Bibliografias temáticas / Student Engagement Detection

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Literatura científica selecionada sobre o tema "Student Engagement Detection"

Autor: Grafiati

Publicado: 25 de janeiro de 2025

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Artigos de revistas sobre o assunto "Student Engagement Detection"

Bustos-López, Maritza, Nicandro Cruz-Ramírez, Alejandro Guerra-Hernández, Laura Nely Sánchez-Morales, Nancy Aracely Cruz-Ramos e Giner Alor-Hernández. "Wearables for Engagement Detection in Learning Environments: A Review". Biosensors 12, n.º 7 (11 de julho de 2022): 509. http://dx.doi.org/10.3390/bios12070509.

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Appropriate teaching–learning strategies lead to student engagement during learning activities. Scientific progress and modern technology have made it possible to measure engagement in educational settings by reading and analyzing student physiological signals through sensors attached to wearables. This work is a review of current student engagement detection initiatives in the educational domain. The review highlights existing commercial and non-commercial wearables for student engagement monitoring and identifies key physiological signals involved in engagement detection. Our findings reveal that common physiological signals used to measure student engagement include heart rate, skin temperature, respiratory rate, oxygen saturation, blood pressure, and electrocardiogram (ECG) data. Similarly, stress and surprise are key features of student engagement.

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Alruwais, Nuha, e Mohammed Zakariah. "Student-Engagement Detection in Classroom Using Machine Learning Algorithm". Electronics 12, n.º 3 (1 de fevereiro de 2023): 731. http://dx.doi.org/10.3390/electronics12030731.

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Student engagement is a flexible, complicated concept that includes behavioural, emotional, and cognitive involvement. In order for the instructor to understand how the student interacts with the various activities in the classroom, it is essential to predict their participation. The current work aims to identify the best algorithm for predicting student engagement in the classroom. In this paper, we gathered data from VLE and prepared them using a variety of data preprocessing techniques, including the elimination of missing values, normalization, encoding, and identification of outliers. On our data, we ran a number of machine learning (ML) classification algorithms, and we assessed each one using cross-validation methods and many helpful indicators. The performance of the model is evaluated with metrics like accuracy, precision, recall, and AUC scores. The results show that the CATBoost model is having higher accuracy than the rest. This proposed model outperformed in all the aspects compared to previous research. The results part of this paper indicates that the CATBoost model had an accuracy of approximately 92.23%, a precision of 94.40%, a recall of 100%, and an AUC score of 0.9624. The XGBoost predictive model, the random forest model, and the multilayer perceptron model all demonstrated approximately the same performance overall. We compared the AISAR model with Our model achieved an accuracy of 94.64% compared with AISAR 91% model and it concludes that our results are better. The AISAR model had only around 50% recall compared to our models, which had around 92%. This shows that our models return more relevant results, i.e., if our models predict that a student has high engagement, they are correct 94.64% of the time.

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Vanneste, Pieter, José Oramas, Thomas Verelst, Tinne Tuytelaars, Annelies Raes, Fien Depaepe e Wim Van den Noortgate. "Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement". Mathematics 9, n.º 3 (1 de fevereiro de 2021): 287. http://dx.doi.org/10.3390/math9030287.

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Computer vision has shown great accomplishments in a wide variety of classification, segmentation and object recognition tasks, but tends to encounter more difficulties when tasks require more contextual assessment. Measuring the engagement of students is an example of such a complex task, as it requires a strong interpretative component. This research describes a methodology to measure students’ engagement, taking both an individual (student-level) and a collective (classroom) approach. Results show that students’ individual behaviour, such as note-taking or hand-raising, is challenging to recognise, and does not correlate with students’ self-reported engagement. Interestingly, students’ collective behaviour can be quantified in a more generic way using measures for students’ symmetry, reaction times and eye-gaze intersections. Nonetheless, the evidence for a connection between these collective measures and engagement is rather weak. Although this study does not succeed in providing a proxy of students’ self-reported engagement, our approach sheds light on the needs for future research. More concretely, we suggest that not only the behavioural, but also the emotional and cognitive component of engagement should be captured.

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Kasatkina, D. A., A. M. Kravchenko, R. B. Kupriyanov e E. V. Nekhorosheva. "Automatic engagement detection in the education: critical review". Современная зарубежная психология 9, n.º 3 (2020): 59–68. http://dx.doi.org/10.17759/jmfp.2020090305.

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This paper reviews the key research of the automatic engagement detection in education. Automatic engagement detection is necessary in enhancing educational process, there is a lack of out-of-the-box technical solutions. Engagement can be detected while tracing learning-centered affects: interest, confusion, frustration, delight, anger, boredom, and their facial and bodily expressions. Most of the researchers reveal these emotions on video using Facial Action Coding System (FACS). But there doesn’t exist a set of ready-made criteria to detect engagement and many scientists use additional techniques like self-reports, audio-data, physiological indicators and others. In this paper we present a review of most recent researches in the field of automatic affect and engagement detection and present our theoretical model of engagement in educational process based on the learning-centered affects’s detection. Engagement is understood as an affective and cognitive state, accompanying learning process. While reaching optimal engagement students experience various affects, where highly positive and negative feelings mean that a student is close to be engaged in the learning process.

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Jiang, Lanlan. "Analysis of Students’ Role Perceptions and their Tendencies in Classroom Education Based on Visual Inspection". Occupational Therapy International 2022 (14 de abril de 2022): 1–11. http://dx.doi.org/10.1155/2022/3650308.

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This paper presents an in-depth study and analysis of students’ role perceptions and their tendencies in classroom education using a visual inspection approach. A multi example learning student engagement assessment method based on a one-dimensional convolutional neural network is proposed. Based on the conceptual composition of student engagement, head posture, eye gaze, and eye-opening and closing states and the most used facial movement units are used as visual features. For feature extraction, the proposed view of relative change features, based on the video features extracted from the Open Face toolset, the standard deviation of the distance between adjacent multiple frames relative to the center point of the three visual features is used as the relative change features of the video. This results in the phenomenon that students are highly motivated in the early stage and significantly increase the rate of absenteeism in the later stage. With the development of information technology injecting new vitality into educational innovation, many researchers have introduced computer vision and image processing technology into students’ online learning activities, and understand students’ current learning situation by analyzing students’ learning status. There are relatively few studies in this area in classroom teaching. Considering the low relative position correlation of the features in the examples, the examples are analyzed using a one-dimensional convolutional neural network to obtain the example-level student engagement, and a multi-example pooling layer is used to infer the student engagement in the video from the example-level student engagement. Finally, the experimental method is used to apply the student classroom attention evaluation detection system to actual classroom teaching activities, and the effectiveness and accuracy of the design of the student classroom attention evaluation detection system are investigated in depth through specific applications and example analysis, and the accuracy of the method of this paper is further verified by communicating feedback with teachers and students in the form of interviews.

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Mai, Tai Tan, Martin Crane e Marija Bezbradica. "Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection". Entropy 25, n.º 8 (17 de agosto de 2023): 1225. http://dx.doi.org/10.3390/e25081225.

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The high dropout rates in programming courses emphasise the need for monitoring and understanding student engagement, enabling early interventions. This activity can be supported by insights into students’ learning behaviours and their relationship with academic performance, derived from student learning log data in learning management systems. However, the high dimensionality of such data, along with their numerous features, pose challenges to their analysis and interpretability. In this study, we introduce entropy-based metrics as a novel manner to represent students’ learning behaviours. Employing these metrics, in conjunction with a proven community detection method, we undertake an analysis of learning behaviours across higher- and lower-performing student communities. Furthermore, we examine the impact of the COVID-19 pandemic on these behaviours. The study is grounded in the analysis of empirical data from 391 Software Engineering students over three academic years. Our findings reveal that students in higher-performing communities typically tend to have lower volatility in entropy values and reach stable learning states earlier than their lower-performing counterparts. Importantly, this study provides evidence of the use of entropy as a simple yet insightful metric for educators to monitor study progress, enhance understanding of student engagement, and enable timely interventions.

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Wang, Zhifeng, Minghui Wang, Chunyan Zeng e Longlong Li. "SBD-Net: Incorporating Multi-Level Features for an Efficient Detection Network of Student Behavior in Smart Classrooms". Applied Sciences 14, n.º 18 (17 de setembro de 2024): 8357. http://dx.doi.org/10.3390/app14188357.

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Detecting student behavior in smart classrooms is a critical area of research in educational technology that significantly enhances teaching quality and student engagement. This paper introduces an innovative approach using advanced computer vision and artificial intelligence technologies to monitor and analyze student behavior in real time. Such monitoring assists educators in adjusting their teaching strategies effectively, thereby optimizing classroom instruction. However, the application of this technology faces substantial challenges, including the variability in student sizes, the diversity of behaviors, and occlusions among students in complex classroom settings. Additionally, the uneven distribution of student behaviors presents a significant hurdle. To overcome these challenges, we propose Student Behavior Detection Network (SBD-Net), a lightweight target detection model enhanced by the Focal Modulation module for robust multi-level feature fusion, which augments feature extraction capabilities. Furthermore, the model incorporates the ESLoss function to address the imbalance in behavior sample detection effectively. The innovation continues with the Dyhead detection head, which integrates three-dimensional attention mechanisms, enhancing behavioral representation without escalating computational demands. This balance achieves both a high detection accuracy and manageable computational complexity. Empirical results from our bespoke student behavior dataset, Student Classroom Behavior (SCBehavior), demonstrate that SBD-Net achieves a mean Average Precision (mAP) of 0.824 with a low computational complexity of just 9.8 G. These figures represent a 4.3% improvement in accuracy and a 3.8% increase in recall compared to the baseline model. These advancements underscore the capability of SBD-Net to handle the skewed distribution of student behaviors and to perform high-precision detection in dynamically challenging classroom environments.

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Jing Yang, Lin Liu,. "Exploring the Path to Improve the Quality of Student Management Education Based on Knowledge Graph and NB-loT Architecture". Journal of Electrical Systems 20, n.º 4s (8 de abril de 2024): 212–25. http://dx.doi.org/10.52783/jes.1907.

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To research the effects of the smart classroom environment on college students' motivation to learn and engagement in that learning, as well as the link between those two factors in the smart classroom environment, a multi-scene student posture detection method using meta-learning is proposed. Through a combination of offline and online learning, the method develops a posture detection metamodel and a reasonable adaptation optimizer to quickly domain modify the posture detection model for certain teaching scenarios. A small amount of labelled sample data from a single teaching scene is all that is required for the metamodel to quickly adapt to the data distribution of that scene with the help of the adaptation optimizer in the online learning phase. The method simulates the process of the pose detection model in various types of teaching through two-layer training to train the parameters of the pose detection metamodel adaptation optimizer. The experimental findings demonstrate that college students' independent learning level and learning engagement are significantly higher in the smart classroom environment than they are in the traditional classroom environment. Additionally, there is a strong positive correlation between their independent learning level and learning engagement in the smart classroom environment.

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Lavanya, R., M. MEENATCHI e R. SARANYA. "Monitoring of Participation Monitoring, Optical Somnolence Recognition and Proctorial Supervision". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 008 (9 de agosto de 2024): 1–15. http://dx.doi.org/10.55041/ijsrem37014.

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The Comprehensive Student Monitoring Solution is a state-of-the-art integrated system designed to streamline attendance tracking, drowsiness detection, and advanced proctoring functionalities within educational settings. This innovative solution combines cutting-edge technologies to provide real-time monitoring and analysis of student activities, ensuring a secure and engaging learning environment. The system offers seamless attendance tracking capabilities, allowing educators to easily monitor and manage student attendance records. Furthermore, the inclusion of drowsiness detection technology enhances student safety by alerting instructors to signs of fatigue or lack of engagement. Additionally, the advanced proctoring functionality of the system enables educators to remotely supervise exams and assessments, ensuring academic integrity and preventing cheating. With its user-friendly interface and robust features, the Comprehensive Student Monitoring Solution is a valuable tool for educators seeking to enhance student engagement and academic performance. Attendance Tracking, Drowsiness Detection, Proctoring Functionality.

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kumar S*, Ashok, Ragul R.N, Praveen Kumar S e Gokula krishnan D. "Student Monitoring System using Machine Learning". International Journal of Innovative Technology and Exploring Engineering 9, n.º 6 (30 de abril de 2020): 1475–79. http://dx.doi.org/10.35940/ijitee.e4213.049620.

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The period behavioral engagement is commonly used to describe the scholar's willingness to participate within the getting to know the system. Emotional engagement describes a scholar's emotional attitude toward learning. Cognitive engagement is a chief part of overall learning engagement. From the facial expressions the involvement of the students in the magnificence can be decided. Commonly in a lecture room it's far difficult to recognize whether the students can understand the lecture or no longer. So that you can know that the comments form will be collected manually from the students. However the feedback given by the students will now not be correct. Hence they will no longer get proper comments. This hassle can be solved through the use of facial expression detection. From the facial expression the emotion of the students may be analyzed. Quantitative observations are achieved in the lecture room wherein the emotion of students might be recorded and statistically analyzed. With the aid of the use of facial expression we will directly get correct information approximately college students understand potential, and determining if the lecture becomes exciting, boring, or mild for the students. And the apprehend capability of the scholar is recognized by the facial emotions.

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Mais fontes

Teses / dissertações sobre o assunto "Student Engagement Detection"

Moufidi, Abderrazzaq. "Machine Learning-Based Multimodal integration for Short Utterance-Based Biometrics Identification and Engagement Detection". Electronic Thesis or Diss., Angers, 2024. http://www.theses.fr/2024ANGE0026.

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Le progrès rapide et la démocratisation de la technologie ont conduit à l’abondance des capteurs. Par conséquent, l’intégration de ces diverses modalités pourrait présenter un avantage considérable pour de nombreuses applications dans la vie réelle, telles que la reconnaissance biométrique ou la détection d’engagement des élèves. Dans le domaine de la multimodalité, les chercheurs ont établi des architectures variées de fusion, allant des approches de fusion précoce, hybride et tardive. Cependant, ces architectures peuvent avoir des limites en ce qui concerne des signaux temporels d’une durée courte, ce qui nécessite un changement de paradigme vers le développement de techniques d’apprentissage automatique multimodales qui promettent une précision et une efficacité pour l’analyse de ces données courtes. Dans cette thèse, nous nous appuyons sur l’intégration de la multimodalité pour relever les défis précédents, allant de l’identification biométrique supervisée à la détection non supervisée de l’engagement des étudiants. La première contribution de ce doctorat porte sur l’intégration de la Wavelet Scattering Transform à plusieurs couches avec une architecture profonde appelée x-vectors, grâce à laquelle nous avons amélioré la performance de l’identification du locuteur dans des scénarios impliquant des énoncés courts tout en réduisant le nombre de paramètres nécessaires à l’entraînement. En s’appuyant sur les avantages de la multimodalité, on a proposé une architecture de fusion tardive combinant des vidéos de la profondeur des lèvres et des signaux audios a permis d’améliorer la précision de l’identification dans le cas d’énoncés courts, en utilisant des méthodes efficaces et moins coûteuses pour extraire des caractéristiques spatio-temporelles. Dans le domaine des défis biométriques, il y a la menace de l’émergence des "deepfakes". Ainsi, nous nous sommes concentrés sur l’élaboration d’une méthode de détection des "deepfakes" basée sur des méthodes mathématiques compréhensibles et sur une version finement ajustée de notre précédente fusion tardive appliquée aux vidéos RVB des lèvres et aux audios. En utilisant des méthodes de détection d’anomalies conçues spécifiquement pour les modalités audio et visuelles, l’étude a démontré des capacités de détection robustes dans divers ensembles de données et conditions, soulignant l’importance des approches multimodales pour contrer l’évolution des techniques de deepfake. S’étendant aux contextes éducatifs, la thèse explore la détection multimodale de l’engagement des étudiants dans une classe. En utilisant des capteurs abordables pour acquérir les signaux du rythme cardiaque et les expressions faciales, l’étude a développé un ensemble de données reproductibles et un plan pour identifier des moments significatifs, tout en tenant compte des nuances culturelles. L’analyse des expressions faciales à l’aide de Vision Transformer (ViT) fusionnée avec le traitement des signaux de fréquence cardiaque, validée par des observations d’experts, a mis en évidence le potentiel du suivi des élèves afin d’améliorer la qualité d’enseignement
The rapid advancement and democratization of technology have led to an abundance of sensors. Consequently, the integration of these diverse modalities presents an advantage for numerous real-life applications, such as biometrics recognition and engage ment detection. In the field of multimodality, researchers have developed various fusion ar chitectures, ranging from early, hybrid, to late fusion approaches. However, these architec tures may have limitations involving short utterances and brief video segments, necessi tating a paradigm shift towards the development of multimodal machine learning techniques that promise precision and efficiency for short-duration data analysis. In this thesis, we lean on integration of multimodality to tackle these previous challenges ranging from supervised biometrics identification to unsupervised student engagement detection. This PhD began with the first contribution on the integration of multiscale Wavelet Scattering Transform with x-vectors architecture, through which we enhanced the accuracy of speaker identification in scenarios involving short utterances. Going through multimodality benefits, a late fusion architecture combining lips depth videos and audio signals further improved identification accuracy under short utterances, utilizing an effective and less computational methods to extract spatiotemporal features. In the realm of biometrics challenges, there is the threat emergence of deepfakes. There-fore, we focalized on elaborating a deepfake detection methods based on, shallow learning and a fine-tuned architecture of our previous late fusion architecture applied on RGB lips videos and audios. By employing hand-crafted anomaly detection methods for both audio and visual modalities, the study demonstrated robust detection capabilities across various datasets and conditions, emphasizing the importance of multimodal approaches in countering evolving deepfake techniques. Expanding to educational contexts, the dissertation explores multimodal student engagement detection in classrooms. Using low-cost sensors to capture Heart Rate signals and facial expressions, the study developed a reproducible dataset and pipeline for identifying significant moments, accounting for cultural nuances. The analysis of facial expressions using Vision Transformer (ViT) fused with heart rate signal processing, validated through expert observations, showcased the potential for real-time monitoring to enhance educational outcomes through timely interventions

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Calado, Jorge Miguel da Silva. "A Framework for Students Profile Detection". Master's thesis, 2017. http://hdl.handle.net/10362/21765.

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Some of the biggest problems tackling Higher Education Institutions are students’ drop-out and academic disengagement. Physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems, are some of the factors that can lead to it. This problematic is worsened by the shortage of educational resources, that can bridge the communication gap between the faculty staff and the affective needs of these students. This dissertation focus in the development of a framework, capable of collecting analytic data, from an array of emotions, affects and behaviours, acquired either by human observations, like a teacher in a classroom or a psychologist, or by electronic sensors and automatic analysis software, such as eye tracking devices, emotion detection through facial expression recognition software, automatic gait and posture detection, and others. The framework establishes the guidance to compile the gathered data in an ontology, to enable the extraction of patterns outliers via machine learning, which assist the profiling of students in critical situations, like disengagement, attention deficit, drop-out, and other sociological issues. Consequently, it is possible to set real-time alerts when these profiles conditions are detected, so that appropriate experts could verify the situation and employ effective procedures. The goal is that, by providing insightful real-time cognitive data and facilitating the profiling of the students’ problems, a faster personalized response to help the student is enabled, allowing academic performance improvements.

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Capítulos de livros sobre o assunto "Student Engagement Detection"

Ravi, Pooja, e M. Ali Akber Dewan. "Real-time Multi-module Student Engagement Detection System". In Communication and Intelligent Systems, 261–78. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2100-3_22.

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Deshpande, Chinar. "AI-Based Student Emotion and Engagement Level Detection Framework". In Lecture Notes on Data Engineering and Communications Technologies, 268–80. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9255-9_18.

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Ahmed, Zeyad A. T., Mukti E. Jadhav, Ali Mansour Al-madani, Mohammed Tawfik, Saleh Nagi Alsubari e Ahmed Abdullah A. Shareef. "Real-Time Detection of Student Engagement: Deep Learning-Based System". In Advances in Intelligent Systems and Computing, 313–23. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2594-7_26.

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Sharma, Prabin, Shubham Joshi, Subash Gautam, Sneha Maharjan, Salik Ram Khanal, Manuel Cabral Reis, João Barroso e Vítor Manuel de Jesus Filipe. "Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning". In Communications in Computer and Information Science, 52–68. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-22918-3_5.

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Toti, Daniele, Nicola Capuano, Fernanda Campos, Mario Dantas, Felipe Neves e Santi Caballé. "Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning". In Advances on P2P, Parallel, Grid, Cloud and Internet Computing, 211–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61105-7_21.

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Okur, Eda, Nese Alyuz, Sinem Aslan, Utku Genc, Cagri Tanriover e Asli Arslan Esme. "Behavioral Engagement Detection of Students in the Wild". In Lecture Notes in Computer Science, 250–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61425-0_21.

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Zavarrone, Emma, Maria Gabriella Grassia, Rocco Mazza e Alessia Forciniti. "Emergency remote teaching: an explorative tool". In Proceedings e report, 61–66. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.12.

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The worldwide rapid spread and severity of the infectious disease caused by Coronavirus forced the WHO to declare a global state of pandemic emergency during March 2020, by leading the governments around the world to adopt policies that created the widest rift of education systems in human history. Italy have temporarily closed each educational institution, by causing the disruption of tertiary education for 16.89% of the Italian learner’s population. To ensure the “pedagogic continuity”, universities adopted the transitioning from traditional face-to-face to online learning. This paradigm shift to fully remote teaching solutions represents the so-called emergency remote teaching (ERT) in contrast to the traditional teaching inspired by Bologna process principles such as teaching quality and student satisfaction. In a landscape of emerging difficulties connected to ERT contexts, the quality assurance of higher education recalled by the Bologna Process may be not appropriate. We propose an evaluation model for the quality and ERT success across two dimensions used as proxy variables: students’ engagement (SE) and success performance (SP). Within the faculties, we analysed the performance and hence the knowledge, skills and/or attitudes acquired by learners, within the students, the focus was the engagement as interest, motivation and involvement. Under this perspective our research question has an explorative nature: we are interested in detecting empirical evidence about the learning assessment and engagement in higher education with focus on students’ engagement and their success performance during ERT. The investigation carried out on Iulm University’s student population (N=775). We integrated textual data related to the students evaluation of ERT and their career data such as credits, marks before and post disease. The results show the relations between the two dimensions taken into account, with a multidimensional approach we created a factorial plan useful to create an agile tool of analysis in the ERT context.

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Churaev, Egor, e Andrey V. Savchenko. "To Kill a Student’s Disengagement: Personalized Engagement Detection in Facial Video". In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky, 329–37. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64315-6_29.

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Arwa, Allinjawi, Altuwairqi Khawlah, Kammoun Jarraya Salma, Abuzinadah Nihal e Alkhuraiji Samar. "CNN-Based Face Emotion Detection and Mouse Movement Analysis to Detect Student’s Engagement Level". In International Conference on Advanced Intelligent Systems for Sustainable Development, 604–26. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_53.

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Kootsookos, Alexandra. "Assessment Practices using Online Tools in Undergraduate Programs". In Student Engagement and Participation, 571–86. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2584-4.ch029.

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This chapter provides an overview of the use of online and Web-based learning technologies as formative and summative assessment. Peer learning and assessment, provision of feedback to students, online tests and quizzes, plagiarism detection systems, and audience response systems are all examined with a view to highlighting best practice and demonstrating that online assessment must still follow sound pedagogy to be both valid and valued by instructors and students alike.

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Trabalhos de conferências sobre o assunto "Student Engagement Detection"

Mazumder, Deepra, Aniruddha Chatterjee, Anwesha Chakraborty e Raja Karmakar. "A Novel Student Engagement Level Detection and Emotion Analysis Using Ensemble Learning". In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725730.

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Lima, Mariam, Kazi Rifat Ahmed, Nusrat Jahan, Imran Mahmud, Md Shahriar Parvez e P. Revathi. "Deep Learning Based Approach For Detecting Student Engagement Through Facial Emotions". In 2024 International Conference on Data Science and Network Security (ICDSNS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691098.

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Bocanumenth, Aurora, e Elizabeth Rendón-Vélez. "Engagement State Definition and Detection in Education: A Review". In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95597.

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Abstract Students’ level of positive learning-centered affective states, like engagement or flow state, has been proved to be strongly related to dropouts’ prevention, higher learning rate, and better student performance in their courses. Measuring users’ engagement state in a more effective and user-independent way may help create a better design of interactive applications and develop intelligent, more sophisticated, and adaptative study environments. The engagement reviews found in the literature go through psychological definitions but do not go deeper into the physiological and behavioral indicators of the state. This review aims to analyze the current state of the art on engagement detection, to identify which are some of the most relevant physiological and behavioral indicators for engagement in students for its prediction during presential or online courses. A computer-aided systematic literature search was performed following the PRISMA methodology. A total of 24 articles were selected after removing duplicates and applying the selection criteria. These studies were analyzed to extract data relative to the physiological behavioral indicators, the classifier used, its’ accuracy, and the number of participants. Indicators, such as leaning forward or backward and parasympathetic activation (such as HR, HRV, and GSR) have proven to be strongly related to students’ engagement states. The multimodal channel systems have been proven to have better performance, but the question of the best channel combination is still on the table. Different classification methods (SVM, RF, NB) have achieved high accuracy performance in experimental setups, but there are still challenges for real-life setups detection systems.

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B, Perumal, Nagaraj P, Thulasi Sai Narsimha Charan, Yellampalli Venkata Siva SaiDeepak, Chennuru Venkata Vignesh Reddy e Sanakam Nagendra. "Student Engagement Detection in Classroom using Deep CNN-based Learning Approach". In 2023 8th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2023. http://dx.doi.org/10.1109/icces57224.2023.10192809.

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Guo, Shouchao. "Detection of Student Engagement in 3-D Design Course Using xAPI and Levenshtein Distance". In 2020 AERA Annual Meeting. Washington DC: AERA, 2020. http://dx.doi.org/10.3102/1577638.

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Vishnumolakala, Sai Krishna, VSNV Sadwika Vallamkonda, Sobin C. C, N. P. Subheesh e Jahfar Ali. "In-class Student Emotion and Engagement Detection System (iSEEDS): An AI-based Approach for Responsive Teaching". In 2023 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2023. http://dx.doi.org/10.1109/educon54358.2023.10125254.

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Shan, J., e Sherin Eliyas. "Exploring AI Facial Recognition for Real-time Emotion Detection: Assessing Student Engagement in Online Learning Environments". In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiiot58432.2024.10574587.

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Pinto, Adam Henrique, e Roseli Aparecida Romero. "EEG signal detection and analysis with application in educational robotics". In VIII Workshop de Teses e Dissertações em Robótica/Concurso de Teses e Dissertações em Robótica. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wtdr_ctdr.2020.14951.

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Brain-Computer Interfaces add information to robots directly from users’ brain, allowing for the interpretation of attention, engagement, and even student mistakes. However, most applications still have low accuracy in recognizing this information. In this paper, an Error Related Potential (ErrP) detection system is being proposed. For this, a new database was created by using a serious game and a humanoid robot aiming to force errors and mental state changings of the user. Wavelets and Fourier Transforms were compared to signal feature extraction, classified using both MultiLayer Perceptron (MLP) and Convolutional Neural Networks (CNN). Experiments demonstrate that the wavelet outperformed Fourier transform to extract the ErrP signal, and CNN had a higher accuracy than MLP in the classification.

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Dunaev, Mihail, George Milescu, Razvan Rughinis e Vlad Posea. "EXPRESSIO: AUTOMATIC FEEDBACK FOR MOOC TEACHERS BASED ON AFFECTIVE COMPUTING". In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-185.

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The increase in popularity of Massive Open Online Courses (MOOCs) raises the issue of effective student feedback. Instructors can examine students' opinions and evaluations through multiple methods, such as eliciting them in feedback forms, or analyzing statistics that are automatically generated on the course platform about students' participation and achievements. While feedback forms can provide valuable information on students' learning experiences, including their interest and emotional reactions, they are vulnerable to low response rates and to various forms of recollection bias. For example, human memory privileges the final elements of an activity over its initial or middle periods (Kahneman 2010). Eliciting feedback on an entire activity also lacks the granularity required to determine what specific aspects were most and least interesting. While the analysis of automatically generated indicators compensates for the low response problem, it also fails to deliver finely-tuned information on student engagement with specific elements of the course. In order to address this issue, we have developed Expressio, an automatic student feedback solution based on affective computing. We start from Ekman's emotion classification, distinguishing six basic emotions: happiness, sadness, surprise, fear, anger and disgust. We rely on several technologies and devices: Creative Senz3D Camera, Intel Perceptual Computing SDK, OpenCV, Windows API and Microsoft Visual Studio 2012. Expressio is a program that identifies user expressions as they occur during an online activity and displays them on the screen in a GUI. Our solution also affords training the program for a more accurate and personalized identification of emotions. Expressio can be integrated with a MOOC to allow the transmission of continuous feedback regarding student interest and emotional experiences during the course, based on automatic detection of facial emotional expressions.

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Bosch, Nigel. "Detecting Student Engagement". In UMAP '16: User Modeling, Adaptation and Personalization Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2930238.2930371.

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