Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

Abstract

This paper presents Moodoo, a system that models how teachers make use of classroom spaces by automatically analysing indoor positioning traces. We illustrate the potential of the system through an authentic study aimed at enabling the characterisation of teachers' instructional behaviours in the classroom. Data were analysed from seven teachers delivering three distinct types of classes to + 190 students in the context of physics education. Results show exemplars of how teaching positioning traces reflect the characteristics of the learning designs and can enable the differentiation of teaching strategies related to the use of classroom space. The contribution of the paper is a set of conceptual mappings from x − y positional data to meaningful constructs, grounded in the theory of Spatial Pedagogy, and its implementation as a composable library of open source algorithms. These are to our knowledge the first automated spatial metrics to map from low-level teacher's positioning data to higher-order spatial constructs.

Footprints at School: Modelling In-class Social Dynamics from Students’ Physical Positioning Traces

“…For example, ClassBeacons [3] deployed tracking devices to teachers and tables around a classroom to model the time teachers spent with each group of students. Similar work is shown in Moodoo [29], an automatic system that models teachers' positioning strategies in a classroom. The authors demonstrated that these tracking devices can be used to generate physical positioning analytics in various learning settings, including collaboration, simulation, and laboratory learning spaces [30].…”

“…Positioning tracking often suffers from missing values due to occlusion or detachment of trackers [15]. Therefore, linear interpolation was used to fill in any missing values between two valid data points, and with a limit of 60 consecutive missing values [29]. For a meaningful attendance, students' positioning must not be missing for more than ten minutes; otherwise, they are excluded from the analysis for that session since it most likely meant that the student did not come to session and their tracker was put away or they were outside of the tracking area during the period, and therefore not attending the session.…”

“…For example, Q1 includes sessions 1-6, and Q2 includes sessions 7-13, and so on. This segmentation enabled capturing how students' social interaction developed over the eight weeks while facilitating interpretation, as it will be demonstrated in the next section [29]. Second, we calculated each students' average weighted degree centrality for each quadrant, resulting in four features for the clustering analysis.…”

Explorations of Designing Spatial Classroom Analytics with Virtual Prototyping

“…Current learning analytics (LA) research has identified that classroom analytics can support learning outcomes and teacher awareness [1,4,14,17,19]. These studies have investigated the importance of analyzing the interactions between the teacher, students, and the space during classroom instruction.…”

“…These studies have investigated the importance of analyzing the interactions between the teacher, students, and the space during classroom instruction. The resulting analyses can provide meaningful feedback to teachers by making teacherstudents' interactions visible [1,5,17], improving teachers' awareness and directing attention to struggling students [4,12,13], or giving early alerts about students' progress and engagement [5,9,13].…”