Videolesson: Interventions based on learning analytics

Watch the video lecture on the application of insights from learning analytics and educational interventions.

 Transcript

	Welcome! Learning Analytics fulfills its purpose when it is used to improve teaching and learning. In this lesson, we show how learning analytics leads to specific educational interventions, those that truly make a difference for students. To begin, let us recall that learning analytics is a cyclical process. The cycle starts with data collection continues through analysis and interpretation, and ends with intervention, but it does not stop there as the cycle begins again, intervention is the key phase, and this refers to a targeted planned change in teaching and learning processes or in support mechanisms based on insights derived from the data. An example of such a cycle is shown in a study by Divjak and colleagues where analytics is used to evaluate alignment between assessment and learning outcomes as part of continuous improvement. The model includes strategic planning and learning design with prioritization of learning outcomes, monitoring and evaluation supported by learning analytics and the implementation of educational interventions. Learning analytics focuses on comparing learning outcomes, priorities, assessment programmes, and actual student results from such analytics. The need for interventions in learning design can be identified. For instance, a discrepancy between student achievement for a specific learning outcome, and the total number of points allocated to that outcome in the assessment program may indicate several issues requiring intervention. It may suggest that students struggle to achieve certain learning outcomes, do not receive enough feedback, in formative assessment, that learning activities are not appropriate for the outcome or that adequate learning resources are lacking. This can serve as a basis for interventions such as placing greater emphasis on a particular learning outcome, in course design and delivery, or revising the assessment program to ensure better alignment with learning outcomes. Interventions may also target the design and relevance in individual learning activities. For example, if analytics show very low participation in a discussion forum, the teacher might introduce interventions such as formulating clearer questions or better explaining the purpose of the activity. Analytics shows that many students make the same mistake in a particular type of task or related to the same concept. This may signal that additional explanation is needed. An intervention might include, for example, adding a new video lesson or providing further examples. If a student is inactive, misses deadlines, or shows a sudden drop in performance, the teacher might send a personalized message or speak directly with the student. In this way, the teacher offers pedagogical support, which can strengthen the student's sense of belonging and encourage responsibility. If analytics shows that students have difficulty submitting assignments on time, it may be worth considering whether deadlines are too clustered or overlapping. Interventions might include redistributing deadlines more evenly or allowing extensions. Educational interventions are a key responsibility of teachers. It is their role to interpret learning analytics and respond based on educational context, pedagogical expertise, and student needs. It is essential to recognize that without proper contextualisation, learning analytics can lead to incorrect interpretations and conclusions. Teachers explain analytics results to students decide on specific interventions in learning and teaching, such as changes in learning activities, and reflect on their courses. However, the responsibility of higher education institutions must also be emphasized, particularly in providing appropriate resources and tools for learning analytics, enabling teachers to develop the skills needed for meaningful use of analytics and applying learning analytics at the institutional level. When learning analytics is available and students receive support in interpretation, students themselves are responsible for reflecting on their learning, practicing self-regulation, adjusting their learning habits, and seeking help when needed. Successful application of Learning Analytics as the basis for educational interventions and ultimately for better achievement of learning outcomes is a collaborative effort involving multiple stakeholders. However, as teachers, we must not forget our central role and the opportunity Learning analytics offers to improve teaching and learning for the benefit of students.