All Stories

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