All Stories

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