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Automatic student engagement measurement using machine learning techniques: A literature study of data and methods

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Abstract

Student engagement is positively related to learning outcomes. The student engagement measurement is studied in varied settings, from the traditional classroom to online learning. Artificial intelligence and machine learning advancements have fueled automatic student engagement analysis. The automated student engagement measurement employed several sensor data such as audio, video, and physiological signals in different settings. This paper presents a literature review of automatic student engagement measurement in the classroom and online learning settings, including data collection and annotation techniques, methods, and evaluation metrics. First, a generalized methodology for automatic student engagement analysis is discussed. Then we describe various data collection techniques and annotation methods widely used in the literature and detail the limitations and advantages. The state-of-the-art machine learning methods and the evaluation metrics used to test those methods are reviewed. Additionally, we extend our literature review to the insight into the existing datasets for evaluating the automatic student engagement methods and recent developments in the machine learning methods on open-source datasets. Finally, we present a comprehensive comparison of the methods proposed on various public datasets based on evaluation metrics and engagement types.

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Fig. 1
Fig. 2

Data availability

The datasets analysed during the current study are not publicly available but are available from the respective corresponding author on reasonable request.

Abbreviations

MOOCs:

Massive Open Online Courses

SOTA:

State-of-the-Art

BPMS:

Body Pressure Measurement System

DAiSEE:

Dataset for Affect States in E-Environment

DT:

Decision Tree

SVM:

Support Vector Machine

KNN:

K-Nearest Neighbors

LMS:

Learning Management System

PCA:

Principal Component Analysis

GBT:

Gradient Boosting Trees

LR:

Logistic Regression

AIC:

Akaike Information Criteria

HMM:

Hidden Markov Model

MLR:

Multinomial logistic regression

CERT:

Computer Expression Recognition Toolbox

2AFC:

2-Alternative Forced-Choice

ANU:

Animation Unit

LBP-TOP:

Local Binary Pattern in Three Orthogonal Planes

UNB:

Updateable Naïve Bayes

BN:

Bayes Nate

RF:

Random Forest

DL:

Deep Learning

SPOC:

Small Private Online Course

SURF:

Speeded Up Robust Features

BOF:

Bag of Features

YFD:

Yale Face Dataset

AAM:

Active Appearance Model

FDDB:

Face Detection Dataset and Benchmark

LFW:

Labeled Faces in the Wild

NN:

Neural Network

BROMP:

Baker Rodrigo Ocumpaugh Monitoring Protocol

MLP:

Multilayer perceptron

SVR:

Support Vector Regression

LSTM:

Long Short-Term Memory

CNN:

Convolutional Neural Network

YOLO:

You Only Look Once

FER:

Facial Expression Recognition

HBCU:

Historically Black College/university

EmotiW:

Emotion Recognition in the Wild

Bi-GRU:

Bidirectional Gated Recurrent Unit

MTCNN:

Multi-Task Cascaded Convolutional Neural Networks

DFSTN:

Deep Facial Spatiotemporal Network

DBN:

Deep Belief Network

LDP:

Local Directional Pattern

ML:

Machine Learning

SIFT:

Scale-Invariant Feature Transform

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  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India

    Sandeep Mandia, Rajendra Mitharwal & Kuldeep Singh

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Mandia, S., Mitharwal, R. & Singh, K. Automatic student engagement measurement using machine learning techniques: A literature study of data and methods. Multimed Tools Appl 83, 49641–49672 (2024). https://doi.org/10.1007/s11042-023-17534-9

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