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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
Shao-Heng Ko; Kristin Stephens-Martinez – ACM Transactions on Computing Education, 2025
Background: Academic help-seeking benefits students' achievement, but existing literature either studies important factors in students' selection of all help resources via self-reported surveys or studies their help-seeking behavior in one or two separate help resources via actual help-seeking records. Little is known about whether computing…
Descriptors: Computer Science Education, College Students, Help Seeking, Student Behavior
Gao, Zhikai; Erickson, Bradley; Xu, Yiqiao; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany – International Educational Data Mining Society, 2022
In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected…
Descriptors: Computer Science Education, College Students, Programming, Coding
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
Coto, Mayela; Mora, Sonia; Grass, Beatriz; Murillo-Morera, Juan – Computer Science Education, 2022
Background and context: Emotions are ubiquitous in academic settings and affect learning strategies, motivation to persevere, and academic outcomes, however they have not figured prominently in research on learning to program at the university level. Objective: To summarize the current knowledge available on the effect of emotions on students…
Descriptors: Programming, Computer Science Education, Psychological Patterns, Emotional Response
Daradoumis, Thanasis; Marquès Puig, Joan Manuel; Arguedas, Marta; Calvet Liñan, Laura – Educational Technology Research and Development, 2021
Recent research has shown a great interest in supporting self-regulated learning (SRL) strategies in online learning. However, there is hardly any study that has investigated how students' self-regulation of behavior could be promoted in online environments for programming learning and assessment, despite the proliferation of automated programming…
Descriptors: Self Management, Student Behavior, Online Courses, Programming
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
Gao, Zhikai; Erickson, Bradley; Xu, Yiqiao; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany – Journal of Educational Data Mining, 2022
Demand for education in Computer Science has increased markedly in recent years. With increased demand has come to an increased need for student support, especially for courses with large programming projects. Instructors commonly provide online post forums or office hours to address this massive demand for help requests. Identifying what types of…
Descriptors: Computer Science Education, Help Seeking, College Faculty, Teacher Role
Arjan J. F. Kok; Lex Bijlsma; Cornelis Huizing; Ruurd Kuiper; Harrie Passier – Informatics in Education, 2024
This paper presents the first experiences of the use of an online open-source repository with programming exercises. The repository is independent of any specific teaching approach. Students can search for and select an exercise that trains the programming concepts that they want to train and that only uses the programming concepts they already…
Descriptors: Programming Languages, Computer Science Education, Open Source Technology, Teaching Methods
Tsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
Anael Kuperwajs Cohen; Alannah Oleson; Amy J. Ko – ACM Transactions on Computing Education, 2024
Collaboration is an important aspect of computing. In a classroom setting, working with others can increase a student's motivation to attempt more challenges, reduce the difficulty of complicated concepts, and bring about greater overall success. Despite extensive research in other domains, there has been minimal exploration within computing on…
Descriptors: College Students, Help Seeking, Student Behavior, Programming
Celis Rangel, Jakeline G.; King, Melissa; Muldner, Kasia – ACM Transactions on Computing Education, 2020
Learning to program requires perseverance, practice, and the mindset that programming skills are improved through these activities (i.e., that everyone has the potential to become good at programming). In contrast to an entity mindset, individuals with an incremental mindset believe that ability is malleable and can be improved with effort. Prior…
Descriptors: Intervention, Cognitive Structures, Programming, Learning Activities
Mangaroska, Katerina; Sharma, Kshitij; Giannakos, Michail; Træteberga, Hallvard; Dillenbourg, Pierre – Journal of Learning Analytics, 2018
This study investigates how multimodal user-generated data can be used to reinforce learner reflection, improve teaching practices, and close the learning analytics loop. In particular, the aim of the study is to utilize user gaze and action-based data to examine the role of a mirroring tool (i.e., Exercise View in Eclipse) in orchestrating basic…
Descriptors: Eye Movements, Student Behavior, Computer Science Education, Programming
Sukhodolsky, Jacob – Online Submission, 2017
The problem of Computer Science students' cheating in their homework assignments so far has been handled mainly through administrative punishment of the cheaters. The success of such an approach depends to a large degree on the ability of the instructor to recognize the fact of cheating, which is a complicated task. With a large number of students…
Descriptors: Computer Science Education, Cheating, Ethics, Homework
Ahadi, Alireza; Hellas, Arto; Lister, Raymond – ACM Transactions on Computing Education, 2017
We describe a method for analyzing student data from online programming exercises. Our approach uses contingency tables that combine whether or not a student answered an online exercise correctly with the number of attempts that the student made on that exercise. We use this method to explore the relationship between student performance on online…
Descriptors: Data Analysis, Online Courses, Computer Science Education, Programming
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