arXiv subjects

Rayhona Nasimova

Publications and source records attributed to Rayhona Nasimova.

3 recordsLinked to original sources

arXiv2026

Exploring the Value of Diverse LLM Explanations in Introductory Programming

Large Language Models (LLMs) have shown the potential to generate code explanations that surpass those of peers in quality, offering promising opportunities for computer science education. While these explanations may not yet match the depth and clarity of instructor-provided explanations, research in computational creativity highlights that the quantity and diversity of ideas can often outweigh a singular focus on quality. Inspired by this, we explore whether combining multiple diverse explanations, each emphasizing distinct aspects (e.g., function, concept, goal), can enhance students' understanding of programming exercises compared to generic explanations that do not emphasize distinct conceptual aspects. In our study 971 first-year computing students were randomly assigned either diverse or generic LLM-generated explanations for two programming exercises. Students completed multiple-choice and open-ended questions for each exercise, followed by Likert-scale questions and open-ended reflections. Our findings outline patterns in student performance and perceived cognitive load across the two explanation conditions. These findings highlight how variation in explanation emphasis may relate to learner engagement and understanding. Across participants, open-ended response accuracy was consistently about 7.7% higher when students received diverse explanations, with no difference in perceived cognitive load.

cs.HC↗

arXiv2026

"I can't read your mind": A Study of Neurodivergent Computing Students' Experiences with Collaborative Active Learning

Computing courses often feature active learning techniques that promote collaboration and social interaction between students. However, neurodivergent students' preferences and experiences with these techniques are not well understood. We conducted a survey of neurodivergent computing students (n=24), specifically autistic students or students with ADHD, and neurotypical computing students (n=20) to understand how the structure of collaborative active learning affects their comfort in computing courses. We also interviewed four computing students on the autism spectrum or with ADHD to gain more contextualized insights into their experiences and accessibility recommendations. Our survey surfaces how team dynamics and assignment structure can impact neurodivergent students' comfort in computing courses. Neurodivergent students expressed discomfort with assignments that lack structure or have ambiguous expectations. Neurodivergent students prefer smaller teams that work together frequently with explicitly defined roles. Our interviews identified ways that neurodivergent students cope with discomfort in collaborative active learning, including self-selecting roles and self-disclosure. While preliminary, our results highlight how instructors can design collaborative active learning to be more equitable and accessible for neurodivergent students.

cs.HC↗

arXiv2025

Neurodiversity in Computing Education Research: A Systematic Literature Review

Ensuring equitable access to computing education for all students-including those with autism, dyslexia, or ADHD-is essential to developing a diverse and inclusive workforce. To understand the state of disability research in computing education, we conducted a systematic literature review of research on neurodiversity in computing education. Our search resulted in 1,943 total papers, which we filtered to 14 papers based on our inclusion criteria. Our mixed-methods approach analyzed research methods, participants, contribution types, and findings. The three main contribution types included empirical contributions based on user studies (57.1%), opinion contributions and position papers (50%), and survey contributions (21.4%). Interviews were the most common methodology (75% of empirical contributions). There were often inconsistencies in how research methods were described (e.g., number of participants and interview and survey materials). Our work shows that research on neurodivergence in computing education is still very preliminary. Most papers provided curricular recommendations that lacked empirical evidence to support those recommendations. Three areas of future work include investigating the impacts of active learning, increasing awareness and knowledge about neurodiverse students' experiences, and engaging neurodivergent students in the design of pedagogical materials and computing education research.

cs.HC↗