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Towards learning analytics adoption: A mixed methods study of data‐related practices and policies in Latin American universities.

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Title: Towards learning analytics adoption: A mixed methods study of data‐related practices and policies in Latin American universities.
Authors: Hilliger, Isabel ihillige@ing.puc.cl, Ortiz‐Rojas, Margarita, Pesántez‐Cabrera, Paola, Scheihing, Eliana, Tsai, Yi‐Shan, Muñoz‐Merino, Pedro J., Broos, Tom, Whitelock‐Wainwright, Alexander, Gašević, Dragan, Pérez‐Sanagustín, Mar
Source: British Journal of Educational Technology. Jul2020, Vol. 51 Issue 4, p915-937. 23p. 7 Charts, 8 Graphs.
Subject Terms: *Mathematical models of learning, *Diversity in education, *Information storage & retrieval systems, *Achievement gains (Education), *Teenagers, *Higher education
Geographic Terms: Latin America
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Educational+Technology%22">British Journal of Educational Technology</searchLink>. Jul2020, Vol. 51 Issue 4, p915-937. 23p. 7 Charts, 8 Graphs.
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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