Browsing by Author "Gasevic, Dragan"

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An overview of the LALA project
(CEUR-WS, 2020) Muñoz Merino, Paul; Delgado Kloos, Carlos; Tsai, Yi Shan; Gasevic, Dragan; Verbert, Katrien; Pérez Sanagustín, María del Mar; Hilliger, Isabel; Zúñiga Prieto, Miguel Ángel; Ortiz Rojas, Margarita; Scheihing, Eliana
The LALA project (“Building Capacity to Use Learning Analytics to Improve Higher Education in Latin America”) is a project that aims at building capacity about the use of data in education for improving education in Latin America. This article presents a general overview of the LALA project including the LALA framework (as a set of guidelines, recommendations and patterns for enabling adoption of learning analytics), the adaptation of learning analytics tools (mainly three different tools used in Europe) and the pilots with learning analytics experiences. The results of this project could serve as an example for other institutions in the Latin American region or other under-represented regions to adopt Learning Analytics as part of their processes.
Detection of learning strategies: a comparison of process, sequence and network analytic approaches
(Springer Link, 2019) Pérez Sanagustín, Mar; Matcha, Wannisa; Gasevic, Dragan; Ahmad Uzir, Noraayu; Jovanovic, Jelena; Pardo, Abelardo; Maldonado Mahauad, Jorge Javier
© 2019, Springer Nature Switzerland AG. Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students’ trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar.
Towards learning analytics adoption: A mixed methods study of data-related practices and policies in Latin American universities
(2020) Hilliger, Isabel; Ortiz Rojas, Margarita Elizabeth; Pesantez Cabrera, Paola Gabriela; Scheihing, Eliana; Tsai, Yi Shan; Muñoz Merino, Pedro J.; Broos, Tom; Whitelock Wainwright, Alexander; Gasevic, Dragan; Pérez Sanagustín, Mar
In Latin American universities, Learning Analytics (LA) has been perceived as a promising opportunity to leverage data to meet the needs of a diverse student cohort. Although universities have been collecting educational data for years, the adoption of LA in this region is still limited due to the lack of expertise and policies for processing and using educational data. In order to get a better picture of how existing data‐related practices and policies might affect the incorporation of LA in Latin American institutions, we conducted a mixed methods study in four Latin American universities (two Chilean and two Ecuadorian). In this paper, the qualitative data were based on 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students; the quantitative data were collected through two surveys answered by 1884 students and 368 teachers, respectively. The findings reveal opportunities to incorporate LA services into existing data practices in the four case studies. However, the lack of reliable information systems and policies to regulate the use of data imposes challenges that need to be overcome for future LA adoption.