Group Admins
Description
Session Description
Research has shown that the most significant challenges that confront higher education institutions in their deployment of learning analytics (LA) are not technical, but social (Ferguson, 2012; Howell, Roberts, Seaman, & Gibson, 2018; Roberts, Howell, Seaman, & Gibson, 2016; Siemens, Dawson, & Lynch, 2013; Tsai et al., 2018). For example, Tsai et al. (2018) identified three areas of prominent challenges: the demand for resources, ethics and privacy, and stakeholder involvement. Resources primarily refer to data, funding, and people. In terms of data, significant issues concern the quality and scope of data that can reflect learning experiences accurately. In terms of funding, studies have shown that LA often needs to compete with other institutional priorities, resulting in a challenge of obtaining sufficient financial support to supply an enabling infrastructure (Arroway, Morgan, O’Keefe, & Yanosky, 2016; Yanosky, 2009). With regards to people, a shortage of skilled people that have the ability to process, analyse and interpret data has been identified as a prominent challenge that worsens the gap between needs and solutions (Norris & Baer, 2013), thereby impeding the scaling of LA research to enterprise solutions (Siemens et al., 2013). In this presentation, we present a European study that looks into these social issues, with a particular focus on the discrepancies in expectations of and concerns about LA among different stakeholders, including institutional leaders, teaching staff, and students. The purpose of the study is to inform a policy framework that can be adopted as guidelines for policy and strategy formation for the use of LA in higher education. This presentation aligns with the conference theme – student data and learning analytics.
In this presentation, we intend to answer two questions: 1) what are the expectations of LA from the perspectives of managers, teachers, and students? 2) what are the concerns about LA from the perspectives of managers, teachers, and students? To answer these questions, we drew on the results of various research activities, including an institutional survey to European higher education institutions (n=46), a student survey (n=3053), a staff survey (n=210), 64 interviews with institutional leaders, 18 student focus groups, and 16 staff focus groups. The study shows that institutional leaders are most interested in using LA to improve institutional performance, teachers are most interested in identifying learning struggles and improving learning support, and students are most interested in gaining personalised support. In terms of concerns about LA, managers are most concerned about returns on investment, teachers are concerned about workload, performance judgement, and the incompleteness of data about students, whereas students are particularly concerned about privacy and ethics related issues, in addition to the accuracy of data about them. We conclude that institutional policy and strategy for LA need to incorporate the perspectives of multi-stakeholders in order to cultivate ownership of LA, meet the needs of different users, and achieve the institutional vision with collective efforts from all the members in the institution. This presentation highlights a people-centered approach to adopting learning analytics. It aims to raise the awareness of tensions between different stakeholders and introduce an evidence-based policy framework that could be useful to anyone who is involved in decisions related to institutional policy and strategy formation.
References
rroway, P., Morgan, G., O’Keefe, M., & Yanosky, R. (2016). Learning Analytics in Higher Education (pp. 1–44). ECAR. Retrieved from https://library.educause.edu/resources/2016/2/learning-analytics-in-higher-education
Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304. https://doi.org/10.1504/IJTEL.2012.051816
Howell, J. A., Roberts, L. D., Seaman, K., & Gibson, D. C. (2018). Are We on Our Way to Becoming a “Helicopter University”? Academics’ Views on Learning Analytics. Technology Knowledge and Learning, 23(1), 1–20. https://doi.org/10.1007/s10758-017-9329-9
Norris, D. M., & Baer, L. L. (2013). Building Organizational Capacity for Analytics (p. 58). Retrieved from https://library.educause.edu/resources/2013/2/building-organizational-capacity-for-analytics
Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. C. (2016). Student attitudes toward learning analytics in higher education: “The fitbit version of the learning world.” Frontiers in Psychology, 7, 1–11. https://doi.org/10.3389/fpsyg.2016.01959
Siemens, G., Dawson, S., & Lynch, G. (2013). Improving the quality and productivity of the higher education sector: policy and strategy for systems-level deployment of learning analytics (pp. 1–35). Society for Learning Analytics Research. Retrieved from http://www.voced.edu.au/content/ngv%3A64739
Tsai, Y.-S., Moreno-Marcos, P. M., Jivet, I., Scheffel, M., Tammets, K., Kollom, K., & Gašević, D. (2018). The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics. Journal of Learning Analytics, 5(3), 5–20. https://doi.org/10.18608/jla.2018.53.2
Yanosky, R. (2009). Institutional Data Management in Higher Education (p. 180). ECAR. Retrieved from https://library.educause.edu/resources/2009/12/institutional-data-management-in-higher-education
