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Multivariate analysis is a statistical solution effectively used to investigate educational phenomena. It operates simultaneously on many variables and data and allows the development of classifications and models by returning data-driven understandings. How does the international educational research community make use of multivariate analysis techniques? We conducted a methodological review to identify trends in applying these methods in education. We extracted only papers written in English, indexed in Scopus, and published from 2018 to 2022 in journals in the Education category. Our review included bibliometrics such as years of publications, leading journals, and most cited articles. We detected an increase in papers using multivariate analysis in the educational research in Scopus publications over the past five years, particularly in journals in quartiles Q1 and Q2. MANOVA represents the main method used for the analysis along with regression methods; the former may be overestimated due to the overlap of names with terms searched in the string. University students represent the preferred experimental subjects for investigation; the administration of surveys and questionnaires is the most practiced way to collect data; preferred analysis tools among those declared are non-free. Based on the topics, some research categories emerged: Teaching, Medical Education, STEM, Digital Education, Professional Development, Inclusion, Wellbeing. However, the number of citations is low (less than 8) for three-quarters of the articles in our selection. To increase the effective use, confidence, and understanding of multivariate analysis processes, appropriate skills in education, statistical analysis, and interpretation of results need to be strengthened.

The advent, in 2004, of online universities in Italy followed the global trend of open and distance learning institutions. Within a brief span, the authorization of as many as 11 online universities was granted. Despite ongoing regulatory revisions and a prevailing skepticism regarding the actual quality of education these institutions furnish, student enrollment continues to rise annually. This study explores enrollment trends in Italian online universities from their inception through 2021, considering concurrent enrollment trends in traditional public and private universities. Additionally, we examine in detail the gender composition, subject areas of degree programs, and, most importantly, the geographic origins of enrolled students.

Annotated corpora, provided that they adopt international standards and expose data in open format, have many more chances to be easily exploited and reused for different objectives than traditional, analogue corpora. This paper aims at presenting the results of the early adhesion to best practices and principles afterward codified as Open Science and FAIR principles in the frame of projects concerned with digital textual corpora, in a niche area of research such as the pre-Islamic Arabian epigraphy. The case study analysed in this paper is the Digital Archive for the Study of pre-Islamic Arabian inscriptions – DASI, an online annotated corpus of the textual sources from Ancient Arabia, which also exposes its records in standard formats (oai_dc, EpiDoc, EDM) in an OAI-PMH repository. The initiatives of reuse of DASI open data in the frame of the recently ANR-funded project Maparabia (CNRS-CNR) are discussed in the paper, focusing on the exploitation of DASI’s onomastic and geographic data in a new reference tool, the Gazetteer of Ancient Arabia. After introducing DASI and Maparabia projects and highlighting the objectives of the Gazetteer, the paper describes the conceptual model of its database and the module importing data from DASI. The population of the Gazetteer, implying also a data entry and manipulation phase, is exemplified by the case-study of the Ancient South Arabian place ‘Barāqish/Yathill’. Based on the above experience, limitations and opportunities of data reuse and synchronisation issues between systems are discussed.

In recent years, due to technological advancement, research has been directed to the development and analysis of resources and tools related to educational robotics with particular attention to the field of special needs and training actions aimed at learners, teachers, professionals, and families. The use of robotics in all levels of education can support the development of logical and computational thinking, interaction, communication, and socialization, and the acquisition of particularly complex work practices, for example, in the medical field. The adoption of successful educational robotics training practices can be a potential tool to support rehabilitation interventions for disabilities and comprehensive training for students or future professionals in healthcare. A scoping review was conducted on the main topics “education” AND “robotics” with three specific focuses on complementary themes in educational research about ER: (1) teaching and computational thinking, (2) training in the health sector, and (3) education and special needs. The authors systematically searched two online databases, Scopus and Web of Science, up to April 2022. A total of 164 articles were evaluated, and 59 articles were analyzed, in a particular way N = 33 related to computational thinking, N = 15 related to e-health, and N = 11 related to special needs. The following four questions guided our research: (1) What are the educational and experimental experiences conducted through robotics in transdisciplinary fields? (2) What tools and resources are most used in such experiments (educational robotics kit, humanoid robots, telepresence robots etc.)? (3) What are the constitutive elements of the experiments and studies involving robotics and health in educational contexts? and (4) What are those explicitly related to students with special needs? In this study, part of the research project “Robotics and E-health: new Challenges for Education” (RECE) activated at the University of Modena and Reggio Emilia. RECE aims to investigate the training, educational, cognitive, and legal processes induced by the increasing diffusion of educational robotics and telemedicine in clinical and surgical contexts.

In this paper we introduce an approach for selecting a linear model to estimate, in a predictive way, the completion rate of massive open online courses (MOOCs). Data are derived from LMS analytics and nominal surveys. The sample comprises 723 observations (users) carried out in seven courses on EduOpen, the Italian MOOCs platform. We used 24 independent variables (predictors), categorised into four groups (User Profile, User Engagement, User Behaviour, Course Profile). As response variables we examined both the course completion status and the completion rate of the learning activities. A first analysis concerned the correlation between the predictors within each group and between the different groups, as well as that between all the dependent variables and the two response variables. The linear regression analysis was conducted by means of a stepwise approach for model selection using the asymptotic information criterion (AIC). For each of the response variables we estimated predictive models using the different groups of predictors both separately and in combination. The models were validated using the usual statistical tests. The main results suggest a high degree of dependence of course completion and completion rate on variables measuring the user’s behavioural profile in the course and a weak degree of dependence on the user’s profile, motivation and course pattern. In addition, residual analysis indicates the potential occurrence of interaction effects among variables and non-linear dynamics.

On-line teaching environments acquire extremely high granularity data, both on users' personal profiles and on their behaviour and results. Learning Analytics open to a numerous possible research scenario, thanks to development of technology and the speed to collect data. One of the characteristic elements is that the data is not anonymous but reproduces a personalization and identification of the profiles. Identifiability of the student is implicit in a teaching process, but access to Analytics techniques reveals a fundamental question: “What is the limit?”. The answer to this question should be preliminary to any use of data by users (students), teachers, instructors and managers of the online learning environments. Nowadays, we’re also experiencing a particular moment of change: the producing of effects concretely of the European General Data Protection Regulation (GDPR) 679/2016, the general regulation on the protection of personal data which aims to standardize all national legislation and adapt it to the new needs dictated by the evolving technological context. The objective of this work is to propose a three-points checklist list of the questions connected to management and limit of teacher’s use of data in Learning Analytics and student right of transparency in the context of Higher Digital Education to take into account before every work and research. To this end, paper contain an examination of the literatures on privacy and ethic debate in LA. Work continues with legislation’ review, with particular reference to the Italian path, and the discussion about online data management in our current universities two-speed context: that of technology and that of legislation.