bradley; Xu, Yiqiao; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany – International Educational Data Mining Society, 2022
In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected…
Descriptors: Computer Science Education, College Students, Programming, Coding
Karnalim, Oscar; Simon; Chivers, William; Panca, Billy Susanto – ACM Transactions on Computing Education, 2022
To help address programming plagiarism and collusion, students should be informed about acceptable practices and about program similarity, both coincidental and non-coincidental. However, current approaches are usually manual, brief, and delivered well before students are in a situation where they might commit academic misconduct. This article…
Descriptors: Computer Science Education, Programming, Plagiarism, Formative Evaluation
Tay, Hui Yong; Lam, Karen W. L. – Educational Research for Policy and Practice, 2022
The provision of feedback is widely practised as part of formative assessment. However, studies that examine the impact of feedback are usually from the teachers' perspective, focusing on why and how they provide feedback. Fewer studies examine feedback from the students' perspective, especially in the way they experience, make sense of and take…
Descriptors: Learner Engagement, Feedback (Response), Student Attitudes, Essays
Parks-Leduc, Laura; Guay, Russell P.; Mulligan, Leigh M. – Journal of Academic Ethics, 2022
In this study we examine college cheating behaviors of business students compared to non-business students, and investigate possible antecedents to cheating in an effort to better understand why and when students cheat. We specifically examine power values; we found that they were positively related to academic cheating in our sample, and that…
Descriptors: Values, Cheating, Business Administration Education, Majors (Students)
Krienert, Jessie L.; Walsh, Jeffrey A.; Cannon, Kevin D. – College Teaching, 2022
Academic dishonesty is pervasive among college students throughout the country. Current research suggests that more than half of students report engaging in cheating behavior while in college. While traditional forms of cheating behavior remain, technology has ushered in new opportunities making cheating more accessible by more students and harder…
Descriptors: Cheating, Student Behavior, Ethics, Achievement Need

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