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Fein, Benedikt; Graßl, Isabella; Beck, Florian; Fraser, Gordon – International Educational Data Mining Society, 2022
The recent trend of embedding source code for machine learning applications also enables new opportunities in learning analytics in programming education, but which code embedding approach is most suitable for learning analytics remains an open question. A common approach to embedding source code lies in extracting syntactic information from a…
Descriptors: Artificial Intelligence, Learning Analytics, Programming, Programming Languages
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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
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Höppner, Frank – International Educational Data Mining Society, 2021
Various similarity measures for source code have been proposed, many rely on edit- or tree-distance. To support a lecturer in quickly assessing live or online exercises with respect to "approaches taken by the students," we compare source code on a more abstract, semantic level. Even if novice student's solutions follow the same idea,…
Descriptors: Coding, Classification, Programming, Computer Science Education
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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
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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
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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
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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
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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
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Carina Büscher – International Journal of Science and Mathematics Education, 2025
Computational thinking (CT) is becoming increasingly important as a learning content. Subject-integrated approaches aim to develop CT within other subjects like mathematics. The question is how exactly CT can be integrated and learned in mathematics classrooms. In a case study involving 12 sixth-grade learners, CT activities were explored that…
Descriptors: Mathematics Instruction, Thinking Skills, Teaching Methods, Computer Science Education
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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
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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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McCall, Davin; Kölling, Michael – ACM Transactions on Computing Education, 2019
The types of programming errors that novice programmers make and struggle to resolve have long been of interest to researchers. Various past studies have analyzed the frequency of compiler diagnostic messages. This information, however, does not have a direct correlation to the types of errors students make, due to the inaccuracy and imprecision…
Descriptors: Computer Software, Programming, Error Patterns, Novices
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Ben-Yaacov, Anat; Hershkovitz, Arnon – Journal of Educational Computing Research, 2023
Block programming has been suggested as a way of engaging young learners with the foundations of programming and computational thinking in a syntax-free manner. Indeed, syntax errors--which form one of two broad categories of errors in programming, the other one being logic errors--are omitted while block programming. However, this does not mean…
Descriptors: Programming, Computation, Thinking Skills, Error Patterns
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Mao, Ye; Shi, Yang; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2021
As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…
Descriptors: Programming, Computer Science Education, Learning Processes, Learning Analytics
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Barbosa Rocha, Hemilis Joyse; Cabral De Azevedo Restelli Tedesco, Patrícia; De Barros Costa, Evandro – Informatics in Education, 2023
In programming problem solving activities, sometimes, students need feedback to progress in the course, being positively affected by the received feedback. This paper presents an overview of the state of the art and practice of the feedback approaches on introductory programming. To this end, we have carried out a systematic literature mapping to…
Descriptors: Classification, Computer Science Education, Feedback (Response), Problem Solving
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