Toward an artificial intelligence-based decision framework for developing adaptive e-learning systems to impact learners’ emotions

References

• Acampora, G., Gaeta, M., & Loia, V. (2011). Hierarchical optimization of personalized experiences for e-Learning systems through evolutionary models. Neural Computing and Applications, 20, 641–657. https://doi.org/10.1007/s00521-009-0273-z
   Web of Science ®Google Scholar
• Acampora, G., Loia, V., & Gaeta, M. (2010). Exploring e-learning knowledge through ontological memetic agents. IEEE Computational Intelligence Magazine, 66–77. https://doi.org/10.1109/MCI.2010.936306
   Web of Science ®Google Scholar
• Albano, G., Gaeta, M., & Salerno, S. (2006). E-learning: A model and process proposal. International Journal of Knowledge and Learning, 2(1–2), 73–88. https://doi.org/10.1504/IJKL.2006.009680
   Google Scholar
• Altuwairqi, K., Jarraya, S., Allinjawi, A., & Hammami, M. (2021). A new emotion-based effective model to detect student’s engagement. Journal of King Saud University - Computer and Information Sciences, 99–109. https://doi.org/10.1016/j.jksuci.2018.12.008
   Web of Science ®Google Scholar
• Ashwin, T. S., & Guddeti, R. M. R. (2020). Affective database for e-learning and classroom environments using Indian students’ faces, hand gestures and body postures. Future Generation Computer Systems, , 334–348. https://doi.org/10.1016/j.future.2020.02.075
   Web of Science ®Google Scholar
• Bahreini, K., Nadolski, R., & Westera, W. (2016a). Towards multimodal emotion recognition in E-learning environments. Interactive Learning Environments, 24(3), 590–605. https://doi.org/10.1080/10494820.2014.908927
   Web of Science ®Google Scholar
• Bahreini, K., Nadolski, R., & Westera, W. (2016b). Towards real-time speech emotion recognition for affective E-learning. Education and Information Technologies, 21, 1367–1386. https://doi.org/10.1007/s10639-015-9388-2
   Google Scholar
• Bahreini, K., Nadolski, R., & Westera, W. (2016c). Data fusion for real-time multimodal emotion recognition through webcams and microphones in E-learning. International Journal of Human–Computer Interaction, 32(5), 415–430. https://doi.org/10.1080/10447318.2016.1159799
   Web of Science ®Google Scholar
• Birjali, M., Beni-Hssane, A., & Erritali, M. (2018). A novel adaptive e-learning model based on big data by using competency-based knowledge and social learner activities. Applied Soft Computing, 14–32. https://doi.org/10.1016/j.asoc.2018.04.030
   Web of Science ®Google Scholar
• Capuano, N., Gaeta, M., Micarelli, A., & Sangineto, E. (2002). An intelligent web teacher system for learning personalization. IEEE International Conference on Advanced Learning Technologies (ICALT 02) (pp. 322–326). Google Scholar.
   Google Scholar
• Clore, G., & Gasper, K. (2000). Feeling is believing: Some affective influences on belief. In N. Frijda, A. Manstead, & S. Bem (Eds.), Emotions and beliefs: How feelings influence thoughts (pp. 1–9). Cambridge University Press.
   Google Scholar
• Dagger, D., O’Connor, A., Lawless, S., Eddie, W., & Wade, V. P. (2007). Service-oriented E-learning platforms: From monolithic systems to flexible services. IEEE Internet Computing, 28–35. https://doi.org/10.1109/MIC.2007.70
   Web of Science ®Google Scholar
• Danvers, E. C. (2016). Criticality's affective entanglements: Rethinking emotion and critical thinking in higher education. Gender and Education, 282–297. https://doi.org/10.1080/09540253.2015.1115469
   Web of Science ®Google Scholar
• Darejeh, A., Marcus, N., & Sweller, J. (2021). The effect of narrative-based E-learning systems on novice users’ cognitive load while learning software applications. Educational Technology Research and Development, 69(5), 2451–2473. https://doi.org/10.1007/s11423-021-10024-5
   Web of Science ®Google Scholar
• Darejeh, A., Marcus, N., & Sweller, J. (2022a). Increasing learner interactions with E-learning systems can either decrease or increase cognitive load depending on the nature of the interaction. L'Année psychologique, (3), 405–437. https://doi.org/10.3917/anpsy1.223.0405
   Web of Science ®Google Scholar
• Darejeh, A., Mashayekh, S., & Marcus, N. (2022b). Cognitive-based methods to facilitate learning of software applications via E-learning systems. Cogent Education, 9(1), 2082085. https://doi.org/10.1080/2331186X.2022.2082085
   Web of Science ®Google Scholar
• De Carolis, B., D’Errico, F., Macchiarulo, N., Paciello, M., & Palestra, G. (2021). Recognizing cognitive emotions in E-learning environment. In L. S. Agrati et al. (Ed.), Bridges and mediation in higher distance education: Second international workshop, HELMeTO 2020 , Bari, BA, Italy, September 17–18, 2020, Revised Selected Papers 2. (pp. 17–27). Springer International Publishing. https://doi.org/10.1007/978-3-030-67435-9_2
   Google Scholar
• De Winter, J. C. (2013). Using the Student’s t-test with extremely small sample sizes. Practical Assessment, Research, and Evaluation, 18(1), 1–12. https://doi.org/10.7275/e4r6-dj05
   Google Scholar
• Dwivedi, P., Kant, V., & Bharadwaj, K. (2018). Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 819–836. https://doi.org/10.1007/s10639-017-9637-7
   Web of Science ®Google Scholar
• Ferro, T. R. (1993). The influence of affective processing in education and training. New Directions for Adult and Continuing Education, 1993(59), 25–33. https://doi.org/10.1002/ace.36719935905
   Google Scholar
• Fidalgo-Blanco, A., Martinez-Nuñez, M., Borrás-Gene, O., & Sanchez-Medina, J. J. (2017). Micro flip teaching An innovative model to promote the active involvement of students. Computers in Human Behavior, 713–723. https://doi.org/10.1016/j.chb.2016.07.060
   Web of Science ®Google Scholar
• Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. International Journal of Applied Mathematics and Theoretical Physics, 4(2), 50–54. https://doi.org/10.11648/j.ijamtp.20180402.13
   Google Scholar
• Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 907–928. https://doi.org/10.1006/ijhc.1995.1081
   Web of Science ®Google Scholar
• Guarino, N. (1998). Formal ontology in information systems. IOS Press.
   Google Scholar
• Hascher, T. (2010). Learning and emotion: Perspectives for theory and research. European Educational Research Journal, 13–28. https://doi.org/10.2304/eerj.2010.9.1.13
   Google Scholar
• Heidegger, M. (1996). Being and time. Translated by J. Stambaugh. State University of New York Press, 126.
   Google Scholar
• Hssina, B., & Erritali, M. (2019). A personalized pedagogical objectives based on a genetic algorithm in an adaptive learning system. Procedia Computer Science, 1152–1157. https://doi.org/10.1016/j.procs.2019.04.164
   Google Scholar
• Ilić, V., Batić, D., Mirković, M., Vukmirović, S., Ćulibrk, D., Bosakov, D., & Popović, I. (2021). Automatic emotion detection as a teaching aid in online knowledge assessment. 20th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1–5). IEEE.
   Google Scholar
• Jacobs, S. E., & Gross, J. J.. (2014). Emotion regulation in education. International handbook of emotions in education (pp. 183–217).
   Google Scholar
• Jithendran, A., Pranav Karthik, P., Santhosh, S., & Naren, J. (2020). Emotion recognition on E-learning community to improve the learning outcomes using machine learning concepts: A pilot study. Smart Systems and IoT: Innovations in Computing, 521–530. https://doi.org/10.1007/978-981-13-8406-6_50
   Google Scholar
• Kim, S., Cho, S., & Park, Y. (2022). Daily microbreaks in a self-regulatory resources lens: Perceived health climate as a contextual moderator via microbreak autonomy. Journal of Applied Psychology, 107(1), 60–77. https://doi.org/10.1037/apl0000891
   PubMed Web of Science ®Google Scholar
• Kim, S., Park, Y., & Niu, Q. (2017). Micro-break activities at work to recover from daily work demands. Journal of Organizational Behavior, 28–44. https://doi.org/10.1002/job.2109
   Web of Science ®Google Scholar
• Kort, B., Reilly, R., & Picard, R. W.. (2001). External representation of learning process and domain knowledge: Affective state as a determinate of its structure and function. In Workshop on artificial intelligence in education (AI-ED 2001) (pp. 64–69).
   Google Scholar
• Kratica, J., Tošic, D., Filipović, V., & Ljubić, I. (2001). Solving the simple plant location problem by genetic algorithm. RAIRO - Operations Research, 127–142. https://doi.org/10.1051/ro:2001107
   Web of Science ®Google Scholar
• Lee, H., Choi, Y. S., & Kim, Y.-J. (2011). An adaptive user interface based on spatiotemporal structure learning. IEEE Communications Magazine, 118–124. https://doi.org/10.1109/MCOM.2011.5783996
   Web of Science ®Google Scholar
• Li, G., & Wang, Y. (2018). Research on learner’s emotion recognition for intelligent education system. In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (pp. 754–758). IEEE.
   Google Scholar
• Livingston, E. H. (2004). Who was student and why do we care so much about his t-test?. Journal of Surgical Research, 118(1), 58–65.
   PubMed Web of Science ®Google Scholar
• Martin, E., & Carro, R. M. (2009). Supporting the development of mobile adaptive learning environment: A case study. IEEE Transactions on Learning Technologies, 23–36. https://doi.org/10.1109/TLT.2008.24
   Web of Science ®Google Scholar
• Megahed, M., & Mohammed, A. (2020). Modeling adaptive E-learning environment using facial expressions and fuzzy logic. Expert Systems with Applications, Article 113460. https://doi.org/10.1016/j.eswa.2020.113460
   Web of Science ®Google Scholar
• Mortari, L. (2015). Emotion and education: Reflecting on the emotional experience emotion and education. European Journal of Educational Research, 157–176. https://doi.org/10.12973/eu-jer.4.4.157
   Google Scholar
• Muehlenbrock, M. (2005). Formation of learning groups by using learner profiles and context information. In M. Muehlenbrock (Ed.), Artificial intelligence in education (pp. 507–514). http://ebooks.iospress.nl/volumearticle/2349
   Google Scholar
• Mukhopadhyay, M., Pal, S., Nayyar, A., D, N., Dutta Pramanik, P., Dasgupta, N., & Choudhury, P. (2020). Facial emotion detection to assess learner's state of mind in an online learning system. In 5th International Conference on Intelligent Information Technology (pp. 107–115). ICIIT.
   Google Scholar
• Nie, Q., Zhang, J., Peng, J., & Chen, X. (2021). Daily micro-break activities and workplace well-being: A recovery perspective. Current Psychology, 1–14. https://doi.org/10.1007/s12144-021-02300-7
   Web of Science ®Google Scholar
• Nussbaum, M. C. (2001). Upheavals of thought: The intelligence of emotions. Cambridge University Press.
   Google Scholar
• O’Regan, K. (2003). Emotion and E-Learning. Journal of Asynchronous Learning Networks, 7(3), 78–92.
   Google Scholar
• Pise, A., Vadapalli, H., & Sanders, I. (2020). Facial emotion recognition using temporal relational network: An application to E-learning. Multimedia Tools and Applications, 1–21. https://doi.org/10.1007/s11042-020-10133-y
   Web of Science ®Google Scholar
• Roberts, K., Roach, M. A., Johnson, J., Guthrie, J., & Harabagiu, S. M. (2012). EmpaTweet: Annotating and detecting emotions on Twitter. Eight International Conference on Language Resources and Evaluation (pp. 3806–3813). Istanbul: European Language Resources Association.
   Google Scholar
• Seli, P., Wammes, D. J., Risko, F. E., & Smilek, D. (2016). On the relation between motivation and retention in educational contexts: The role of intentional and unintentional mind wandering. Psychonomic Bulletin & Review, 1280–1287. https://doi.org/10.3758/s13423-015-0979-0
   PubMed Web of Science ®Google Scholar
• Shen, L., Wang, M., & Shen, R. (2009). Affective e-learning: Using “emotional” data to improve learning in pervasive learning environment. Journal of Educational Technology & Society, 12(2), 176–189.
   Web of Science ®Google Scholar
• Solomon, R C. (2003). I. Emotions, thoughts and feelings: What is a ‘cognitive theory' of the emotions and does it neglect affectivity?Royal Institute of Philosophy Supplements, 52, 1–18.
   Google Scholar
• Tang, T., Lim, M. H., & Ong, M. H. (2007). Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Computing, 873–888. https://doi.org/10.1007/s00500-006-0139-6
   Web of Science ®Google Scholar
• Tyng, C. M., Amin, H. U., Saad, M. N., & Malik, A. S. (2017). The influences of emotion on learning and memory. Frontiers in Psychology, (8). https://doi.org/10.3389/fpsyg.2017.01454
   PubMedGoogle Scholar
• Vanitha, V., Krishnan, P., & Elakkiya, R. (2019). Collaborative optimization algorithm for learning path construction in E-learning. Computers & Electrical Engineering, 325–338. https://doi.org/10.1016/j.compeleceng.2019.06.016
   Web of Science ®Google Scholar
• Wang, M., & Kang, M. (2006). Cybergogy for engaged learning: A framework for creating learner engagement through information and communication technology. Engaged Learning with Emerging Technologies, 225–253. https://doi.org/10.1007/1-4020-3669-8_11
   Google Scholar
• Weiss, P. R. (2000). Emotion and learning. Training and Development, 54(11), 44–48.
   Google Scholar
• Worthman, C. (1999). Emotions: You can feel the difference. Hinton, A. Biocultural approaches to the emotions. Cambridge University Press, 41–74.
   Google Scholar
• Yim, K. H., Nahm, F. S., Han, K. A., & Park, S. Y. (2010). Analysis of statistical methods and errors in the articles published in the Korean journal of pain. The Korean Journal of Pain, 23(1), 35–41.
   PubMedGoogle Scholar
• Zakka, B., & Vadapalli, H. (2019). Detecting learning affect in E-Learning platform using facial emotion expression. International Conference on Soft Computing and Pattern Recognition (pp. 217–225). Springer.
   Google Scholar
• Zembylas, M. (2020). Affect/emotion and securitising education: Re-orienting the methodological and theoretical framework for the study of securitisation in education. British Journal of Educational Studies, 487–506. https://doi.org/10.1080/00071005.2019.1711018
   Web of Science ®Google Scholar
• Zulkifli, S. F., Shiang, C. W.,. A.,. b. K., & Bt Jali, N. (2020). Modeling emotion oriented approach through agent-oriented approach. International Journal on Advanced Science Engineering and Information Technology, 647–653. https://doi.org/10.18517/ijaseit.10.2.10644
   Google Scholar