Affective computing in online higher education: A systematic literature review

Agrawal, A., & Paepcke, A. The Stanford MOOCPosts data set, datastage. Stanford. edu. https://datastage.stanford.edu/StanfordMoocPosts/ (Accessed 2022).

[4]

Agarwal "Identifying the ethical values and norms of artificial intelligence in education: a systematic literature review" EdArXiV (2014)

[5]

Akhand "Improved eeg-based emotion recognition through information enhancement in connectivity feature map" Scientific Reports (2023) 10.1038/s41598-023-40786-2

[6]

Alkaabi "Detecting emotions behind the screen" AI (2022) 10.3390/ai3040056

[7]

Altuwairqi "Student behavior analysis to measure engagement levels in online learning environments" Signal, Image and Video Processing (2021) 10.1007/s11760-021-01869-7

[8]

Aly "Enhancing facial expression recognition system in online learning context using efficient deep learning model" IEEE Access (2023) 10.1109/access.2023.3325407

[9]

AlZu’bi "A novel deep learning technique for detecting emotional impact in online education" Electronics (2022) 10.3390/electronics11182964

[10]

Amsel "Arousal, suppression, and persistence: Frustration theory, attention, and its disorders" Cognition & Emotion (1990) 10.1080/02699939008410798

[11]

Ashwath "Early vs. Late multimodal fusion for recognizing confusion in collaborative tasks" (2023)

[12]

Athauda "CNN for facial emotion recognition in online learning platforms to identify learner engagement" (2023)

[13]

Bajaj "Classification of student affective states in online learning using neural networks" (2022)

[14]

Bala "An approach for learner categorization based on emotions in intelligent adaptive e-learning environment" Journal of Mobile Multimedia (2022)

[15]

Bian "Spontaneous facial expression database for academic emotion inference in online learning" IET Computer Vision (2019) 10.1049/iet-cvi.2018.5281

[16]

Bloom (1956)

[17]

Böttger "Privacy-preserving and scalable affect detection in online synchronous learning" (2022)

[18]

Boulanger "Lightweight and interpretable detection of affective engagement for online learners" (2021)

[19]

Cárdenas-López "Semantic fusion of facial expressions and textual opinions from different datasets for learning-centered emotion recognition" Soft Computing (2023) 10.1007/s00500-023-08076-1

[20]

Chango (2020)

[21]

Chen "Research on emotion recognition for online learning in a novel computing model" Applied Sciences (2022) 10.3390/app12094236

[22]

Chiaro "Unveiling engagement in virtual classrooms: a multimodal analysis" (2023)

[23]

Clore "Affective causes and consequences of social information processing" Handbook of Social Cognition (2014)

[24]

Corza-Vargas "Students’ ethical, privacy, design, and cultural perspectives on visualizing cognitive-affective states in online learning" Journal of Learning Analytics (2024)

[25]

Dynamics of affective states during complex learning

Sidney D’Mello, Art Graesser

Learning and Instruction 2012 10.1016/j.learninstruc.2011.10.001

[26]

D’Mello "A selective meta-analysis on the relative incidence of discrete affective states during learning with technology" Journal of Educational Psychology (2013) 10.1037/a0032674

[27]

Daily "Affective computing: historical foundations, current applications, and future trends" (2017)

[28]

Darwin "The expression of the emotions in man and animals (1872)" (1993)

[29]

Delgado "Student engagement dataset" (2021)

[30]

Dhall "Emotiw 2020: Driver gaze, group emotion, student engagement and physiological signal based challenges" (2020)

[31]

Online Learning: A Panacea in the Time of COVID-19 Crisis

Shivangi Dhawan

Journal of Educational Technology Systems 2020 10.1177/0047239520934018

[32]

Ding "Emotion recognition and achievement prediction for foreign language learners under the background of network teaching" Frontiers in Psychology (2022) 10.3389/fpsyg.2022.1017570

[33]

An argument for basic emotions

Paul Ekman

Cognition and Emotion 1992 10.1080/02699939208411068

[34]

Evangeline "Facial emotion recognition of online learners using a hybrid deep learning model" International Journal of Intelligent Engineering & Systems (2024)

[35]

Ez-Zaouia "Emodash: A dashboard supporting retrospective awareness of emotions in online learning" International Journal of Human-Computer Studies (2020) 10.1016/j.ijhcs.2020.102411

[36]

Fan "A student emotions recognition based online texts with large language models for learning prediction" (2024)

[37]

Fang "Facial expression recognition in educational research from the perspective of machine learning: A systematic review" IEEE Access (2023) 10.1109/access.2023.3322454

[38]

Farahmand "Improving students’ self-awareness by analyzing course discussion forum data" (2023)

[39]

Fwa "Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks" (2022)

[40]

Ganesan "Deep learning-based interactive dashboard for enhancing online classroom experience through student emotion analysis" IEEE Access (2024) 10.1109/access.2024.3421282

[41]

Gao "A parallel neural network structure for sentiment classification of MOOCs discussion forums" Journal of Intelligent & Fuzzy Systems (2020)

[42]

Geyu "Student engagement detection: Case study on using peer-to-peer emotion comparison with context regularization" (2023)

[43]

Gillett-Swan "The challenges of online learning: supporting and engaging the isolated learner" Journal of Learning Design (2017) 10.5204/jld.v9i3.293

[44]

Goodfellow "Challenges in representation learning: a report on three machine learning contests" (2013)

[45]

Graesser "Emotions during the learning of difficult material" Psychology of Learning and Motivation (2012) 10.1016/b978-0-12-394293-7.00005-4

[46]

Guerrero-Bote "A further step forward in measuring journals’ scientific prestige: The sjr2 indicator" Journal of Informetrics (2012) 10.1016/j.joi.2012.07.001

[47]

Guo "Engagement recognition in online learning based on an improved video vision transformer" (2023)

[48]

Gupta

[49]

Gupta "Affective teacher tools: affective class report card and dashboard" (2021)

[50]

Gupta "A multimodal facial cues based engagement detection system in e-learning context using deep learning approach" Multimedia Tools and Applications (2023) 10.1007/s11042-023-14392-3

Showing 50 of 143 references

Metrics

1

Citations

143

References

Details

Published: Jun 01, 2026
Vol/Issue: 10
Pages: 100499
License: View

Authors

Cite This Article

Krist Shingjergji, Deniz Iren, Corrie Urlings, et al. (2026). Affective computing in online higher education: A systematic literature review. Computers and Education: Artificial Intelligence, 10, 100499. https://doi.org/10.1016/j.caeai.2025.100499

Affective computing in online higher education: A systematic literature review

You May Also Like