Artificial intelligence based cognitive state prediction in an e-learning environment using multimodal data

A Literature Review on Impact of COVID-19 Pandemic on Teaching and Learning

Pokhrel, S., & Chhetri, R. (2021) · Higher Education for the Future · doi:10.1177/2347631120983481 · Cited by 2,400 · see citing works

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103795 · Cited by 848 · see citing works

The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions

Whitehill, J., Serpell, Z. N., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2316163 · Cited by 694 · see citing works

ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors

Subramanian, R., Wache, J., Abadi, M. K., Vieriu, R. L., WINKLER, S. S., & Sebe, N. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2625250 · Cited by 536 · see citing works

The Challenges of Online Learning during the COVID-19 Pandemic: An Essay Analysis of Performing Arts Education Students

Simamora, R. M. (2020) · Studies in Learning and Teaching · doi:10.46627/silet.v1i2.38 · Cited by 424 · see citing works

Facial Landmark Detection: A Literature Survey

Wu, Y., & Ji, Q. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1097-z · Cited by 421 · see citing works

Multimodal sentiment analysis using hierarchical fusion with context modeling

Majumder, N., Hazarika, D., Gelbukh, A. F., Cambria, E., & Poria, S. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.07.041 · Cited by 385 · see citing works

Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning

D’Mello, S. K., Dieterle, E., & Duckworth, A. L. (2017) · Educational Psychologist · doi:10.1080/00461520.2017.1281747 · Cited by 378 · see citing works

Gaze tutor: A gaze-reactive intelligent tutoring system

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A selective meta-analysis on the relative incidence of discrete affective states during learning with technology.

D’Mello, S. K. (2013) · Journal of Educational Psychology · doi:10.1037/a0032674 · Cited by 305 · see citing works

Pay attention!: designing adaptive agents that monitor and improve user engagement

Szafir, D., & Mutlu, B. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2207679 · Cited by 304 · see citing works

Audio-Visual Emotion Recognition in Video Clips

Noroozi, F., Marjanovic, M., Njeguš, A., Escalera, S., & Anbarjafari, G. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2713783 · Cited by 269 · see citing works

Towards an intelligent framework for multimodal affective data analysis

Poria, S., Cambria, E., Hussain, A., & Huang, G.-B. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.005 · Cited by 236 · see citing works

Facial emotion recognition based real-time learner engagement detection system in online learning context using deep learning models

Gupta, S., Kumar, P., & Tekchandani, R. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13558-9 · Cited by 232 · see citing works

Emotion Recognition From Multimodal Physiological Signals Using a Regularized Deep Fusion of Kernel Machine

Zhang, X., Liu, J., Shen, J. G., Li, S., Hou, K., Hu, B., Gao, J. G., Zhang, T., & Hu, B. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2987575 · Cited by 209 · see citing works

EmotiCon: Context-Aware Multimodal Emotion Recognition Using Frege’s Principle

Mittal, T., Guhan, P., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01424 · Cited by 187 · see citing works

Automatic Detection of Learning-Centered Affective States in the Wild

Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701397 · Cited by 178 · see citing works

Students’ behavior mining in e-learning environment using cognitive processes with information technologies

Jalal, A., & Mahmood, M. (2019) · Education and Information Technologies · doi:10.1007/s10639-019-09892-5 · Cited by 172 · see citing works

Student Emotion Recognition System (SERS) for e-learning Improvement Based on Learner Concentration Metric

Krithika, L. B., & Priya, G. G. L. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.264 · Cited by 166 · see citing works

Audio-visual emotion fusion (AVEF): A deep efficient weighted approach

Ma, Y., Hao, Y., Chen, M., Chen, J., Lu, P., & Košir, A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.06.003 · Cited by 155 · see citing works

E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics

Moubayed, A., Injadat, M., Nassif, A. B., Lutfiyya, H. L., & Shami, A. A. (2018) · IEEE Access · doi:10.1109/access.2018.2851790 · Cited by 154 · see citing works

Automatic Recognition of Student Engagement Using Deep Learning and Facial Expression

Nezami, O. M., Dras, M., Hamey, L., Richards, D., Wan, S., & Paris, C. L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46133-1_17 · Cited by 138 · see citing works

Towards multimodal emotion recognition in e-learning environments

Bahreini, K., Nadolski, R., & Westera, W. (2016) · Interactive Learning Environments · doi:10.1080/10494820.2014.908927 · Cited by 137 · see citing works

Breeze

Frey, J., Grabli, M., Slyper, R., & Cauchard, J. R. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174219 · Cited by 135 · see citing works

Secondary education teachers’ perceptions of mobile phone and tablet use in classrooms: benefits, constraints and concerns

Νικολοπούλου, Κ. (2020) · Journal of Computers in Education · doi:10.1007/s40692-020-00156-7 · Cited by 135 · see citing works

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Iwendi, C., Khan, S. A., Anajemba, J. H., et al. (2020) · Sensors · doi:10.3390/s20092559 · Cited by 130 · see citing works

Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods

Acı, Ç. İ., Kaya, M., & Mishchenko, Y. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.057 · Cited by 127 · see citing works

Mental effort detection using EEG data in E-learning contexts

Lin, F., & Kao, C.-M. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.03.020 · Cited by 122 · see citing works

Analysis of security and energy efficiency for shortest route discovery in low‐energy adaptive clustering hierarchy protocol using Levenberg‐Marquardt neural network and gated recurrent unit for intrusion detection system

Mittal, M., Iwendi, C., Khan, S. A., & Javed, A. R. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3997 · Cited by 120 · see citing works

Predicting student engagement in classrooms using facial behavioral cues

Thomas, C., & Jayagopi, D. B. (2017) · doi:10.1145/3139513.3139514 · Cited by 111 · see citing works

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed, A. R., Sarwar, M. U., Khan, S. A., Iwendi, C., Mittal, M., & Kumar, N. (2020) · Sensors · doi:10.3390/s20082216 · Cited by 110 · see citing works

Multi-cue fusion for emotion recognition in the wild

Yan, J., Zheng, W., Cui, Z., Tang, C., Zhang, T., & Zong, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.068 · Cited by 98 · see citing works

The role of usability on e-learning user interactions and satisfaction: a literature review

Gunesekera, A., Bao, Y., & Kibelloh, M. (2019) · Journal of Systems and Information Technology · doi:10.1108/jsit-02-2019-0024 · Cited by 96 · see citing works

Augmented Reality Experience: Initial Perceptions of Higher Education Students

Süral, İ. (2018) · International Journal of Instruction · doi:10.12973/iji.2018.11435a · Cited by 93 · see citing works

Locally Confined Modality Fusion Network With a Global Perspective for Multimodal Human Affective Computing

Mai, S., Xing, S., & Hu, H. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2925966 · Cited by 89 · see citing works

Multimodal Deep Learning Framework for Mental Disorder Recognition

Zhang, Z., Lin, W., Liu, M., & Mahmoud, M. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00033 · Cited by 89 · see citing works

Identification of malnutrition and prediction of BMI from facial images using real‐time image processing and machine learning

Dhanamjayulu, C., Nizhal, U. N., Maddikunta, P. K. R., Gadekallu, T. R., Iwendi, C., Wei, C., & Xin, Q. (2021) · IET Image Processing · doi:10.1049/ipr2.12222 · Cited by 88 · see citing works

Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems

Behera, A., Matthew, P. W., Keidel, A., Vangorp, P., Fang, H., & Canning, S. (2020) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00195-2 · Cited by 85 · see citing works

A Survey of Computational Methods for Online Mental State Assessment on Social Media

Ríssola, E. A., Losada, D. E., & Crestani, F. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3437259 · Cited by 84 · see citing works

N-Sanitization: A semantic privacy-preserving framework for unstructured medical datasets

Iwendi, C., Moqurrab, S. A., Anjum, A., Khan, S., Mohan, S., & Srivastava, G. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.07.032 · Cited by 80 · see citing works

Facial Expression Recognition With Two-Branch Disentangled Generative Adversarial Network

Xie, S., Hu, H., & Chen, Y. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3024201 · Cited by 76 · see citing works

Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks

Mittal, M., de Prado, R. P., Kawai, Y., et al. (2021) · Energies · doi:10.3390/en14113125 · Cited by 72 · see citing works

Understanding Attention to Adaptive Hints in Educational Games: An Eye-Tracking Study

Conati, C., Jaques, N., & Muir, M. (2013) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-013-0002-8 · Cited by 68 · see citing works

Deep Convolutional Neural Network for Facial Expression Recognition Using Facial Parts

Nwosu, L., Wang, H., Lu, J., et al. (2017) · IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) · doi:10.1109/dasc-picom-datacom-cyberscitec.2017.213 · Cited by 68 · see citing works

Support vector machine (SVM) classification of cognitive tasks based on electroencephalography (EEG) engagement index

Nuamah, J. K., & Seong, Y. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2017.1338012 · Cited by 65 · see citing works

EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings

Han, J., Zhang, Z., Ren, Z. X., & Schuller, B. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2928297 · Cited by 64 · see citing works

Face Detection Based on Viola-Jones Algorithm Applying Composite Features

Wen-yao, L. U., & Yang, M. (2019) · International Conference on Robots & Intelligent System · doi:10.1109/icris.2019.00029 · Cited by 63 · see citing works

COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients

Iwendi, C., Huescas, C. G. Y., Chakraborty, C., & Mohan, S. (2022) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2022.2058097 · Cited by 58 · see citing works

Online Learners’ Reading Ability Detection Based on Eye-Tracking Sensors

Zhan, Z., Zhang, L., Hu, M. Y., & Fong, P. S. W. (2016) · Sensors · doi:10.3390/s16091457 · Cited by 53 · see citing works

DeepCU: Integrating both Common and Unique Latent Information for Multimodal Sentiment Analysis

Verma, S., Wang, C., Zhu, L., & Liu, W. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/503 · Cited by 53 · see citing works

An EEG-Based Attentiveness Recognition System Using Hilbert–Huang Transform and Support Vector Machine

Peng, C.-J., Chen, Y., Chen, C., Chen, S.-J., Cagneau, B., & Chassagne, L. (2019) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-019-00500-y · Cited by 52 · see citing works

Mobile devices in early childhood education: teachers’ views on benefits and barriers

Νικολοπούλου, Κ. (2021) · Education and Information Technologies · doi:10.1007/s10639-020-10400-3 · Cited by 50 · see citing works

A Technology Acceptance Model-Based Analytics for Online Mobile Games Using Machine Learning Techniques

Chauhan, S., Mittal, M., Woźniak, M., Gupta, S., & de Prado, R. P. (2021) · Symmetry · doi:10.3390/sym13081545 · Cited by 47 · see citing works

Toward active and unobtrusive engagement assessment of distance learners

Booth, B. M., Ali, A., Narayanan, S. S., Bennett, I., & Farag, A. A. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273641 · Cited by 45 · see citing works

A Neuro-Fuzzy Approach for Intrusion Detection in Energy Efficient Sensor Routing

Mittal, M., Saraswat, L. K., Iwendi, C., et al. (2019) · International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU) · doi:10.1109/iot-siu.2019.8777501 · Cited by 42 · see citing works

Detection of Human Attention Using EEG Signals

Alirezaei, M., & Sardouie, S. H. (2017) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2017.8430244 · Cited by 39 · see citing works

EEG-based recognition of attention state using wavelet and support vector machine

Djamal, E. C., Pangestu, D. P., & Dewi, D. A. (2016) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia.2016.7828648 · Cited by 38 · see citing works

Learning Representations for Facial Actions From Unlabeled Videos

Li, Y., Zeng, J., & Shan, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3011063 · Cited by 38 · see citing works

Combining Skin Conductance and Heart Rate Variability for Adaptive Automation During Simulated IFR Flight

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Exploring the Predictive Role of E-Learning Readiness and E-Learning Style on Student Engagement

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Gauging attention of students in an e-learning environment

Revadekar, A., Oak, S., Gadekar, A., & Bide, P. J. (2020) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict51604.2020.9312048 · Cited by 35 · see citing works

A Correction Model for Real-word Errors

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Improved Advance Encryption Standard with a Privacy Database Structure for IoT Nodes

Anajemba, J. H., Iwendi, C., Mittal, M., et al. (2020) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt48778.2020.9115741 · Cited by 34 · see citing works

Network Lifetime Enhancement of Homogeneous Sensor Network Using ART1 Neural Network

Mittal, M., & Kumar, K. (2014) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn.2014.110 · Cited by 31 · see citing works

Multimodal Framework for Analyzing the Affect of a Group of People

Huang, X., Dhall, A., Goecke, R., Pietikäinen, M., & Zhao, G. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2818015 · Cited by 31 · see citing works

Attention monitoring for synchronous distance learning

Abate, A. F., Cascone, L., Nappi, M., Narducci, F., & Passero, I. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.07.026 · Cited by 30 · see citing works

A dynamic multimodal approach for assessing learners' interaction experience

Jraidi, I., Chaouachi, M., & Frasson, C. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2522896 · Cited by 29 · see citing works

Teaching automation with Factory I/O under a competency-based curriculum

Vargas, H., Heradio, R., Donoso, M., et al. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14047-9 · Cited by 29 · see citing works

An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches

Mittal, M., Kobielnik, M., Gupta, S., et al. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00344-z · Cited by 26 · see citing works

Attention Recognition System in Online Learning Platform Using EEG Signals

Gupta, S., & Kumar, P. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-1550-4_15 · Cited by 25 · see citing works

Student engagement study based on multi-cue detection and recognition in an intelligent learning environment

Liu, Y., Chen, J., Zhang, M., & Rao, C. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6017-2 · Cited by 24 · see citing works

M3ER: Multiplicative Multimodal Emotion Recognition using Facial, Textual, and Speech Cues

Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i02.5492 · Cited by 23 · see citing works

Joint low rank embedded multiple features learning for audio–visual emotion recognition

Wang, Z., Wang, L., & Huang, H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.017 · Cited by 23 · see citing works

Combining Facial Expression and Touch for Perceiving Emotional Valence

Tsalamlal, M. Y., Amorim, M., Martin, J., & Ammi, M. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2631469 · Cited by 20 · see citing works

Copula Ordinal Regression Framework for Joint Estimation of Facial Action Unit Intensity

Walecki, R., Rudovic, O. O., Pavlović, V., & Pantić, M. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2728534 · Cited by 18 · see citing works

Facial Features Detection and Localization

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Smart Learning System Based on EEG Signals

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Electroencephalography based detection of cognitive state during learning tasks: An extensive approach

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Type-2 fuzzy ontology-based multi-agents system for wireless sensor network

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Towards changing the paradigm of software development in software industries: An emergence of agile software development

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Audio-Visual Emotion Forecasting: Characterizing and Predicting Future Emotion Using Deep Learning

Shahriar, S., & Kim, Y. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756599 · Cited by 10 · see citing works

Cross-modality interaction between EEG signals and facial expression

Rayatdoost, S. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3137034 · Cited by 5 · see citing works

Investigating the Use of Spatialized Audio Augmented Reality to Enhance the Outdoor Running ExperiencePreprint

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Case Study-Based Predictive Linear Regression Model to Measure Anxiety and Depression as the Impact of COVID- 19 among Students in Higher EducationPreprint

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Experimental Study on Deep Learning Oriented to Learning Engagement Recognition

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Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis

Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works

Comparing the quality of signals recorded with a rapid response EEG and conventional clinical EEG systems

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Time Series of Awake Background EEG Generated by a Model Reflecting the EEG Report

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Which electroencephalography (EEG) for epilepsy? The relative usefulness of different EEG protocols in patients with possible epilepsy

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Sleep stage detection using chaotic feature analysis of Electroencephalography (EEG) signals

Zarei, F. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns7.13647

Comparison of awake Electroencephalography, Electroencephalography after Sleep Deprivation, and Melatonin-Induced Sleep Electroencephalography Sensitivity in the Diagnosis of Epilepsy in Adults

Demirtas, B. S., Uludağ, İ. F., Şener, U., & Zorlu, Y. (2022) · Neurological Sciences and Neurophysiology · doi:10.4103/nsn.nsn_101_22