Accessibility and Inclusion in Education: Enabling Digital Technologies

Dear Colleagues,

Technology has considerable but largely unused potential to support inclusive education: it can provide multiple means of presenting, representing, and expressing learning and to overcome barriers that several learners (e.g., people with special education needs, specific learning disorders or disability) would otherwise experience by participating in the curriculum. It also has the potential to increase enjoyment and motivation. This special issue focuses on novel education strategies and enabling technologies to avoid students’ isolation and improve inclusive teaching-learning practices both in formal and informal educational contexts. Topics of interest include but are not limited to the followers: Design of inclusive interactive systems for formal and informal educational setting; Technologies to support individualized educational planning and Life Project; Innovative use of technology in the classroom, from primary to higher education; Computer and web-based software, and mobile applications for distance education and blended education; Extended reality and metaverse for education; Affective computing in educational contexts.

Dr. Silvia Ceccacci
Dr. Catia Giaconi
Dr. Noemi Del Bianco
Guest Editors

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• human-computer interaction
• special education
• inclusive teaching
• extended reality
• affective computing

Title: Exploring Moodle Resources and Activities for Enhanced Teaching Analytics
Authors: Ricardo Queirós; Carla M. A. Pinto; Mário Cruz; Daniela Mascarenhas
Affiliation: Polytechnic of Porto
Abstract: This paper presents a study examining the use of Moodle resources and activities across diverse courses. The primary objective was to gain insights into the ways these tools are employed and the difficulties found, tailored to the nature of each course. Through this distinct approach, we aimed to uncover patterns of usage and shed light on context-specific challenges faced by educators. The findings contribute to a deeper understanding of e-learning tool dynamics within varying educational contexts. The study also holds potential implications for the advancement of teaching analytics. By examining the different approaches that educators adopt in response to the prescribed resources and activities, this research can offer insights into the practical use of the learning management system (LMS) and provide a valuable resource for refining the design of the LMS optimizing the user experience for both instructors and students.

Title: The Position of Technical Competence in Establishing Digital Data Security Awareness
Authors: Stamatis Papadakis
Affiliation: University of Crete, Department of Preschool Education
Abstract: This research aimed to investigate the explanatory and predictive relationships between digital literacy components and digital data security. The study was carried out with 322 students enrolled in the education faculties of 8 randomly selected universities. In the research, the attitudes, cognitive and social variables of digital literacy were exogenous. A mediation model has been established in which the technical variable is the mediator, and the digital data security awareness is endogenous. According to the analysis results of the mediation model, the technical variable fully mediates between the attitude, cognitive and social variables, and the awareness of digital data security. These results show that developing digital literacy skills and if these skills are supported with technical knowledge can contribute to developing digital data security awareness. As a result, digital literacy skills will only have the expected effect on creating digital data security awareness with the technical information tool variable. According to our mediation model, the technical competence to be developed with the support of digital literacy skills and the creation of digital data security awareness can guide researchers and instructors working in the field.

Title: Comparison of Machine Learning Approaches for Predicting Student's Dropout from Multiple Online Educational Entities.
Authors: Cristóbal Romero
Affiliation: University of Cordoba
Abstract: Predicting student dropout is a very important task in education, mainly in online educational environments. Traditionally, each educational entity such an institution, faculty, department, enterprise, etc. create and use its own prediction model starting from its own data. However, that approach is not always feasible or advisable and may depend on the availability of data, local infrastructure, and resources. In those cases, there exist different machine learning approaches for sharing of data and/or prediction models between different educational entities, using a classical centralized machine learning approach or other more advanced approaches such as Transfer Learning or Federated Learning. Our case study addresses the challenge of comparing different existing approaches for generating prediction dropout models using data from multiple homo-geneous educational entities of different sizes. We have used data from three different LMS Moodle servers representing different-sized educational entities. We carried out experiments to test the performance of the different machine learning approaches for the problem of predicting student dropout with multiple educational entities involved, having used a deep learning algo-rithm as a predictive method. Our preliminary findings provide useful information on the benefits and drawbacks of each approach, as well as suggestions for enhancing the performance when there are multiple institutions. In our case, the repurposed transfer learning, the stacked transfer learning and the centralized approaches obtained similar or better results than the locally trained models for most of all entities.