All Stories

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