All Stories

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