Where are You? Exploring Micro-Location in Indoor Learning Environments

Abstract

Classroom teaching methodologies are gradually changing from masterclasses to active learning practices, and peer collaboration emerges as an essential skill to be developed. However, there are several challenges in evaluating collaborative activities more objectively, as well as to generate valuable information to teachers and appropriate feedback to students about their learning processes. In this context, multimodal learning analytics facilitate the evaluation of complex skills using data from multiple data sources. In this work, we propose the use of beacons to collect geolocation data from students who carry out collaborative tasks that involve movement and interactions through space. Furthermore, we suggest new ways to analyze, visualize, and interpret the data obtained. As a first practical approach, we carried out an exploratory, collaborative activity with sixteen undergraduate students working in a library, with bookshelves and work tables monitored by beacons. From the analysis of student movement dynamics, three types of well-differentiated student roles were identified: the collectors, those who go out to collect data from the bookshelves, ambassadors, those who communicate with other groups, and the secretaries, those who stay at their work desk to shape the requested essay. We believe these findings are valuable feedback for the enhancement of the learning activity and the first step towards MMLA-driven Teaching Process Improvement method.

The role of indoor positioning analytics in assessment of simulation‐based learning

“…In contrast, the models outperformed the baseline model regarding each collaboration assessment item (T4–T6) with an acceptable level of differentiation (Hosmer Jr et al, 2013) (AUC of around 0.74). This finding was expected as students' spatial behaviours has been frequently found to contain meaningful insights about their interaction and collaboration in physical learning spaces (Riquelme et al, 2020; Saquib et al, 2018; Yan et al, 2021). Whereas in Scenario B (Table 4), the models performed poorly for every assessment item with low AUC and large variability.…”

Scalability, Sustainability, and Ethicality of Multimodal Learning Analytics

“…Most of the MMLA research (n=83) did not include any information about the ethical issues of the technologies used in MMLA research. Among the 13 studies that have included ethical-related information, two studies [9,60] only briefly mentioned ethical concerns in the introduction or the literature review section but did not elaborate on the ethical implications of the technologies used in the research. In the remaining 11 studies, we have identified three major ethical challenges, including privacy, equality, and risks of bias.…”

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

“…Wearable tracking devices have also demonstrated potentials in generating insights about different types of student interactions [10]. Riquelme et al [40] used beacons to track the movement and interaction patterns of students with other student groups and various objects within a library. Through clustering and pattern analysis, these positioning data provide insights about potential collaborative learning behaviours under experimental conditions.…”

“…These approaches have utilised various forms of technologies and data sources, including WiFi data [35], computer vision algorithms [1,6], thermal sensors [7], and wearable technologies [30,42]. They have also focused on a range of learning spaces, such as small fabrication rooms [9], healthcare simulation rooms [12], the library [40], laboratories [30], regular classrooms [1,42], and lecture rooms [6]. Out of these approaches, physical positioning tacking is one of the promising methods in providing fine-grained data for capturing in-class social interactions because of its high spatial-temporal precision [30].…”