NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing 1 to 15 of 58 results Save | Export
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Gitinabard, Niki; Gao, Zhikai; Heckman, Sarah; Barnes, Tiffany; Lynch, Collin F. – Journal of Educational Data Mining, 2023
Few studies have analyzed students' teamwork (pairwork) habits in programming projects due to the challenges and high cost of analyzing complex, long-term collaborative processes. In this work, we analyze student teamwork data collected from the GitHub platform with the goal of identifying specific pair teamwork styles. This analysis builds on an…
Descriptors: Cooperative Learning, Computer Science Education, Programming, Student Projects
Peer reviewed Peer reviewed
Direct linkDirect link
Chih-Yueh Chou; Wei-Han Chen – Educational Technology & Society, 2025
Studies have shown that students have different help-seeking behavior patterns and tendencies and furthermore, that students with certain help-seeking behavior patterns and tendencies may have poor performance (i.e., at-risk students). This study applied an educational data mining approach, including clustering and classification, to analyze…
Descriptors: Student Behavior, Help Seeking, Problem Solving, Information Retrieval
Peer reviewed Peer reviewed
Direct linkDirect link
Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garci´a, Agusti´n Alejo; Bringas, Mauro; Morzan, Ezequiel; Onna, Diego – Journal of Chemical Education, 2021
Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks…
Descriptors: Undergraduate Students, Chemistry, Electronic Learning, Artificial Intelligence
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Ragonis, Noa; Shmallo, Ronit – Informatics in Education, 2022
Object-oriented programming distinguishes between instance attributes and methods and class attributes and methods, annotated by the "static" modifier. Novices encounter difficulty understanding the means and implications of "static" attributes and methods. The paper has two outcomes: (a) a detailed classification of aspects of…
Descriptors: Programming, Computer Science Education, Concept Formation, Thinking Skills
Peer reviewed Peer reviewed
Direct linkDirect link
Yun Huang; Christian Dieter Schunn; Julio Guerra; Peter L. Brusilovsky – ACM Transactions on Computing Education, 2024
Programming skills are increasingly important to the current digital economy, yet these skills have long been regarded as challenging to acquire. A central challenge in learning programming skills involves the simultaneous use of multiple component skills. This article investigates why students struggle with integrating component skills--a…
Descriptors: Programming, Computer Science Education, Error Patterns, Classification
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas – International Educational Data Mining Society, 2023
Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…
Descriptors: Computer Science Education, Data Analysis, Programming, Coding
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Tunnel, Raimond-Hendrik; Norbisrath, Ulrich – Journal of Education and Learning, 2023
As in any professional field, aspiring video game artists, designers, and developers must acquire the necessary skills and knowledge for a successful career. Higher education institutions offer varying video game Bachelor's degree programs to meet the diverse needs of the industry. Our objective in this study was to explore these curricula to gain…
Descriptors: Classification, Video Games, Bachelors Degrees, College Curriculum
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Melina Verger; Chunyang Fan; Sébastien Lallé; François Bouchet; Vanda Luengo – Journal of Educational Data Mining, 2024
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…
Descriptors: Algorithms, Prediction, Bias, Classification
Peer reviewed Peer reviewed
Direct linkDirect link
Rebeckah K. Fussell; Megan Flynn; Anil Damle; Michael F. J. Fox; N. G. Holmes – Physical Review Physics Education Research, 2025
Recent advancements in large language models (LLMs) hold significant promise for improving physics education research that uses machine learning. In this study, we compare the application of various models for conducting a large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in…
Descriptors: Physics, Computational Linguistics, Classification, Laboratory Experiments
Peer reviewed Peer reviewed
Direct linkDirect link
Shaheen, Muhammad – Interactive Learning Environments, 2023
Outcome-based education (OBE) is uniquely adapted by most of the educators across the world for objective processing, evaluation and assessment of computing programs and its students. However, the extraction of knowledge from OBE in common is a challenging task because of the scattered nature of the data obtained through Program Educational…
Descriptors: Undergraduate Students, Programming, Computer Science Education, Educational Objectives
Peer reviewed Peer reviewed
Direct linkDirect link
Boussaha, Karima; Mokhati, Farid; Hanneche, Amira – International Journal of Web-Based Learning and Teaching Technologies, 2021
This article introduces a new learner's self-assessment environment as CEHL that allows comparison of learners' programs with those elaborated by the teacher. The subjacent idea is to indirectly compare programs through their graphical representations described by ontologies. So, CEHL developed so-called S_Onto_ALPPWA which allows comparing…
Descriptors: Self Evaluation (Individuals), Programming, Computer Uses in Education, Comparative Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Craig, Clay Martin; Brooks, Mary Elizabeth; Bichard, Shannon – International Journal of Listening, 2023
Despite the pervasive nature of podcasts, little research has examined college student's affinity for and motivations to listen to podcasts. This study investigated college students' motivations, attitudes and behaviors in association with podcasts utilizing the appreciative listening framework in conjunction with uses and gratification theory.…
Descriptors: College Students, Handheld Devices, Audio Equipment, Information Dissemination
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Gitinabard, Niki; Okoilu, Ruth; Xu, Yiqao; Heckman, Sarah; Barnes, Tiffany; Lynch, Collin – International Educational Data Mining Society, 2020
Teamwork, often mediated by version control systems such as Git and Apache Subversion (SVN), is central to professional programming. As a consequence, many colleges are incorporating both collaboration and online development environments into their curricula even in introductory courses. In this research, we collected GitHub logs from two…
Descriptors: Teamwork, Group Activities, Student Projects, Programming
Peer reviewed Peer reviewed
Direct linkDirect link
Thrall, Elizabeth S.; Lee, Seung Eun; Schrier, Joshua; Zhao, Yijun – Journal of Chemical Education, 2021
Techniques from the branch of artificial intelligence known as machine learning (ML) have been applied to a wide range of problems in chemistry. Nonetheless, there are very few examples of pedagogical activities to introduce ML to chemistry students in the chemistry education literature. Here we report a computational activity that introduces…
Descriptors: Undergraduate Students, Artificial Intelligence, Man Machine Systems, Science Education
Peer reviewed Peer reviewed
Direct linkDirect link
Baosen Zhang; Ariana Frkonja-Kuczin; Zhong-Hui Duan; Aliaksei Boika – Journal of Chemical Education, 2023
Computer vision (CV) is a subfield of artificial intelligence (AI) that trains computers to understand our visual world based on digital images. There are many successful applications of CV including face and hand gesture detection, weather recording, smart farming, and self-driving cars. Recent advances in computer vision with machine learning…
Descriptors: Classification, Laboratory Equipment, Visual Aids, Optics
Previous Page | Next Page »
Pages: 1  |  2  |  3  |  4