Journal of e-Learning and Knowledge Society

Second Cover

2025-10-29T02:17:11+00:00

Assessing Digital Competence in Indonesian students: demographic and Internet usage factors through the Rasch Model

2025-09-14T07:27:06+00:00

In today’s rapidly evolving digital landscape, technological advancements continue to reshape human lifestyles, making robust digital competence (DC) essential in an interconnected world. This study addresses existing gaps in the literature by evaluating the digital competence of Indonesian students and examining the influence of parental educational backgrounds and daily internet usage frequency. Utilizing convenience sampling and online questionnaires, data were collected from 251 students and analyzed using the Rasch Model with Winsteps software version 5.7.3.0. The findings reveal gender-based differences in digital skills, indicating the need for tailored educational strategies. Additionally, students with less educated parents tend to prioritize personal data protection, while those with highly educated parents display broader digital competencies. Although high internet usage is associated with enhanced digital competence, it also carries risks to mental health, such as increased internalizing symptoms and cognitive distortions. This study contributes to ongoing discussions on improving student digital competence and underscores the importance of balanced internet usage strategies.

Academic performance in AI Era: salient factors in higher education

2025-09-14T07:27:05+00:00

This research integrates teacher AI competence (TAC), student learning agility (SLA), and student engagement (SE), as factors affecting student academic performance (SAP). We employed a survey methodology in which the instrument’s validation was conducted through content and face validity, as well as a content validity index and measurement model in SmartPLS. A total of 380 lecturers from three universities participated as respondents in this survey study. Partial least squares structural equation modeling (PLS-SEM) procedures were employed for the primary data analysis of the study. The findings informed the validity and reliability of the model, highlighting the important roles of SLA and SA in relation to SAP. In addition, TAC was also correlated with SAP and SLA, while it has no relationship with SA.

Assessing the Usability of Federated Access to T4EU Online Courses in Higher Education Mobility Programs

2025-09-14T07:27:02+00:00

Facilitating access to online courses in higher education mobility programs is essential for creating a more interconnected educational ecosystem within the European Education Area. Federated e-infrastructures have emerged as effective solutions to enhance the interoperability, accessibility, and scalability of academic services under a standardized trust model. However, assessing their usability for end-users is critical. This study aims to identify and adapt an instrument for measuring the usability of federated access to a Moodle ecosystem implemented by the Transform4Europe alliance for students participating in mobility programs. The paper outlines the process of adapting and validating a questionnaire based on Nielsen’s Usability Attributes model to meet the unique characteristics of this context. An iterative, multi-method approach was employed, incorporating feedback from students and usability experts for content validation. The resulting instrument was administered to 145 students at the University of Trieste during lectures. Exploratory factor analysis confirmed the tool’s reliability and validity while highlighting the need for refinements, including revising two items with low factor loadings, methodological adjustments in questionnaire administration, and increased sample size for more robust results. Although further validation of the final instrument is recommended, the results obtained in this study provide a significant starting point for advancing usability assessment practices in federated learning environments aimed at enhancing the student mobility experience.

Conceptual Knowledge Representation: a semantic model for Smart Learning Environments in an IoT-enabled Smart Campus

2025-11-03T12:10:28+00:00

Smart learning environments (SLE) have been greatly enhanced lately by the adoption of cutting-edge technologies such as Internet-of-Things (IoT), Artificial Intelligence, Augmented Reality, Cloud Computing and Learning Analytics among others. Huge amounts of heterogeneous data are being exchanged between numerous devices, sensors and “things” used by students, educators and educational institutions. This heterogeneity hinders seamless communication among different systems pertaining to SLE. A smart campus is an example of a smart learning environment involving different systems such as smart learning management system, personalized learning, e-learning, assessment, smart classroom and smart library system among others. These systems often need to collaborate to enhance the teaching and learning process. To allow seamless communication among these systems, semantic interoperability has to be tackled by the adoption of a shared common data model. Ontologies are viewed as a potential way to ensure semantic interoperability. Several ontologies exist in the smart learning domain. However, none of them represents a smart learning environment for an IoT-enabled smart campus. This paper presents a semantic model entitled SmartLearningOnto that aims to model different aspects of a smart learning environment in a smart campus. The proposed ontology facilitates exchange of data among several systems in a smart campus by defining the concepts related to smart learning in an appropriate way. Furthermore, it infers new knowledge to enrich the learning experience of learners. SPARQL queries have been used to answer competency questions. Furthermore, several metrics along with expert evaluation have been used to evaluate SmartLearningOnto.

Exploring Generative AI tools in higher education: insights for policies

2025-11-03T12:10:27+00:00

The public availability of the Generative Artificial Intelligence (Gen-AI) tools, such as ChatGPT, led to diverse reactions in society. In higher education, these emerging technologies have brought several challenges, particularly with regard to ethical considerations, assessment frameworks, and new paradigms in teaching and research practices. In this article, we intend to explore the issues related to integration and ways of using the Gen-AI tools in higher education, especially in initial teacher education, and the implications of this use for education policies. A qualitative approach was used with recourse to non-participant observation and narrative research methods through the analysis of experiences developed in Initiation to Professional Practice curricular unit of a Master’ in Teaching. It was found that future teachers were able to use the ChatGPT as a tool to plan lessons and create digital educational resources, but the results obtained from its use always need careful and rigorous scrutiny and verification. Developing an entrepreneurial mindset in learning is important to increase creativity, innovation, and adaptability among preservice teachers. One also concludes that it is relevant to address and include issues relating to artificial intelligence in higher education, reflecting them particularly in regulations, legislation, and educational policy.