All Stories

  1. The Impact of Generative AI on the Perpetuation and Detection of Greenwashing in Sustainability Reports: Insights From an Experiment
  2. Assessing the Role of Diversity in LLM Explanations for Enhancing Student Understanding
  3. Knowledge Component-Driven Alignment of CS1 Textbooks and Exercises
  4. ACM Generative AI Task Force Special Session: Teaching with Generative AI: Tools You Can Use Today
  5. Fine-Tuning Open-Source Models as a Viable Alternative to Proprietary LLMs for Explaining Compiler Messages
  6. Unraveling Ambiguities: Analyzing Student Approaches to Solving Probeable Problems
  7. Ambiguity by Design: Practicing Requirement Clarification through Natural-Language Dialogue with LLMs
  8. Enabling Postgraduate Projects in Computing Education through Synthetic Research Data Generation
  9. Proceedings of the 25th Koli Calling International Conference on Computing Education Research
  10. From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions
  11. Prompts First, Precision Later: Reviving the Vision of Natural Language Programming for Computing Education
  12. Adaptive Learning Curve Analytics with LLM-KC Identifiers for Knowledge Component Refinement
  13. Howzat? Appealing to Expert Judgement for Evaluating Human and AI Next-Step Hints for Novice Programmers
  14. Koli Calling: Call for Participation
  15. The Role of Generative AI in Software Student CollaborAItion
  16. Probing the Unknown: Exploring Student Interactions with Probeable Problems at Scale in Introductory Programming
  17. Fostering Responsible AI Use Through Negative Expertise: A Contextualized Autocompletion Quiz
  18. Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
  19. Koli Calling 2025: Call for Submissions
  20. Using Generative AI to Scaffold the Teaching of Software Engineering Team Skills
  21. Evaluating Language Models for Generating and Judging Programming Feedback
  22. Exploring Student Reactions to LLM-Generated Feedback on Explain in Plain English Problems
  23. Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners
  24. LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education
  25. On the Opportunities of Large Language Models for Programming Process Data
  26. Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools
  27. Koli Calling 2024 Conference Recap
  28. Integrating Natural Language Prompting Tasks in Introductory Programming Courses
  29. Experiences from Integrating Large Language Model Chatbots into the Classroom
  30. Synthetic Students: A Comparative Study of Bug Distribution Between Large Language Models and Computing Students
  31. "Sometimes You Just Gotta Risk It for the Biscuit": A Portrait of Student Risk-Taking
  32. 2024 Working Group Reports on 1st ACM Virtual Global Computing Education Conference
  33. Proceedings of the 24th Koli Calling International Conference on Computing Education Research
  34. Post Primary Teachers' Perspectives on Machine Learning and Artificial Intelligence in the Leaving Certificate Computer Science Curriculum
  35. GenAI in education: the first step towards personalization
  36. The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
  37. How Instructors Incorporate Generative AI into Teaching Computing
  38. Analyzing Students' Preferences for LLM-Generated Analogies
  39. Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skills
  40. Self-Regulation, Self-Efficacy, and Fear of Failure Interactions with How Novices Use LLMs to Solve Programming Problems
  41. Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge
  42. "Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students Using Large Language Models
  43. Koli Calling 2024: Call for Participation
  44. On the comprehensibility of functional decomposition: An empirical study
  45. Koli Calling 2024: Call for Submissions
  46. Using Large Language Models for Teaching Computing
  47. Discussing the Changing Landscape of Generative AI in Computing Education
  48. AI in Computing Education from Research to Practice
  49. Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models
  50. Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study
  51. Solving Proof Block Problems Using Large Language Models
  52. Prompt Problems: A New Programming Exercise for the Generative AI Era
  53. Evaluating LLM-generated Worked Examples in an Introductory Programming Course
  54. Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
  55. Computing Education in the Era of Generative AI
  56. Detecting Learning Behaviour in Programming Assignments by Analysing Versioned Repositories
  57. The Robots Are Here: Navigating the Generative AI Revolution in Computing Education
  58. Understanding Student Evaluation of Teaching in Computer Science Courses
  59. Leveraging Large Language Models for Analysis of Student Course Feedback
  60. The Forum Factor: Exploring the Link between Online Discourse and Student Achievement in Higher Education
  61. Could ChatGPT Be Used for Reviewing Learnersourced Exercises?
  62. Exploring the Interplay of Achievement Goals, Self-Efficacy, Prior Experience and Course Achievement
  63. “It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers
  64. Evaluating Distance Measures for Program Repair
  65. Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests
  66. Transformed by Transformers: Navigating the AI Coding Revolution for Computing Education: An ITiCSE Working Group Conducted by Humans
  67. Evaluating the Performance of Code Generation Models for Solving Parsons Problems With Small Prompt Variations
  68. Chat Overflow: Artificially Intelligent Models for Computing Education - renAIssance or apocAIypse?
  69. Comparing Code Explanations Created by Students and Large Language Models
  70. Seeing Program Output Improves Novice Learning Gains
  71. Factors Affecting Compilable State at Each Keystroke in CS1
  72. Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book
  73. G is for Generalisation
  74. Using Large Language Models to Enhance Programming Error Messages
  75. Automatically Generating CS Learning Materials with Large Language Models
  76. Computing Education Postdocs and Beyond
  77. The Implications of Large Language Models for CS Teachers and Students
  78. Automated Questionnaires About Students’ JavaScript Programs: Towards Gauging Novice Programming Processes
  79. Experiences from Learnersourcing SQL Exercises: Do They Cover Course Topics and Do Students Use Them?
  80. Lessons Learned From Four Computing Education Crowdsourcing Systems
  81. Facilitating API lookup for novices learning data wrangling using thumbnail graphics
  82. Automated Program Repair Using Generative Models for Code Infilling
  83. Parsons Problems and Beyond
  84. Finding Significant p in Coffee or Tea: Mildly Distasteful
  85. Experiences With and Lessons Learned on Deadlines and Submission Behavior
  86. Trends From Computing Education Research Conferences: Increasing Submissions and Decreasing Acceptance Rates
  87. Piloting Natural Language Generation for Personalized Progress Feedback
  88. Speeding Up Automated Assessment of Programming Exercises
  89. Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models
  90. Planning a Multi-institutional and Multi-national Study of the Effectiveness of Parsons Problems
  91. Can Students Review Their Peers?
  92. Who Continues in a Series of Lifelong Learning Courses?
  93. Digital Education For All: Multi-University Study of Increasing Competent Student Admissions at Scale
  94. Seeking flow from fine-grained log data
  95. Time-on-task metrics for predicting performance
  96. Pausing While Programming: Insights From Keystroke Analysis
  97. Seeking Flow from Fine-Grained Log Data
  98. A Comparison of Immediate and Scheduled Feedback in Introductory Programming Projects
  99. Time-on-Task Metrics for Predicting Performance
  100. CodeProcess Charts: Visualizing the Process of Writing Code
  101. Methodological Considerations for Predicting At-risk Students
  102. Visual recipes for slicing and dicing data: teaching data wrangling using subgoal graphics
  103. Persistence of Time Management Behavior of Students and Its Relationship with Performance in Software Projects
  104. Digital Education For All: Better Students Through Open Doors?
  105. Does the Early Bird Catch the Worm? Earliness of Students' Work and its Relationship with Course Outcomes
  106. Morning or Evening? An Examination of Circadian Rhythms of CS1 Students
  107. Exploring Personalization of Gamification in an Introductory Programming Course
  108. Promoting Early Engagement with Programming Assignments Using Scheduled Automated Feedback
  109. Exploring the Effects of Contextualized Problem Descriptions on Problem Solving
  110. Koli Calling '20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research
  111. Students’ Preferences Between Traditional and Video Lectures: Profiles and Study Success
  112. Programming Versus Natural Language
  113. Choosing Code Segments to Exclude from Code Similarity Detection
  114. Selection of Code Segments for Exclusion from Code Similarity Detection
  115. Crowdsourcing Content Creation for SQL Practice
  116. A Study of Keystroke Data in Two Contexts
  117. Comparing Pass Rates in Introductory Programming and in other STEM Disciplines
  118. Admitting Students through an Open Online Course in Programming
  119. Non-restricted Access to Model Solutions
  120. Pass Rates in STEM Disciplines Including Computing
  121. Does Creating Programming Assignments with Tests Lead to Improved Performance in Writing Unit Tests?
  122. Exploring the Applicability of Simple Syntax Writing Practice for Learning Programming
  123. Experimenting with Model Solutions as a Support Mechanism
  124. Analysis of Students' Peer Reviews to Crowdsourced Programming Assignments
  125. Crowdsourcing programming assignments with CrowdSorcerer
  126. Predicting academic performance: a systematic literature review
  127. Taxonomizing features and methods for identifying at-risk students in computing courses
  128. A Study of Pair Programming Enjoyment and Attendance using Study Motivation and Strategy Metrics
  129. Supporting Self-Regulated Learning with Visualizations in Online Learning Environments
  130. Identification based on typing patterns between programming and free text
  131. Thought crimes and profanities whilst programming
  132. Predicting Academic Success Based on Learning Material Usage
  133. Comparison of Time Metrics in Programming
  134. Student Modeling Based on Fine-Grained Programming Process Snapshots
  135. Plagiarism in Take-home Exams
  136. Using and Collecting Fine-Grained Usage Data to Improve Online Learning Materials
  137. Preventing Keystroke Based Identification in Open Data Sets
  138. Adolescent and Adult Student Attitudes Towards Progress Visualizations
  139. Tracking Students' Internet Browsing in a Machine Exam
  140. Performance and Consistency in Learning to Program
  141. SHORT PAUSES WHILE STUDYING CONSIDERED HARMFUL
  142. Automatic Inference of Programming Performance and Experience from Typing Patterns
  143. Pauses and spacing in learning to program
  144. Typing Patterns and Authentication in Practical Programming Exams
  145. Identification of programmers from typing patterns