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Showing all 8 results Save | Export
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Abdullahi Yusuf; Norah Md Noor; Shamsudeen Bello – Education and Information Technologies, 2024
Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In…
Descriptors: Learning Analytics, Student Behavior, Video Technology, Classification
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Hellings, Jan; Haelermans, Carla – Higher Education: The International Journal of Higher Education Research, 2022
We use a randomised experiment to study the effect of offering half of 556 freshman students a learning analytics dashboard and a weekly email with a link to their dashboard, on student behaviour in the online environment and final exam performance. The dashboard shows their online progress in the learning management systems, their predicted…
Descriptors: Learning Analytics, College Freshmen, Student Behavior, Electronic Learning
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Denis Zhidkikh; Ville Heilala; Charlotte Van Petegem; Peter Dawyndt; Miitta Jarvinen; Sami Viitanen; Bram De Wever; Bart Mesuere; Vesa Lappalainen; Lauri Kettunen; Raija Hämäläinen – Journal of Learning Analytics, 2024
Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first…
Descriptors: Learning Analytics, Prediction, School Holding Power, Academic Achievement
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Hsu, Ting-Chia; Abelson, Hal; Patton, Evan; Chen, Shih-Chu; Chang, Hsuan-Ning – International Journal of Computer-Supported Collaborative Learning, 2021
In order to promote the practice of co-creation, a real-time collaboration (RTC) version of the popular block-based programming (BBP) learning environment, MIT App Inventor (MAI), was proposed and implemented. RTC overcomes challenges related to non-collocated group work, thus lowering barriers to cross-region and multi-user collaborative software…
Descriptors: Self Efficacy, Behavior Patterns, Student Behavior, Programming
Mohammed Alzaid – ProQuest LLC, 2022
Distributed self-assessments and reflections empower learners to take the lead on their knowledge gaining evaluation. Both provide essential elements for practice and self-regulation in learning settings. Nowadays, many sources for practice opportunities are made available to the learners, especially in the Computer Science (CS) and programming…
Descriptors: Learning Analytics, Self Evaluation (Individuals), Programming, Problem Solving
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Gallego-Romero, Jesús Manuel; Alario-Hoyos, Carlos; Estévez-Ayres, Iria; Delgado Kloos, Carlos – Educational Technology Research and Development, 2020
Massive Open Online Courses (MOOCs) can be enhanced with the so-called learning-by-doing, designing the courses in a way that the learners are involved in a more active way in the learning process. Within the options for increasing learners' interaction in MOOCs, it is possible to integrate (third-party) external tools as part of the instructional…
Descriptors: Learner Engagement, Student Behavior, Learning Analytics, Online Courses
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Qian, Yizhou; Lehman, James – Journal of Educational Computing Research, 2020
This study implemented a data-driven approach to identify Chinese high school students' common errors in a Java-based introductory programming course using the data in an automated assessment tool called the Mulberry. Students' error-related behaviors were also analyzed, and their relationships to success in introductory programming were…
Descriptors: High School Students, Error Patterns, Introductory Courses, Computer Science Education
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Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology