REAL-TIME LEARNING ANALYTICS FOR FACE-TO-FACE LESSONS

Abstract

Even though the use of digital technology and e-learning has grown over the years, most of the time spent in schools around the world is still in face-to-face lessons. Traditional classroom teaching encounters fundamental constraints like the difficulty faced by one educator to track the understanding of a group of learners. Numerous tools exist to help educators but they are mostly detached from the actual teaching and learning materials, and hence necessitate a breaking away from the flow of the lesson to collect, visualize and understand the data collected. In this paper, we present a real-time learning analytics system that can provide both educators and learners with a real-time view of the data collected from learners’ interaction with a mobile-optimized lesson embedded in a learning management system and accessible via mobile phones or computers. Data collection and visualization are automated and achieved with no friction to the flow of the lesson. The educator could use the data to keep track of individual students’ responses, as well as moderate the pace of the whole class. Action research was done on a total of four classes of students to test the benefits of using the real-time learning analytics system. Quantitative sentiment feedback was collected and the number of targeted interventions by the educators was recorded. Targeted interventions are defined as moments when the educator spot a learning gap or misconception and intervene immediately to address the issue. Both categories of data captured showed positive results for the use of real-time learning analytics in the classroom. The system has the potential to be used in any domain as it is domain-neutral and built on open-source technology. Usage of the system does not require much technical know-how, and the lessons created can be easily exported into any major Learning Management Systems (LMSs).