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