All Stories

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