Higher Education Students’ Perspectives on Learning Analytics Use as Support for Academic Paths
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Linnanmaa, auditorium L2
Topic of the dissertation
Higher Education Students’ Perspectives on Learning Analytics Use as Support for Academic Paths
Doctoral candidate
Master of Education Anni Silvola
Faculty and unit
University of Oulu Graduate School, Faculty of Education and Psychology, Learning and Learning Processes
Subject of study
Educational Psychology
Opponent
Professor Dirk Ifenthaler, University of Mannheim
Custos
Professor Hanni Muukkonen, University of Oulu
Higher education students identify a lot of potential in the use of learning analytics as a support for study paths
Anni Silvola's doctoral research in educational psychology examined the perspectives of higher education students on the utilization of learning analytics to support their study paths. The research addressed that the automatic feedback generated with learning analytics can be effectively used to support the study paths of higher education students in various ways.
For students, the fluent mediation of information between stakeholders, awareness of themselves as learners and the development of their professional skills, solution-oriented insights on their study situation, making their individual options on study paths visible with the data emerged as the four key elements of learning analytics use as a support for their engagement and learning on study paths. Ethical considerations are in a significant role in the design and implementation of learning analytics. From students’ perspectives, the themes of student agency and participation, privacy, transparency, and their readiness to use provided automatic feedback emerged as key themes in this study.
The study address timely findings for the discussion of the applications of artificial intelligence and data-analytics in the field of education. Learning analytics can provided personalized support for learners, enabling them to improve their capacities as learners. Such tools are particularly needed in the context of higher education, where increasing student intakes and the practices of hybrid learning and teaching cause new needs to provide adequate support for students in different life situations. Previous studies have identified a need to better understand students’ perspectives on learning analytics to ensure that the provided information is meaningful and actionable for students with different support needs.
This doctoral study was conducted with mixed-methods approach, focusing on students’ support needs and evaluations of learning analytics. The study identified that students’ self-efficacy beliefs and help-seeking skills may influence on the perceived value of provided support.
The results of this study provide timely information on the utilization and the design of data-driven technologies as a support for learning. The findings are valuable in planning technology-enhanced learning and teaching in the context of higher education and they can provide meaningful information for teachers, academic advisors to develop new technology-enhanced practices in education.
For students, the fluent mediation of information between stakeholders, awareness of themselves as learners and the development of their professional skills, solution-oriented insights on their study situation, making their individual options on study paths visible with the data emerged as the four key elements of learning analytics use as a support for their engagement and learning on study paths. Ethical considerations are in a significant role in the design and implementation of learning analytics. From students’ perspectives, the themes of student agency and participation, privacy, transparency, and their readiness to use provided automatic feedback emerged as key themes in this study.
The study address timely findings for the discussion of the applications of artificial intelligence and data-analytics in the field of education. Learning analytics can provided personalized support for learners, enabling them to improve their capacities as learners. Such tools are particularly needed in the context of higher education, where increasing student intakes and the practices of hybrid learning and teaching cause new needs to provide adequate support for students in different life situations. Previous studies have identified a need to better understand students’ perspectives on learning analytics to ensure that the provided information is meaningful and actionable for students with different support needs.
This doctoral study was conducted with mixed-methods approach, focusing on students’ support needs and evaluations of learning analytics. The study identified that students’ self-efficacy beliefs and help-seeking skills may influence on the perceived value of provided support.
The results of this study provide timely information on the utilization and the design of data-driven technologies as a support for learning. The findings are valuable in planning technology-enhanced learning and teaching in the context of higher education and they can provide meaningful information for teachers, academic advisors to develop new technology-enhanced practices in education.
Last updated: 23.1.2024