Sep 2026· Proceedings of the 2026 United Kingdom and Ireland Computing Education Research· 0 citations· 10 references
TL;DR
A web-based misconception repository for Java/Processing is presented, with each entry connecting misconceptions to origins, typical errors, and concrete teaching opportunities, and two teaching-relevance criteria are introduced: Learner Discoverability and Learning Barrier.
Abstract
Despite a wealth of documented programming misconceptions, collections remain difficult to use in practice: static lists with no priority guidance leave educators without support for deciding which misconceptions deserve attention. We present a web-based misconception repository for Java/Processing designed to change this, with each entry connecting misconceptions to origins, typical errors, and concrete teaching opportunities. To support navigation, we introduce two teaching-relevance criteria: Learner Discoverability (how likely learners are to notice a misconception themselves) and Learning Barrier (how strongly it hinders progress if unaddressed). These criteria were developed iteratively using LLM-based pilot ratings to validate and refine definitions before involving human raters, reducing an initial set of five dimensions to two. Three human instructors rated all misconceptions, yielding moderate inter-rater reliability (α = 0.57 and α = 0.60), with a majority vote raising consistency to α = 0.9 and α = 1.0. Combined with observed frequency data and topic/subtopic filtering, the criteria enable sorting and compound filters (such as Silent Blockers or Common Obstacles) that translate ratings into actionable entry points for lesson preparation and tutor training, going beyond existing collections that offer no priority guidance or instructional filtering.
Many programming misconception collections contain hundreds of entries with little guidance on which misconceptions to prioritize. We report on a refinement of a teaching-relevance framework for a web-based misconception repository for Java/Processing. Three instructors evaluated two criteria, Learner Discoverability and Learning Barrier (Krippendorff's α=0.57;0.60, rising to 0.9;1.0 after a majority-vote). Together with frequency data, the criteria enable compound filters like Silent Blockers or Rare Traps that, combined with teaching opportunities such as peer instruction quizzes, bridge the gap between misconception knowledge and classroom action.
Corinna List, Moritz Mueller, Michael Kipp· Annual Conference on Innovat...· 0 citations
Findings are interpreted as evidence that course-aware style feedback is promising as a pre-submission revision aid, but that future versions should combine deterministic rule checks with LLM-generated explanations, rule citations, and stronger verification support.
Ethan Dickey, L. Vento, Peter Kurto et al.· 0 citations
This paper introduces CodeOwl, an AI-driven tool that automates the generation of tiered Parsons problems automatically, and evaluated CodeOwl with a mixed-method framework comprising complexity analysis, expert ratings, and user studies.
Luca Cisternino, Florian Obermuller, Gordon Fraser· 0 citations
Generative AI chatbots are increasingly used in introductory programming courses, but whether they support learning or encourage over-reliance remains unclear. To examine which interaction features are associated with productive persistence, we analyzed 198 CS1 student–chatbot conversation logs using a five-dimensional coding scheme: query type, response relevance, response type, dialogue outcome, and effectiveness marker. Persistence was measured as the number of student prompts per log. Negative binomial models showed that relevance failures predicted longer interactions, particularly false positives (IRR = 2.49) and false negatives (IRR = 1.65), both p < 0.001. Logistic models showed that false negatives strongly predicted unresolved sessions (OR = 9.94, p = 0.008), suggesting that the inability to answer accelerates abandonment. In contrast, conceptual (Socratic) responses predicted progressive effectiveness markers. Overall, interaction quality, not mere usage, appears to drive productive persistence, highlighting the importance of minimizing false negatives and encouraging Socratic scaffolding in educational chatbot design and deployment.
Rubaina Khan, Joshua Siderius, Kyle James Ross et al.· Proceedings of the Canadian...· 0 citations
As demand for data-centric skills grows across various fields, mastering Structured Query Language (SQL) has become increasingly important for both professionals and students. Despite SQL's English-like syntax, students consistently struggle with advanced concepts like aggregation, joins, and subqueries. While quantitative studies analyzing large student submission datasets identify common errors, they fail to explain the underlying reasons for these difficulties, leaving a critical gap in understanding the root causes of SQL learning challenges. To address this gap, we conducted semi-structured think-aloud interviews with 12 students solving advanced SQL problems, analyzing our data using the Constant Comparative Method to explore how students define and understand SQL concepts, how they apply them in practice, and where understanding and application misalign. Our findings reveal that students confuse similar constructs (WHERE vs. HAVING, JOIN types, etc.) due to linguistic similarities, over-rely on familiar yet inefficient or incorrect patterns, and demonstrate surface-level understanding of grouping and aggregation concepts, often resorting to trial-and-error and excessive subqueries rather than conceptual reasoning. These findings suggest that effective SQL instruction should provide side-by-side comparisons of similar constructs with identical sample data, teach SQL's logical execution order explicitly, use query visualization exercises where students trace data transformations step-by-step, and implement scaffolded problem sets that require students to apply different SQL constructs rather than defaulting to familiar approaches.
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SocraticTrap-CS is introduced, a publicly available benchmark that probes the capacity of open-weight LLMs to generate strategic misconceptions on demand and reframes the evaluation of educational LLMs around pedagogical trustworthiness rather than factual correctness alone.
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