Detecting Learner Engagement in MOOCs using Automatic Facial Expression Recognition

Abhilash, Dubbaka; Gopalan, Anandha

doi:10.1109/educon45650.2020.9125149

Cited by 22 publications

(22 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data collected via these approaches are employed in modeling and designing adaptive learning environments that will support student learning. A variety of studies (Monkaresi et al, 2017 ; Dubbaka and Gopalan, 2020 ; Erkan et al, 2020 ; Raj and Renumol, 2022 ) reported many students' online learning behaviors and physiological responses that can be extracted and applied in building models that will automatically predict students' engagement levels. The studies reported the accuracy of their models to indicate the extent to which engagement levels could be dynamically detected using their approach.…”

Section: Discussionmentioning

confidence: 99%

“…Hussain et al ( 2018 ) developed a dashboard which incorporated a model of student engagement built using their interaction data, grade scores, and machine learning into an online education system to help instructors in assessing student engagement levels in online courses in relation to various activities and resources, and to offer additional interventions for students prior to their final exam. Dubbaka and Gopalan ( 2020 ) reported an accuracy of 95% for the prediction of student engagement using facial expression data and convolutional neural network model. The research achieved the best performance in terms of accuracy among all the reviewed papers that predicted student engagement levels using physiological data.…”

Section: Discussionmentioning

confidence: 99%

“…Based on this literature review, the most predominant approach for detecting engagement using physiological data is facial expression tracking. A number of studies (Monkaresi et al, 2017 ; Dubbaka and Gopalan, 2020 ; Mohamad Nezami et al, 2020 ; Altuwairqi et al, 2021 ) analyzed student engagement during interaction with online educational systems using facial expressions. The studies focused on capturing and analyzing facial expressions to identify disengagement and engagement states of students.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Orji

Vassileva²

2022

Front. Artif. Intell.

View full text Add to dashboard Cite

Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of student characteristics in online educational systems has led to application of machine learning methods in modeling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modeling of various student characteristics. The review found six student characteristics that can be modeled automatically and highlighted the data types, collection methods, and machine learning techniques used to model them. Researchers, educators, and online educational systems designers will benefit from this study as it could be used as a guide for decision-making when creating student models for adaptive educational systems. Such systems can detect students' needs during the learning process and adapt the learning interventions based on the detected needs. Moreover, the study revealed the progress made in the application of machine learning for automatic modeling of student characteristics and suggested new future research directions for the field. Therefore, machine learning researchers could benefit from this study as they can further advance this area by investigating new, unexplored techniques and find new ways to improve the accuracy of the created student models.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Orji

Vassileva²

2022

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…ExpresSense has been developed for Android Platforms, using Android Studio library. Finally, the trained model is uploaded to the Heroku 10 platform and connected to the smartphone application using an intermediate FLASK-API 11 . It is to be noted that the entire signal processing part is executed locally in the user's smartphone and only the numeric values of generated amplitude and phase are sent to the remote server for being predicted.…”

Section: Implementation Apparatusmentioning

confidence: 99%

ExpresSense: Exploring a Standalone Smartphone to Sense Engagement of Users from Facial Expressions Using Acoustic Sensing

Kar¹,

Shyamvanshikumar²,

Mandal³

et al. 2023

Preprint

View full text Add to dashboard Cite

Facial expressions have been considered a metric reflecting a person's engagement with a task. While the evolution of expression detection methods is consequential, the foundation remains mostly on image processing techniques that suffer from occlusion, ambient light, and privacy concerns. In this paper, we propose ExpresSense, a lightweight application for standalone smartphones that relies on near-ultrasound acoustic signals for detecting users' facial expressions. ExpresSense has been tested on different users in lab-scaled and large-scale studies for both posed as well as natural expressions. By achieving a classification accuracy of ≈ 75% over various basic expressions, we discuss the potential of a standalone smartphone to sense expressions through acoustic sensing.

show abstract

“…Nonetheless, several works focused on uncovering the relationship between attention and specific action units (AUs), i.e., relaxation or contraction of one or more facial muscles [23], while others directly dealt with basic emotions. Dubbaka et al [24] explored the usage of cameras to monitor attention of students in massive open online courses (MOOCs). Roohi et al [25] introduced a deep learning-based methodology to analyze players' facial expressions and verify that neural networks, trained with the common six basic emotions, could link the brief moments of intense concentration required to kill enemies to the expression of anger.…”

Section: Introductionmentioning

confidence: 99%

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

et al. 2021

View full text Add to dashboard Cite

Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques.

show abstract

Detecting Learner Engagement in MOOCs using Automatic Facial Expression Recognition

Cited by 22 publications

References 40 publications

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

ExpresSense: Exploring a Standalone Smartphone to Sense Engagement of Users from Facial Expressions Using Acoustic Sensing

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

Contact Info

Product

Resources

About