arXiv subjects

Sonsoles López-Pernas

Publications and source records attributed to Sonsoles López-Pernas.

15 recordsLinked to original sources

arXiv2026

The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy

Higher education institutions are increasingly expected to ensure that both students and staff develop Generative AI (GenAI) literacies. In response, they are introducing professional development programs and embedding GenAI skills within student curricula. However, current educational frameworks typically assume a linear progression of GenAI literacy, implying that foundational technical understanding must precede creative application. This paper challenges such an assumption through a psychometric analysis of a taxonomy-based self-assessment instrument (n = 158). We applied Rasch measurement theory and Guttman ordering to map the latent perceived order of difficulty of GenAI skills across students, academics, and professional staff. Results reveal a fundamental divergence in perceived competence profiles: while academics follow a more traditional linear path, students exhibit an "inverted" profile, frequently mastering high-level creation tasks before acquiring foundational conceptual understanding. Furthermore, the correlation of skill difficulty between students and academics was weak (r = 0.188). We argue that this "skill bypass" creates a fragile sense of fluency, where high self-efficacy in prompting masks low literacy in AI mechanics. These findings challenge the "one-size-fits-all" curricula and provide the empirical basis for diagnostic-driven, modular interventions that foster genuine human-AI synergy.

cs.CY↗

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

AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report

Code review is central to software engineering education but hard to scale in capstone projects due to tight deadlines, uneven peer feedback, and limited prior experience. We investigate an LLM-as-reviewer integrated directly into GitHub pull requests (human-in-the-loop) across two cohorts (more than 100 students, 2023--2024). Using a mixed-methods design -- GitHub data, reflective reports, and a targeted survey -- we examine engagement and responsiveness as behavioral indicators of self-regulated learning processes. Quantitatively, the 2024 cohort produced more iterative activity (1176 vs. 581 PRs), while technical issues observed in 2023 (227 failed AI attempts) dropped to zero after tool and instructional refinements. Despite different adoption levels (93\% vs. 50\% of teams using the tool), responsiveness was stable: 32\% (2023) and 33\% (2024) of successfully AI-reviewed PRs were followed by subsequent commits on the same PR. Qualitatively, students used the LLM's structured comments to focus reviews and discuss code quality, while guidance reduced over-reliance. We contribute: (i) an in-workflow design for an AI reviewer that scaffolds learning while mitigating cognitive offloading; (ii) a repeated cross sectional comparison across two cohorts in authentic settings; (iii) a mixed-methods analysis combining objective GitHub metrics with student self-reports; and (iv) evidence-based pedagogical recommendations for responsible, student-led AI-assisted review.

cs.SE↗

arXiv2026

Early Warning Signals Appear Long Before Dropping Out: An Idiographic Approach Grounded in Complex Dynamic Systems Theory

The ability to sustain engagement and recover from setbacks (i.e., resilience) -- is fundamental for learning. When resilience weakens, students are at risk of disengagement and may drop out and miss on opportunities. Therefore, predicting disengagement long before it happens during the window of hope is important. In this article, we test whether early warning signals of resilience loss, grounded in the concept of critical slowing down (CSD) can forecast disengagement before dropping out. CSD has been widely observed across ecological, climate, and neural systems, where it precedes tipping points into catastrophic failure (dropping out in our case). Using 1.67 million practice attempts from 9,401 students who used a digital math learning environment, we computed CSD indicators: autocorrelation, return rate, variance, skewness, kurtosis, and coefficient of variation. We found that 88.2% of students exhibited CSD signals prior to disengagement, with warnings clustering late in activity and before practice ceased (dropping out). Our results provide the first evidence of CSD in education, suggesting that universal resilience dynamics also govern social systems such as human learning. These findings offer a practical indicator for early detection of vulnerability and supporting learners across different applications and contexts long before critical events happen. Most importantly, CSD indicators arise universally, independent of the mechanisms that generate the data, offering new opportunities for portability across contexts, data types, and learning environments.

cs.CY↗

arXiv2025

Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?

While research on human-AI collaboration exists, it mainly examined language learning and used traditional counting methods with little attention to evolution and dynamics of collaboration on cognitively demanding tasks. This study examines human-AI interactions while solving a complex problem. Student-AI interactions were qualitatively coded and analyzed with transition network analysis, sequence analysis and partial correlation networks as well as comparison of frequencies using chi-square and Person-residual shaded Mosaic plots to map interaction patterns, their evolution, and their relationship to problem complexity and student performance. Findings reveal a dominant Instructive pattern with interactions characterized by iterative ordering rather than collaborative negotiation. Oftentimes, students engaged in long threads that showed misalignment between their prompts and AI output that exemplified a lack of synergy that challenges the prevailing assumptions about LLMs as collaborative partners. We also found no significant correlations between assignment complexity, prompt length, and student grades suggesting a lack of cognitive depth, or effect of problem difficulty. Our study indicates that the current LLMs, optimized for instruction-following rather than cognitive partnership, compound their capability to act as cognitively stimulating or aligned collaborators. Implications for designing AI systems that prioritize cognitive alignment and collaboration are discussed.

cs.HC↗

arXiv2025

Delving Into the Psychology of Machines: Exploring the Structure of Self-Regulated Learning via LLM-Generated Survey Responses

Large language models (LLMs) offer the potential to simulate human-like responses and behaviors, creating new opportunities for psychological science. In the context of self-regulated learning (SRL), if LLMs can reliably simulate survey responses at scale and speed, they could be used to test intervention scenarios, refine theoretical models, augment sparse datasets, and represent hard-to-reach populations. However, the validity of LLM-generated survey responses remains uncertain, with limited research focused on SRL and existing studies beyond SRL yielding mixed results. Therefore, in this study, we examined LLM-generated responses to the 44-item Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich \& De Groot, 1990), a widely used instrument assessing students' learning strategies and academic motivation. Particularly, we used the LLMs GPT-4o, Claude 3.7 Sonnet, Gemini 2 Flash, LLaMA 3.1-8B, and Mistral Large. We analyzed item distributions, the psychological network of the theoretical SRL dimensions, and psychometric validity based on the latent factor structure. Our results suggest that Gemini 2 Flash was the most promising LLM, showing considerable sampling variability and producing underlying dimensions and theoretical relationships that align with prior theory and empirical findings. At the same time, we observed discrepancies and limitations, underscoring both the potential and current constraints of using LLMs for simulating psychological survey data and applying it in educational contexts.

cs.AI↗

arXiv2025

Human-AI Collaboration or Academic Misconduct? Measuring AI Use in Student Writing Through Stylometric Evidence

As human-AI collaboration becomes increasingly prevalent in educational contexts, understanding and measuring the extent and nature of such interactions pose significant challenges. This research investigates the use of authorship verification (AV) techniques not as a punitive measure, but as a means to quantify AI assistance in academic writing, with a focus on promoting transparency, interpretability, and student development. Building on prior work, we structured our investigation into three stages: dataset selection and expansion, AV method development, and systematic evaluation. Using three datasets - including a public dataset (PAN-14) and two from University of Melbourne students from various courses - we expanded the data to include LLM-generated texts, totalling 1,889 documents and 540 authorship problems from 506 students. We developed an adapted Feature Vector Difference AV methodology to construct robust academic writing profiles for students, designed to capture meaningful, individual characteristics of their writing. The method's effectiveness was evaluated across multiple scenarios, including distinguishing between student-authored and LLM-generated texts and testing resilience against LLMs' attempts to mimic student writing styles. Results demonstrate the enhanced AV classifier's ability to identify stylometric discrepancies and measure human-AI collaboration at word and sentence levels while providing educators with a transparent tool to support academic integrity investigations. This work advances AV technology, offering actionable insights into the dynamics of academic writing in an AI-driven era.

cs.CL↗

arXiv2025

Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes

This paper presents a novel learning analytics method: Transition Network Analysis (TNA), a method that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and identify transition patterns in the learning process data. Combining the relational and temporal aspects into a single lens offers capabilities beyond either framework, including centralities to capture important learning events, community detection to identify behavior patterns, and clustering to reveal temporal patterns. Furthermore, TNA introduces several significance tests that go beyond either method and add rigor to the analysis. Here, we introduce the theoretical and mathematical foundations of TNA and we demonstrate the functionalities of TNA with a case study where students (n=191) engaged in small-group collaboration to map patterns of group dynamics using the theories of co-regulation and socially-shared regulated learning. The analysis revealed that TNA can map the regulatory processes as well as identify important events, patterns, and clusters. Bootstrap validation established the significant transitions and eliminated spurious transitions. As such, TNA can capture learning dynamics and provide a robust framework for investigating the temporal evolution of learning processes. Future directions include -- inter alia -- expanding estimation methods, reliability assessment, and building longitudinal TNA.

cs.SI↗

arXiv2025

Complex Dynamic Systems in Education: Beyond the Static, the Linear and the Causal Reductionism

Traditional methods in educational research often fail to capture the complex and evolving nature of learning processes. This chapter examines the use of complex systems theory in education to address these limitations. The chapter covers the main characteristics of complex systems such as non-linear relationships, emergent properties, and feedback mechanisms to explain how educational phenomena unfold. Some of the main methodological approaches are presented, such as network analysis and recurrence quantification analysis to study relationships and patterns in learning. These have been operationalized by existing education research to study self-regulation, engagement, and academic emotions, among other learning-related constructs. Lastly, the chapter describes data collection methods that are suitable for studying learning processes from a complex systems' lens.

cs.CY↗

arXiv2025

Classroom Activities and New Classroom Apps for Enhancing Children's Understanding of Social Media Mechanisms

Young people are increasingly exposed to adverse effects of data-driven profiling, recommending, and manipulation on social media platforms, most of them without adequate understanding of the mechanisms that drive these platforms. In the context of computing education, educating learners about mechanisms and data practices of social media may improve young learners' data agency, digital literacy, and understanding how their digital services work. A four-hour technology -- supported intervention was designed and implemented in 12 schools involving 209 5th and 8th grade learners. Two new classroom apps were developed to support the classroom activities. Using Likert-scale questions borrowed from a data agency questionnaire and open-ended questions that mapped learners' data-driven reasoning on social media phenomena, this article shows significant improvement between pre- and post-tests in learners' data agency and data-driven explanations of social media mechanisms. Results present an example of improving young learners' understanding of social media mechanisms.

cs.CY↗

arXiv2024

Empowering Database Learning Through Remote Educational Escape Rooms

Learning about databases is indispensable for individuals studying software engineering or computer science or those involved in the IT industry. We analyzed a remote educational escape room for teaching about databases in four different higher education courses in two consecutive academic years. We employed three instruments for evaluation: a pre- and post-test to assess the escape room's effectiveness for student learning, a questionnaire to gather students' perceptions, and a Web platform that unobtrusively records students' interactions and performance. We show novel evidence that educational escape rooms conducted remotely can be engaging as well as effective for teaching about databases.

cs.CY↗

arXiv2023

Have Learning Analytics Dashboards Lived Up to the Hype? A Systematic Review of Impact on Students' Achievement, Motivation, Participation and Attitude

While learning analytics dashboards (LADs) are the most common form of LA intervention, there is limited evidence regarding their impact on students learning outcomes. This systematic review synthesizes the findings of 38 research studies to investigate the impact of LADs on students' learning outcomes, encompassing achievement, participation, motivation, and attitudes. As we currently stand, there is no evidence to support the conclusion that LADs have lived up to the promise of improving academic achievement. Most studies reported negligible or small effects, with limited evidence from well-powered controlled experiments. Many studies merely compared users and non-users of LADs, confounding the dashboard effect with student engagement levels. Similarly, the impact of LADs on motivation and attitudes appeared modest, with only a few exceptions demonstrating significant effects. Small sample sizes in these studies highlight the need for larger-scale investigations to validate these findings. Notably, LADs showed a relatively substantial impact on student participation. Several studies reported medium to large effect sizes, suggesting that LADs can promote engagement and interaction in online learning environments. However, methodological shortcomings, such as reliance on traditional evaluation methods, self-selection bias, the assumption that access equates to usage, and a lack of standardized assessment tools, emerged as recurring issues. To advance the research line for LADs, researchers should use rigorous assessment methods and establish clear standards for evaluating learning constructs. Such efforts will advance our understanding of the potential of LADs to enhance learning outcomes and provide valuable insights for educators and researchers alike.

cs.HC↗

arXiv2023

Modeling Digital Twin Data and Architecture: A Building Guide with FIWARE as Enabling Technology

The use of Digital Twins in the industry has become a growing trend in recent years, allowing to improve the lifecycle of any process by taking advantage of the relationship between the physical and the virtual world. Existing literature formulates several challenges for building Digital Twins, as well as some proposals for overcoming them. However, in the vast majority of the cases, the architectures and technologies presented are strongly bounded to the domain where the Digital Twins are applied. This article proposes the FIWARE Ecosystem, combining its catalog of components and its Smart Data Models, as a solution for the development of any Digital Twin. We also provide a use case to showcase how to use FIWARE for building Digital Twins through a complete example of a Parking Digital Twin. We conclude that the FIWARE Ecosystem constitutes a real reference option for developing DTs in any domain.

cs.CY↗

arXiv2023

A modern approach to transition analysis and process mining with Markov models: A tutorial with R

This chapter presents an introduction to Markovian modeling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapters, Markovian models are probabilistic models, focusing on the transitions between states instead of studying sequences as a whole. The chapter provides an introduction to this method and differentiates between its most common variations: first-order Markov models, hidden Markov models, mixture Markov models, and mixture hidden Markov models. In addition to a thorough explanation and contextualization within the existing literature, the chapter provides a step-by-step tutorial on how to implement each type of Markovian model using the R package seqHMM. The chaper also provides a complete guide to performing stochastic process mining with Markovian models as well as plotting, comparing and clustering different process models.

stat.ME↗

arXiv2023

An introduction and tutorial to model-based clustering in education via Gaussian mixture modelling

Heterogeneity has been a hot topic in recent educational literature. Several calls have been voiced to adopt methods that capture different patterns or subgroups within students behavior or functioning. Assuming that there is an average pattern that represents the entirety of student populations requires the measured construct to have the same causal mechanism, same development pattern, and affect students in exactly the same way. Using a person-centered method (Finite Gaussian mixture model or latent profile analysis), the present tutorial shows how to uncover the heterogeneity within engagement data by identifying three latent or unobserved clusters. This chapter offers an introduction to the model-based clustering that includes the principles of the methods, a guide to choice of number of clusters, evaluation of clustering results and a detailed guide with code and a real-life dataset. The discussion elaborates on the interpretation of the results, the advantages of model-based clustering as well as how it compares with other methods.

stat.ME↗