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.

Escape rooms have evolved from recreational activities to engaging educational tools, combining storytelling, puzzlesolving, and teamwork. Despite their popularity, research on escape rooms remains highly decentralized with no clear focus or pathway. To shed some light on this growing field, this study analyzes 1,051 published articles on escape rooms using natural language processing, specifically keyword analysis and structured topic modeling. Our study addresses four key research questions related to (1) commonly used phrases, (2) research topics, (3) the evolution of these topics, and (4) their interconnections. We identified 24 distinct topics categorized into design and development, field of application, participants, and technology. The analysis reveals an almost exclusive focus on educational applications (>90%), particularly in healthcare and STEM education, highlighting soft skills and practical knowledge development. Based on our findings, we provide a research agenda where we highlight future research opportunities which include expanding research methods, incorporating generative artificial intelligence and easing the barriers to adoption for teachers.

This paper presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students aged 11-16. The tool was designed for interventions on the fundamental processes behind social media platforms, focusing on four AI- and data-driven core concepts: data collection, user profiling, engagement metrics, and recommendation algorithms. An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engaging for young learners. The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence their personal experience on the platform as well as the experience of others. This approach seeks to enhance learners' data agency, AI literacy, and sensitivity to AI ethics. The paper includes a case example from 12 two-hour test sessions involving 209 children, using learning analytics to demonstrate how they navigated their social media feeds and the browsing patterns that emerged.

This paper presents AIQUIZ, an open-source webbased platform designed for the automatic generation of multiplechoice questions (MCQs) using large language models (LLMs). AIQUIZ aims to streamline the labor-intensive process of manually creating MCQs to enhance the learning experience by providing adaptive questions tailored to students' past performance. The platform supports A/B testing, allowing educators and researchers to experiment and evaluate different prompts and LLMs to optimize the quality and relevance of the generated questions. AIQUIZ was applied to four engineering courses at Polytechnic University of Madrid (UPM), where it demonstrated promising results, including a high accuracy rate in student responses and a low rate of reported errors in generated questions. The paper describes the platform's key functionalities and results.

The rapid adoption of ChatGPT and other large language models (LLMs) in education has created new opportunities for human-AI collaboration research, e.g., studying interactions, automating support or implementing novel ways of assessment. However, existing methods for retrieving ChatGPT conversation data -either through OpenAI's API or manual transcription-are limited by technical, financial, and scalability constraints. This paper introduces chatGPTscrapeR, an open-source R package and Shiny web application that automates the extraction of ChatGPT conversation data from URLs. Thus, it enables researchers and educators to efficiently retrieve, organize, and subsequently analyze interaction logs, and their metadata. The retrieved data are ready to be assessed if they are part of an assignment or analyzed using different methods. In all such cases, automating the retrieval of human-AI interactions is instrumental for an efficient analysis of such interactions and for creating modern AI-enabled learning systems.

It stands to reason that if we want to offer "personalized" education, our methods should be designed to capture the person and the intraindividual processes. However, an idiographic approach that investigates within-person processes and provides insights on the person has been so far lagging. We conducted a scoping review to explore how the idiographic approach has been applied in educational research (i.e., what methods, topics, data, and statistical approaches). We found that person-specific analysis has mostly been used to investigate education psychology constructs. In addition,Except for a few exceptions in learning analytics, many idiographic studies have employed basic statistical techniques, whereas advanced statistical methods have been applied only recently. Therefore, considering the recent development of educational data science, the potential of idiographic methodology needs to be explored further.

Research on academic emotions has explored different granularities that range from a full program to a single task. Yet, most of the existing research stems from cross-sectional studies. While immensely useful, lacking a temporal depth obfuscates the process of emotions into a flat process. To fill this gap, this study takes a process-oriented approach to study the momentary changes in academic emotions as they unfold in time into phases, changes, and successions of sequences during two lectures. We use intensive longitudinal data from 104 students attending a German University in the form of ecological momentary surveys. We rely on mixture models to cluster the data into states, use sequence analysis to map the longitudinal unfolding and mixture hidden Markov models to answer why certain longitudinal patterns emerge. Our findings point to differences among students in their reactions to contextual variables, yet, such reactions are relatively stable within students. In other words, students may have different emotional profiles, but these emotional profiles are surprisingly stable across time and contexts.

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.

Making research advances available to the community in the shape of open source software has the potential to introduce cutting-edge innovations from early on, foster collaborative development, and revolutionize industrial applications. However, including open source software resulting from a research project as part of a production system poses some risks and must be evaluated in detail, considering all pros and cons. This is especially delicate when that piece of software is in charge of authentication and authorization. This article reports on an experience of integrating open source identity and access management (IAM) software that is the result of multiple research projects, the FIWARE Keyrock IAM, into three educational web-based platforms: two learning object repositories and a course management platform. We intend to draw the lessons learned from this experience so they can guide software practitioners when deciding if they should integrate open source software developed in research projects.

Abstract Producing high-quality educational videos usually requires a large budget as it involves the use of expensive recording studios, the presence of a technician during the entire recording session and often post-production tasks. The high costs associated with video production represent a major hindrance for many educational institutions and, thus, many teachers regard high-quality video recording as inaccessible. As a remedy to this situation, this article presents SAGA (Autonomous Advanced Recording Studio in its Spanish acronym), a low-cost autonomous recording set that allows teachers to produce educational content in video format in an agile way and without the need for post-production. The article provides an overview of SAGA, including a description of its hardware and software so that anyone with basic technical knowledge can replicate and operate the system. SAGA has been used to record more than 1,500 videos including the contents of six MOOCs hosted on the MiriadaX platform, as well as four courses at UPM. SAGA has been evaluated in two ways: (1) from the video producers’ perspective, it was evaluated with a questionnaire based on the Technology Acceptance Model, and (2) from the video consumers’ perspective, a questionnaire was conducted among MOOC participants to assess the perceived technical quality of the videos recorded with SAGA. The results show a very positive general opinion of the SAGA system, the recorded videos and the technical features thereof. Thus, SAGA represents a good opportunity for all those educational institutions and teachers interested in producing high-quality educational videos at a low cost.

IEEE/ASEE Frontiers in Education turned 50 at the 2020 virtual conference in Uppsala, Sweden. This paper presents an historical retrospective on the first 50 years of the conference from a scientometric perspective. That is to say, we explore the evolution of the conference in terms of prolific authors, communities of co-authorship, clusters of topics, and internationalization, as the conference transcended its largely provincial US roots to become a truly international forum through which to explore the frontiers of educational research and practice. The paper demonstrates the significance of FIE for a core of 30% repeat authors, many of whom have been members of the community and regular contributors for more than 20 years. It also demonstrates that internal citation rates are low, and that the co-authoring networks remain strongly dominated by clusters around highly prolific authors from a few well known US institutions. We conclude that FIE has truly come of age as an international venue for publishing high quality research and practice papers, while at the same time urging members of the community to be aware of prior work published at FIE, and to consider using it more actively as a foundation for future advances in the field.

Idiographic methods have emerged as a way to examine individual behavior by using several data points from each subject to create person-specific insights. In the field of learning analytics, such methods could overcome the limitations of cross-sectional group-level data that may fail to capture the dynamic processes that unfold within each individual learner and less likely to offer relevant personalized learning or support. In this study, we provide a definition of idiographic learning analytics and we explore the possible potentials of this method to zoom in on the fine-grained dynamics of a single student. Specifically, we make use of Gaussian Graphical Models -an emerging trend in network science- to analyze a single student's dispositions and devise insights specific to him/her. Our findings offer a proof of concept of the potential of this novel method in revealing personalized valuable insights about students' self-regulation. While our specific findings apply to a single student, our method applies to every student regardless of context.

Nowadays, the use of authoring tools has become the most popular way in which teachers create learning resources. These tools commonly facilitate the creation of educational content by combining text, self-assessed activities, and multimedia resources, often allowing authors to export the result in a standard format. Although there are many tools available, it is difficult to find a tool that meets the needs of all users. In order to fulfill the requirements of specific learning scenarios, one possible solution is to adapt an existing e-Learning authoring tool. However, to do so in an easy way, authoring tools should be built on the principles of modular architecture to facilitate and encourage developers to contribute, which is often not the case. This article presents Ediphy, a modular and extensible open-source web authoring tool for the creation of interactive educational content. Its modular nature, based on plugins, makes it easy to extend the tool with new functionalities to meet the requirements of specific educational settings. In order to evaluate Ediphy, two surveys have been conducted: one among end users who have used Ediphy to create learning resources and another one among developers who have contributed to its improvement. Results show that end users have a great opinion of Ediphy and developers find it easy to contribute.