Analysis of interactions between lecturers and students

Watanabe, Eiji; Ozeki, Takashi; Kohama, Takeshi

doi:10.1145/3170358.3170360

Cited by 16 publications

(7 citation statements)

References 4 publications

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“…Facial features based on local binary pattern and bag-of-words are extracted from a video captured when a subject is doing different tasks, such as watching movie clips, reading texts, interviews, etc., and a support vector machine is used to predict presence of melancholia in subject. Watanabe et al analysed facial region pixels to get interactions between students and teachers using a neural network in a classroom environment [21].…”

Section: Related Workmentioning

confidence: 99%

Learners’ Efficiency Prediction Using Facial Behavior Analysis

Verma

Nakashima

Kobori

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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In the e-learning context, how much the learner is concentrated and engaged, or the learners' efficiency, is essential for providing adaptive and flexible materials, timely suggestions, etc., which can lead to efficient learning. In this work, we explore to predict learners' efficiency with a realistic configuration, in which we use a webcam or a laptop PC's built-in camera. Specifically, we first provide a feasible definition of the learners' efficiency, and based on this definition, we predict one's efficiency from facial behavior. We predict the learners' efficiency using various convolutional neural networks. Results are discussed using different evaluation metrics.

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Section: Related Workmentioning

confidence: 99%

Learners’ Efficiency Prediction Using Facial Behavior Analysis

Verma

Nakashima

Kobori

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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“…There has been a growing interest in exploring physical aspects of the classroom [16]. For example, authors have used automated video analysis to model students' posture [45] and gestures [1], teacher's walking [10], interactions between teachers and students [1,53] during a lecture, and characterising the types of social interactions of students in makerspaces [15]. Wearable sensors have also been used to track teachers' orchestration tasks by using a combination of sensors (eye tracker, accelerometer, and a camera) [44] and students' mobility strategies while working in teams in the contexts of primary education [48], healthcare simulation [18] and firefighting training [51].…”

Section: Spatial Analysis and Positioning Technology In The Classroommentioning

confidence: 99%

“…There is a growing interest in using novel sensing technologies (e.g. wearable and computer vision systems) to automatically analyse classroom activity traces to model behaviours such as engagement [30], teacher-student interactions [10] and students' physical activity [1,53]. Previous research has found that teachers' positioning in the classroom and proximity to students can strongly influence critical aspects such as students' engagement [14], motivation [19], disruptive behaviour [27], and self-efficacy [31] (see review in [43]).…”

Section: Introductionmentioning

confidence: 99%

Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

Martínez-Maldonado

Echeverria

Schulte

et al. 2020

Lecture Notes in Computer Science

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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.

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“…Instructional quality [5][6][7], [12], [17], [23], [49], [50], [59], [61] Modelling of teacher actions [33], [42], [43] Classroom Teacher-student rapport [4], [12], [22]. [45], [46], [49], [50], [61] Modelling classroom activities [9], [13], [15], [16], [38], [42], [43], [59], [60]…”

Section: Types Of Outcomes Referencesmentioning

confidence: 99%

Technologies for automated analysis of co-located, real-life, physical learning spaces

Chua

Dauwels

Tan³

2019

Proceedings of the 9th International Conference on Learning Analytics &Amp; Knowledge

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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. CCS CONCEPTS• Human-centred computing~Visualization design and evaluation methods •Information systems~Data analytics KEYWORDSFace-to-face classroom analysis, co-located learning, physical learning analytics, educational data mining, educational technologies We utilized a three-pronged approach to select studies for review. First, relevant journals and conference proceedings, such as journals and proceedings of known conferences targeted at showcasing advancements and innovation in the field of learning sciences and learning analytics, were shortlisted and searched using a targeted search strategy. We include the following journals:

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Analysis of interactions between lecturers and students

Cited by 16 publications

References 4 publications

Learners’ Efficiency Prediction Using Facial Behavior Analysis

Learners’ Efficiency Prediction Using Facial Behavior Analysis

Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

Technologies for automated analysis of co-located, real-life, physical learning spaces

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