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Gerardo Ibarra-Vazquez; Maria Soledad Ramirez-Montoya; Mariana Buenestado-Fernandez – IEEE Transactions on Learning Technologies, 2024
This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and…
Descriptors: Gender Differences, Open Education, Cross Cultural Studies, Student Attitudes
Udi Alter; Carmen Dang; Zachary J. Kunicki; Alyssa Counsell – Teaching Statistics: An International Journal for Teachers, 2024
The biggest difference in statistical training from previous decades is the increased use of software. However, little research examines how software impacts learning statistics. Assessing the value of software to statistical learning demands appropriate, valid, and reliable measures. The present study expands the arsenal of tools by reporting on…
Descriptors: Statistics Education, Student Attitudes, Course Descriptions, Social Sciences
Nick Hopwood; Tracey-Ann Palmer; Gloria Angela Koh; Mun Yee Lai; Yifei Dong; Sarah Loch; Kun Yu – International Journal of Research & Method in Education, 2025
Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in…
Descriptors: Psychological Patterns, Student Evaluation, Learning Analytics, Learner Engagement
Conrad Borchers; Cindy Peng; Qianru Lyu; Paulo F. Carvalho; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2025
Many AIED systems support self-regulated learning, yet, support for setting and achieving practice goals has received little attention. We examine how middle school students respond to system-recommended practice goals, building on the success of similar data-driven recommendations in other domains. We introduce an adaptive dashboard in an…
Descriptors: Goal Orientation, Student Attitudes, Self Control, Intelligent Tutoring Systems
Oxman, Steven – ProQuest LLC, 2023
The vast amount of data collected during online learning offers opportunities to advance newer interventions that might aid learning. One such intervention has been learning analytics dashboards, visualizations designed to translate learning-related data into usable information. However, many student-facing dashboards compare learners' performance…
Descriptors: Courseware, Computer Software, Learning Analytics, Mastery Learning
Muhittin Sahin – Interactive Learning Environments, 2024
Learning analytics aims to improve learning and teaching in digital learning environments by optimizing them. Real-time feedback, suggestions, directions, and interventions are structured in the digital learning environments. In order to structure more effective interventions, it is crucial to ascertain which of these services offered to students…
Descriptors: Learning Analytics, Intervention, Student Attitudes, Preferences
Investigating Student Self-Beliefs and Learning Metrics in Online Courseware: A Quantitative Inquiry
Van Campenhout, Rachel – ProQuest LLC, 2022
Online courseware is an emerging educational technology that has the potential to reach students at scale. Designed with cognitive and learning science principles, courseware utilizes effective methods to maximize learning outcomes for students. Mindset (implicit theories of ability) and self-efficacy are two widely researched self-belief topics…
Descriptors: Student Attitudes, Beliefs, Online Courses, Courseware
Maya Usher; Noga Reznik; Gilad Bronshtein; Dan Kohen-Vacs – Journal of Learning Analytics, 2025
Computational thinking (CT) is a critical 21st-century skill that equips undergraduate students to solve problems systematically and think algorithmically. A key component of CT is computational creativity, which enables students to generate novel solutions within programming constraints. Humanoid robots are increasingly explored as promising…
Descriptors: Computation, Thinking Skills, Creativity, Robotics
Ignacio Villagrán; Rocio Hernández; Gregory Schuit; Andrés Neyem; Javiera Fuentes; Loreto Larrondo; Elisa Margozzini; María T. Hurtado; Zoe Iriarte; Constanza Miranda; Julián Varas; Isabel Hilliger – Journal of Learning Analytics, 2024
Remote technology has been widely incorporated into health professions education. For procedural skills training, effective feedback and reflection processes are required. Consequently, supporting a self-regulated learning (SRL) approach with learning analytics dashboards (LADs) has proven beneficial in online environments. Despite the potential…
Descriptors: Feedback (Response), Independent Study, Skill Development, Learning Analytics
Riina Kleimola; Laura Hirsto; Heli Ruokamo – Education and Information Technologies, 2025
Learning analytics provides a novel means to support the development and growth of students into self-regulated learners, but little is known about student perspectives on its utilization. To address this gap, the present study proposed the following research question: what are the perceptions of higher education students on the utilization of a…
Descriptors: Self Management, College Students, Learning Analytics, Student Development
Stanislav Pozdniakov; Jonathan Brazil; Mehrnoush Mohammadi; Mollie Dollinger; Shazia Sadiq; Hassan Khosravi – Journal of Learning Analytics, 2025
Engaging students in creating high-quality novel content, such as educational resources, promotes deep and higher-order learning. However, students often lack the necessary training or knowledge to produce such content. To address this gap, this paper explores the potential of incorporating generative AI (GenAI) to review students' work and…
Descriptors: Student Evaluation, Artificial Intelligence, Student Developed Materials, Feedback (Response)
Zhou, Jin; Ye, Jun-min – Interactive Learning Environments, 2023
Sentiment analysis (SA) is widespread across all fields and has become one of the most active topics in education research, and there is a growing body of papers published. So far, however, there has been little discussion about comprehensive literature reviews in SA in education. Therefore, this study aims to review the high-qualified scientific…
Descriptors: Educational Research, Electronic Learning, Psychological Patterns, Literature Reviews
Greenhalgh, Spencer P.; DiGiacomo, Daniela K.; Barriage, Sarah – Information and Learning Sciences, 2023
Purpose: The purpose of this paper is to examine how higher education students think about educational technologies they have previously used -- and the implications of this understanding for their awareness of datafication and privacy issues in a postsecondary context. Design/methodology/approach: The authors conducted two surveys about students'…
Descriptors: Ethics, Privacy, Learning Management Systems, Learning Analytics
Damien S. Fleur; Max Marshall; Miguel Pieters; Natasa Brouwer; Gerrit Oomens; Angelos Konstantinidis; Koos Winnips; Sylvia Moes; Wouter van den Bos; Bert Bredeweg; Erwin A. van Vliet – Journal of Learning Analytics, 2023
Personalized feedback is important for the learning process, but it is time consuming and particularly problematic in large-scale courses. While automatic feedback may help for self-regulated learning, not all forms of feedback are effective. Social comparison offers powerful feedback but is often loosely designed. We propose that intertwining…
Descriptors: Feedback (Response), Peer Influence, Learning Analytics, Undergraduate Students
Tal Soffer; Anat Cohen – Australasian Journal of Educational Technology, 2024
The rapid recent use of learning analytics (LA) in higher education, specifically during the COVID-19 pandemic, allows the monitoring of users' behavior while learning. Using LA may promote students' learning outcomes but also intrude into their privacy. This study aimed to explore students' behaviour and perceptions towards privacy and data…
Descriptors: Privacy, Educational Practices, College Students, Student Attitudes

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