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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
Gabbay, Hagit; Cohen, Anat – International Educational Data Mining Society, 2022
The challenge of learning programming in a MOOC is twofold: acquiring programming skills and learning online, independently. Automated testing and feedback systems, often offered in programming courses, may scaffold MOOC learners by providing immediate feedback and unlimited re-submissions of code assignments. However, research still lacks…
Descriptors: Automation, Feedback (Response), Student Behavior, MOOCs
Yukiko Maruyama – International Association for Development of the Information Society, 2022
The increased focus on computational thinking has led to the acceptance of computer programming as one of the ways of teaching computational thinking. In 2020, Japan introduced programming education in elementary schools. To understand the current situation of parental involvement at the beginning of programming education, this study aimed to know…
Descriptors: Parent Participation, Programming, Computer Science Education, Thinking Skills
Lockwood, Elise; De Chenne, Adaline – North American Chapter of the International Group for the Psychology of Mathematics Education, 2020
Computational activity is increasingly relevant in education and society, and researchers have investigated its role in students' mathematical thinking and activity. More work is needed within mathematics education to explore ways in which computational activity might afford development of mathematical practices. In this paper, we specifically…
Descriptors: Undergraduate Students, Computation, Problem Solving, Programming
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
Singla, Adish; Theodoropoulos, Nikitas – International Educational Data Mining Society, 2022
Block-based visual programming environments are increasingly used to introduce computing concepts to beginners. Given that programming tasks are open-ended and conceptual, novice students often struggle when learning in these environments. AI-driven programming tutors hold great promise in automatically assisting struggling students, and need…
Descriptors: Programming, Computer Science Education, Task Analysis, Introductory Courses
Saira Anwar; Ahmed Ashraf Butt; Muhsin Menekse – Grantee Submission, 2022
This work-in-progress research paper examines the relationship between two aspects of students' engagement and academic performance. With the boom of technology-mediated learning environments, many educational applications are integrated into STEM courses. However, the effectiveness of these applications in the learning environments is contingent…
Descriptors: Learner Engagement, Academic Achievement, College Freshmen, Engineering Education
An, Truong-Sinh; Krauss, Christopher; Merceron, Agathe – International Educational Data Mining Society, 2017
The emergence of Massive Open Online Courses (MOOCs) has enabled new research to analyze typical behaviors of learners. In this paper, we investigate whether this research is generalizable to other courses that are backed by a learning management system (LMS) as MOOCs are. Building on methods developed by others, we characterize individual…
Descriptors: Large Group Instruction, Online Courses, Student Behavior, College Students
Majid al-Rifaie, Mohammad; Yee-King, Matthew; d'Inverno, Mark – International Educational Data Mining Society, 2016
This paper proposes a new technique for analysing the behaviour of students on an online course. This work considers a range of social learning behaviours supported in our recently designed and implemented collaborative learning system which supports students giving and receiving feedback on each other's developing work and practice. The course…
Descriptors: Student Behavior, Online Courses, Social Behavior, Data Analysis
McBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – International Educational Data Mining Society, 2016
Effective mining of data from online submission systems offers the potential to improve educational outcomes by identifying student habits and behaviours and their relationship with levels of achievement. In particular, it may assist in identifying students at risk of performing poorly, allowing for early intervention. In this paper we investigate…
Descriptors: Data Collection, Student Behavior, Academic Achievement, Correlation
Lu, Yihan; Hsiao, I-Han – International Educational Data Mining Society, 2016
Online programming discussion forums have grown increasingly and have formed sizable repositories of problem solving-solutions. In this paper, we investigate programming learners' information seeking behaviors from online discussion forums. We design engines to collect students' information seeking processes, including query formulation,…
Descriptors: Programming, Advanced Students, Reading Processes, Computer Mediated Communication
Seels, Barbara; And Others – 1996
This paper summarizes an integrated research study which was conducted of the literature on learning from mass media and instructional television. The body of literature reviewed encompasses 17,500 citations from the Educational Resources Information Clearinghouse (ERIC) and 1,882 citations from Psychological Abstracts. Major research areas…
Descriptors: Academic Achievement, Beliefs, Cognitive Processes, Comparative Analysis