Elvira Popescu

2020–2022 Distinguished Speaker
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Elvira Popescu is a full professor at the Computers and Information Technology Department, University of Craiova, Romania. She received the Ph.D. degree in information and systems technologies from the University of Technology of Compiègne, France, and the University of Craiova, Romania, in 2009. Her research interests include technology-enhanced learning, adaptation and personalization in Web-based systems, learner modeling, computer-supported collaborative learning, learning analytics, and intelligent and distributed computing. Dr. Popescu has authored and co-authored over 100 publications, including two books, journal articles, book chapters, and conference papers, which have received over 1400 citations. In addition, she has co-edited six journal special issues, as well as 15 international conference proceedings published by Springer and one published by IEEE. She has also given several invited and panel talks at various international conferences. She has participated in over 15 national and international research projects, three of which as a principal investigator (grant director).
Prof. Popescu also serves as the Vice Chair for the IEEE Women in Engineering Romania Section Affinity Group and is an Executive Board Member for the IEEE Technical Committee on Learning Technology and the International Association of Smart Learning Environments. She received several scholarships and awards, including five best paper distinctions (IEEE EUROCON 2007, IEEE ICALT 2013, ICWL 2015, ICWL 2018, IEETeL 2019). She is actively involved in the academic community by serving as associate editor for two journals (IEEE Transactions on Learning Technologies, Smart Learning Environments), member of five other journal editorial boards, organizing a series of international workshops in the area of social and personal computing for e-learning (SPeL 2008–2020), serving as a conference chair, program committee chair, and track chair for 18 conferences.

Computers and Information Technology Department
Faculty of Automation, Computers and Electronics
University of Craiova
Address: Bvd. Decebal 107, 200440 Craiova, Romania
E-mail: elvira.popescu@gmail.com
Web page: http://software.ucv.ro/~epopescu
Phone: +40 743 023 073

DVP term expires December 2023


Social learning environments – design solutions and trends

Social learning environments aim at providing learners with a medium in which they can actively engage with each other and with their teachers, co-create knowledge, share experiences, work and learn collaboratively. These environments help the learner find the right content (e.g., the most suitable resources according to the student’s need and purpose), connect with the right people (e.g., peers that the student could collaborate with) and motivate them by making learning more gratifying. In this context, Web 2.0 and social media appear well suited to support this new mode of learning and facilitate the design of social learning environments. These technologies can be used to foster communication and collaboration between learners and help create online learning networks, actively engaging students in their learning. Indeed, Web 2.0 tools have been reportedly used for a variety of educational purposes, in wide-ranging contexts and disciplines of study, following various pedagogical approaches and instructional scenarios and in support of different cognitive processes and learning objectives.

In an attempt to exploit this potential, many researchers have started to design special-purpose Web 2.0 tools for educational use, enhanced with dedicated learning support features. These can take the form of extensions / plugins / mashups of branded social media applications or the form of dedicated stand-alone tools, built from scratch. The added instructional features refer to assessment support, learner tracking and monitoring, collaborative learning facilities, ranging up to complex integrated learning environments. We provide a classification and synthesis of the state-of-the-art regarding social media-based learning spaces, surfacing design solutions and trends and offering a comparative review. While still far from the popularity and widespread adoption of learning management systems, Web 2.0-enhanced learning spaces are definitely on the rise. This is due to two main reasons: i) increasing adoption rates of social media among students and teachers, both in and out of school contexts; ii) rising awareness of the importance played by the pedagogical features and instructional strategies in addition to the technologies. Therefore, the trend is toward the development of dedicated Web 2.0-based learning environments, which incorporate pedagogically valuable features alongside social media tools.

Learning analytics – a multiple perspectives analysis of student data

Learning analytics (LA) is a growing research area, which aims at selecting, analyzing and reporting student data (in their interaction with the online learning environment), finding patterns in student behavior, displaying relevant information in suggestive formats; the end goal is the prediction of student performance, the optimization of the educational platform and the implementation of personalized interventions. The topic is highly interdisciplinary, including machine learning techniques, educational data mining, statistical analysis, social network analysis, natural language processing, but also knowledge from learning sciences, pedagogy and sociology.

Despite its increasing popularity, LA has been applied less in the context of social media-based environments; hence in this talk we focus especially on research in social learning analytics area. In particular, we explore academic performance predictors and the relationships between students’ learning styles and their social media use; we also investigate students’ collaboration patterns and the community of inquiry supported by social media tools. Four research directions are tackled: analytics dashboards, predictive analytics, social network analytics and discourse analytics. As far as analysis techniques are concerned, we apply various approaches, such as: classification, regression, clustering and PCA algorithms, textual complexity analysis, social network analysis techniques, content analysis based on Community of Inquiry. We thus address the “trinity” of methodological approaches: i) network analysis (representing actor-actor / social relations); ii) process-oriented analysis (based on action logs and pattern detection); iii) content-oriented analysis (based on learner created artefacts); hence a more comprehensive learning analytics perspective is provided.

Peer assessment in technology-enhanced learning

Peer assessment is gaining increasing popularity in recent years, especially in the context of collaborative learning. Also known as peer review or peer evaluation, it refers to the involvement of students in the process of assessing the work of fellow learners and providing feedback and sometimes grades. Peer review has several benefits, both for the provider and the receiver of the assessment. Students who play the role of evaluators are exposed to peers’ work and ideas, which offers them new perspectives on the field and helps them extend their own knowledge and understanding. Furthermore, performing an evaluation contributes to the development of advanced critical thinking, reflection and meta-cognitive skills. It also improves evaluators’ motivation and responsibility and fosters self-confidence. Students who receive their peers’ reviews benefit from timely and more detailed feedback, as compared to the limited formative assessment which can be provided by the instructor, especially in large classes.

Nevertheless, peer review also has potential pitfalls, such as validity, reliability and fairness issues, especially in case of peer grading. Some students may resent evaluating their peers’ work, find it too time consuming or lack confidence in their evaluation ability; other students may not take the peer review process seriously, unless it is monitored and graded by the instructor. However, on the whole, students’ engagement is increased by means of peer evaluation, since their motivation for learning has a strong social dimension and they pay more attention to peers’ opinions and feedback. Furthermore, an increased interactivity level between students and a more active role in learning are achieved.

Several platforms for managing the peer review process have been proposed in the literature; we provide an overview of these systems, as well as a description of our innovative general-purpose peer assessment platform called LearnEval.


Social learning environments – design solutions and trends
Learning analytics – a multiple perspectives analysis of student data
Peer assessment in technology-enhanced learning

Read the abstracts for each of these presentations