All Stories

  1. Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks
  2. Steering AI Tutors Through System Prompts: A Crossover Study on Self-Regulated Learning and Cognitive Engagement Scaffolds in CS1
  3. Scaffolding Autocomplete: Improving Guidance for Learners using Generative Code Suggestions
  4. When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code
  5. Fast and Forgettable: A Controlled Study of Novices' Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms
  6. Effective Use of Large Language Models for Social Constructivism in Computer Science Education
  7. Say What? Examining Text and Voice Input Modalities for Prompt-Based Programming in Computing Education
  8. Interleaving Natural Language Prompting with Code Editing for Solving Programming Tasks with Generative AI Models
  9. Personalized Worked Example Generation from Student Code Submissions Using Pattern-based Knowledge Components
  10. The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness
  11. The Impact of Generative AI on the Perpetuation and Detection of Greenwashing in Sustainability Reports: Insights From an Experiment
  12. Assessing the Role of Diversity in LLM Explanations for Enhancing Student Understanding
  13. Knowledge Component-Driven Alignment of CS1 Textbooks and Exercises
  14. ACM Generative AI Task Force Special Session: Teaching with Generative AI: Tools You Can Use Today
  15. Fine-Tuning Open-Source Models as a Viable Alternative to Proprietary LLMs for Explaining Compiler Messages
  16. Unraveling Ambiguities: Analyzing Student Approaches to Solving Probeable Problems
  17. Ambiguity by Design: Practicing Requirement Clarification through Natural-Language Dialogue with LLMs
  18. Enabling Postgraduate Projects in Computing Education through Synthetic Research Data Generation
  19. The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance
  20. 10th Educational Data Mining in Computer Science Education (CSEDM) Workshop
  21. Proceedings of the 25th Koli Calling International Conference on Computing Education Research
  22. From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions
  23. Prompts First, Precision Later: Reviving the Vision of Natural Language Programming for Computing Education
  24. Developing Written Communication Skills in Engineering Education Using Automated Short Answer Grading
  25. Investigating Students' Programming Plan Knowledge with Time-Constrained Code Recall Tasks
  26. Adaptive Learning Curve Analytics with LLM-KC Identifiers for Knowledge Component Refinement
  27. Howzat? Appealing to Expert Judgement for Evaluating Human and AI Next-Step Hints for Novice Programmers
  28. Koli Calling: Call for Participation
  29. The Role of Generative AI in Software Student CollaborAItion
  30. Probing the Unknown: Exploring Student Interactions with Probeable Problems at Scale in Introductory Programming
  31. Fostering Responsible AI Use Through Negative Expertise: A Contextualized Autocompletion Quiz
  32. Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
  33. Koli Calling 2025: Call for Submissions
  34. Using Generative AI to Scaffold the Teaching of Software Engineering Team Skills
  35. Evaluating Language Models for Generating and Judging Programming Feedback
  36. Exploring Student Reactions to LLM-Generated Feedback on Explain in Plain English Problems
  37. Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners
  38. LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education
  39. On the Opportunities of Large Language Models for Programming Process Data
  40. Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools
  41. Koli Calling 2024 Conference Recap
  42. Integrating Natural Language Prompting Tasks in Introductory Programming Courses
  43. Experiences from Integrating Large Language Model Chatbots into the Classroom
  44. Synthetic Students: A Comparative Study of Bug Distribution Between Large Language Models and Computing Students
  45. "Sometimes You Just Gotta Risk It for the Biscuit": A Portrait of Student Risk-Taking
  46. 2024 Working Group Reports on 1st ACM Virtual Global Computing Education Conference
  47. Proceedings of the 24th Koli Calling International Conference on Computing Education Research
  48. Post Primary Teachers' Perspectives on Machine Learning and Artificial Intelligence in the Leaving Certificate Computer Science Curriculum
  49. GenAI in education: the first step towards personalization
  50. The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
  51. How Instructors Incorporate Generative AI into Teaching Computing
  52. Analyzing Students' Preferences for LLM-Generated Analogies
  53. Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skills
  54. Self-Regulation, Self-Efficacy, and Fear of Failure Interactions with How Novices Use LLMs to Solve Programming Problems
  55. Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge
  56. "Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students Using Large Language Models
  57. Koli Calling 2024: Call for Participation
  58. On the comprehensibility of functional decomposition: An empirical study
  59. Koli Calling 2024: Call for Submissions
  60. Using Large Language Models for Teaching Computing
  61. Discussing the Changing Landscape of Generative AI in Computing Education
  62. AI in Computing Education from Research to Practice
  63. Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models
  64. Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study
  65. Solving Proof Block Problems Using Large Language Models
  66. Prompt Problems: A New Programming Exercise for the Generative AI Era
  67. Evaluating LLM-generated Worked Examples in an Introductory Programming Course
  68. Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
  69. Computing Education in the Era of Generative AI
  70. Detecting Learning Behaviour in Programming Assignments by Analysing Versioned Repositories
  71. The Robots Are Here: Navigating the Generative AI Revolution in Computing Education
  72. Understanding Student Evaluation of Teaching in Computer Science Courses
  73. Leveraging Large Language Models for Analysis of Student Course Feedback
  74. The Forum Factor: Exploring the Link between Online Discourse and Student Achievement in Higher Education
  75. Could ChatGPT Be Used for Reviewing Learnersourced Exercises?
  76. Exploring the Interplay of Achievement Goals, Self-Efficacy, Prior Experience and Course Achievement
  77. “It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers
  78. Evaluating Distance Measures for Program Repair
  79. Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests
  80. Transformed by Transformers: Navigating the AI Coding Revolution for Computing Education: An ITiCSE Working Group Conducted by Humans
  81. Evaluating the Performance of Code Generation Models for Solving Parsons Problems With Small Prompt Variations
  82. Chat Overflow: Artificially Intelligent Models for Computing Education - renAIssance or apocAIypse?
  83. Comparing Code Explanations Created by Students and Large Language Models
  84. Seeing Program Output Improves Novice Learning Gains
  85. Factors Affecting Compilable State at Each Keystroke in CS1
  86. Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book
  87. G is for Generalisation
  88. Using Large Language Models to Enhance Programming Error Messages
  89. Automatically Generating CS Learning Materials with Large Language Models
  90. Computing Education Postdocs and Beyond
  91. The Implications of Large Language Models for CS Teachers and Students
  92. Automated Questionnaires About Students’ JavaScript Programs: Towards Gauging Novice Programming Processes
  93. Experiences from Learnersourcing SQL Exercises: Do They Cover Course Topics and Do Students Use Them?
  94. Lessons Learned From Four Computing Education Crowdsourcing Systems
  95. Facilitating API lookup for novices learning data wrangling using thumbnail graphics
  96. Automated Program Repair Using Generative Models for Code Infilling
  97. Parsons Problems and Beyond
  98. Finding Significant p in Coffee or Tea: Mildly Distasteful
  99. Experiences With and Lessons Learned on Deadlines and Submission Behavior
  100. Trends From Computing Education Research Conferences: Increasing Submissions and Decreasing Acceptance Rates
  101. Piloting Natural Language Generation for Personalized Progress Feedback
  102. Speeding Up Automated Assessment of Programming Exercises
  103. Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models
  104. Planning a Multi-institutional and Multi-national Study of the Effectiveness of Parsons Problems
  105. Can Students Review Their Peers?
  106. Who Continues in a Series of Lifelong Learning Courses?
  107. Digital Education For All: Multi-University Study of Increasing Competent Student Admissions at Scale
  108. Seeking flow from fine-grained log data
  109. Time-on-task metrics for predicting performance
  110. Pausing While Programming: Insights From Keystroke Analysis
  111. Seeking Flow from Fine-Grained Log Data
  112. A Comparison of Immediate and Scheduled Feedback in Introductory Programming Projects
  113. Time-on-Task Metrics for Predicting Performance
  114. CodeProcess Charts: Visualizing the Process of Writing Code
  115. Methodological Considerations for Predicting At-risk Students
  116. Visual recipes for slicing and dicing data: teaching data wrangling using subgoal graphics
  117. Persistence of Time Management Behavior of Students and Its Relationship with Performance in Software Projects
  118. Digital Education For All: Better Students Through Open Doors?
  119. Does the Early Bird Catch the Worm? Earliness of Students' Work and its Relationship with Course Outcomes
  120. Morning or Evening? An Examination of Circadian Rhythms of CS1 Students
  121. Exploring Personalization of Gamification in an Introductory Programming Course
  122. Promoting Early Engagement with Programming Assignments Using Scheduled Automated Feedback
  123. Exploring the Effects of Contextualized Problem Descriptions on Problem Solving
  124. Koli Calling '20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research
  125. Students’ Preferences Between Traditional and Video Lectures: Profiles and Study Success
  126. Programming Versus Natural Language
  127. Choosing Code Segments to Exclude from Code Similarity Detection
  128. Selection of Code Segments for Exclusion from Code Similarity Detection
  129. Crowdsourcing Content Creation for SQL Practice
  130. A Study of Keystroke Data in Two Contexts
  131. Comparing Pass Rates in Introductory Programming and in other STEM Disciplines
  132. Admitting Students through an Open Online Course in Programming
  133. Non-restricted Access to Model Solutions
  134. Pass Rates in STEM Disciplines Including Computing
  135. Does Creating Programming Assignments with Tests Lead to Improved Performance in Writing Unit Tests?
  136. Exploring the Applicability of Simple Syntax Writing Practice for Learning Programming
  137. Experimenting with Model Solutions as a Support Mechanism
  138. Analysis of Students' Peer Reviews to Crowdsourced Programming Assignments
  139. Crowdsourcing programming assignments with CrowdSorcerer
  140. Predicting academic performance: a systematic literature review
  141. Taxonomizing features and methods for identifying at-risk students in computing courses
  142. A Study of Pair Programming Enjoyment and Attendance using Study Motivation and Strategy Metrics
  143. Supporting Self-Regulated Learning with Visualizations in Online Learning Environments
  144. Identification based on typing patterns between programming and free text
  145. Thought crimes and profanities whilst programming
  146. Predicting Academic Success Based on Learning Material Usage
  147. Comparison of Time Metrics in Programming
  148. Student Modeling Based on Fine-Grained Programming Process Snapshots
  149. Plagiarism in Take-home Exams
  150. Using and Collecting Fine-Grained Usage Data to Improve Online Learning Materials
  151. Preventing Keystroke Based Identification in Open Data Sets
  152. Adolescent and Adult Student Attitudes Towards Progress Visualizations
  153. Tracking Students' Internet Browsing in a Machine Exam
  154. Performance and Consistency in Learning to Program
  155. SHORT PAUSES WHILE STUDYING CONSIDERED HARMFUL
  156. Automatic Inference of Programming Performance and Experience from Typing Patterns
  157. Pauses and spacing in learning to program
  158. Typing Patterns and Authentication in Practical Programming Exams
  159. Identification of programmers from typing patterns