arXiv subjects

Marcus Specht

Publications and source records attributed to Marcus Specht.

5 recordsLinked to original sources

arXiv2026

Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming

Generative AI is reshaping higher education programming through vibe coding, where students collaborate with AI via natural language rather than writing code line-by-line. We conceptualize this practice as help-seeking, analyzing 19,418 interaction turns from 110 undergraduate students. Using inductive coding and Heterogeneous Transition Network Analysis, we examined interaction sequences to compare top- and low-performing students. Results reveal that top performers engaged in instrumental help-seeking -- inquiry and exploration -- eliciting tutor-like AI responses. In contrast, low performers relied on executive help-seeking, frequently delegating tasks and prompting the AI to assume an executor role focused on ready-made solutions. These findings indicate that currently generative AI mirrors student intent (whether productive or passive) rather than optimizing for learning. To evolve from tools to teammates, AI systems must move beyond passive compliance. We argue for pedagogically aligned design that detect unproductive delegation and adaptively steer educational interactions toward inquiry, ensuring student-AI partnerships augment rather than replace cognitive effort.

cs.AI↗

arXiv2026

What makes an Expert? Comparing Problem-solving Practices in Data Science Notebooks

The development of data science expertise requires tacit, process-oriented skills that are difficult to teach directly. This study addresses the resulting challenge of empirically understanding how the problem-solving processes of experts and novices differ. We apply a multi-level sequence analysis to 440 Jupyter notebooks from a public dataset, mapping low-level coding actions to higher-level problem-solving practices. Our findings reveal that experts do not follow fundamentally different transitions between data science phases than novices (e.g., Data Import, EDA, Model Training, Visualization). Instead, expertise is distinguished by the overall workflow structure from a problem-solving perspective and cell-level, fine-grained action patterns. Novices tend to follow long, linear processes, whereas experts employ shorter, more iterative strategies enacted through efficient, context-specific action sequences. These results provide data science educators with empirical insights for curriculum design and assessment, shifting the focus from final products toward the development of the flexible, iterative thinking that defines expertise-a priority in a field increasingly shaped by AI tools.

cs.CY↗

arXiv2025

LLM Chatbots in High School Programming: Exploring Behaviors and Interventions

This study uses a Design-Based Research (DBR) cycle to refine the integration of Large Language Models (LLMs) in high school programming education. The initial problem was identified in an Intervention Group where, in an unguided setting, a higher proportion of executive, solution-seeking queries correlated strongly and negatively with exam performance. A contemporaneous Comparison Group demonstrated that without guidance, these unproductive help-seeking patterns do not self-correct, with engagement fluctuating and eventually declining. This insight prompted a mid-course pedagogical intervention in the first group, designed to teach instrumental help-seeking. The subsequent evaluation confirmed the intervention's success, revealing a decrease in executive queries, as well as a shift toward more productive learning workflows. However, this behavioral change did not translate into a statistically significant improvement in exam grades, suggesting that altering tool-use strategies alone may be insufficient to overcome foundational knowledge gaps. The DBR process thus yields a more nuanced principle: the educational value of an LLM depends on a pedagogy that scaffolds help-seeking, but this is only one part of the complex process of learning.

cs.HC↗

arXiv2025

JELAI: Integrating AI and Learning Analytics in Jupyter Notebooks

Generative AI offers potential for educational support, but often lacks pedagogical grounding and awareness of the student's learning context. Furthermore, researching student interactions with these tools within authentic learning environments remains challenging. To address this, we present JELAI, an open-source platform architecture designed to integrate fine-grained Learning Analytics (LA) with Large Language Model (LLM)-based tutoring directly within a Jupyter Notebook environment. JELAI employs a modular, containerized design featuring JupyterLab extensions for telemetry and chat, alongside a central middleware handling LA processing and context-aware LLM prompt enrichment. This architecture enables the capture of integrated code interaction and chat data, facilitating real-time, context-sensitive AI scaffolding and research into student behaviour. We describe the system's design, implementation, and demonstrate its feasibility through system performance benchmarks and two proof-of-concept use cases illustrating its capabilities for logging multi-modal data, analysing help-seeking patterns, and supporting A/B testing of AI configurations. JELAI's primary contribution is its technical framework, providing a flexible tool for researchers and educators to develop, deploy, and study LA-informed AI tutoring within the widely used Jupyter ecosystem.

cs.HC↗

arXiv2023

The Sequence Matters in Learning -- A Systematic Literature Review

Describing and analysing learner behaviour using sequential data and analysis is becoming more and more popular in Learning Analytics. Nevertheless, we found a variety of definitions of learning sequences, as well as choices regarding data aggregation and the methods implemented for analysis. Furthermore, sequences are used to study different educational settings and serve as a base for various interventions. In this literature review, the authors aim to generate an overview of these aspects to describe the current state of using sequence analysis in educational support and learning analytics. The 74 included articles were selected based on the criteria that they conduct empirical research on an educational environment using sequences of learning actions as the main focus of their analysis. The results enable us to highlight different learning tasks where sequences are analysed, identify data mapping strategies for different types of sequence actions, differentiate techniques based on purpose and scope, and identify educational interventions based on the outcomes of sequence analysis.

cs.CY↗