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Showing 1 to 15 of 34 results Save | Export
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Leah Bidlake; Eric Aubanel; Daniel Voyer – ACM Transactions on Computing Education, 2025
Research on mental model representations developed by programmers during parallel program comprehension is important for informing and advancing teaching methods including model-based learning and visualizations. The goals of the research presented here were to determine: how the mental models of programmers change and develop as they learn…
Descriptors: Schemata (Cognition), Programming, Computer Science Education, Coding
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David P. Bunde; John F. Dooley – PRIMUS, 2024
We present a detailed description of a Cryptography and Computer Security course that has been offered at Knox College for the last 15 years. While the course is roughly divided into two sections, Cryptology and Computer Security, our emphasis here is on the Cryptology section. The course puts the cryptologic material into its historical context…
Descriptors: Technology, Coding, Computer Security, Mathematics Education
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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
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Mark Frydenberg; Anqi Xu; Jennifer Xu – Information Systems Education Journal, 2025
This study explores student perceptions of learning to code by evaluating AI-generated Python code. In an experimental exercise given to students in an introductory Python course at a business university, students wrote their own solutions to a Python program and then compared their solutions with AI-generated code. They evaluated both solutions…
Descriptors: Student Attitudes, Programming, Computer Software, Quality Assurance
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Tarling, Georgie; Melro, Ana; Kleine Staarman, Judith; Fujita, Taro – Pedagogies: An International Journal, 2023
Coding bootcamps targeting diverse learners are increasingly popular. However, little research has focused on the student experience of these courses: what pedagogic practices make learning coding meaningful for them and why. In a previous paper, we proposed a conceptual framework outlining three dimensions of learning opportunities in relation to…
Descriptors: Student Attitudes, Coding, Programming, Computer Science Education
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Fowler, Max; Smith, David H., IV; Hassan, Mohammed; Poulsen, Seth; West, Matthew; Zilles, Craig – Computer Science Education, 2022
Background and Context: Lopez and Lister first presented evidence for a skill hierarchy of code reading, tracing, and writing for introductory programming students. Further support for this hierarchy could help computer science educators sequence course content to best build student programming skill. Objective: This study aims to replicate a…
Descriptors: Programming, Computer Science Education, Correlation, Introductory Courses
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Luce, Thom – Information Systems Education Journal, 2021
This paper describes changes, precipitated by the COVID-19 pandemic, to a capstone MIS class using Microsoft ASP.NET MVC for team development with live-clients. The advent of the pandemic required that the entire development effort of the class immediately transition from a largely in-class development effort with local SQL Server and Web Server…
Descriptors: Computer Science Education, Educational Change, COVID-19, Pandemics
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Webb, Kevin C.; Zingaro, Daniel; Liao, Soohyun Nam; Taylor, Cynthia; Lee, Cynthia; Clancy, Michael; Porter, Leo – ACM Transactions on Computing Education, 2022
A Concept Inventory (CI) is an assessment to measure student conceptual understanding of a particular topic. This article presents the results of a CI for basic data structures (BDSI) that has been previously shown to have strong evidence for validity. The goal of this work is to help researchers or instructors who administer the BDSI in their own…
Descriptors: Measures (Individuals), Concept Formation, Computer Science Education, Test Results
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Deepak Dawar – Information Systems Education Journal, 2024
Learning computer programming is typically difficult for newcomers. Demotivation and learned helplessness have received much attention. Besides the subject's intricacy, low in-class participation has been associated with poor student achievement. This paper presents a follow-up, stage 2 study on the novel instructional technique, Student-Driven…
Descriptors: College Students, Computer Science Education, Required Courses, Elective Courses
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Lyon, Louise Ann; Green, Emily – ACM Transactions on Computing Education, 2021
College-educated women in the workforce are discovering a latent interest in and aptitude for computing motivated by the prevalence of computing as an integral part of jobs in many fields as well as continued headlines about the number of unfilled, highly paid computing jobs. One of these women's choices for retraining are the so-called coding…
Descriptors: Computer Science Education, Coding, Programming, Females
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Zhizezhang Gao; Haochen Yan; Jiaqi Liu; Xiao Zhang; Yuxiang Lin; Yingzhi Zhang; Xia Sun; Jun Feng – International Journal of STEM Education, 2025
Background: With the increasing interdisciplinarity between computer science (CS) and other fields, a growing number of non-CS students are embracing programming. However, there is a gap in research concerning differences in programming learning between CS and non-CS students. Previous studies predominantly relied on outcome-based assessments,…
Descriptors: Computer Science Education, Mathematics Education, Novices, Programming
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Dawar, Deepak – Journal of Information Systems Education, 2023
For most beginners, learning computer programming is a complex undertaking. Demotivation and learned helplessness have been widely reported. In addition to the subject's complexity, low in-class involvement has been linked to poor student performance. This work introduces a novel instructional technique called Student-Driven Probe Instruction…
Descriptors: Computer Science Education, Programming, Introductory Courses, Teaching Methods
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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
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Hsu, Wen-Chin; Gainsburg, Julie – Journal of Educational Computing Research, 2021
Block-based programming languages (BBLs) have been proposed as a way to prepare students for learning to program in more sophisticated, text-based languages, such as Java. Hybrid BBLs add the ability to view and edit the block commands in auto-generated, text-based code. We compared the use of a non-hybrid BBL (Scratch), a hybrid BBL (Pencil…
Descriptors: Computer Science Education, Introductory Courses, Teaching Methods, Student Attitudes
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Mangaroska, Katerina; Sharma, Kshitij; Gaševic, Dragan; Giannakos, Michail – Journal of Computer Assisted Learning, 2022
Background: Problem-solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE-log data).…
Descriptors: Cognitive Processes, Psychological Patterns, Problem Solving, Programming
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