All Stories

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