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Showing 1 to 15 of 29 results Save | Export
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Oscar Karnalim; Hapnes Toba; Meliana Christianti Johan – Education and Information Technologies, 2024
Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to be completed independently. There are a number of automated detectors for AI assisted work. However, most of them are not dedicated…
Descriptors: Artificial Intelligence, Academic Achievement, Integrity, Introductory Courses
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Michael E. Ellis; K. Mike Casey; Geoffrey Hill – Decision Sciences Journal of Innovative Education, 2024
Large Language Model (LLM) artificial intelligence tools present a unique challenge for educators who teach programming languages. While LLMs like ChatGPT have been well documented for their ability to complete exams and create prose, there is a noticeable lack of research into their ability to solve problems using high-level programming…
Descriptors: Artificial Intelligence, Programming Languages, Programming, Homework
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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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Xiner Liu; Andres Felipe Zambrano; Ryan S. Baker; Amanda Barany; Jaclyn Ocumpaugh; Jiayi Zhang; Maciej Pankiewicz; Nidhi Nasiar; Zhanlan Wei – Journal of Learning Analytics, 2025
This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies -- Zero-shot, Few-shot, and Fewshot with…
Descriptors: Coding, Artificial Intelligence, Automation, Data Analysis
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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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Lakshminarayanan, Srinivasan; Rao, N. J. – Higher Education for the Future, 2022
There are many grey areas in the interpretation of academic integrity in the course on Introduction to Programming, commonly known as CS1. Copying, for example, is a method of learning, a method of cheating and a reuse method in professional practice. Many institutions in India publish the code in the lab course manual. The students are expected…
Descriptors: Integrity, Cheating, Duplication, Introductory Courses
Kathryn Irene Cunningham – ProQuest LLC, 2020
Introductory programming courses typically focus on building generalizable programming knowledge by focusing on a language's syntax and semantics. Assignments often involve "code tracing" problems, where students perform close tracking of code's execution, typically in the context of 'toy' problems. "Reading-first" approaches…
Descriptors: Programming, Coding, Teaching Methods, Introductory Courses
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Daniele Traversaro; Giorgio Delzanno; Giovanna Guerrini – Informatics in Education, 2024
Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a…
Descriptors: Misconceptions, Programming Languages, Computer Science Education, Undergraduate Students
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Bettin, Briana; Jarvie-Eggart, Michelle; Steelman, Kelly S.; Wallace, Charles – IEEE Transactions on Education, 2022
In the wake of the so-called fourth industrial revolution, computer programming has become a foundational competency across engineering disciplines. Yet engineering students often resist the notion that computer programming is a skill relevant to their future profession. Here are presented two activities aimed at supporting the early development…
Descriptors: College Freshmen, Engineering Education, Programming, Coding
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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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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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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
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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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Loksa, Dastyni; Margulieux, Lauren; Becker, Brett A.; Craig, Michelle; Denny, Paul; Pettit, Raymond; Prather, James – ACM Transactions on Computing Education, 2022
Metacognition and self-regulation are important skills for successful learning and have been discussed and researched extensively in the general education literature for several decades. More recently, there has been growing interest in understanding how metacognitive and self-regulatory skills contribute to student success in the context of…
Descriptors: Metacognition, Programming, Computer Science Education, Learning Processes
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