Browsing by Author "Scheihing, Eliana"

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    An overview of the LALA project
    (2020) Muñoz-Merino, Pedro J.; Delgado Kloos, Carlos; Tsai, Yi-Shan; Gasevic, Dragan; Verbert, Katrien; Pérez Sanagustín, 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.
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    Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach
    (ELSEVIER SCIENCE INC, 2020) Hilliger, Isabel; Ortiz Rojas, Margarita; Pesantez Cabrera, Paola; Scheihing, Eliana; Tsai, Yi Shan; Munoz Merino, Pedro J.; Broos, Tom; Whitelock Wainwright, Alexander; Perez Sanagustin, Mar
    Learning Analytics (LA) is perceived to be a promising strategy to tackle persisting educational challenges in Latin America, such as quality disparities and high dropout rates. However, Latin American universities have fallen behind in LA adoption compared to institutions in other regions. To understand stakeholders' needs for LA services, this study used mixed methods to collect data in four Latin American Universities. Qualitative data was obtained from 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students, whereas quantitative data was obtained from surveys answered by 1884 students and 368 teaching staff. According to the triangulation of both types of evidence, we found that (1) students need quality feedback and timely support, (2) teaching staff need timely alerts and meaningful performance evaluations, and (3) managers need quality information to implement support interventions. Thus, LA offers an opportunity to integrate data-driven decision-making in existing tasks.