EXECUTE: Exploring Eye Tracking to Support E-learning

Khan, Ahsan Raza; Khosravi, Sara; Hussain, Sajjad; Ghannam, Rami; Zoha, Ahmed; Imran, Muhammad

doi:10.1109/educon52537.2022.9766506

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…Examples of commercial devices include Microsoft’s Hololens [ 89 ]. In addition to the range of wearable sensors that currently exist for the learning enhancement, wearable eye trackers, enabling them to record subtle eye movements in different head movements and directions [ 90 , 91 ].…”

Section: Results and Discussionmentioning

confidence: 99%

Wearable Sensors for Learning Enhancement in Higher Education

Khosravi

Bailey

Parvizi

et al. 2022

Sensors

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Wearable sensors have traditionally been used to measure and monitor vital human signs for well-being and healthcare applications. However, there is a growing interest in using and deploying these technologies to facilitate teaching and learning, particularly in a higher education environment. The aim of this paper is therefore to systematically review the range of wearable devices that have been used for enhancing the teaching and delivery of engineering curricula in higher education. Moreover, we compare the advantages and disadvantages of these devices according to the location in which they are worn on the human body. According to our survey, wearable devices for enhanced learning have mainly been worn on the head (e.g., eyeglasses), wrist (e.g., watches) and chest (e.g., electrocardiogram patch). In fact, among those locations, head-worn devices enable better student engagement with the learning materials, improved student attention as well as higher spatial and visual awareness. We identify the research questions and discuss the research inclusion and exclusion criteria to present the challenges faced by researchers in implementing learning technologies for enhanced engineering education. Furthermore, we provide recommendations on using wearable devices to improve the teaching and learning of engineering courses in higher education.

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Section: Results and Discussionmentioning

confidence: 99%

Wearable Sensors for Learning Enhancement in Higher Education

Khosravi

Bailey

Parvizi

et al. 2022

Sensors

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“…The bell rang at regular intervals, and participants had to press the feedback button to report distractions. The details of EXECUTE interface and data collection process are given in our previous study [10]. This work proposes a feature engineering mechanism for attention classification of students in a remote learning experiment using gaze metric fixations and saccades with the time-bounded operation.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…attentive and non-attentive. The details on data labelling process and obtaining the attention score matrix are discussed in our previous study [10].…”

Section: A Data Processing and Feature Engineeringmentioning

confidence: 99%

Feature Selection Mechanism for Attention Classification using Gaze Tracking Data

Khan

Bokhari

Khosravi

et al. 2022

2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

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The Covid-19 outbreak has caused disruptions in the education sector, making remote education the dominant mode for lecture delivery. The lack of visual feedback and physical interaction makes it very hard for teachers to measure the engagement level of students during lectures. This paper proposes a time-bounded window operation to extract statistical features from raw gaze data, captured in a remote teaching experiment and link them with the student's attention level. Feature selection or dimensionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores. The model trained using the optimal feature subset showed significant improvement in accuracy and computational complexity. For instance, a support vector classifier (SVC) led 2.39% improvement in accuracy along with approximately 66% reduction in convergence time.

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“…For this purpose, they adopted a multimodal approach, thus using information related to facial expression, eye tracking, and mouse dynamics. Students' attention during learning from online learning materials is of great interest to Robal et al (2018), Khan et al (2022) and Madsen et al (2021). Khan et al (2022) reacted to the pandemic situation and the subsequent conversion of in-person lectures to an e-learning environment.…”

Section: Exploring Educational Horizons: Webcam Eye Trackers and Lear...mentioning

confidence: 99%

A Scoping Review of Webcam Eye Tracking in Learning and Education

Dostálová,

Plch

2024

Studia paedagogica

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The use of eye tracking in educational research has shown great potential in recent decades. There are various approaches to the usage of eye tracking technology in this area, including investigation of self-regulated learning from different types of learning environments. Nonetheless, the majority of published research studies have one tremendous limitation: using expensive remote or tower-based eye trackers to provide high-quality data in laboratory conditions. Now, new webcam eye trackers may offer an easily affordable approach allowing eye tracking measurements in the real environment, such as the investigation of learning behavior in online learning environments. The main aim of this scoping review is to explore the use of webcam eye tracking technology in the field of learning and education. We established three specific purposes: 1) to introduce educational topics being explored using webcam eye tracking, 2) to discuss the methodological aspects when exploring educational topics with webcam eye tracking, and 3) to investigate the eye tracking aspects used for the analysis. To do this, we analyzed 16 studies that used webcam eye tracking. The results of the scoping review show that 1) selected studies focus mainly on students’ behavior in online learning environments, such as engagement, lack of attention, cheating and others; 2) a wide range of studies aimed at the development of automatized detection tools; and 3) studies are mainly focused on extracting raw and event data features using them mostly for automatized detection purposes.

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EXECUTE: Exploring Eye Tracking to Support E-learning

Cited by 12 publications

References 13 publications

Wearable Sensors for Learning Enhancement in Higher Education

Wearable Sensors for Learning Enhancement in Higher Education

Feature Selection Mechanism for Attention Classification using Gaze Tracking Data

A Scoping Review of Webcam Eye Tracking in Learning and Education

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