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 opensource, 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.

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

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.

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.

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.

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.

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.

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.

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).

As the use of online interactive tutorials becomes more widespread, there will be more opportunities to use fine-grained interaction log data to deduce student behavior. Log data can help debug usability or pedagogical problems with the tutorials, or guide redesign to discourage pedagogically poor student behavior. OpenDSA is a collection of open source interactive materials for teaching data structures and algorithms. We present a case study analysis of the activity logs from use of OpenDSA tutorials by roughly 150 students over several weeks. We identified clusters of student use based on when they completed exercises, verified the reliability of estimated time requirements for exercises, provided evidence that a majority of students do not read the text, and found evidence that students complete additional exercises after obtaining credit. Furthermore, we determined that slideshow use was fairly high, but that skipping to the end of slideshows was common.