Student Concentration Evaluation Index in an E-learning Context Using Facial Emotion Analysis

Sharma, Prabin; Esengönül, Meltem; Khanal, Salik Ram; Khanal, T; Filipe, Vítor; Reis, Manuel J. C. S.

doi:10.1007/978-3-030-20954-4_40

Cited by 26 publications

(14 citation statements)

References 17 publications

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“…An FER analysis has been carried out to monitor driver's mood during the whole experiment. As mentioned in the previous Section, in order to identify the concentration through a FER methodology, some works ( [22,26]) suggest to identify the neutral expression. Furthermore, the present work also considered the assumption that emotion detection can reveal driver's distraction with particular criticality in the presence of anger, sadness, and emotional agitation, as stated by Dingus et al [30] in their work about crash risk factors, and, on the contrary, the concentration can be associated with the neutral expression.…”

Section: Methodsmentioning

confidence: 99%

“…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. Sharma et al [26] proposed a system to figure out the concentration level of students in front of a webcam, identifying concentration with the neutral facial expression. Furthermore, according to Kowalczuk et al [22], emotions are suppressed over time, hence emotion detection can reveal distraction in specific scenarios.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Những nghiên cứu liên quan đến cảm xúc, có thể kể đến, Turabzadeh và cộng sự [12] dựa trên nhận dạng cảm xúc khuôn mặt trong thời gian thực, sử dụng thuật toán mẫu nhị phân cục bộ (Local Binary Point -LBP), Kamath, Biswas và Balasubramania [13] sử dụng thuật toán phát hiện khuôn mặt Vilola Jones để phân tích hình ảnh đầu vào và sau đó là biểu đồ phân định hướng (HOG, Sharma và cộng sự đã đề xuất một hệ thống thời gian thực, dựa trên những biểu hiện cảm xúc trên khuôn mặt, để kiểm tra sự tham gia của học sinh trong bối cảnh học tập điện tử, tự động điều chỉnh nội dung theo mức độ tập trung của học sinh, bằng cách phân tích cảm xúc của học sinh và đưa ra ba mức độ tập trung khác nhau (cao, trung bình và thấp) [14].…”

Section: Giới Thiệuunclassified

Một Kỹ Thuật Đánh Giá Sự Tập Trung Của Sinh Viên Dựa Trên Trạng Thái Đóng/Mở Của Mắt

Phương¹,

Lăng²,

Toàn³

2021

TNUJST

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Phân tích học tập tự động đang trở thành một chủ đề thiết yếu trong lĩnh vực giáo dục, trong đó cần các hệ thống giám sát quá trình học tập và đo lường mức độ tham gia của sinh viên để giúp cho giáo viên có những điều chỉnh hợp lý hơn về chiến lược giảng dạy và môi trường học tập. Bài báo này đề xuất một kỹ thuật ước lượng mức độ buồn ngủ dựa trên trạng thái đóng/mở của mắt để đánh giá sự tập trung của sinh viên trong giờ học. Dữ liệu đầu vào của thuật toán được ghi nhận bởi camera trong lớp học và việc phân tích đánh giá được thực hiện trên từng sinh viên trong suốt quá trình học tập. Kỹ thuật đã được cài đặt thử nghiệm và tỏ ra hiệu quả đối với các lớp học lý thuyết với quy mô trung bình của các ngành học kỹ thuật.

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“…Being alone in front of a screen in an online learning environment is a huge challenge for students. A prototype was proposed by Sharma et al (2018) to determine students' attention level in real-time by using their facial expressions during an online lecture. They experimented with testing the framework of the prototype.…”

Section: Facial Coding and Learning Performancementioning

confidence: 99%

Enhancing the Motivation and Learning Performance in an Online Classroom With the Use of Neuromarketing

Šola¹,

Qureshi

Khawaja³

2021

EJMMS

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In recent years, the newly emerging discipline of neuromarketing, which employs brain (emotions and behaviour) research in an organisational context, has grown in prominence in academic and practice literature. With the increasing growth of online teaching, COVID-19 left no option for higher education institutions to go online. As a result, students who attend an online course are more prone to lose focus and attention, resulting in poor academic performance. Therefore, the primary purpose of this study is to observe the learner's behaviour while making use of an online learning platform. This study presents neuromarketing to enhance students' learning performance and motivation in an online classroom. Using a web camera, we used facial coding and eye-tracking techniques to study students' attention, motivation, and interest in an online classroom. In collaboration with Oxford Business College's marketing team, the Institute for Neuromarketing distributed video links via email, a student representative from Oxford Business College, the WhatsApp group, and a newsletter developed explicitly for that purpose to 297 students over the course of five days. To ensure the research was both realistic and feasible, the instructors in the videos were different, and students were randomly allocated to one video link lasting 90 seconds (n=142) and a second one lasting 10 minutes (n=155). An online platform for self-service called Tobii Sticky was used to measure facial coding and eye-tracking. During the 90-second online lecture, participants' gaze behaviour was tracked overtime to gather data on their attention distribution, and emotions were evaluated using facial coding. In contrast, the 10-minute film looked at emotional involvement. The findings show that students lose their listening focus when no supporting visual material or virtual board is used, even during a brief presentation. Furthermore, when they are exposed to a single shareable piece of content for longer than 5.24 minutes, their motivation and mood decline; however, when new shareable material or a class activity is introduced, their motivation and mood rise. JEL: I20; I21 Article visualizations:<img src="/-counters-/edu_01/0805/a.php" alt="Hit counter" />

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Student Concentration Evaluation Index in an E-learning Context Using Facial Emotion Analysis

Cited by 26 publications

References 17 publications

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

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

Một Kỹ Thuật Đánh Giá Sự Tập Trung Của Sinh Viên Dựa Trên Trạng Thái Đóng/Mở Của Mắt

Enhancing the Motivation and Learning Performance in an Online Classroom With the Use of Neuromarketing

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