Contextualizing computer science education has been recognized as a key factor in enhancing student engagement and learning outcomes. This study investigates the initial perceptions of university computer science faculty in Africa regarding the benefits, adoption challenges, and institutional support required for the successful integration of contextually relevant materials into introductory computer science (CS1) courses. Faculty then assessed a set of previously developed contextually tailored materials, grounded in Banks' Additive Approach to curriculum reform and aligned to the CS curricula 2023. The research adopted qualitative methods, gathering data through open-ended surveys from 22 CS faculty across 9 African countries. Thematic analysis identified key patterns in the responses from faculty, who generally expressed positive perceptions of integrating contextualized materials. They agreed such materials could enhance engagement without distracting from core objectives, but emphasized the need for careful integration. Insights from faculty highlighted that successful implementation requires substantial institutional support, including curriculum reform, textbook development, and faculty training, with universities playing a critical role in adoption.

Introduction. This study introduces CodeKids, a novel web-based platform to teach computer science to elementary and middle school-aged students using interactive digital storybooks. Several books on the CodeKids website were created to target common misconceptions young students form when learning foundational computer science concepts, including variables, conditionals, and loops. The primary objectives of this research are to evaluate student engagement and perceptions of the storybook format and determine if the books help prevent common computer science misconceptions for K-8 students. Methodology and Study Design. This research was approved by the Virginia Tech IRB under protocol number 23-1266. A study was conducted with middle school students in the 6th and 7th grades, where students used two CodeKids books created to teach variables while also targeting commonly associated misconceptions. The books were designed to align with the Computer Science Standards of Virginia Public Schools [4] so teachers can seamlessly integrate the books into their classrooms. Common misconceptions were identified from previous literature from authors including Swidan et al. [3], Žanko et al. [5], and Grover and Basu [2], and questions were created in the books to target the misconceptions. Misconceptions often stem from variable manipulation and the sequential execution of code. 97 students participated in a survey designed to gauge their perception of the books and prior programming experience, and 115 students completed a paper-based examination consisting of multiple-choice and matching questions that targeted specific misconceptions related to variables. Results. Survey results indicated strong engagement with the books, with 82.5% of students responding positively to the interactive activities and examples provided within the books. Additionally, statistical analysis revealed a significant increase in students’ perceived understanding of variables post-intervention (Figure 2). However, despite the positive perception of the books and positive self-assessment, examination results highlighted ongoing challenges with addressing misconceptions. Misconceptions remained a problem, particularly those that pertain to the sequential execution of code and variable manipulation. Furthermore, Furthermore, the research revealed a gap between the students’ current computer science knowledge and the expectations set in the Computer Science Standards of Virginia Public Schools [4] (Figure 3), with most students indicating they have never used variables in programming despite variables first appearing in the fourth grade in the learning standards. This underscores a need for accessible educational material such as CodeKids to ensure schools meet the learning standards. Future Research. Future research directions include the development and integration of adaptive learning elements within the storybooks to allow for varying difficulty based on individual student knowledge. Additionally, the development of more interactive program visualization tools is needed to keep students engaged and increase learning outcomes [1]. The books currently incorporate Python Tutor as a program visualization tool; however, students had mixed perceptions of this tool’s usefulness (Figure 4). A new tool where students answer questions as they click through the program will likely be more engaging for students. Lastly, a study should be done with experimental and control groups to understand the sustained impact of these digital books on misconceptions and overall learning outcomes.

Motivation and Background. Many K–12 students struggle with programming concepts. While LLMs offer scalable, timely support, overly direct answers can reduce reasoning and engagement [8], prompting the question: How can LLMs support learning without encouraging overreliance? In our study with 105 students, 31.4% showed misconceptions about variable assignment and data types, and in another survey, only 20% correctly solved conditional problems. This highlights the need for scaffolding to address conceptual gaps in K–12 programming. To address these gaps, we designed an answer-aware hint generation system using LLMs to support learning without reducing cognitive demand. We developed the system for CodeKids—an open-source, curriculum-aligned platform built with Virginia Tech and local public schools. It helps students practice grade-level programming through interactive activities, using LLM-generated hints to guide thinking without revealing answers [1, 11]. Based on Vygotsky’s Zone of Proximal Development [12], our approach balances support and autonomy through structured prompting that preserves productive struggle. Methodology. Building on research showing that machine learning supports K–12 learners without compromising cognitive development [15], we implemented a mindful answer-aware prompting approach [5, 7] grounded in two principles. The first principle, cognitive scaffolding, draws from ZPD and ITS research [10, 12], and ensures hints progress from general to specific while preserving learner autonomy. The second principle, technical safeguards, applies semantic similarity thresholds and constraint-based prompting to prevent answer leakage [13]. The system is deployed across 12 advanced CodeKids books covering core topics like variables, data types, conditionals, loops, and logical operators. Hints are concise, pedagogically sound, and generated by GPT-4 when students request help or load a page. Each request includes the topic, question, answer choices, and correct answer sent to the LLM, enabling context-aware adaptation to the activity and content. Our prompt design constrains hints to one sentence, emphasizes conceptual clarity, and gradually increases specificity to preserve student agency. This aligns with research on scaffold types—such as sense-making, elaboration, and motivational cues—that support self-regulated learning [9]. To support diverse learners, the system includes text-to-speech for reading hints aloud. Our approach combines learning sciences and prompt engineering to foster scalable support, student agency, and conceptual understanding. Evaluation. We evaluated semantic hint alignment using sentence embeddings: 98.1% of hints scored ≥ 0.30 in content alignment and 44.2% ≥ 0.20 in answer alignment, indicating strong relevance with minimal over-reliance. GPT-4, used as an LLM-as-a-judge due to its > 85% agreement with human ratings [14], gave an average score of 0.958 for hints on convergence, pedagogical value, and context. Combining LLM and cosine scores (0.7/0.3), we computed a Hint Quality Score of 0.749 [3]. To assess real-world impact, we developed surveys to collect feedback on clarity, usefulness, and learning [4]. Ongoing Work and Vision. We are investigating hint convergence across LLMs (e.g., Claude 3, Gemini 1.5 Pro) and exploring alternative prompting strategies to improve diversity. Future work includes personalizing hints through difficulty adaptation and embedding-based models for curriculum-aligned scaffolding [6], reducing reliance on proprietary LLMs, and incorporating retrieval-augmented generation (RAG) for contextualization [2].

Building on our ITiCSE-Working Group Report 2024 study, this proposal aims to expand and refine contextualized CS1 materials in partnership with African researchers and educators. Earlier findings showed that faculty see value in locally relevant materials for boosting engagement and problem-solving, though challenges remain in adapting them across contexts. This working group will co-design new materials using a structured framework and evaluate them through mixed-methods research with students centrally involved. The project promotes inclusive pedagogy, cross-institutional collaboration, and scalable strategies to better align global computing standards with local needs.

This ITiCSE Working Group (WG) has two goals: first, to increase awareness of computing education research in the African countries, and second, to create and strengthen connections between computing education researchers in African countries and those in the larger computing education research community. To achieve these goals, the WG will focus on two tasks: writing a literature review of publications related to computing education in African countries and building a set of contextually relevant materials -- introductory programming examples and assignments at university level -- designed for students in African countries. The final report will include an overview of past computing education research related to Africa, a current project (the new course materials), and suggestions for future work.

This report begins with a literature review of computing education in Africa. We found a substantial body of work, scattered over more than 80 venues, which we have brought together here for the first time. Several important themes emerge in this dataset, including the need to contextualise computing education. In the second part of this report we investigate contextualisation further. We present a pilot study, grounded in the literature review, of the development of course materials, sample code, and programming assignments for introductory programming, contextualised for six African countries: Botswana, Egypt, Ghana, Nigeria, South Africa, and Zambia. We include the materials, report on a preliminary evaluation of the materials by fellow educators in African countries, and suggest a process by which other educators could develop materials for their local contexts.

Data and visualizations are powerful tools that provide insights, analysis, and conclusions in a logical and easy-to-understand manner. However, the current school curriculum lacks adequate preparation for students to understand, analyze, interpret, or create complex data visualizations, which can hinder their potential careers in data science. To address this gap, our project aimed to develop a user-friendly web-based tool that provides interactive lessons on data and visualizations for elementary school children. The website consists of 12 lessons, categorized by grade levels (1st–2nd grade, 3rd–4th grade, and 5th–6th grade), and includes an interactive question-answer section. Users can scroll down after reading the lessons and practice questions based on the visualizations. The website also has the potential to incorporate games related to data and visualization. The lessons are implemented using React.js and Java with the Spring framework, and new lessons can easily be added by storing them in a markdown folder. The website features a navigation bar with tabs for Home, Lessons, Games, About, and Contact. Additionally, a feedback form is included to gather user feedback for further improvements. The website is currently in the testing stage, and future surveys for teachers and elementary school students will be added to enhance the features provided. Our study presents preliminary findings and serves as a foundational exploration. We acknowledge that further research and experimentation are required to validate and expand upon the results discussed herein.

Data visualization tools can help teachers engage students who are visual learners. Current web-tools that focus on the creation of visualizations are not beginner-friendly and require little support for teachers wishing to implement visualizations into their classrooms. As a result, teachers rarely use visualization tools. This paper addresses the lack of visualization tool usage in classrooms, analyzes why this is based on previous research and current research we conducted, and offers potential focuses a visualization tool should have—through the lens of our own tool SimpleChartsRI—for pedagogical use. SimpleChartsRI aims to help high-school teachers create effective visualizations through a simple user-friendly interface that guides them on best practices.

Recursion in binary trees has proven to be a hard topic. There was not much research on enhancing student understanding of this topic. We present a tutorial to enhance learning through practice of recursive operations in binary trees, as it is typically taught post-CS2. We identified the misconceptions students have in recursive operations on binary trees. We designed a code writing exam question to measure those misconceptions. We built a tutorial that trains students on avoiding those misconceptions through the use of a semantic code analyzer that detects misconceptions and provides appropriate feedback. Our results show an improvement in student performance when using the tutorial along with the practice exercises, and even more improvement when the same exercises are used with a semantic code analyzer. The best way to use our tutorial to enhance student performance on advanced recursion is to allow students solving the tutorial exercises with the the semantic feedback.

Peer review is a mainstay of academic publication -- indeed, it is the peer-review process that provides much of the publications' credibility. As the number of computing education conferences and the number of submissions increase, the need for reviewers grows. This report does not attempt to set standards for reviewing; rather, as a first step toward meeting the need for well qualified reviewers, it presents an overview of the ways peer review is used in various venues, both inside computing education and, for comparison, in closely-related areas outside our field. It considers four key components of peer review in some depth: criteria, the review process, roles and responsibilities, and ethics and etiquette. To do so, it draws on relevant literature, guidance and forms associated with peer review, interviews with journal editors and conference chairs, and a limited survey of the computing education research community. In addition to providing an overview of practice, this report identifies a number of themes running through the discourse that have relevance for decision making about how best to conduct peer review for a given venue.

Peer review is a mainstay of academic publication - indeed, it is the peer-review process that provides much of the publications' credibility. This working group is examining the ways peer review is used in various computing education venues and will use this examination to articulate community standards for peer review in this discipline.

Subgroup discovery is a problem in machine learning and data mining in which the population data is mined to discover interesting subgroups with respect to a target property. The goal of subgroup discovery is to find rules describing subsets of the population. In this paper, a new solving approach is proposed (FDG-SD). The new approach adopts fuzzy rule induction that uses a dynamic programming like algorithm to discover fuzzy subgroups. FDG- SD surpasses disadvantages of existing approaches. It is able to find better solutions for almost half out of 30 UCI machine learning repository datasets based on significance, unusualness, support, confidence and running time quality measures. According to Friedmann test results, the new approach (FDG-SD) is ranked first among mostly used algorithms with respect to significance, unusualness, support and running time quality measures.

We present an analysis of the visual portrayals of data (including results) in the full papers and working group reports of ITiCSE from 2013 to 2018. We find that tables are the most common visual portrayal of data in these publications, but that a number of graphical forms are also widely used. We examine the quality of the data portrayals for tables, graphs, and images using visual quality indicators derived from the literature. Overall, our findings are not positive. We find that many papers present data in such a way that it cannot be readily interpreted. The most common problem is captions that do not adequately describe the table, figure, or image. In tables, the main issues affecting readability of numeric data are poor alignment of numbers within a column, unnecessary notations, and unwarranted precision. In graphs and images, the prevalent problem is text that is too small to be read. We conclude with guidelines for future authors to ITiCSE and other computing education venues, in the hope that we can contribute to an improvement in the quality of computing education publications.

The goal of most computing education research is to effect positive change in how computing is taught and learned. Statistical techniques are one important tool for achieving this goal. In this paper we report on an analysis of ICER papers that use inferential statistics. We present the most commonly used techniques; an overview of the techniques the ICER community has used over its first 14 years of papers, grouped according to the purpose of the technique; and a detailed analysis of three of the most commonly used techniques (t-test, chi-squared test, and Mann-Whitney-Wilcoxon). We identify common flaws in reporting and give examples of papers where statistics are reported well. In sum, the paper draws a picture of the use of inferential statistics by the ICER community. This picture is intended to help orient researchers who are new to the use of statistics in computing education research and to encourage reflection by the ICER community on how it uses statistics and how it can improve that use.

Conferences such as ITiCSE have recently seen an increase in the number of papers presenting empirical research in computing education. While empirical research need not be quantitative, there has been a corresponding increase in the number of papers that present some level of statistical analysis to support their arguments. This poster introduces a project that is exploring how - and how well statistics are used and reported in computing education research.

Recursion is one of the most important and hardest topics in lower division computer science courses. As it is an advanced programming skill, the best way to learn it is through targeted practice exercises. But the best practice problems are time consuming to manually grade by an instructor. As a consequence, students historically have completed only a small number of recursion programming exercises as part of their coursework. We present a new way for teaching such programming skills. Students view examples and visualizations, then practice a wide variety of automatically assessed, small-scale programming exercises that address the sub-skills required to learn recursion. The basic recursion tutorial (RecurTutor) teaches material typically encountered in CS2 courses. Students who used RecurTutor had significantly better grades on recursion exam questions than did students who used typical instruction. Students who experienced RecurTutor spent significantly more time on solving recursive programming exercises than students who experienced typical instruction, and came out with a significantly higher confidence level.

Recursion is both an important and a difficult topic for introductory Computer Science students. Students often develop misconceptions about the topic that need to be diagnosed and corrected. In this paper, we report on our initial attempts to develop a concept inventory that measures student misconceptions on basic recursion topics. We present a collection of misconceptions and difficulties encountered by students when learning introductory recursion as presented in a typical CS2 course. Based on this collection, a draft concept inventory in the form of a series of questions was developed and evaluated, with the question rubric tagged to the list of misconceptions and difficulties.

Data Structures and Algorithms (DSA) courses are considered critical in any computer science curriculum. DSA courses emphasize topics related to procedural dynamics (how an algorithm works) and algorithm analysis (the algorithm's efficiency). Historically, algorithm visualizations (AVs) have dealt almost exclusively with portraying algorithm dynamics, and there are few examples of visualizations related to algorithm analysis topics. We have developed a new generation of visualizations that we term Algorithm Analysis Visualizations (AAVs) to convey algorithm analysis concepts. We present the motivation behind AAVs, and outlines a methodology for their evaluation. We present results from student surveys and the analysis of student interaction logs from the OpenDSA eTextbook used by several CS3-level classes during the period of Fall 2014 through Fall 2015. Initial results from Fall 2014 revealed that students were not spending enough time reading the algorithm analysis material presented as textual content. Our results from a preliminary deployment of AAVs in Spring 2015 showed that students interacted with AAVs for significantly longer than the control group spent reading the previous text-based algorithm analysis material. We will present additional results from our ongoing experiment in Fall2015 (control group without AAVs) and Spring2016 (test group with additional AAVs).

In this paper, we study the relationship between the use of “crib sheets” or “cheat sheets” and performance on in-class exams. Our extensive survey of the existing literature shows that it is not decisive on the questions of when or whether crib sheets actually help students to either perform better on an exam or better learn the material. We report on our own detailed analysis for a body of crib sheets created for the final exam in a junior-level Data Structures and Algorithms course. We wanted to determine whether there is any feature of the crib sheets that correlates to good exam scores. Exam performance was compared against a number of potential indicators for quality in a crib sheet. We have found that students performed significantly better on questions at the comprehension level of Bloom’s taxonomy when their crib sheet contained good information on the topic, while performance on questions at higher levels of the taxonomy did not show correlation to crib sheet contents. We have also seen that students at certain levels of performance on the final exam (specifically, medium-to-high performance) did relatively better on certain questions than other students at that performance level when they had good coverage of that question’s topic on their crib sheet.