All Stories

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