DISTRIBUTED PARAMETER MONITORING FOR WIRELESS DEPLOYMENTS

Using machine learning (ML) to make observations of network operations is faced with many constraints, including collection constraints, storage constraints, and processing constraints. Additionally, in many instances, data collected from a network may be unusable and will incur collection, storage, and processing costs with potentially limited return. Presented herein are techniques through which pre-filtering tasks can be distributed to wireless access points (APs) to highlight valuable metrics and learn from network deployments

Camera-based estimation of student's attention in class

Two essential elements of classroom lecturing are the teacher and the students. This human core can easily be lost in the overwhelming list of technological supplements aimed at improving the teaching/learning experience. We start from the question of whether we can formulate a technological intervention around the human connection, and find indicators which would tell us when the teacher is not reaching the audience. Our approach is based on principles of unobtrusive measurements and social signal processing. Our assumption is that students with different levels of attention will display different non-verbal behaviour during the lecture. Inspired by information theory, we formulated a theoretical background for our assumptions around the idea of synchronization between the sender and receiver, and between several receivers focused on the same sender. Based on this foundation we present a novel set of behaviour metrics as the main contribution. By using a camera-based system to observe lectures, we recorded an extensive dataset in order to verify our assumptions. In our first study on motion, we found that differences in attention are manifested on the level of audience movement synchronization. We formulated the measure of ``motion lag'' based on the idea that attentive students would have a common behaviour pattern. For our second set of metrics we explored ways to substitute intrusive eye-tracking equipment in order to record gaze information of the entire audience. To achieve this we conducted an experiment on the relationship between head orientation and gaze direction. Based on acquired results we formulated an improved model of gaze uncertainty than the ones currently used in similar studies. In combination with improvements on head detection and pose estimation, we extracted measures of audience head and gaze behaviour from our remote recording system. From the collected data we found that synchronization between student's head orientation and teacher's motion serves as a reliable indicator of the attentiveness of students. To illustrate the predictive power of our features, a supervised-learning model was trained achieving satisfactory results at predicting student's attention

Translating Head Motion into Attention - Towards Processing of Student’s Body-Language

Evidence has shown that student's attention is a crucial factor for engagement and learning gain. Although it can be accurately assessed ad-hoc by an experienced teacher, continuous contact with all students in a large class is difficult to maintain and requires training for novice practitioners. We continue our previous work on investigating unobtrusive measures of body-language in order to predict student's attention during the class, and provide teachers with a support system to help them to "scale-up" to a large class. Our work here is focused on head-motion, by which we aim to mimic large-scale gaze tracking. By using new computer vision techniques we are able to extract head poses of all students in the video-stream from the class. After defining several measures about head motion, we checked their significance and attempted to demonstrate their value by fitting a mixture model and training support vector machines (SVM) classifiers. We show that drops in attention are reflected in a decreased intensity of head movement. We were also able to reach 65.72% correct classifications of student attention on a 3-point scale

Student motion and it's potential as a classroom performance metric

Can we predict students attention from a classroom video? We present our system for analysing movement of students in classroom during a lecture. We go into details about the technical side for analysing motion of people with limited visibility, and we present preliminary results which show how the neighbourhood and location in the classroom affect individual movement

Sleepers’ lag - study on motion and attention

Human body-language is one of the richest and most obscure sources of information in inter-personal communication which we aim to re-introduce into the classroom’s ecosystem. In this paper we present our observations of student-to-student influence and measurements. We show parallels with previous theories and formulate a new concept for measuring the level of attention based on synchronization of student actions. We observed that the students with lower levels of attention are slower to react then focused students, a phenomenon we named “sleepers’ lag”

Tracking Multiple Players using a Single Camera

It has been shown that multi-people tracking could be successfullly formulated as a Linear Program to process the output of multiple fixed and synchronized cameras with overlapping fields of view. In this paper, we extend this approach to the more challenging single-camera case and show that it yields excellent performance, even when the camera moves. We validate our approach on a number of basketball matches and argue that using a properly retrained people detector is key to producing the probabilities of presence that are used as input to the Linear Program

System for Assessing Classroom Attention

In this paper we give a preview of our system for automatically evaluating attention in the classroom. We demonstrate our current behaviour metrics and preliminary observations on how they reflect the reactions of people to the given lecture. We also introduce foundations of our hypothesis on peripheral awareness of students during lectures

Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now?

The motivation for this paper is derived from the fact that there has been increasing interest among researchers and practitioners in developing technologies that capture, model and analyze learning and teaching experiences that take place beyond computer-based learning environments. In this paper, we review case studies of tools and technologies developed to collect and analyze data in educational settings, quantify learning and teaching processes and support assessment of learning and teaching in an automated fashion. We focus on pipelines that leverage information and data harnessed from physical spaces and/or integrates collected data across physical and digital spaces. Our review reveals a promising field of physical classroom analysis. We describe some trends and suggest potential future directions. Specifically, more research should be geared towards a) deployable and sustainable data collection set-ups in physical learning environments, b) teacher assessment, c) developing feedback and visualization systems and d) promoting inclusivity and generalizability of models across populations.Nanyang Technological UniversityAccepted versionThis project is supported by a grant from Centre for Research and Development in Learning (CRADLE@NTU)