A systematic analysis of learning analytics using multi-source data in the context of Spain

ABSTRACT

Learning analytics (LA) employs educational data to improve the timeliness of support for learners. Apart from technical aspects, there is a need to understand social complexities brought about by different stakeholders, so as to systematise the adoption of LA in Higher Education (HE). We present an analysis of the situation, needs and challenges of LA in the context of Spanish HE, considering managers’, teachers’ and students’ perspectives. Qualitative research is employed using recursive abstraction. Results reveal that the level of institutional adoption is low and none of the analysed institutions had an LA policy. Furthermore, only two of these institutions had an initial LA strategy. While the institutions shared some commonalities in their objectives for LA, chosen tools and adoption challenges, the distinct differences in the political contexts and institutional practices among the institutions reaffirmed that LA solutions and services cannot be implemented in the same manner. Moreover, different needs for LA and concerns are identified about its adoption among managers, students and teachers. These observations lead to our conclusion that the main challenges to implement LA in Spain are not related to technological issues but to the social and cultural issues rooted in institutions and those associated with different stakeholders.

KEYWORDS:

• Higher education
• learning analytics
• stakeholders’ perspectives
• systematic analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Questions of the Interview – available at: https://www.it.uc3m.es/pedmume/files/Institutional%20interview_questions2.pdf; Student focus groups questions – available at: https://www.it.uc3m.es/pedmume/files/Student%20FG_questions2.pdf; Teacher focus group question – available at: https://www.it.uc3m.es/pedmume/files/Staff%20FG_questions2.pdf.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the SHEILA project which was funded with support from the European Commission [grant number [562080-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD]; by the Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación for the Research Projects ‘Smartlet’, ‘H2O’ and ‘SNOLA’ whose codes are TIN2017-85179-C3-1-R, PID2020-112584RB-C31, and RED2018-102725-T, respectively; and by the Comunidad de Madrid, through the project e-Madrid-CM (S2018/TCS-4307). The last four were also co-financed by the European Structural Funds (European Social Fund and European Regional Development Fund). It was also supported by Ministerio de Ciencia, Innovación y Universidades, under an FPU fellowship (FPU016/00526). This publication reflects the views only of the authors, and funders cannot be held responsible for any use which may be made of the information contained therein.