Personalized support of student motivation based on learning analytics

12 april 2023

Educational project

Personalized support of student motivation based on learning analytics

Effectively learning in large university courses is a challenge for both students and teachers. Support from teachers cannot always be offered in the best possible way for each individual student, leaving students to rely on mostly their motivation during courses. This project aims to create a platform for students to take timely tests regarding their understanding of the material and immediately follow-up with helpful notes to improve in underperformed areas. 

Background

Effective learning support in a large university course can be challenging as the link between a teacher and a student becomes very thin (Ward et al., 1992). Teachers may lose their grip on accurate estimations of the overall level of understanding in a class, resulting in students missing necessary guidance and timely feedback. A students’ motivation then becomes a crucial factor: Supporting students’ motivation helps them stay engaged with learning material and resolve some of the learning difficulties on their own. Such a support can come in the form of communicating clear learning goals, informing about the current progress, or personalized feedback. One of the ways to enhance students’ motivation is to inform them about their current level of achievement and its relation to the learning goals. In this project, we plan to explore different methods for such personalized support.

Students will be able to take periodic formative tests in the StudyLens platform. As a result of this assessment, the StudyLens dashboard will display to the students their progress profiles enhanced with personalized navigation cues. Students will be guided by the dashboard to the parts of the curriculum where they underperform and subsequently get access to useful remedial material.

Project description

The project has three main phases: (1) preparation; (2) experiment; (3) data analysis and dissemination. The phases will interleave due to the several experiments planned in Periods 1 and 3 of the 2022/2023 academic year (Figure 1). Such an arrangement will allow us to conduct a preliminary analysis of data after the first experiment and use its result to inform a potential adjustment of the developed dashboard for the 2nd round of experiments. 

Figure 1. Project schedule

During the preparation phase, three main tasks will be completed:

1a) pedagogical preparations will include the development of online assessment and remedial material;
1b) technical preparation will be devoted to the design of the new motivation dashboard for the StudyLens platform and implementation of the learning material developed during task 1a);
1c) experimental preparations will cover writing and approval of the Ethical Review Board (ERB) application and the Data Management Plan (DMP). 

During the experiment phase, the motivation dashboard will be used in three Biology courses. 

During the third phase, data collected from students will be analysed to verify the effect of the developed technology on students’ motivation and learning. In particular, we will examine: students’ activity with the system, students’ grades outside the system and possible changes in students’ motivational profile. 

Aims

The project aims to improve the current teaching / learning practices, as well as develop and facilitate the adoption of a new educational technology. This project is well aligned with the strategic plans of the Faculty with respect to offering high-quality education with the most suitable and quality-enhancing teaching methods. The proposed technology is aimed at supporting students of large courses using methods of blended learning. Students will be guided to the necessary remedial material during the self-regulated learning phase. Personalized guidance supporting students’ motivation and independent learning is an innovative technology that is firmly based on the existing research in this area. Yet, it remains a topic that has not been fully explored. From a practical perspective, personalized learning support is an underused educational technology that has a great potential especially as the modern system of education moves towards wider adoption of distant forms of learning and teaching. 

Results

The desired outcome of the project is a dashboard that can effectively motivate students to engage with supplementary learning material and guide them to the learning resources that help them resolve their learning difficulties. This dashboard can be further used in future instalments of the target three courses. All learning and assessment material as well as the developed software will remain available for the teachers and students of these courses. It can also be used (with some additional work) in other courses taught at the Faculty. 

We expect to observe that students in general will become more motivated to work with remedial learning material, will choose the right learning material effectively and score higher on course exams. The proposed technology also has a potential to further inform a teacher about potential problems that students experience in her/his course. 

 Project results will be disseminated on two levels. Local dissemination will seek to promote the developed technology among UU teachers and facilitate its wider uptake in real classrooms. Academic dissemination will inform the international community about the results of educational research conducted at UU. As the project involves two PhD students, active in the field of Research on Education, it will be very important for the project team to disseminate its results in the form of academic publications. It will target international research community in the field of learning analytics and educational technology. We consider such conferences  as LAK (https://www.solaresearch.org/events/lak/), EC-TEL (https://ea-tel.eu/ec-tel-conference) and AIED (https://iaied.org/).

References

Ward, Andrew, and Alan Jenkins (1992). “The problems of learning and teaching in large classes.” Teaching large classes in higher education: How to maintain quality with reduced resources: 23-36. 

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