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Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
Marijn Martens; Ralf De Wolf; Lieven De Marez – Technology, Knowledge and Learning, 2025
Algorithmic decision-making systems such as Learning Analytics (LA) are widely used in an educational setting ranging from kindergarten to university. Most research focuses on how LA is used and adopted by teachers. However, the perspective of students and parents who experience the (in)direct consequences of these systems is underexplored. This…
Descriptors: Algorithms, Decision Making, Learning Analytics, Secondary School Students
Ozan Rasit Yürüm; Tugba Taskaya-Temizel; Soner Yildirim – Technology, Knowledge and Learning, 2024
The purpose of this study was to investigate the use of predictive video analytics in online courses in the literature. A systematic literature review was performed based on a hybrid search strategy that included both database searching and backward snowballing. In total, 77 related publications published between 2011 and April 2023 were…
Descriptors: Literature Reviews, Distance Education, Online Courses, Video Technology
Lars de Vreugd; Anouschka van Leeuwen; Marieke van der Schaaf – Journal of Computer Assisted Learning, 2025
Background: University students need to self-regulate but are sometimes incapable of doing so. Learning Analytics Dashboards (LADs) can support students' appraisal of study behaviour, from which goals can be set and performed. However, it is unclear how goal-setting and self-motivation within self-regulated learning elicits behaviour when using an…
Descriptors: Learning Analytics, Educational Technology, Goal Orientation, Learning Motivation
Earl H. McKinney Jr.; Simon Ginzinger – Journal of Information Systems Education, 2024
The growing use of analytics has increased the demand for more highly data literate graduates. Awareness of ambiguity in data has been suggested as a new data literacy skill. Here, we describe a student-centered semester-long project that can be used to teach this skill in an introductory analytics or database course. The project requires students…
Descriptors: Student Centered Learning, Student Projects, Consciousness Raising, Ambiguity (Context)
Wenyi Lu; Joseph Griffin; Troy D. Sadler; James Laffey; Sean P. Goggins – Journal of Learning Analytics, 2025
Game-based learning (GBL) is increasingly recognized as an effective tool for teaching diverse skills, particularly in science education, due to its interactive, engaging, and motivational qualities, along with timely assessments and intelligent feedback. However, more empirical studies are needed to facilitate its wider application in school…
Descriptors: Game Based Learning, Predictor Variables, Evaluation Methods, Educational Games
Using Analytics to Predict Students' Interactions with Learning Management Systems in Online Courses
Ali Alshammari – Education and Information Technologies, 2024
In online education, it is widely recognized that interaction and engagement have an impact on students' academic performance. While previous research has extensively explored interactions between students, instructors, and content, there has been limited exploration of course design elements that promote the fourth type of interaction:…
Descriptors: Learning Analytics, Learning Management Systems, Academic Achievement, Correlation
Umar Bin Qushem; Solomon Sunday Oyelere; Gökhan Akçapinar; Rogers Kaliisa; Mikko-Jussi Laakso – Technology, Knowledge and Learning, 2024
Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine…
Descriptors: Predictor Variables, Learning Analytics, At Risk Students, Computer Science
Sudeshna Pal; Patsy Moskal; Anchalee Ngampornchai – International Journal on E-Learning, 2024
This study investigated the effectiveness of blended instruction in enhancing student success in an advanced undergraduate engineering course. The research used learning analytics captured from pre-recorded lecture videos, course grade data, and student surveys. Results revealed positive correlations between lecture video viewership and course…
Descriptors: Blended Learning, Advanced Courses, Engineering Education, Undergraduate Students
Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
Hung Tan Ha; Duyen Thi Bich Nguyen; Tim Stoeckel – Language Testing, 2024
Word frequency has a long history of being considered the most important predictor of word difficulty and has served as a guideline for several aspects of second language vocabulary teaching, learning, and assessment. However, recent empirical research has challenged the supremacy of frequency as a predictor of word difficulty. Accordingly,…
Descriptors: Word Frequency, Vocabulary Skills, Second Language Learning, Second Language Instruction