All Stories

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