Browsing by Author "Perez Sanagustin, Mar"

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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.
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    My Learning Mentor: A Mobile App to Support Learners Participating in MOOCs
    (2015) Alario-Hoyos, Carlos; Estevez-Ayres, Iria; Perez Sanagustin, Mar; Leony, Derick; Delgado Kloos, Carlos
    MOOCs have brought a revolution to education. However, their impact is mainly benefiting people with Higher Education degrees. The lack of support and personalized advice in MOOCs is causing that many of the learners that have not developed work habits and self-learning skills give them up at the first obstacle, and do not see MOOCs as an alternative for their education and training. My Learning Mentor (MLM) is a mobile application that addresses the lack of support and personalized advice for learners in MOOCs. This paper presents the architecture of MLM and practical examples of use. The architecture of MLM is designed to provide MOOC participants with a personalized planning that facilitates them following up the MOOCs they enroll. This planning is adapted to learners' profiles, preferences, priorities and previous performance (measured in time devoted to each task). The architecture of MLM is also designed to provide tips and hints aimed at helping learners develop work habits and study skills, and eventually become self-learners.