New digital guidance practices: supporting student engagement with learning analytics
Analytiikka Opetusteknologia opintojen ohjaus Yksilöllinen oppiminen
Paikka: Esityssali 12 -
Puheenjohtaja: Ilomäki Liisa
Ajankohta: 2.12.2021 17.15 - 17.45 (30 minuuttia)
Kohderyhmä: Yliopistot: Asiantuntijat, suunnittelijat, ohjaajat, koordinaattorit

New digital guidance practices: supporting student engagement with learning analytics
Doctoral candidate  at University of Oulu

Muukkonen Hanni
Muukkonen Hanni Professor in Educational Psychology  at University of Oulu

Silvola Anni
Silvola Anni doctoral candidate  at University of Oulu

Academic advising plays a significant role in supporting fluent study paths in the university. Nevertheless, available digital tools do not always support teacher tutors in monitoring students’ study progress in real time, but are  often experienced as time consuming and difficult to use. This study describes a process of developing learning analytics (LA) tools that enable teacher tutors to better monitor the timely progress of university students’ studies and similarly developed guidance practices with designed LA tools. From students’ perspective, development of new visualizations of study progress and guidance practices aim to support students in developing and maintaining their study engagement by facilitating the interaction between a teacher tutor and the student during the guidance situation. Tools for academic advising have been in a minor role in the previous research of LA (Tsai et al., 2018; Charleer et al., 2018).

The development process focuses particularly on capturing the key factors involved in 1) supporting student engagement, planning and timely progress in their studies, 2) teacher tutors’ possibilities to monitor individual and tutored group level indicators of engagement, planning, progress and study success, and use that information for timely guidance, and 3) study program leadership level visualization into these factors. The study examines the guidance of bachelor-degree students as a major development challenge at the institution, especially identifying early indicators for student and teacher tutor interaction with the assistance of unsupervised machine learning and clustering techniques. 

Based on the acquired knowledge from previously conducted user needs research, institutional development of teacher tutors practices and development of visualizations, an experimental study was designed to test the functionality of the teacher tutor tool visualizing individual students’ study progress and study success. In the ongoing pilot study, each participating teacher tutor gives individual academic guidance for 2nd year students with and without the tool (control group). During the structured guidance conversation, the teacher tutor and the student discuss about students’ study progress and study success by utilizing the new visualizations. Pre- and post questionnaires are collected from participants to analyze the understandability and usability of the tool from teacher tutors’ and students’ perspectives, and to understand users with different ICT-self-efficacy beliefs, experience as a teacher tutor, and students with different regulation and monitoring practices. Finally, all participants are interviewed to analyze their experiences from the guidance situation and the use of the tools. 

During the ongoing research, our understanding about the multidisciplinary development process and different issues of designing LA tools has increased. For further research, the results of the pilot study will be used to develop the visualizations and guidance practices further. In the pilot study, the important development objectives are 1) to specify the indicators of monitoring study progress based on the data collected from different guidance situations, study participant experiences and different user profiles, and 2) to map the possible intervention steps to facilitate student engagement in different situations with automatic or agent identified indicators.