Journal of e-Learning and Knowledge Society 2019-10-16T17:16:14+00:00 Editorial Staff Open Journal Systems <h1 class="page-header" style="font-family: Raleway; margin-top: -50px;">Bibliometrics</h1> <p style="font-size: 18px; margin-top: -20px;"><strong>ANVUR Ranking<br></strong>A-Class for Sector 10, 11-D1 and 11-D2</p> <p style="font-size: 18px; margin-bottom: -0px;"><strong>Publish-or-Perish (2019)<br></strong>- H-Index: <strong>22<br><br></strong><strong>Scopus (2018)<br></strong>- SJR Index (2018): <strong>0.345</strong><br>- H Index: <strong>12</strong><em><br></em><span style="text-decoration: underline;">- Rankings (2018)&nbsp;</span><br>&nbsp; - #40 out of 83 in e-Learning category (<em>#1 Italian Journals</em>);<br>&nbsp; - #565 out of 1222 in Education category (<em>#1 Italian Journal</em>s);<br>&nbsp;- #397 out of 1823 in Computer Science Applications category (<em>#1 Italian Journals</em>)</p> <p style="font-size: 18px; margin-bottom: -45px;"><strong>Web of Science (from 2015)<br></strong>- H-Index: <strong>6</strong></p> Second Cover 2019-10-16T10:20:45+00:00 Managing Editor 2019-10-15T19:37:37+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) In memory of Luigi Colazzo 2019-10-16T10:20:51+00:00 Nicola Villa 2019-10-12T10:58:51+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Editorial 2019-10-16T10:20:50+00:00 Antonio Marzano Antonella Poce 2019-10-12T11:00:07+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Disciplinary and didactic profiles in the EduOpen network MOOCs 2019-10-16T10:20:51+00:00 Bojan Fazlagic Luciano Cecconi <table class="data" width="100%"> <tbody> <tr valign="top"> <td class="label">&nbsp;</td> <td class="value">This paper describes the quantitative and qualitative characteristics of the MOOCs available in the EduOpen platform. In particular, data (analytics) concerning the variables “didactic disciplines” and “didactic structuring” are presented in order to identify the main trend lines and possible critical aspects. From the data analysis some useful elements emerge for a greater understanding of the main characteristics of the MOOCs offered by the EduOpen network, in particular: a) the quantitative dimensions of the supply and demand of the MOOCs; in this regard, a greater flow of enrolments towards the courses belonging to a scientific and technological nature is evident; b) the degree of didactic structuring of the courses; in this regard, the presence of assessment tools appears to be the element that more than others characterises the didactic structure of the MOOCs EduOpen. The conclusions suggest awareness-raising actions aimed at building dashboards that can report in real time to instructors and students critical and action needed issues and therefore provide a useful guidance both to prevent situations at risk and to support teachers in the design and development of new courses.</td> </tr> </tbody> </table> 2019-10-12T10:55:15+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) The Design of a Learning Analytics Dashboard: EduOpen Mooc platform redefinition procedures 2019-10-16T10:20:49+00:00 Anna Dipace Bojan Fazlagic Tommaso Minerva <table class="data" width="100%"> <tbody> <tr valign="top"> <td class="label">&nbsp;</td> <td class="value">The current EduOpen dashboard is not capable of monitoring performances and trends over the medium to long term both for the students as for the instructors; summarising and synthesising the adequate information; allowing implementation of any sort of predictive actions and functions (learning prediction). The article aims to expose the process of innovation and redefinition of a learning analytics dashboard in the EduOpen MOOC platform in order to define a model to design it accurately in terms of productivity for all users (teachers and students above all). <br>From the literature analysis, main MOOC platform comparisons and the insights from the round tables a time spent variable is identified as at the basis of the entire user experience in online training paths. A concrete experimentation, through the design of a learning timeline and a constructive feedback system of an upcoming course in the EduOpen catalog, is designed and explained relaying on the hypothesis of the existence of a correlation between the “time spent” (time value) and the final performance of the student.</td> </tr> </tbody> </table> 2019-10-12T11:03:40+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Analyzing Learning Analytics techniques applied on open online courses 2019-10-16T10:20:49+00:00 Marina Marchisio Sergio Rabellino Fabio Roman Matteo Sacchet Daniela Salusso <p>Nowadays learning analytics has been growing as a science, and at the University of Turin we are interested in its potential to enhance both the teaching and the learning experience. In the last few years we have gathered data from two projects: Orient@mente, an online platform where students can prepare for university entry tests and browse online courses before enrolling in one, and start@unito, which offers open online university courses in various disciplines. In addition, we have also studied and analyzed the results of the teacher training experience carried out for the start@unito project, as well as those obtained from a survey involving secondary school teachers and the possible employment of the start@unito OERs in their everyday teaching. Our sources of data are students’ activity online, the results of formative automatic assessment, and the questionnaires given to the learners; the types of questions range from Likert scale evaluations to multiple choice, yes/no and a few open questions. In this way, the insights gained from both usage tracking and questionnaires can be used to make interventions to improve the teaching and the learning experience. In this paper we discuss the different ways we employ LA in our projects and try to evaluate their effectiveness in terms of outcomes, structure, availability, statistics and interventions.</p> 2019-10-12T11:06:49+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) The presence and role of assessment in UNIMORE MOOCs 2019-10-16T10:20:48+00:00 Luciano Cecconi Bojan Fazlagic <p>The contribution presents a reflection on the relationship between the use of assessment tools and the two-sided phenomenon of the completion rate and dropout rate in MOOCs. In support of this reflection, the experience of the MOOCs proposed by the University of Modena and Reggio Emilia (UNIMORE) within the EduOpen network is described. In particular, the data relating to the quantity and quality of the assessment tools used in the MOOCs UNIMORE and the data on the completion rates of the 5 pathways currently active in the training offer on EduOpen and, specifically, of a MOOC with a complex evaluation system and particularly high completion rates are reported.</p> 2019-10-12T11:12:27+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Learning Analytics to improve Formative Assessment strategies 2019-10-16T10:20:47+00:00 Alice Barana Alberto Conte Cecilia Fissore Marina Marchisio Sergio Rabellino <p>In digital education, learning analytics should support active monitoring and dynamic decision-making during learning processes; they are mainly based on digital assessment, through which it is possible to collect and elaborate data about students’ progresses. In this paper we start from Black and Wiliam’s theoretical framework on formative assessment, which identified 5 key strategies that 3 agents (student, peers and teacher) pursue when enacting formative practices in a context of traditional learning, and we integrate it in a framework of innovative didactics. In particular, we consider the use of a Digital Learning Environment integrated with an Automatic Assessment System based on the engine of an Advanced Computing Environment to build interactive materials with automatic assessment according to a specific model of formative assessment. In this framework, rooted in activity theory, the Digital Learning Environment plays the role of mediating artifact in the activity of enacting the strategies of formative assessment. Though several examples of application of automatic formative assessment in several contexts and modalities, we show how it is possible to use the data gathered through the Digital Learning Environment to improve the enactment of Black and Wiliam’s strategies of formative assessment, strengthen and evaluate their action.</p> 2019-10-12T11:15:08+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Use of Learning Analytics in formative and summative evaluation 2019-10-16T10:20:47+00:00 Carlo Palmiero Luciano Cecconi <p>This study belongs the field of learning analytics (LA) and considers data produced within digital learning environments. This study investigates the relationship between the standard psychometric properties of test questions and the information obtained from the log files produced, on a large scale, during test administration by computer. The results of this type of survey can help to make visible the intersections between formative and summative assessment and to renew the evaluation practices of a rapidly expanding sector such as digital education.</p> 2019-10-12T11:17:14+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Learning Analytics to support learners and teachers: the navigation among contents as a model to adopt 2019-10-16T10:20:46+00:00 Sergio Miranda Rosa Vegliante <p>Learners have different needs and abilities; teachers have the ambition to intervene before it is too late. How may e-learning systems support this? Learning Analytics may be the answer but there is not a general-purpose model to adopt. Many learning analytics tools examine data related to the activities of learners in on-line systems. Research efforts in learning analytics tried to examine data coming from LMS tracks in order to define predictive model of students’ performances and failure risks and to intervene to improve the learning outcomes. The analytical methods are widely used but no theoretical references are clear. In this paper, we tried to define a prediction model for learning analytics. In particular, we adopted a Moodle-based LMS in a blended course and collected all data of more than 400 undergraduate students in terms of resource accesses and exam performances. The model we defined was able to identify the learners at risk during their learning processes only by analysing their navigation paths among the contents.</p> 2019-10-12T11:20:16+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) How to use assessment data collected through writing activities to identify participants’ Critical Thinking levels 2019-10-16T10:20:45+00:00 Maria Rosaria Re Francesca Amenduni Carlo De Medio Mara Valente <p>The present paper aims at presenting the Critical Thinking (CT) Skills assessment results in teachers participating in the Erasmus+ KA203 CRITHINKEDU summit (Critical Thinking Across the European Higher Education Curricula), organised in Leuven in June 2019. Within the summit, a workshop was organized to promote in participants’ CT skills knowledge, especially in terms of CT assessment methods through open-ended questions. Based on our theoretical assumptions, description and interpretation activities of written text promote skills such as Analysis, Argumentation, Inference and Critical evaluation, which can also be defined in terms of improvement of language skills. Teachers participating in the workshop were assessed through a test composed by literary text paraphrase and commentary exercises; a prototype for the automatic assessment of CT in open-ended answers was used to evaluate the open-answers. Also three human raters evaluated the answers’ texts. The goal of the present research was to verify the assessment method reliability and to collect some data useful for the implementation of the automatic prototype.</p> 2019-10-12T11:25:17+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Data management in Learning Analytics: terms and perspectives 2019-10-16T10:20:59+00:00 Claudia Bellini Annamaria De Santis Katia Sannicandro Tommaso Minerva <p>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.<br>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.<br>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.<br>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.&nbsp;<br>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.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Predictive model selection for completion rate in Massive Open Online Courses 2019-10-16T17:16:14+00:00 Annamaria De Santis Katia Sannicandro Claudia Bellini Tommaso Minerva <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>The models were validated using the usual statistical tests.</p> <p>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.</p> <p>In addition, residual analysis indicates the potential occurrence of interaction effects among variables and non-linear dynamics.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) University Dropout Prediction through Educational Data Mining Techniques: A Systematic Review 2019-10-16T10:20:59+00:00 Francesco Agrusti Gianmarco Bonavolontà Mauro Mezzini <p>The dropout rates in the European countries is one of the major issues to be faced in a near future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training according to Eurostat’s statistics. The main aim of this review is to identify studies which uses educational data mining techniques to predict university dropout in traditional courses. In Scopus and Web of Science (WoS) catalogues, we identified 241 studies related to this topic from which we selected 73, focusing on what data mining techniques are used for predicting university dropout. We identified 6 data mining classification techniques, 53 data mining algorithms and 14 data mining tools.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Reflecting A… “Bit”. What Relationship Between Metacognition And ICT? 2019-10-16T10:20:58+00:00 Alessia Cadamuro Elisa Bisagno Chiara Pecini Loris Vezzali <p>Using Information and Communication Technologies (ICT) in educational environments has become widespread in latest years. Since research underlined the important role played by metacognition and self-regulation abilities in fostering learning outcomes, the relationship between these aspects appears to be particularly worthy of investigation. In this review, we present 14 studies that have deepened the relationship between ICT, metacognitive skills and learning outcomes by identifying two main categories. Some articles investigated the effects of ICT environments combined with metacognitive aspects of learning outcomes, while others investigated the reciprocal relationship between ICT and metacognition. In general, from our review, the interaction between ICT and metacognition in producing better learning outcomes appears well established and the results highlight a bi-directional relationship between metacognition and ICT, but also allow to draw attention to gaps requiring further research.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) An agnostic monitoring system for Italian as second language online learning 2019-10-16T10:20:53+00:00 Gerardo Fallani Stefano Penge Paola Carmelina Piera Tettamanti <p>This contribution follows the trend in educational research to collect data and create an information-based system to improve learning effectiveness.<br>However, the value of quantitative data collected through online platforms is a subject of debate: when starting from data (inductively) meaningful interpretations are hard to discover; on the other hand, when starting from a priori schema (deductively), there is a risk of lack of flexibility and responsiveness to the changes. Hence, the need to hypothesize a different approach.<br>For this purpose, a monitoring system whose architecture we defined as agnostics has been built and tested. That system was connected to an online learning environment with free educational resources, whose operating learning fulcrum is the Digital Learning Unit (DLU), an original theoretical-practical device which allows interpretative assumptions to be made on the data obtainable from the system.<br>Although minimal, the results achieved through the piloting are sufficient to enable the monitoring system as an information provider about learning experiences, resources, and the environment itself.<br>The interpretative hypotheses made possible by the DLU legitimize the assumption of an abductive approach which, without incurring in the aporias mentioned above, allows us to transform mere quantitative data into useful information in order to support the learning process.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Data-Driven Modeling of Engagement Analytics for Quality Blended Learning 2019-10-16T10:20:57+00:00 Nan Yang Patrizia Ghislandi Juliana Raffaghelli Giuseppe Ritella <p>Engagement analytics is a branch of learning analytics (LA) that focuses on student engagement, with the majority of studies conducted by computer scientists.Thus, rather than focusing on learning, research in this field usually treats education as a scenario for algorithms optimization and it rarely concludes with implications for practice. While LA as a research field is reaching ten years, its contribution to our understanding of teaching and learning and its impact on learning enhancement are still underdeveloped. This paper argues that data-driven modeling of engagement analytics is helpful to assess student engagement and to promote reflections on the quality of teaching and learning. In this article, the authors a) introduce four key constructs (student engagement, learning analytics, engagement analytics, modeling and data-driven modeling); b) explain why data-driven modeling is chosen for engagement analytics and the limitations of using a predefined framework; c) discuss how to use engagement analytics to promote pedagogical reflection using a pilot study as a demonstration. As a final remark, the authors see the need of interdisciplinary collaboration on engagement analytics between computer science and educational science. In fact, this collaboration should enhance the use of machine learning and data mining methods to explore big data in education as a means to provide effective insights for quality educational practice.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) User ratings as a predictor of linguistic feedback quality in Question and Answer portals 2019-10-16T10:20:53+00:00 Simone Torsani <p>Social Question and Answer portals allow users to post and answer questions on different issues, among which foreign languages. The present paper focuses on feedback requests, i.e. questions in which users of the site ask for linguistic feedback on short sentences or phrases. In particular, it reports on a research on how reliable is the evaluation of answers provided by the portal’s users to identify correct and good linguistic feedback. An observational approach was adopted on about 600 answers in the Italian version of Yahoo! Answers. Each feedback was evaluated by two expert teachers and their rating was then compared with the evaluation provided by the site’s users. Results show that, while the correlation between the votes of the community and the rating of the experts is rather weak, answers with a positive evaluation generally contain a correct feedback. We conclude, therefore, that caution must be exercised when using users’ evaluation as guidance on feedback choice.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) A Social Network Analysis approach to a Digital Interactive Storytelling in Mathematics 2019-10-16T10:20:54+00:00 Maria Polo Umberto Dello Iacono Giuseppe Fiorentino Anna Pierri <p>In this paper we present a social analysis of the interactions among the students involved in a trial of the PRIN project “Digital Interactive Storytelling in Mathematics: a Competence-based Social Approach”. The instructional design is based on collaborative scripts within a digital storytelling framework where the story follows the interactions among the characters played by the students and an expert (teacher or researcher). We report the results of a trial that involved teachers and students from the upper secondary school analysing from a Social Network Analysis point of view the interventions of the expert, the involvement / participation of the students and the interactions among peers and with the expert. We also briefly discuss the potential and limitations of the currently available tools to perform this kind of analysis, in view of the much broader perspective offered by the Learning Analytics approach.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Learning Analytics - Scientific Description and Heuristic Validation of Languages NLG 2019-10-16T10:20:54+00:00 Ritamaria Bucciarelli Roberto Capone Francesco Saverio Tortoriello Marianna Greco Giulia Savarese Javier Enriquez <p>Educators do not have to be mere "Translators" as manufacturers of algorithms for teaching in the infosphere, but must modulate it for everyone's purposes. This work aims to validate the moments of transformation, through which, over the centuries, the mathematical sciences, with the help of philosophy, have promoted the Natural Language Generation to formal models. The starting hypothesis intends to corroborate an epistemological framework, which assigns logical-mathematical reasoning to mental processes following four models: Chomsky, who, with descriptive grammar marks a new model for rewriting languages; Gross, who, with the relationship between linguistics, computer science, and mathematics generates a relationship concerning a strongly transdisciplinary domain, in which linguistics realizes computer-based models and procedures; Silberzstein's Nooj system for processing, describing and analyzing INLG fixed sentences. The focal part of the research is the work of comparison that the team carried out to validate the development of languages according to the Transformational Analysis of Direct Transitive by M. Silberzstein and the lexicon-grammar; probabilistic calculus, according to the Probabilistic latent semantic Analysis.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Learning analytics in online social interactions. The case of a MOOC on ‘language awareness’ promoted by the European Commission 2019-10-16T10:20:57+00:00 Letizia Cinganotto Daniela Cuccurullo <p>The contribution is aimed at reporting and commenting on some significant data collected from a MOOC on language awareness, addressed to teachers, trainers and educators from all over the world, promoted by the European Commission through the School Education Gateway platform and moderated by the authors.<br>The role of MOOCs for teachers' continuous quality professional development will represent the starting point of the discussion.&nbsp;<br>After a brief overview of the inspirational background and of the MOOC syllabus, data will be highlighted and commented on with reference to the attendees’ participation, motivation and online social interaction, according to the following categories: learner issues, pedagogical issues, technical issues.&nbsp;<br>Among the different learning environments and media channels used during the course, the Facebook Group, the forum and the Twitter chat will be described and commented on as crucial dimensions of the learning experience.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Improving learning with augmented: a didactic re-mediation model from Inf@nzia Digitale 3.6 2019-10-16T10:20:56+00:00 Marta De Angelis Angelo Gaeta Francesco Orciouli Mimmo Parente <p>The diffusion of information and communication technologies, in the last decades, appears to be a great opportunity in teaching and learning processes, not attributable to a simple differentiation of learning supports.</p> <p>Augmented reality (AR), in particular, appears capable of radically transforming learning methods, supporting students to have an educational experience able to involving all the senses, facilitating attention and the storage of content.</p> <p>This paper provides a brief overview on the use of augmented reality in learning, in order to present a didactic re-mediation model and its trial application developed in the context of the Inf@nzia Digitales 3.6 project.</p> <p>The illustrated learning experiences are designed for children from three to six years for the discovery of geometric shapes in a smart city, and their understanding as road signs.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Improving schools’ setting and climate: what role for the National Operative Programme? Some empirical insights from a Learning Analytics perspective 2019-10-16T10:20:52+00:00 Rosalba Manna Samuele Calzone Rocco Palumbo <p>Even though students are increasingly involved in extra learning activities which are aimed at enriching the contents and the attributes of conventional educational programmes, still little is known on the main implications of such initiatives. Exploiting Learning Analytics, the article shed light on the effects triggered by students’ involvement in the educational activities co-financed by the Call no. 10862/2016 of the PON 2014/2020. We implemented a three-steps study design, which consisted of: 1) a descriptive analysis; 2) a principal component analysis; and 3) a regression logit analysis. Our findings stressed that educational activities delivered were especially effective in improving social relationships at school and in increasing the willingness of students to expand their horizons.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L) Which Learning Analytics for a socio-constructivist teaching and learning blended experience? 2019-10-16T10:20:52+00:00 Nadia Sansone Donatella Cesareni <p>The contribution describes and problematizes the use of learning analytics within a blended university course based on a socio-constructivist approach and aimed at constructing artefacts and knowledge. Specifically, the authors focus on the evaluation system adopted in the course, deliberately inspired by the principles of formative assessment: an ongoing evaluation in the form of feedback shared with the students, and which integrates the teacher's evaluation with self-evaluation and peer-evaluation. This system obviously requires the integration of qualitative procedures - from teachers and tutors - and quantitative - managed through the reporting functions of the LMS and online tools used for the course. The contribution ends with a reflection on the possibilities of technological development of learning analytics within the learning environment, such as to better support constructivist teaching: Learning Analytics that comes closest to social LA techniques providing the teacher with a richer picture of the student's behaviour and learning processes.</p> 2019-10-12T00:00:00+00:00 Copyright (c) 2019 Italian e-Learning Association (SIe-L)