Research project
Advisory dashboards: Using learning analytics to explore higher educations students’ online and offline feedback processes
Learning analytics tracks students’ online learning activities in order to support their learning. But what if most of their learning takes place offline, out of sight of digital eyes?
- Duration
- 2021 - 0
- Contact
- Arjen de Vetten
Researchers
- Dr. A.J. de Vetten
- dr. A.A.J. van den Beemt (TU Eindhoven)
- Prof.dr. N. Saab
Social relevance
Nowadays, technological tools allow students to engage in online learning. In higher education institutions, many courses adopt blended learning: online activities, such as educational videos and quizzes, and offline learning activities, such reading, making assignments and classes, are combined. One example is formative prior knowledge tests, in which students test their prior knowledge using a formative test. Learning analytics can be used to construct advisory dashboards that students with their results and personalized and actionable feedback information, suggesting how to remedy any knowledge deficiencies.
Scientific relevance
Various studies have shown significant positive effects of actionable feedback information on course engagement and course grades. What previous studies did not consider are students’ offline learning activities. Our previous research suggests many students prefer to employ offline learning activities, such as taking and reviewing notes.
Topic and research questions
We investigate both the online and offline learning activities of students in response to actionable feedback information provided in advisory dashboards.
The guiding research question is: What are the effects of personalized actionable feedback, based on the results of a formative prior knowledge test, on higher education students’ online and offline learning activities to remedy knowledge deficiencies?
Material and methods
We conducted two studies. In both studies, students enrolled in a second-year law course took a prior knowledge test and received personalized actionable feedback information, shown on an advisory dashboard. The dashboard suggested which knowledge clips and quizzes could be used to remediate their prior knowledge. The second study included a quasi-experimental setup, comparing students who did and not receive personalized recommendations. We used digital trace and questionnaire data to investigate the relationship between students’ test results and online and offline remediation activities in response to the actionable feedback information. Interviews were used to explore students’ reasons for their remediation activities.
Results and conclusions
We found no significant relationship between actionable feedback information and remediation activities, and whether. However, students who took the test engaged more often in remediation activities than those who did not, suggesting the test and feedback information primarily had a signalling function. Furthermore, more students used offline remediation activities than online remediation activities to remediate their prior knowledge. Including offline learning data in addition to online learning data thus provides a more comprehensive picture of feedback processes.
Follow-up
The findings call for further studies on students’ offline learning activities in response to personalized actionable feedback.