Elena Barberà

Este estudio exploratorio, de enfoque de caso, analiza comparativamente la calidad textual y el desempeño en actividades de evaluación en línea de distinto nivel cognitivo ante la disponibilidad de inteligencia artificial generativa (IAG) en educación superior. Se examinan seis producciones textuales: dos respuestas de un estudiante en línea (caso típico, rendimiento medio-alto) y cuatro textos generados por dos modelos de IAG (uno de acceso gratuito y otro de pago), todos elaborados a partir de dos enunciados reales de evaluación continua (uno más expositivo y otro más abierto y evaluativo). Se aplicó un diseño mixto que combina una matriz lingüístico-discursiva (cohesión, coherencia, contenido, claridad, concisión, corrección lingüística y adecuación al enunciado) con indicadores automatizados de complejidad y cohesión (p. ej., TAACO y MultiAzterTest), junto con valoración independiente y triangulación consensuada. Los resultados sugieren patrones de diferencia entre las producciones analizadas: el texto del estudiante muestra mayor contextualización, juicio crítico y agencia comunicativa, especialmente en la tarea de mayor demanda cognitiva, mientras que las IAG tienden a generar respuestas formalmente correctas pero más estandarizadas y menos situadas. El modelo de pago presenta un desempeño intermedio superior al gratuito. En conjunto, los hallazgos aportan indicios para revisar el diseño de tareas y criterios de evaluación en contextos mediados por IAG, y orientan futuras investigaciones con muestras ampliadas y procedimientos de fiabilidad reforzados.

En el contexto de la educación superior, las prácticas externas representan una fase de transición hacia la inserción profesional. Este estudio analiza dicho periodo a partir de los marcos conceptuales de los objetos de frontera y el yo dialógico, con el objetivo de identificar mecanismos de autocomprensión y atribución de significado personal. A través del análisis de narrativas estudiantiles, se distinguen posiciones monológicas, centradas en las emociones y las oportunidades laborales, y posiciones dialógicas, que resaltan la importancia de las relaciones interpersonales para el desarrollo personal y profesional. También se identifican elementos vinculados a las tutorías como oportunidades de mejora pedagógica. Los hallazgos configuran un marco conceptual y analítico que permite reconocer prácticas comunes y orientar su optimización. En conjunto, se concibe el prácticum como un objeto de frontera que facilita procesos de reflexión, construcción identitaria y cambio contextual en el tránsito educativo hacia lo profesional.

One of the key areas of interest within learning analytics is identifying student similarities to support collaborative applications such as score prediction, personalized recommendations, and group formation. Clustering is a prominent method for grouping students based on shared learning behaviors, enhancing peer learning, and fostering communities in online courses. This study introduces an intuitive graphical clustering approach using activity logs that track student engagement with different learning resources. These interactions are modeled as multi-dimensional vectors, and a social network of learners is constructed using cosine similarity. Social network analysis (SNA) is then applied to detect learner communities. The dataset for implementation and evaluation includes activity logs and grades from 792 students in an undergraduate study program. Results indicate that learners in the same clusters have similar interaction patterns and grade point averages (GPAs). Statistical measures, such as silhouette index and root mean square standard deviation (RMSSTD), demonstrate the method’s effectiveness and benchmark its performance against K-means clustering. This approach shows significant potential for uncovering and visualizing implicit learner groups.

Microlearning offers a lot of potential for in-service teacher training, as it allows for personalization and flexibility. All the while, it is true that in-service teacher professional development must be tailored to teachers’ needs in order to succeed. This paper reports on teachers' training needs for a microlearning-based professional development aiming to support teachers’ digital competences. The process to develop a survey is explained, including its validation and administration to in-service teachers (n=21). Results show that teachers in Spain do not deploy digital tools to their full potential, especially when it comes to assessment of student learning. In terms of training format and support, it was found that teachers highly appreciate collaborating with other colleagues in this type of training, and that they find micro-credentials highly motivating. The results of this study will inform the design of an online professional development course aimed to foster educators' competences related to digital learning resources.

Course enrolment is a critical aspect of a student's academic journey, and the ability to make informed decisions about which courses to take can greatly impact their educational success. A key factor that often goes unnoticed is the sequence in which courses are enrolled, which can significantly affect a student's ability to excel in their studies. This study delves into the relationship between course enrolment patterns and students’ performance. This paper presents an innovative approach that combines course enrolment association rule mining with the incorporation of grade score measures, aiming to provide a robust framework for informed academic decision support. Association rule mining is applied to identify patterns and relationships among the courses students have previously enrolled in. These patterns reveal the popularity of certain courses being chosen after the other. Interestingly enough, it has been observed that following a common enrolment pattern can cause a big drop or increase in different student groups. The extracted association rules not only assist with course recommendations but also enable academic advisors to caution students or prevent them from enrolling in courses where lower grades are predicted, particularly for students at risk of dropping out of studies. This paper presents a framework for improving the course selection process to advise students with different academic performances to enroll in a course or avoid it, benefiting both students and educational institutions. We used the dataset of Universitat Oberta de Catalunya on Informatics undergraduate studies for eight academic semesters to implement and evaluate the proposed method. The initial results show that integrating association rule mining with grade score measures in course enrolment decision-making offers a promising avenue for enhancing the quality of academic advising for course enrolment recommendations.

Open, social and networked approaches to learning pose challenges for learners, who must assume the role of actively directing their own learning, in interaction and collaboration with others, in an increasingly complex environment. In this context, concepts such as self-direction and self-regulation of learning have attracted renewed interest as umbrella terms for a skill set and provisions that allow subjects to independently guide their own learning process and assume responsibility for it. Moreover, many authors have pointed out various problems regarding MOOC design and quality from a pedagogical perspective. This raises the need for a greater and more holistic understanding of learning regulatory processes and for developing models and instruments to support learners in this regard. This research aims to analyse how to support learning regulation processes as a whole in MOOC environments. This means paying attention to both social and individual dimensions of regulation, by studying how processes of self-regulation, co-regulation and socially shared regulation can be supported and promoted in this type of learning contexts. To this end, we apply the methodology of design-based research in order to intervene directly in the pedagogical practice through an iterative cycle based on stages of design, intervention, reflection and redesign of a design layer to support learning regulation in a MOOC. In this paper we present the design of the regulation support layer proposed in the first research iteration. We begin by presenting its theoretical foundations and then describe the support layer that has been designed as well as the empirical case of an xMOOC were it has been implemented. Finally, some conclusions and recommendations for the design and practice of learning regulation in MOOC are drawn from the results obtained in the first research iteration.

As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners’ behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners’ profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners’ micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.

Background and Purpose: The development of research knowledge, skills, and attitudes among postgraduate vocational-health education students is a crucial outcome of their degree program. This study focuses on the research competences of vocational-health education students and their use of web 2.0 technologies to enhance research productivity. The study employs the Vitae Researcher Development Framework (RDF) and examines the use of web 2.0 technologies. Method: The study surveyed 390 postgraduate vocational-health education students enrolled in universities in Pakistan. Of the participants, 50.5% were male, 49.5% were female, 45.1% were from private universities, and 54.9% were from public sector universities. Moreover, 68.2% were Master’s students, while 31.8% were doctoral students. The data were analyzed through both symmetrical and asymmetrical modeling techniques, including Partial least square equation modeling (PLS-SEM) and fuzzy set Qualitative Comparative Analysis (fsQCA), to measure the direct and indirect specific relationships among the constructs. Results: The results confirmed that research competences and web 2.0 technologies have a direct effect on research productivity. Furthermore, the results revealed that web 2.0 technologies mediate in the relationship between research competences and research productivity. Conclusions: The study concludes that research competences and web 2.0 technologies predict research productivity. Additionally, web 2.0 technologies have an intervening role in the relationship between research competences and research productivity during the COVID-19 pandemic emergency. Implications: This study highlights the broader implications for health education policymakers and institutions to include web 2.0 technologies in their development plans. Future studies can develop web 2.0-based instructional strategies for the professional development of advanced vocational-health education researchers. Originality: This study contributes to the knowledge of research competences, web 2.0 technologies, and research productivity for vocational-health education researchers.

This article examines how a multidimensional, skill-driven curricular framework plays out in diverse primary school settings toward a holistic K-6 curriculum framework. Particularly, it explored how unplugged tasks involve imaginative pedagogies, computational thinking skills, new media literacy skills, and social-emotional skills across three different primary schools by fulfilling two learning conditions, without coding/programming and without using digital devices. Within-case and cross-case analyses were conducted by following the qualitative multiple-case study research design. The evidence stems from lesson analysis, observation field notes, and interview transcripts with 152 participants. We collected 94 lessons from the three schools and we analyzed the data by using a predefined coding scheme grounded upon four frameworks. We conclude that the detected teaching practices uncover promising trajectories by demonstrating a great potential to consider the proposed framework as an effective and age-appropriate pedagogical approach applicable to low-technology and coding-free learning environments. The study highlights the need of teacher professional development, especially on computational thinking in reference to cognitive/intellectual, emotional/affective, social, creative/imaginative, and material dimensions for conscious computational thinking practice implementation. Given the paucity of research on unplugged and, especially, non-programming/coding methods at primary education level, this study can open up prominent issues for further examination and validation.