Abstract:

The collection and analysis of the data trace behind students’ actions in Learning Management Systems (LMS) are known as learning analytics (LA). LA is the "measurement, collection, analysis, and presentation of data on students, their contexts and the interactions that are generated, to understand the learning process that is developing and optimize the environments in which it occurs" (Siemens & Gasevic, 2012, p. 1). These data are usually presented to students in dashboards.
Dashboards are tools that could give support educational practices (Knight & Buckingham Shum, 2017) but, what is more relevant is that they could be a source of feedback provision and benefit students' metacognitive processes (Jivet et al., 2018). For students to interpret the information provided by dashboards and make decisions about their learning, these tools must be useful and provide relevant information. Hence the importance of understanding what the students' preferences are regarding the information to receive about their learning process and the content of the learning analytics dashboards (LADs). In this regard, recent studies explored the need for learning analytics adoption in different contexts (Hilliger et al.,2020; Whitelock-Wainwright et al.,2019; Wollny et al.,2023).
As part of a research project Learning analytics to enhance student self-regulation (Analítiques d'aprenentatge per potenciar l’autoregulació de l’estudiantat-AAPA- REDICE22-2030), we collected data from 1020 higher education students from different courses and faculties from Universitat de Barcelona. One of the research objectives was to explore the expectations and concerns of university students about the learning analytical services at the university. In order to respond to this research objective, the items from the dimension Service and feature expectations from the Student Expectations of Learning Analytics Questionnaire (SELAQ) were used (Whitelock-Wainwright et al., 2019). This dimension is composed of 7 items to be answered on a Likert-scale (strongly disagree; strongly agree). The original items were translated into the Catalan language.

In this study, we present the results obtained from descriptive statical analysis performed in SPSS 27 version. Our findings show that higher education students mostly expect that the teaching staff takes measures if the analyses show that they are at risk of failure and that learning analytics be used to promote the development of academic and professional skills. We discuss these findings considering the previous studies in the field and how they can impact the decision-making of higher education institutions.

References:
[1] Hilliger, I., Ortiz-Rojas, M. .. & Pérez-Sanagustín, M. (2020). Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach. The Internet and Higher Education, 45, 100726
[2] Knight, S., & Shum, S. B. (2017). Theory and learning analytics. Handbook of learning analytics, 17-22
[3] Siemens, G., &  Gasevic, D. (2012). Guest Editorial - Learning and Knowledge Analytics. Educational Technology & Society, 15(3), 1–2.  
[4] Whitelock‐Wainwright, A., Gašević, D. .. & Bennett, K. (2019). The student expectations of learning analytics questionnaire. Journal of Computer Assisted Learning, 35(5), 633-666
[5] Wollny, S., Di Mitri, D.,... & Drachsler, H. (2023). Students' expectations of Learning Analytics across Europe. Journal of computer assisted learning.

Keywords:

Learning analytics, dashboard, student expectations, self-regulation, feedback, higher education.