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Bustos-López, Maritza, Nicandro Cruz-Ramírez, Alejandro Guerra-Hernández, Laura Nely Sánchez-Morales, Nancy Aracely Cruz-Ramos und Giner Alor-Hernández. „Wearables for Engagement Detection in Learning Environments: A Review“. Biosensors 12, Nr. 7 (11.07.2022): 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, und Mohammed Zakariah. „Student-Engagement Detection in Classroom Using Machine Learning Algorithm“. Electronics 12, Nr. 3 (01.02.2023): 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 und Wim Van den Noortgate. „Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement“. Mathematics 9, Nr. 3 (01.02.2021): 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 und E. V. Nekhorosheva. „Automatic engagement detection in the education: critical review“. Современная зарубежная психология 9, Nr. 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 (14.04.2022): 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 und Marija Bezbradica. „Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection“. Entropy 25, Nr. 8 (17.08.2023): 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 und Longlong Li. „SBD-Net: Incorporating Multi-Level Features for an Efficient Detection Network of Student Behavior in Smart Classrooms“. Applied Sciences 14, Nr. 18 (17.09.2024): 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, Nr. 4s (08.04.2024): 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 und R. SARANYA. „Monitoring of Participation Monitoring, Optical Somnolence Recognition and Proctorial Supervision“. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, Nr. 008 (09.08.2024): 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 und Gokula krishnan D. „Student Monitoring System using Machine Learning“. International Journal of Innovative Technology and Exploring Engineering 9, Nr. 6 (30.04.2020): 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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Siswantoro, Joko, Januar Rahmadiarto und Mohammad Farid Naufal. „Facial Expression Recognition to Detect Student Engagement in Online Lectures“. Teknika 13, Nr. 2 (24.06.2024): 226–32. http://dx.doi.org/10.34148/teknika.v13i2.853.
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In synchronous online lectures, the lecturers often provide the lecture material directly through video conference technology. On the other hand, there are many students who do not pay attention to the lecturers when they are participating in online lectures. As a consequence, in this research, an application was developed to assist lecturers in gathering data regarding the degree to which students who participate in online lectures pay attention to the presented information. The application employed a convolutional neural network (CNN) model to recognize each student's facial expressions and place them into one of two classes: either engaged or disengaged. The captured student facial image was preprocessed to facilitate the classification process. The preprocessing stage consisted of image conversion to gray scale, face detection using the Haar-Cascade Classifier model, and a median filter to reduce noise. In the process of designing a CNN model, three different hyperparameter tuning scenarios were implemented. These tuning scenarios aimed to obtain the best possible CNN model by determining which CNN model hyperparameters were the most optimal. The results of the experiments indicate that the CNN model from the second scenario has the highest level of accuracy in terms of recognizing facial expressions, coming in at 86%. The results of this research have been tested to measure the level of student participation in online lectures. The trial results show that the proposed application can help lecturers evaluate student engagement during online lectures.
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Sukumaran, Ajitha, und Arun Manoharan. „A survey on automatic engagement recognition methods: online and traditional classroom“. Indonesian Journal of Electrical Engineering and Computer Science 30, Nr. 2 (01.05.2023): 1178. http://dx.doi.org/10.11591/ijeecs.v30.i2.pp1178-1191.
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Student engagement in a learning environment is directly related to students’ perception and involvement of the educational activities in the class, along with their physical and mental health. This paper presents an extensive survey of the various automatic engagement detection approaches and algorithms based on computer vision, physiological and neurological signals analysis-based methods. The computer vision-based techniques depend on the traits captured by image sensors such as facial expressions, gesture and posture analysis, and gaze direction. The physiological and neurological signal based approach depends on the sensor data, like heart rate (HR), electroencephalogram (EEG), blood pressure (BP), and galvanic skin response (GSR). A brief analysis of the available datasets for Engagement Recognition and its features are also summarized. This study highlights a few commercially available wearables which provides the physiological signals that helps in student’s attentivity recognition. Our study reveal that the accuracy of engagement recognition system will increase if we increase the number of modalities used. In this survey, we intend to support the upcoming researchers as well as tutors of smart education set up by providing an overview of existing or proposed approaches of automatic engagement detection techniques in different scenarios.
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Miller, Claude H., Norah E. Dunbar, Matthew L. Jensen, Zachary B. Massey, Yu-Hao Lee, Spencer B. Nicholls, Chris Anderson et al. „Training Law Enforcement Officers to Identify Reliable Deception Cues With a Serious Digital Game“. International Journal of Game-Based Learning 9, Nr. 3 (Juli 2019): 1–22. http://dx.doi.org/10.4018/ijgbl.2019070101.
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Extant research indicates that professional law enforcement officers (LEOs) are generally no better than untrained novices at detecting deception. Moreover, traditional training methods are often less effective than no training at all at improving successful detection. Compared to the traditional training, interactive digital games can provide an immersive learning environment for deeper internalization of new information through simulated practices. VERITAS—an interactive digital game—was designed and developed to train LEOs to better detect reliable deception cues when questioning suspects and determining the veracity of their answers. The authors hypothesized that reducing players' reactance would mitigate resistance to training, motivate engagement with materials, and result in greater success at deception detection and knowledge. As hypothesized, LEOs playing VERITAS showed significant improvement in deception detection from the first to the second scenario within the game; and the low-reactance version provided the most effective training. The authors also compared various responses to the game between LEOs and a separate undergraduate student sample. Relative to students, findings show LEOs perceived VERITAS to be significantly more intrinsically motivating, engaging, and appealing as a deception detection activity.
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Qiaoqiao Sun und Huiqing Chen. „Computer Vision for E-learning Student Face Reaction Detection for Improving Learning Rate“. Scalable Computing: Practice and Experience 25, Nr. 4 (16.06.2024): 2736–45. http://dx.doi.org/10.12694/scpe.v25i4.2998.
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In the advent of e-learning, understanding student engagement and reaction is crucial for improving the quality of education and enhancing the learning rate. With the advancement of computer vision technologies, there is a significant opportunity to analyze and interpret student reactions in a non-intrusive manner. This study proposes a novel framework employing Faster R-CNN integrated with DenseNet architecture for real-time detection of student facial reactions during e-learning sessions. The proposed method leverages the strengths of Faster R-CNN in generating high-quality region proposals for object detection tasks, coupled with the DenseNet’s efficiency in feature propagation and reduction in the number of parameters, which is well-suited for processing the intricate patterns in facial expressions. Our approach begins with the application of Faster R-CNN to extract potential facial regions with high accuracy and reduced computational cost. The integration of DenseNet as a backbone for feature extraction within Faster R-CNN capitalizes on its densely connected convolutional networks, ensuring maximum information flow between layers in the network. By doing so, the system becomes exceptionally adept at recognizing subtle changes in facial features that indicate various student reactions, such as confusion, engagement, or boredom. We conducted a series of experiments using a diverse dataset of e-learning interactions, collected under various lighting conditions and involving multiple ethnicities to ensure robustness and generalizability. The model was trained and validated on this dataset, and the results demonstrate a significant improvement in detection rates of student reactions compared to existing methods.
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Srivastava, Shubhi, Abdul Manan, Akshith Krishnan, Arvinda H. B und Aslam Asgar Khan. „InsightStream: A Real-Time Perspective on Classroom Environment“. International Journal for Research in Applied Science and Engineering Technology 12, Nr. 3 (31.03.2024): 1353–57. http://dx.doi.org/10.22214/ijraset.2024.58970.
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Abstract: Traditionally, understanding classroom environment relies on subjective observations and post-hoc surveys. "Insight Stream" proposes a paradigm shift, offering a real-time, data-driven perspective through machine learning-powered facial emotion detection. This project leverages AI to analyse student facial expressions during class, capturing the emotional undercurrents in real-time. By delving beyond spoken words, "Insight Stream" aims to: Quantify classroom engagement: Detect emotions like boredom, confusion, and excitement to gauge real-time student engagement and adapt teaching methods accordingly. Identify hidden anxieties: Uncover subtle cues of anxiety or discomfort that may go unnoticed, allowing for proactive support and personalized interventions. Optimize teaching delivery: Track shifts in emotional response to different teaching styles and materials, enabling instructors to fine-tune their methods for maximal impact. Foster well-being: Monitor overall emotional climate to ensure a positive and supportive learning environment, contributing to student well-being and academic success. "Insight Stream" goes beyond just observing the classroom - it delves into the hearts and minds of students, offering a real-time window into their emotional tapestry. This project holds immense potential to revolutionize teaching and learning, creating a dynamic and data-driven environment that caters to the holistic needs of every student.
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Liu, Yuanyuan, Jingying Chen, Mulan Zhang und Chuan Rao. „Student engagement study based on multi-cue detection and recognition in an intelligent learning environment“. Multimedia Tools and Applications 77, Nr. 21 (05.05.2018): 28749–75. http://dx.doi.org/10.1007/s11042-018-6017-2.
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Müller, Carsten, Kareem El-Ansari und Walid El Ansari. „Cross-Sectional Analysis of Mental Health among University Students: Do Sex and Academic Level Matter?“ International Journal of Environmental Research and Public Health 19, Nr. 19 (03.10.2022): 12670. http://dx.doi.org/10.3390/ijerph191912670.
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University students’ mental health and well-being is a growing public health concern. There is a lack of studies assessing a broad range of mental health domains by sex and academic level of study. This cross-sectional online survey of BSc, MSc, and PhD students (n = 3353, 67% female) enrolled at one university in Germany assessed a wide scope of mental health domains, covering positive (i.e., self-rated health, self-esteem, student engagement) and negative aspects (i.e., perceived stress, irritation, and screening positive for depression, anxiety, comorbidity, and psychological distress). We evaluated differences in mental health by sex and academic level. Overall, although self-rated health did not differ by sex and academic level, females and lower academic level were associated with less favorable mental health. Males reported higher prevalence of high self-esteem, and higher engagement (all p ≤ 0.04). Conversely, mean perceived stress and cognitive/emotional irritation were higher among females, as were rates for positive screenings for anxiety, anxiety and depression comorbidity, and psychological distress (p < 0.001 for all). Likewise, lower academic level (BSc) was associated with lower rates of high self-esteem (p ≤ 0.001), increased perceived stress (p < 0.001), and higher prevalence of positive screening for depression, anxiety, comorbidity, and psychological distress (p ≤ 0.002 for all), while higher academic level (PhD) was linked to increased student engagement (p < 0.001 for all). Although the effect sizes of sex and academic level on student mental health were modest, these findings support a need for action to establish and expand early detection and prevention programs, on-campus advisory services, and peer counseling that focus on the sex-specific and academic-study-level-specific factors, as well as mental health and career development resources for students. Academics and policy makers need to consider multipronged intervention strategies to boost confidence of students and their academic career.
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Camp, James, Curtis Smith, Moinak Bhaduri und Samantha Eddy. „Living among immigrants at the U.S.-Mexico border: Community-based learning and the benefits evidenced through network science“. Journal of Educational Impact 1, Nr. 2 (30.12.2024): 32–59. https://doi.org/10.70617/eduimpact.2025.2.
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This study examines immersive community learning during a student trip to the United States-Mexico border, with nine students documenting their experiences through journals and evaluations. Using innovative techniques from statistical network science, we analyzed commonalities and differences in students' experiences, quantitatively assessing sentiment variations and exploring their first-hand observations. Novel methods using cluster centrality and community detection were deployed to identify broad areas of observation and concern. Emotion fluctuations, recorded with the National Research Council (American English) dictionary, are placed on a firm numerical basis, and thematic currents are unearthed with the presence or absence of topical diversity. Students reported strong emotional engagement with their experiences, reflecting on challenged worldviews through phrases like "border dynamics," "strong stories" from immigrant narratives, and "emotional farewell." These findings demonstrate that community-based learning curricula, which extend beyond traditional classroom limits, can effectively address and overcome misinformation regarding border issues. The study spotlights the transformative potential of education focused on community engagement, empathy, and solidarity, providing a framework for future community-based educational projects and emphasizing their substantial benefits to student learning experience. Four pedagogical contributions and actionable insights to take from this research: (1) preparation and design, (2) emphasis on “communal living,” (3) reflection, and (4) establishing trust.
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Rajae, Amimi, Radgui Amina und Ibn El Haj El Hassane. „An improved student’s facial emotions recognition method using transfer learning“. Indonesian Journal of Electrical Engineering and Computer Science 36, Nr. 2 (01.11.2024): 1199. http://dx.doi.org/10.11591/ijeecs.v36.i2.pp1199-1208.
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<p>Instructors endeavour to encourage active participation and interaction among learners. However, in settings with a large number of students, such as universities or online platforms, obtaining real-time feedback and evaluating teaching methodology presents a significant challenge. In this paper, we introduce a student engagement recognition system based on a hybrid method using handcrafted features and transfer learning. The research is conducted on two databases for emotion detection based on facial cues (FER13) benchmarked dataset and our database. We use the local binary patterns (LBP) method combined with pre-trained MobileNet model for feature extraction and classification. The proposed system adeptly discerns students’ facial expressions and categorizes their engagement states as either ‘engaged’ or ‘disengaged’. We determine the most effective model by evaluating and comparing several deep learning models, including Inception-V3, VGG16, EfficientNet, ResNet, and DenseNet. Experimental results underscore the efficacy of our approach, revealing a remarkable accuracy, surpassing benchmarks set by state-of-the-art models.</p>
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Shiri, Farhad Mortezapour, Ehsan Ahmadi, Mohammadreza Rezaee und Thinagaran Perumal. „Detection of Student Engagement in E-Learning Environments Using EfficientnetV2-L Together with RNN-Based Models“. Journal on Artificial Intelligence 6 (24.04.2024): 85–103. http://dx.doi.org/10.32604/jai.2024.048911.
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K, Ramya. „DRONE POWERED CLASSROOM PRESENCE TRACKER“. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, Nr. 05 (06.05.2024): 1–5. http://dx.doi.org/10.55041/ijsrem32683.
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In today's rapidly evolving educational landscape, the traditional methods of manual attendance tracking in classrooms are proving to be increasingly inefficient and prone to inaccuracies. This project introduces an innovative solution to modernize classroom attendance tracking through the integration of drone technology and advanced computer vision algorithms. By combining autonomous navigation, object detection, data processing, real-time reporting, and user interface functionalities, the system streamlines attendance management processes in educational environments. Utilizing drones equipped with advanced computer vision algorithms like YOLOv4, the system autonomously navigates classroom spaces to accurately detect and count individuals in real-time. The captured attendance data undergoes processing to filter irrelevant information, enabling educators and administrators to access up-to-date attendance information instantly through real-time reporting features. The user-friendly interface enhances accessibility and usability, making the system easily adaptable to educational settings of varying sizes and layouts. Additionally, the project's flexibility, precision, and proactive monitoring capabilities contribute to a more efficient, accurate, and proactive approach to attendance management, ultimately improving student engagement and success in the classroom. The proactive monitoring enabled by real-time reporting features allows educators to identify attendance trends promptly and intervene as needed to support student engagement and success. Keywords: Drone Technology, Computer Vision, Object Detection, YOLOv4, Real Time Reporting, Attendance Tracking
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Wani, Abid Hussain. „Leveraging Emotions in Student Feedback to Improve Course Content and Delivery“. Scalable Computing: Practice and Experience 25, Nr. 5 (01.08.2024): 3388–93. http://dx.doi.org/10.12694/scpe.v25i5.3114.
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Emotions play a vital role in almost all the activities we perform, including learning. In fact, the success of any learning system is largely dependent upon its ability to deliver the course content in such a form so as to meet the learning requirements of the target audience. Learning Systems can be tailored to effectively utilize the feedback from learners to improve the course content, and thus the feedback can prove to be a valuable asset. There is an increased demand for focusing on a learner-centric approach to content delivery. In this study we attempt at detecting different learning-relevant emotions from the feedback for a course so as to enable course designers to incorporate the type of content that matches a learners requirements. Rather than taking into account six basic emotions (sadness, happiness, fear, anger, surprise and disgust) we consider interest, engagement, confusion, frustration, disappointment, boredom, hopefulness and satisfaction emotions for the purpose of our study since they are more relevant in a learning setup. We employed a supervised algorithm, Support Vector Machine, for affect detection from the textual feedback in our experiments.
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Yin Albert, Chuck Chung, Yuqi Sun, Guang Li, Jun Peng, Feng Ran, Zheng Wang und Jie Zhou. „Identifying and Monitoring Students’ Classroom Learning Behavior Based on Multisource Information“. Mobile Information Systems 2022 (25.08.2022): 1–8. http://dx.doi.org/10.1155/2022/9903342.
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Understanding human activity and behavior, particularly real-time understanding in video feeds, is one of the most active areas of research in Computer Vision (CV) and Artificial Intelligence (AI) nowadays. To advance the topic of integrating learning engagement research with university teaching practice, accurate and efficient assessment, and analysis of students’ classroom learning behavior engagement is very important. The recently proposed classroom behavior recognition algorithms have some limitations, such as the inability to quickly and accurately identify students’ classroom behaviors because they do not consider the motion information of students between consecutive frames. In recent years, action recognition algorithms based on Convolutional Neural Networks (CNN) have improved significantly. To address the limitations of existing algorithms, in this study, a 3D-CNN is selected as a network model for classroom student behavior recognition, which increases information multisourcing and classroom student localization with high accuracy and robustness. For better analysis of human behavior in videos, the 3D convolution extends the 2D convolution to the spatial–temporal domain. In the proposed system, first of all, a real-time picture stream of each student is obtained by combining real-time target detection and tracking. Then, a deep spatiotemporal residual CNN is used to learn the spatiotemporal features of each student’s behavior, so, as to achieve real-time recognition of classroom behaviors for multistudent targets in classroom teaching scenarios. To verify the effectiveness of the proposed model, different experiments are conducted using the labeled classroom behavior dataset. The experimental results demonstrate that the proposed model exhibits better performance in classroom behavior recognition. The accurate recognition of classroom behaviors can assist the teachers and students to understand the classroom learning situation and help to promote the development of smart classroom.
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Yu, Zefang, Mingye Xie, Jingsheng Gao, Ting Liu und Yuzhuo Fu. „From Raw Video to Pedagogical Insights: A Unified Framework for Student Behavior Analysis“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 21 (24.03.2024): 23241–49. http://dx.doi.org/10.1609/aaai.v38i21.30371.
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Understanding student behavior in educational settings is critical in improving both the quality of pedagogy and the level of student engagement. While various AI-based models exist for classroom analysis, they tend to specialize in limited tasks and lack generalizability across diverse educational environments. Additionally, these models often fall short in ensuring student privacy and in providing actionable insights accessible to educators. To bridge this gap, we introduce a unified, end-to-end framework by leveraging temporal action detection techniques and advanced large language models for a more nuanced student behavior analysis. Our proposed framework provides an end-to-end pipeline that starts with raw classroom video footage and culminates in the autonomous generation of pedagogical reports. It offers a comprehensive and scalable solution for student behavior analysis. Experimental validation confirms the capability of our framework to accurately identify student behaviors and to produce pedagogically meaningful insights, thereby setting the stage for future AI-assisted educational assessments.
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Avital, Nuphar, Idan Egel, Ido Weinstock und Dror Malka. „Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing“. Inventions 9, Nr. 6 (04.11.2024): 113. http://dx.doi.org/10.3390/inventions9060113.
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In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices.
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Al-Nafjan, Abeer, und Mashael Aldayel. „Predict Students’ Attention in Online Learning Using EEG Data“. Sustainability 14, Nr. 11 (27.05.2022): 6553. http://dx.doi.org/10.3390/su14116553.
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In education, it is critical to monitor students’ attention and measure the extents to which students participate and the differences in their levels and abilities. The overall goal of this study was to increase the quality of distance education. In particular, in order to craft an approach that will effectively augment online learning using objective measures of brain activity, we propose a brain–computer interface (BCI) system that aims to use electroencephalography (EEG) signals for the detection of student’s attention during online classes. This system will aid teachers to objectively assess student attention and engagement. To this end, experiments were conducted on a public dataset; we extracted power spectral density (PSD) features using used a fast Fourier transform. Different attention indexes were calculated. Then, we built three different classification algorithms: k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF). Our proposed random forest classifier achieved a higher accuracy (96%) than KNN and SVM. Moreover, our results compared to state-of-the-art attention-detection systems with respect to the same dataset. Our findings revealed that the proposed RF approach can be used to effectively distinguish the attention state of a user.
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Anwar, Aamir, Ikram Ur Rehman, Moustafa M. Nasralla, Sohaib Bin Altaf Khattak und Nasrullah Khilji. „Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning“. Education Sciences 13, Nr. 9 (08.09.2023): 914. http://dx.doi.org/10.3390/educsci13090914.
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In recent years, the rapid growth of online learning has highlighted the need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, and satisfaction in online learning environments, particularly in complex STEM subjects. In this context, sentiment analysis has emerged as a promising tool to detect and classify emotions expressed in textual and visual forms. This study offers an extensive literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique on the role of sentiment analysis in student satisfaction and online learning in STEM subjects. The review analyses the applicability, challenges, and limitations of text- and facial-based sentiment analysis techniques in educational settings by reviewing 57 peer-reviewed research articles out of 236 articles, published between 2015 and 2023, initially identified through a comprehensive search strategy. Through an extensive search and scrutiny process, these articles were selected based on their relevance and contribution to the topic. The review’s findings indicate that sentiment analysis holds significant potential for improving student experiences, encouraging personalised learning, and promoting satisfaction in the online learning environment. Educators and administrators can gain valuable insights into students’ emotions and perceptions by employing computational techniques to analyse and interpret emotions expressed in text and facial expressions. However, the review also identifies several challenges and limitations associated with sentiment analysis in educational settings. These challenges include the need for accurate emotion detection and interpretation, addressing cultural and linguistic variations, ensuring data privacy and ethics, and a reliance on high-quality data sources. Despite these challenges, the review highlights the immense potential of sentiment analysis in transforming online learning experiences in STEM subjects and recommends further research and development in this area.
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Serrano-Mamolar, Ana, Miguel Arevalillo-Herráez, Guillermo Chicote-Huete und Jesus G. Boticario. „An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations“. Sensors 21, Nr. 5 (04.03.2021): 1777. http://dx.doi.org/10.3390/s21051777.
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Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.
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Jain, Prisha, und Chaya Ravindra. „Classifying Emotional Engagement in Online Learning Via Deep Learning Architecture“. International Journal of Advanced Engineering, Management and Science 10, Nr. 5 (2024): 063–70. http://dx.doi.org/10.22161/ijaems.105.2.
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The world has seen a phenomenal rise in online learning over the past decade, with universities shifting courses to online modes, MOOCs(Massive Open Online Course) emerging and laptop and tab-based initiatives being extensively promoted. However, educators face significant challenges in analyzing learning environments due to issues like lack of in-person cues, small video size, etc. To address these challenges, it is crucial to analyze the engagement levels of online classes. Out of the various subcategories of engagement, emotional engagement is one that is overlooked, but integral to analysis and deterministic in its approach. In response, we developed a deep learning architecture to analyze emotional engagement in online classes. Our method utilizes a ResNet50-based algorithm, refined through experimentation with various techniques such as transfer learning, optimizers, and pre-trained weights. The model adds a unique layer to the analysis of different algorithms used for engagement detection in academia while also achieving stellar rates of 81.34% validation accuracy and 81.04% training accuracy. Unlike other models, our approach employs high-quality image data for training, ensuring more reliable results. Moreover, we constructed a novel framework for applying emotional engagement to real-world scenarios, thus bridging the pre-existing gap between implementation and academia. The integration of this technology into online learning has immense potential, and can bring with it a shift in the quality of education. By fostering a safe and healthy learning space for every student, we can significantly enhance the effectiveness of online education systems.
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Arianto, Arianto, Fadlia Dwi Hudaibah, Nurhalifah Nurhalifah, Mariatul Qippiah und Suharsono Bantun. „Learning Innovations in Coastal Areas Through Augmented Reality and Gamification“. Jurnal Media Informasi Teknologi 1, Nr. 2 (31.10.2024): 95–102. http://dx.doi.org/10.69616/mit.v1i2.193.
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This research investigates the development of an augmented reality (AR) application with gamification elements to enhance natural science learning in coastal areas. Validation by media and content experts, along with trials conducted among students, showed high feasibility and strong appeal of the application. Functionality tests, including marker detection range, demonstrated the application's capability to operate effectively in diverse coastal environments. The study highlights the successful integration of AR and gamification as a tool to improve engagement and understanding of natural science concepts in under-resourced coastal schools. This application holds great potential for enhancing the quality of education in coastal areas by offering an interactive and immersive learning experience. Further research is recommended to refine the application and assess its long-term impact on student comprehension and educational outcomes in various coastal communities. This study represents a significant step forward in using innovative AR technology to support learning in coastal regions.
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AlQuraan, Mahmoud. „The effect of insufficient effort responding on the validity of student evaluation of teaching“. Journal of Applied Research in Higher Education 11, Nr. 3 (01.07.2019): 604–15. http://dx.doi.org/10.1108/jarhe-03-2018-0034.
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Purpose The purpose of this paper is to investigate the effect of insufficient effort responding (IER) on construct validity of student evaluations of teaching (SET) in higher education. Design/methodology/approach A total of 13,340 SET surveys collected by a major Jordanian university to assess teaching effectiveness were analyzed in this study. The detection method was used to detect IER, and the construct (factorial) validity was assessed using confirmatory factor analysis (CFA) and principal component analysis (PCA) before and after removing detected IER. Findings The results of this study show that 2,160 SET surveys were flagged as insufficient effort responses out of 13,340 surveys. This figure represents 16.2 percent of the sample. Moreover, the results of CFA and PCA show that removing detected IER statistically enhanced the construct (factorial) validity of the SET survey. Research limitations/implications Since IER responses are often ignored by researchers and practitioners in industrial and organizational psychology (Liu et al., 2013), the results of this study strongly suggest that higher education administrations should give the necessary attention to IER responses, as SET results are used in making critical decisions Practical implications The results of the current study recommend universities to carefully design online SET surveys, and provide the students with clear instructions in order to minimize students’ engagement in IER. Moreover, since SET results are used in making critical decisions, higher education administrations should give the necessary attention to IER by examining the IERs rate in their data sets and its consequences on the data quality. Originality/value Reviewing the related literature shows that this is the first study that investigates the effect of IER on construct validity of SET in higher education using an IRT-based detection method.
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Jambhulkar, Prof P. J., Rushikesh Mane, Niraj Karande, Sumit Sunke und Sarthak Nirgude. „Emotion Analysis from Face and Speech: A Comprehensive Review of Current Techniques and Models“. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, Nr. 12 (04.12.2024): 1–6. https://doi.org/10.55041/ijsrem39454.
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Emotion detection in facial expressions and speech plays a crucial role in enhancing interactive platforms, particularly in learning and assessment systems. This study explores advanced techniques for integrating dynamic mock test generation and interview simulation modules in an Advanced Placement Preparation Platform. The dynamic mock test uses real-time feedback to recommend questions based on a student’s performance, leveraging machine learning algorithms for adaptive learning. Additionally, the interview simulation module incorporates facial expression recognition using Convolutional Neural Networks (CNNs) and speech analysis using Recurrent Neural Networks (RNNs) to evaluate student performance. Initial results show that traditional models struggle with real-time adaptability and emotion classification accuracy, underscoring the need for specialized algorithms for complex data. To address these limitations, the system evaluates deep learning models designed for adaptive learning and emotion analysis, such as transfer learning models in emotion detection. The findings highlight the potential for using multimodal data to improve user engagement and performance evaluation in educational settings, paving the way for more immersive and intelligent learning platforms.
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Tam, Yi Qian, Anis Nur Shasha Abdul Halim, Aemi Syazwani Abdul Keyon und Nur Safwati Mohd Nor. „Development of an Arduino-Based Photometer for Reactive Red 120 Dye Detection COVID-19“. Asean Journal of Engineering Education 8, Nr. 2 (29.12.2024): 90–96. https://doi.org/10.11113/ajee2024.8n2.161.
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During the COVID-19 pandemic, traditional laboratory access was limited, prompting innovative approaches to education and research. An undergraduate chemistry student undertook the development of an Arduino-based photometer as a remote learning final year project. This project aimed to measure RR-120 dye concentrations in water using affordable and accessible technology, providing practical experience in photometry, electronics, and programming despite the constraints of the pandemic. The removal of Reactive Red 120 from water bodies is a significant environmental concern. The photometer, constructed using an Arduino UNO microcontroller, a green LED (500-570 nm), resistors, a plastic cuvette, and a BH1750FVI digital light intensity sensor module, is designed to be a cost-effective and user-friendly device for classroom and laboratory use. The device's operation is controlled via Arduino IDE 1.8.19 software, providing hands-on experience with programming and electronics. Calibration with seven RR-120 solutions (0.2 to 1.4 mg/L) produced an R² value of 0.9823, with a detection limit of 0.47 mg/L. The photometer achieved 98.66% accuracy demonstrating its reliability. Performance comparison with a commercial benchtop UV-Visible spectrophotometer (R² = 0.9956) further demonstrates the photometer’s reliability. This Arduino-based photometer not only offers a practical application for teaching principles of photometry and spectroscopy but also illustrates the integration of affordable technology in scientific education, enhancing student engagement and learning in STEM fields during challenging times. Most importantly, this device also provided hands-on experience in photometry, enhancing remote learning during restricted lab access. The device’s scalability suggests potential applications in broader environmental monitoring and educational settings, beyond the initial scope of dye detection.
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Kumar, Vishesh, und Marcelo Worsley. „Scratch for Sports: Athletic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 13 (26.06.2023): 16011–16. http://dx.doi.org/10.1609/aaai.v37i13.26901.
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Culturally relevant and sustaining implementations of computing education are increasingly leveraging young learners' passion for sports as a platform for building interest in different STEM (Science, Technology, Engineering, and Math) concepts. Numerous disciplines spanning physics, engineering, data science, and especially AI based computing are not only authentically used in professional sports in today's world, but can also be productively introduced to introduce young learnres to these disciplines and facilitate deep engagement with the same in the context of sports. In this work, we present a curriculum that includes a constellation of proprietary apps and tools we show student athletes learning sports like basketball and soccer that use AI methods like pose detection and IMU-based gesture detection to track activity and provide feedback. We also share Scratch extensions which enable rich access to sports related pose, object, and gesture detection algorithms that youth can then tinker around with and develop their own sports drill applications. We present early findings from pilot implementations of portions of these tools and curricula, which also fostered discussion relating to the failings, risks, and social harms associated with many of these different AI methods – noticeable in professional sports contexts, and relevant to youths' lives as active users of AI technologies as well as potential future creators of the same.
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Pellegrino, PhD, Jeffrey L., Brian Miller, MS, Jamillee Krob, DHEd und Travis Darago. „Intention to vaccinate against COVID-19 and potential outreach strategies for a residential research university in Northeastern Ohio“. Journal of Emergency Management 21, Nr. 7 (28.02.2023): 185–202. http://dx.doi.org/10.5055/jem.0729.
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Introduction: A university’s understanding of student, faculty, and staff members’ intention to vaccinate against COVID-19 has been vital in returning safely to in-person education, research, and engagement with communities and professions. We employed a novel survey to describe intentions across subpopulations of one campus and consider key issues in their rationales for intentions and hesitancies. Materials and methods: 1,077 surveys based on Theory of Planned Behavior were completed from randomly selected pools of undergraduate students, graduate students, part-time faculty, full-time faculty, and staff. Chi-Squared Automated Interaction Detection algorithm analysis provided paths for evaluation. Results and discussion: 83 percent of respondents said they would receive the vaccine at their first opportunity, while 5 percent said they would never get the vaccine; the remaining 12 percent wanted more evidence before getting the vaccine. Findings included negative health perceptions of the vaccine, misinformation on the process, as well as negative rhetorical responses differentiated between political partisanship and membership within the campus community, eg, faculty, staff, or student.Implications: Universities seeking to raise campus vaccination rates should concentrate limited resources on the largest populations with the most opportunity to vaccinate. In this study, newer students, with conservative political views, represented a population of opportunity. Their formative beliefs may be influenced by messaging and in collaboration with students’ personal physician and/or friend groups. A theory-based approach leads to focused efforts for safer campuses and resumption/continuation of face-to-face interactions for students, faculty, and staff.
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Abiodun, Pelumi, Oludare Owolabi, Adebayo Olude und Petronella James-Okeke. „The impact of teaching noise detection and control strategies among historically black college and university student using hands-on pedagogy on student's motivation and curiosity“. INTER-NOISE and NOISE-CON Congress and Conference Proceedings 266, Nr. 1 (25.05.2023): 1321–31. http://dx.doi.org/10.3397/nc_2023_0182.
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Noise engineering is not a new field of study but statistics showed that experts in the field are on a decline. Observing that motivation and curiosity are among the hallmarks of any workforce development pipeline, the study developed an experiment-centric pedagogy to detect and measure noise from pollution using low-cost hands-on devices with the aim of motivating learners. The study design was a pre- and post-test method. The learners were enrolled in a transportation course and the noise detection and measurement strategies course module was used for the study. Motivated Strategies Learning Questionnaire was adopted for the study. Learners response to the use of technological tools incorporated in learning was predominantly positive revealing that the learners' gain extensively. More so, significant improvement was observed in the critical thinking of leaners (p < 0 .05) and overall, there was an increase in their motivation at the post-test. Significant improvement in academic performance of learners was also observed at post-test (p < 0 .05). It is therefore posited that there is need for effective engagement with learners with similar low-cost hands-on to lead to better understand and motivation that can lead to development of workforce in noise engineering.
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Komaravalli, Purushottama Rao, und Janet B. „Detecting Academic Affective States of Learners in Online Learning Environments Using Deep Transfer Learning“. Scalable Computing: Practice and Experience 24, Nr. 4 (17.11.2023): 957–70. http://dx.doi.org/10.12694/scpe.v24i4.2470.
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Online Learning Environments (OLEs) have become essential in global education, especially during and after the COVID-19 pandemic. However, OLEs face a challenge in recognizing student emotions, hindering educators' ability to provide effective support. To address this issue, researchers emphasize the importance of a balanced dataset and a precise model for academic emotion detection in OLEs. However, the widely-used DAiSEE dataset is imbalanced and contains videos captured in well-lit environments. However, real-time observations reveal students' diverse lighting conditions and proximity to cameras. Consequently, models trained on DAiSEE dataset exhibit poor accuracy. In response, this work suggests a customized DAiSEE dataset and proposes the Xception-based transfer learned model and AffectXception model. Our customization process involves selectively extracting single-label frames with intensity levels 2 or 3 from the original DAiSEE dataset. To enhance dataset diversity and tackle the issue of dataset imbalance, we meticulously apply data augmentation techniques on these extracted frames. This results in frames that showcase variations in lighting, both low and high, as well as diverse camera perspectives. As a result, the customized DAiSEE dataset is now well-balanced and exceptionally suitable for training deep learning models to detect academic emotions in online learners. Then we trained and tested both proposed models on this dataset. The AffectXception model outperforms existing models, achieving significant improvements. For Boredom, Engagement, Confusion, and Frustration, it attains accuracy rates of 77%, 79.28%, 83.76%, and 91.87%, respectively. Additionally, we evaluate the AffectXception model on the Online Learning Spontaneous Facial Expression Database (OL-SFED), obtaining competitive results across various emotion classes. This work empowers educators to adjust their content and delivery methods based on learners' emotional states, resulting in more effective and informative online sessions. As OLEs continue to play a crucial role in education, our approach enhances their capacity to address students' emotional needs.
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Selvakumar, Jashan, Jiann Lin Loo und May Honey Ohn. „OpenMinds on Mental Health Literacy: A Reflective Journey of a Medical Student“. BJPsych Open 8, S1 (Juni 2022): S34. http://dx.doi.org/10.1192/bjo.2022.151.
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AimsAs a medical student from a local university, the first author undertook a mental health education course, i.e. OpenMinds at the King's College University. The aim of the course is to improve literacy about key mental health issues that children and adolescents face and the stigma against mental illnesses. Upon completion of training, a medical student will be able to lead intervention workshops to share the mental health knowledge with local school audiences on these issues, promote early detection of mental illnesses among the audiences and their peers with the aim of improving health-seeking behaviour by providing information of where to access help to reduce the duration of untreated illness. This article is aimed to describe the personal reflective experience of a medical student and the lessons learnt.MethodsThe OpenMinds course was an eight-week workshop on important mental health topics such as depression, anxiety, coping strategies and psychosis. This was followed by a session on effective teaching detailing various techniques including maintaining children's concentration, increasing engagement by utilising different learning techniques, safeguarding and maintaining well-being during conversations about difficult and sensitive topics.ResultsAfter attending the OpenMinds educational workshop, the first author had delivered three workshops (one primary school and two secondary schools) as part of the bigger organising team from the other university. Overall, the verbal feedback from the local schools on the workshops was positive (Kirkpatrick's evaluation outcome level one). The challenge faced was virtual teaching due to the COVID-19 pandemic which meant not being able to read facial expressions or body language while delivering information. This limitation could be mitigated by having a trained teacher moderating the sessions on-site and making sure the workshops ran smoothly. Online lessons emphasised the use of technology which was proven to be useful as videos and other audiovisual aids had the ability to keep the children engaged and provide different sources of learning concurrently.ConclusionHaving participated in this course, the first author has learned teaching skills and a better way of communicating mental health issues to vulnerable audiences. Although face-to-face workshops are still not possible at the time of writing, the first author is keen to set up an OpenMinds branch at his university and be able to share with his fellow colleagues these skills in the future.
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Uçar, Mustafa Uğur, und Ersin Özdemir. „Recognizing Students and Detecting Student Engagement with Real-Time Image Processing“. Electronics 11, Nr. 9 (07.05.2022): 1500. http://dx.doi.org/10.3390/electronics11091500.
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With COVID-19, formal education was interrupted in all countries and the importance of distance learning has increased. It is possible to teach any lesson with various communication tools but it is difficult to know how far this lesson reaches to the students. In this study, it is aimed to monitor the students in a classroom or in front of the computer with a camera in real time, recognizing their faces, their head poses, and scoring their distraction to detect student engagement based on their head poses and Eye Aspect Ratios. Distraction was determined by associating the students’ attention with looking at the teacher or the camera in the right direction. The success of the face recognition and head pose estimation was tested by using the UPNA Head Pose Database and, as a result of the conducted tests, the most successful result in face recognition was obtained with the Local Binary Patterns method with a 98.95% recognition rate. In the classification of student engagement as Engaged and Not Engaged, support vector machine gave results with 72.4% accuracy. The developed system will be used to recognize and monitor students in the classroom or in front of the computer, and to determine the course flow autonomously.
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MARTYNYUK, OLENA, und OLHA ORLOVSKA. „INTRODUCING ACTIVE LANGUAGE LEARNING TECHNIQUES INTO A VIRTUAL CLASSROOM: REFLECTION ON THE AMERICAN PRACTICES“. Comparative Professional Pedagogy 13, Nr. 1 (25.05.2023): 44–52. http://dx.doi.org/10.31891/2308-4081/2023-13(1)-6.
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The paper considers American active learning (AL) practices that can be used in a virtual language learning classroom in Ukrainian higher education institutions to encourage students’ engagement, collaboration and evaluate their performance. The authors study the concept of AL, its main techniques and peculiarities of application; outline technologies and tools that have the potential to influence active language learning (ALL) in a virtual classroom; define the techniques that can be used to promote ALL in a virtual classroom (polls and surveys, discussions and forums, case studies, interactive lectures, simulations and games, collaborative projects, personalized learning).
Particular attention is paid to online AL strategies applied by Columbia University and Cornell University (USA). Here belong Online Polling, Think-Pair-Share, a Minute Paper, Small Group Discussion and Short Student Presentation strategies, each of them requiring appropriate online learning platforms, services and tools for its effective implementation. They include Zoom videoconferencing platform with its breakout rooms, polling, screen sharing, whiteboard and nonverbal feedback features, Poll Everywhere, CourseWorks Quiz, Canvas Quiz features, collaborative online tools such as LionMail (Google) Docs, Sheets, Slides, etc. Another important issue considered in the paper is assessment and evaluation of students’ progress in AL.
Assessment techniques used in Cornell University comprise Grading Rubrics, plagiarism detection, self-assessment, peer assessment, surveys and classroom polling. In this context, Canvas Rubrics, Canvas Assignments, FeedbackFruits, Gradescope, Qualtrics, Turnitin can be used as effective assessment tools. The authors conclude that the main advantages of ALL are its flexibility, collaborative learning opportunities, customization options, access to online resources while its challenges involve lack of face-to-face interaction, technical difficulties, high pricing plans for some online services and tools, variety of distractions such as social media, email, or other online activities, and limited learning environment.
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Mujtaba, Bahaudin. „Clarifying Ethical Dilemmas in Sharpening Students’ Artificial Intelligence Proficiency: Dispelling Myths About Using AI Tools in Higher Education“. Business Ethics and Leadership 8, Nr. 2 (03.07.2024): 107–27. http://dx.doi.org/10.61093/bel.8(2).107-127.2024.
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Artificial intelligence has been talked about for over half a century now. Still, it became a fast-growing reality in 2023 through modern technologies, such as Meta AI, Open AI, or ChatGPT, and has created some ethical concerns. This research provides examples of how AI is being used in academia, how it can be used, and how to assess college students’ familiarity with such technologies, their perception of it, and level of usage. Using an AI-generated short survey to gather quantitative and qualitative data through a discussion exercise, 126 undergraduates with four different professors were asked to share their answers and views. The findings show that many of today’s college students in South Florida see the usage of AI as ethical and legal. However, a few respondents remain uncertain due to a lack of clear guidelines from professors and the institution. Thus, most respondents reported that they are familiar with AI as they use it multiple times weekly. Consequently, educators and administrators must sharpen their students’ AI skills so they can be ethical and competitive in the workplace. Implications for students, educators and administrators in the higher education arena are explored. Besides serving as a person’s second brain, using AI can be an excellent way for students to mitigate and overcome procrastination, enhance their productivity, and comprehensively complete academic projects on time. Furthermore, the proper use of AI tools can reduce errors, quickly assess large amounts of data, automate repetitive functions, lead to better decisions, and help learners move forward amid challenging obstacles. As such, academic institutions must do more to ensure they are “sharpening their students’ AI saw” before they graduate and embark on their professional endeavors. Artificial intelligence, when used properly, ethically, and legally following established industry norms and guidelines, offers many transformative benefits across diverse fields to benefit human beings and society. Students pursuing a healthcare career can use AI to aid in early disease detection, accelerate drug discovery, and improve patient care through precision medicine. Graduates in the engineering or transportation industries can use AI to optimize traffic flow, enhance safety with autonomous vehicles, and reduce emissions through predictive maintenance. Moreover, those who remain in the education field after graduation can use AI to facilitate personalized learning experiences tailored to individual student needs while fostering greater engagement and academic success for all learners. The latest advancements underscore AI’s potential to drive innovation, increase efficiency, and address complex challenges while ultimately shaping a more interconnected and prosperous future for everyone in society.
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Khenkar, Shoroog Ghazee, Salma Kammoun Jarraya, Arwa Allinjawi, Samar Alkhuraiji, Nihal Abuzinadah und Faris A. Kateb. „Deep Analysis of Student Body Activities to Detect Engagement State in E-Learning Sessions“. Applied Sciences 13, Nr. 4 (17.02.2023): 2591. http://dx.doi.org/10.3390/app13042591.
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In this paper, we propose new 3D CNN prediction models for detecting student engagement levels in an e-learning environment. The first generated model classifies students’ engagement to high positive engagement or low positive engagement. The second generated model classifies engagement to low negative engagement or disengagement. To predict the engagement level, the proposed prediction models learn the deep spatiotemporal features of the body activities of the students. In addition, we collected a new video dataset for this study. The new dataset was collected in realistic, uncontrolled settings from real students attending real online classes. Our findings are threefold: (1) Spatiotemporal features are more suitable for analyzing body activities from video data; (2) our proposed prediction models outperform state-of-the-art methods and have proven their effectiveness; and (3) our newly collected video dataset, which reflects realistic scenarios, contributed to delivering comparable results to current methods. The findings of this work will strengthen the knowledge base for the development of intelligent and interactive e-learning systems that can give feedback based on user engagement.
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Zhang, Zhaoli, Zhenhua Li, Hai Liu, Taihe Cao und Sannyuya Liu. „Data-driven Online Learning Engagement Detection via Facial Expression and Mouse Behavior Recognition Technology“. Journal of Educational Computing Research 58, Nr. 1 (08.02.2019): 63–86. http://dx.doi.org/10.1177/0735633119825575.
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Online learning engagement detection is a fundamental problem in educational information technology. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real time. To improve the accuracy of learning engagement detection, we have collected two aspects of students’ behavior data: face data (using adaptive weighted Local Gray Code Patterns for facial expression recognition) and mouse interaction. In this article, we propose a novel learning engagement detection algorithm based on the collected data (students’ behavior), which come from the cameras and the mouse in the online learning environment. The cameras were utilized to capture students’ face images, while the mouse movement data were captured simultaneously. In the process of image data labeling, we built two datasets for classifier training and testing. One took the mouse movement data as a reference, while the other did not. We performed experiments on two datasets using several methods and found that the classifier trained by the former dataset had a better performance, and its recognition rate is higher than that of the latter one (94.60% vs. 91.51%).
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Hasnine, Mohammad Nehal, Huyen T. T. Bui, Thuy Thi Thu Tran, Ho Tran Nguyen, Gökhan Akçapınar und Hiroshi Ueda. „Students’ emotion extraction and visualization for engagement detection in online learning“. Procedia Computer Science 192 (2021): 3423–31. http://dx.doi.org/10.1016/j.procs.2021.09.115.
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Hasnine, Mohammad Nehal, Ho Tan Nguyen, Thuy Thi Thu Tran, Huyen T. T. Bui, Gökhan Akçapınar und Hiroshi Ueda. „A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States“. Sensors 23, Nr. 9 (24.04.2023): 4243. http://dx.doi.org/10.3390/s23094243.
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Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students’ affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners’ affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners’ affective states on lecturers’ screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners’ five types of engagement (“strong engagement”, “high engagement”, “medium engagement”, “low engagement”, and “disengagement”) and two types of concentration levels (“focused” and “distracted”). Furthermore, the dashboard is designed to provide insight into students’ emotional states, the clusters of engaged and disengaged students’, assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment.
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Qi, Yongfeng, Liqiang Zhuang, Huili Chen, Xiang Han und Anye Liang. „Evaluation of Students’ Learning Engagement in Online Classes Based on Multimodal Vision Perspective“. Electronics 13, Nr. 1 (29.12.2023): 149. http://dx.doi.org/10.3390/electronics13010149.
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The method of evaluating student engagement in online classrooms can provide a timely alert to learners who are distracted, effectively improving classroom learning efficiency. Based on data from online classroom scenarios, a cascaded analysis network model integrating gaze estimation, facial expression recognition, and action recognition is constructed to recognize student attention and grade engagement levels, thereby assessing the level of student engagement in online classrooms. Comparative experiments with the LRCN model, C3D network model, etc., demonstrate the effectiveness of the cascaded analysis network model in evaluating engagement, with evaluations being more accurate than other models. The method of evaluating student engagement in online classrooms compensates for the shortcomings of single-method evaluation models in detecting student engagement in classrooms.
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Bowden, Vanessa, Luke Ren und Shayne Loft. „Supervising High Degree Automation in Simulated Air Traffic Control“. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, Nr. 1 (September 2018): 86. http://dx.doi.org/10.1177/1541931218621019.
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Implementing high degree automation in future air traffic control (ATC) systems will be crucial for coping with increased air traffic demand and maintaining safety. However, issues associated with the passive monitoring role assumed by operators in these systems continue to be of concern. Passive monitoring can lead to a range of human operator performance problems when overseeing automation. The performance cost when human operators are placed in a passive monitoring role has been conceptualized as the out-of-the-loop (OOTL) performance problem: where adding more automation to a system makes it less likely that the operator will notice an automation failure and intervene appropriately (Endsley & Kiris, 1995). The OOTL performance problem has been attributed to numerous factors including vigilance decrements, fatigue, task disengagement, and poor situation awareness. This study tested two different approaches to addressing the OOTL performance problem associated with high degree automation in a simulation of en-route ATC (ATC-labAdvanced; Fothergill, Loft, & Neal, 2009). Following a 60-min training and practice session, 115 university student participants completed two 30-min ATC scenarios; one under manual control and one where they supervised high degree automation (counterbalanced order). The automation performed all acceptances for aircraft entering the sector of controlled airspace, handed off all departing aircraft, and resolved all conflicts between aircraft pairs that would otherwise have violated the minimum safe separation standards (except for a single automation failure event). Participants were instructed that the automation was highly reliable, but not infallible. The first aim was to confirm that while high degree automation can reduce workload, it can also lead to increased task disengagement and fatigue when compared to manual control. Furthermore, to determine how well participants supervised the automation, the conflict detection automation failed once late in the automation scenario. This failure involved two aircraft violating the minimum lateral and vertical separation standard and being missed by the automation. We expected to find that participants would fail to detect this conflict more often, or be slower to detect it, when under automation conditions, compared to a comparable conflict event presented when under manual control. Our second aim was to investigate whether these costs of automation could be ameliorated by techniques designed to improve task engagement. Participants were assigned to one of three automation conditions, including automation with (1) no acknowledgements, (2) acknowledgments, or (3) queries. In the no acknowledgements condition, automation failure monitoring was the only task performed. In the acknowledgements condition, similar to Pop et al. (2012), participants were additionally instructed to click to acknowledge each automated action, thereby potentially improving engagement by adding an active component to an otherwise passive monitoring task. In the queries condition, participants were queried regarding the past, present, and future state of aircraft on the display. The goal was to help participants maintain an accurate mental model (aka. situation awareness) when using automation. We found that automation reduced workload, increased disengagement and fatigue, and impaired detection of a single conflict detection failure event compared to manual task performance. Consistent with previous research, this shows that as a higher degree of automation is added to a system, it becomes less likely that the operator will notice automation failures and intervene appropriately (e.g. Pop et al., 2012). The first intervention tested whether adding automation acknowledgement requirements to the task made it easier for participants to detect and resolve a single automation failure event. The results showed that there was no difference between automation with and without acknowledgement requirements on workload, task disengagement, fatigue, and the detection of the automation failure event. The second intervention tested whether adding queries regarding aircraft on the display would improve failure detection performance. The queries intervention successfully reduced task disengagement and trended towards reducing fatigue, while workload was maintained at a level similar to that of manual control. These findings suggest that the manipulation successfully reduced some of the subjective deficits associated with the passive monitoring of automation. However, there was a significant cost to participants’ ability to detect and resolve the automation failure event relative to manual performance, where half the participants in the queries condition missed the automation failure entirely, compared to 25% in the no queries condition. Response times to detect the failure event were also considerably longer when queries were included compared to no queries. One explanation is that the queries condition may have been engaging to the point of distraction. This is supported by qualitative information provided by participants, where 40% mentioned that they found the queries to be distracting. Future studies may wish to examine the effectiveness of auditory queries instead of visual queries, potentially with verbal instead of typed responses. This may allow queries to reduce task disengagement and fatigue while potentially improving participants’ ability to intervene to automation failures.
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Pan, Hui-Ling Wendy. „Advancing Student Learning Power by Operating Classrooms as Learning Communities: Mediated Effects of Engagement Activities and Social Relations“. Sustainability 15, Nr. 3 (30.01.2023): 2461. http://dx.doi.org/10.3390/su15032461.
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Schools are responsible for developing students’ learning abilities in order to prepare them for the future. However, learning power was rarely explored in previous studies. This study considered classrooms as a proximal level of influences from ecologically-oriented systems theory and therefore centered on exploring the effects of operating classrooms as learning communities (CaLC) on students’ learning power. Learner-centered teaching, which includes the components of inquiry, collaboration, and expression, was used to assess how far CaLC has progressed. It comprises the classroom processes, along with student engagement activities (i.e., inquiry, collaboration, and expression), and classroom social relations. By employing a mediation model, this study aimed to disentangle the effects of classroom processes on learning power. A total of 1478 students from 14 junior high schools in Taiwan participated in the survey. The findings indicate that student perceptions of learner-centered teaching, engagement activities, social relations, and learning power all reached a high-intermediate level. It also found that learner-centered teaching directly affected learning power and exerted indirect effects through engagement activities and social relations. This study contributes to the research on the learning community by providing a more comprehensive analytical framework for detecting the impact of classroom processes. Besides, the three identified components (i.e., inquiry, collaboration, and expression) of CaLC can be a practical guide for the instructional practice of learner-centeredness.
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Xiao, Guangrun, Qi Xu, Yantao Wei, Huang Yao und Qingtang Liu. „Occlusion Robust Cognitive Engagement Detection in Real-World Classroom“. Sensors 24, Nr. 11 (03.06.2024): 3609. http://dx.doi.org/10.3390/s24113609.
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Cognitive engagement involves mental and physical involvement, with observable behaviors as indicators. Automatically measuring cognitive engagement can offer valuable insights for instructors. However, object occlusion, inter-class similarity, and intra-class variance make designing an effective detection method challenging. To deal with these problems, we propose the Object-Enhanced–You Only Look Once version 8 nano (OE-YOLOv8n) model. This model employs the YOLOv8n framework with an improved Inner Minimum Point Distance Intersection over Union (IMPDIoU) Loss to detect cognitive engagement. To evaluate the proposed methodology, we construct a real-world Students’ Cognitive Engagement (SCE) dataset. Extensive experiments on the self-built dataset show the superior performance of the proposed model, which improves the detection performance of the five distinct classes with a precision of 92.5%.
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Lasekan, Olusiji Adebola, Vengalarao Pachava, Margot Teresa Godoy Pena, Siva Krishna Golla und Mariya Samreen Raje. „Investigating Factors Influencing Students’ Engagement in Sustainable Online Education“. Sustainability 16, Nr. 2 (12.01.2024): 689. http://dx.doi.org/10.3390/su16020689.
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Due to the COVID-19 pandemic, there has been a rapid shift from traditional classroom-based education to sustainable online classrooms. This has brought attention to the importance of comprehending the intricacies of students’ engagement during virtual learning. Drawing upon the concept of community of inquiry in cognitive, social, and teaching presence, a mixed-methods approach involved data collected via a structured questionnaire administered to 452 university students to identify the factors that influence students’ participation during online classes. Through the application of the CHAID (Chi-Squared Automatic Interaction Detection) decision tree algorithm, the quality of course content is identified as a cognitive predictor of students’ engagement. It is worth mentioning that a significant proportion of students, specifically 61.7%, demonstrated a considerable degree of engagement with faculty content due to its high quality. With respect to the role of social presence, possession of a designated private space boost (69.2%) and requiring students to use their webcams are found to be critical for students’ engagement. Lastly, teaching presence as a factor in enhancing students’ engagement is demonstrated by promoting interactive classrooms and providing rapid feedback to students by teachers. The findings demonstrate the importance of teachers’ quality course material, interactive classes, and the benefits of a physical environment free from distractions for optimizing students’ engagement.