On the joint use of Artificial Intelligence and Brain-Imaging Techniques in Technology-enhanced Learning Environments: A Systematic Literature Review
DOI:
https://doi.org/10.5753/rbie.2021.29.0.502Keywords:
Brain-imaging Techniques, Technology-enhanced Learning Environments, Artificial Intelligence AlgorithmsAbstract
Recent meta-analysis and literature reviews support that adaptive learning systems are components of effective instruction. These exciting results are motivating researchers to explore new technologies that provide relevant students’ information to promote a better-personalized experience for students to achieve better learning outcomes in technology-enhanced learning environments. A new trend is related to the studies that use brain-imaging techniques to provide relevant students’ information for educational systems, aiming to enable an enhanced personalized experience. Some of these studies are making use of artificial intelligence to provide real-time monitoring of students’ cognitive phenomena supplied by brain-imaging techniques such as electroencephalography and functional magnetic resonance imaging. Therefore, considering the relevance of the application of artificial intelligence in studies that use brain-imaging techniques combined with technology-enhanced learning environments and the lack of a current understanding of how these techniques have been used in this context, we present a systematic literature review (SLR) that aims to explore which artificial intelligence algorithms have been adopted, what are their purposes in studies that apply brain-imaging techniques in educational technologies and which were the results reported in these studies related to the use of artificial intelligence algorithms. The systematic literature review was conducted according to the recommendation of a well-accepted guideline to perform a rigorous review of the current literature. The search was conducted in seven academic databases in January 2020 and resulted in a total of 6089 studies that was reduced to 20 studies for the final analysis.
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Akbulut, Y., & Cardak, C. S. (2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers and Education, 58(2), 835–842. doi: 10.1016/j.compedu.2011.10.008 [GS Search]
Azcarraga, J., & Suarez, M. T. (2013). Recognizing Student Emotions Using Brainwaves and Mouse Behavior Data. Int. J. Distance Educ. Technol., 11(2), 1–15. doi: 10.4018/jdet.2013040101 [GS Search]
Babiker, A., Faye, I., Mumtaz, W., Malik, A. S., & Sato, H. (2019). EEG in classroom: EMD features to detect situational interest of students during learning. Multimedia Tools and Applications, 78(12), 16261–16281. doi: 10.1007/s11042-018-7016-z [GS Search]
Bamatraf, S., Hussain, M., Aboalsamh, H., Qazi, E.-U.-H., Malik, A. S., Amin, H. U., ... Imran, H. M. (2016). A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals. Computational Intelligence and Neuroscience, 2016, 8491046. doi: 10.1155/2016/8491046 [GS Search]
Bano, M., Zowghi, D., Kearney, M., Schuck, S., & Aubusson, P. (2018). Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Computers and Education, 121, 30–58. doi: 10.1016/j.compedu.2018.02.006 [GS Search]
Bauer, M., Bräuer, C., Schuldt, J., Niemann, M., & Krömker, H. (2019). Application of Wearable Technology for the Acquisition of Learning Motivation in an Adaptive E-Learning Platform. In T. Z. Ahram (Ed.), Advances in human factors in wearable technologies and game design (pp. 29–40). Campinas: Springer, Cham. doi: 10.1007/978-3-319-94619-1_4 [GS Search]
Chaouachi, M., Jraidi, I., & Frasson, C. (2011). Modeling Mental Workload Using EEG Features for Intelligent Systems. In J. A. Konstan, R. Conejo, J. L. Marzo, & N. Oliver (Eds.), User modeling, adaption and personalization (pp. 50–61). Berlin, Heidelberg: Springer Berlin Heidelberg. doi: 10.1007/978-3-642-22362-4_5 [GS Search]
Das, D., Chatterjee, D., & Sinha, A. (2013). Unsupervised approach for measurement of cognitive load using EEG signals. In 13th ieee international conference on bioinformatics and bioengineering, ieee bibe 2013. doi: 10.1109/BIBE.2013.6701686 [GS Search]
Deunk, M. I., Smale-Jacobse, A. E., de Boer, H., Doolaard, S., & Bosker, R. J. (2018). Effective differentiation Practices:A systematic review and meta-analysis of studies on the cognitive effects of differentiation practices in primary education. Educational Research Review, 24, 31–54. doi: 10.1016/j.edurev.2018.02.002 [GS Search]
Fang, Y., Ren, Z., Hu, X., & Graesser, A. C. (2018). A meta-analysis of the effectiveness of ALEKS on learning. Educational Psychology, 1278–1292. doi: 10.1080/01443410.2018.1495829 [GS Search]
Fincham, J. M., Anderson, J. R., Betts, S., & Ferris, J. L. (2010). Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System. In Educational data mining 2010 - 3rd international conference on educational data mining (pp. 51–60). doi: 10.1184/R1/6618986.v1 [GS Search]
Ghiani, G., Manca, M., & Paternò, F. (2015). Dynamic User Interface Adaptation Driven by Physiological Parameters to Support Learning. In Proceedings of the 7th acm sigchi symposium on engineering interactive computing systems (pp. 158–163). New York, NY, USA: ACM. doi: 10.1145/2774225.2775081 [GS Search]
Gruenewald, A., Kroenert, D., Poehler, J., Brueck, R., Li, F., Littau, J., . . . Niehaves, B. (2018). Biomedical Data Acquisition and Processing to Recognize Emotions for Affective Learning. In 2018 ieee 18th international conference on bioinformatics and bioengineering (bibe) (pp. 126–132). doi: 10.1109/BIBE.2018.00031 [GS Search]
Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge Taylor & Francis Group. doi: 10.4324/9780203887332 [GS Search]
Hu, B., Li, X., Sun, S., & Ratcliffe, M. (2018). Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(1), 38–45. doi: 10.1109/TCBB.2016.2616395 [GS Search]
Indriasari, T. D., Luxton-Reilly, A., & Denny, P. (2020). Gamification of student peer review in education: A systematic literature review. Education and Information Technologies, 25(6), 5205–5234. doi: 10.1007/s10639-020-10228-x [GS Search]
Kang, J.-S., Ojha, A., & Lee, M. (2015). Development of Intelligent Learning Tool for Improving Foreign Language Skills Based on EEG and Eye Tracker. In Proceedings of the 3rd international conference on human-agent interaction (pp. 121–126). New York, NY, USA: ACM. doi: 10.1145/2814940.2814951 [GS Search]
Käser, T., Baschera, G. M., Busetto, A. G., Klingler, S., Solenthaler, B., Buhmann, J. M., & Gross, M. (2013). Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains. In International journal of artificial intelligence in education (Vol. 22, pp. 59–83). Springer New York LLC. doi: 10.3233/JAI-130026 [GS Search]
Kaszuba, K., & Kostek, B. (2012). Employing a Biofeedback Method Based on Hemispheric Synchronization in Effective Learning. In Z. S. Hippe, J. L. Kulikowski, & T. Mroczek (Eds.), Human-computer systems interaction: Backgrounds and applications 2: Part 2 (pp. 295– 309). Berlin, Heidelberg: Springer Berlin Heidelberg. doi: 10.1007/978-3-642-23172-8_20 [GS Search]
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. In (Vol. 2). Keele University and Durham University Joint Report. [GS Search]
Klašnja-Milic ́evic ́, A., Vesin, B., Ivanovic ́, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers and Education, 56(3), 885–899. doi: 10.1016/j.compedu.2010.11.001 [GS Search]
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86(1), 42–78. doi: 10.3102/0034654315581420 [GS Search]
Kumar, B. A., & Chand, S. S. (2019). Mobile learning adoption: A systematic review. In Education and information technologies (Vol. 24, pp. 471–487). Springer New York LLC. doi: 10.1007/s10639-018-9783-6 [GS Search]
Li, X., Zhao, Q., Hu, B., Liu, L., Peng, H., Qi, Y., . . . Liu, Q. (2010). Improve Affective Learning with EEG Approach. Computing and Informatics, 29, 557–570. [GS Search]
Li, Y., Li, X., Ratcliffe, M., Liu, L., Qi, Y., & Liu, Q. (2011). A Real-time EEG-based BCI System for Attention Recognition in Ubiquitous Environment. In Proceedings of 2011 international workshop on ubiquitous affective awareness and intelligent interaction (pp. 33–40). New York, NY, USA: ACM. doi: 10.1145/2030092.2030099 [GS Search]
Lin, F.-R., & Kao, C.-M. (2018). Mental effort detection using EEG data in E-learning contexts. Computers & Education, 122, 63–79. doi: 10.1016/j.compedu.2018.03.020 [GS Search]
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. doi: 10.1037/a0037123 [GS Search]
Mailhot, T., Lavoie, P., Maheu-Cadotte, M.-A., Fontaine, G., Cournoyer, A., Côté, J., . . . Cossette, S. (2018). Using a Wireless Electroencephalography Device to Evaluate E-Health and E- Learning Interventions. Nursing Research, 67, 43–48. doi: 10.1097/NNR.0000000000000260 [GS Search]
Mohamed, Z., Halaby, M. E., Said, T., Shawky, D., & Badawi, A. (2020). Facilitating Classroom Orchestration Using EEG to Detect the Cognitive States of Learners. In Advances in intelligent systems and computing (Vol. 921, pp. 209–217). Springer Verlag. doi: 10.1007/978-3-030-14118-9_21 [GS Search]
Naik, V., & Kamat, V. (2015). Adaptive and Gamified Learning Environment (AGLE). In 2015 ieee seventh international conference on technology for education (t4e) (pp. 7–14). doi: 10.1109/T4E.2015.23 [GS Search]
Ni, Z., Yuksel, A. C., Ni, X., Mandel, M. I., & Xie, L. (2017). Confused or Not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks. In Proceedings of the 8th acm international conference on bioinformatics, computational biology,and health informatics (pp. 241–246). New York, NY, USA: ACM. doi: 10.1145/3107411.3107513 [GS Search]
Normadhi, N. B. A., Shuib, L., Nasir, H. N. A., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers and Education,130,168–190. doi: 10.1016/j.compedu.2018.11.005 [GS Search]
Parsons, S. A., Vaughn, M., Scales, R. Q., Gallagher, M. A., Parsons, A. W., Davis, S. G., . . . Allen, M. (2018). Teachers’ Instructional Adaptations: A Research Synthesis. Review of Educational Research, 88(2), 205–242. doi: 10.3102/0034654317743198 [GS Search]
Phobun, P., & Vicheanpanya, J. (2010). Adaptive intelligent tutoring systems for e- learning systems. Procedia - Social and Behavioral Sciences, 2(2), 4064–4069. doi: 10.1016/j.sbspro.2010.03.641 [GS Search]
Pinto, A., Nardari, G., Mijam, M., Morya, E., & Romero, R. (2019). A Serious Game to Build a Database for ErrP Signal Recognition. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 11507 LNCS, pp. 186–197). Springer Verlag. doi: 10.1007/978-3-030-20518-8_16 [GS Search]
Santos, W. O. d., Bittencourt, I. I., Isotani, S., Dermeval, D., Marques, L. B., & Silveira, I. F. (2018). Flow Theory to Promote Learning in Educational Systems: Is it Really Relevant? Revista Brasileira de Informática na Educação, 29–59(02), 29. doi: 10.5753/rbie.2018.26.02.29 [GS Search]
Smale-Jacobse, A. E., Meijer, A., Helms-Lorenz, M., & Maulana, R. (2019). Differentiated Instruction in Secondary Education: A Systematic Review of Research Evidence. Frontiers in Psychology, 10, 23–66. doi: 10.3389/fpsyg.2019.02366 [GS Search]
Spüler, M., Walter, C., Rosenstiel, W., Gerjets, P., Moeller, K., & Klein, E. (2016). EEG-based prediction of cognitive workload induced by arithmetic: a step towards online adaptation in numerical learning. ZDM, 48(3), 267–278. doi: 10.1007/s11858-015-0754-8 [GS Search]
Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent tutoring systems on K–12 students’ mathematical learning. Journal of Educational Psychology, 105(4), 970–987. doi: 10.1037/a0032447 [GS Search]
Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106(2), 331–347. doi: 10.1037/a0034752 [GS Search]
Tahmassebi, A., Gandomi, A., & Meyer-Base, A. (2018). An Evolutionary Online Framework for MOOC Performance Using EEG Data. In Ieee congress on evolutionary computation (pp. 1–8). doi: 10.1109/CEC.2018.8477862 [GS Search]
VanLehn, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems, Educational Psychologist. Educational Psychologist, 46(4), 197– 221. doi: 10.1080/00461520.2011.611369 [GS Search]
Verdú, E., Regueras, L. M., Verdú, M. J., De Castro, J. P., & Pérez, M. (2008). An Analysis of the Research on Adaptive Learning: The Next Generation of e-Learning. WSEAS Trans. Info. Sci. and App., 5(6), 859–868. [GS Search]
Wang, C.-C., & Hsu, M.-C. (2014). An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction. Information & Management, 51(7), 912–923. doi: 10.1016/j.im.2014.05.010 [GS Search]
Wang, H., Li, Y., Hu, X., Yang, Y., Meng, Z., & Chang, K.-M. (2013). Using EEG to improve massive open online courses feedback interaction. In Ceur workshop proceedings (Vol. 1009, pp. 59–66). [GS Search]
Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers and Education, 140, 103599. doi: 10.1016/j.compedu.2019.103599 [GS Search]
Zatarain Cabada, R., Rodriguez Rangel, H., Barron Estrada, M. L., & Cardenas Lopez, H. M. (2019, 10). Hyperparameter optimization in CNN for learning-centered emotion recognition for intelligent tutoring systems. Soft Computing, 1–10. doi: 10.1007/s00500-019-04387-4 [GS Search]
Zhou, Y., Xu, T., Li, S., & Shi, R. (2019). Beyond engagement: an EEG-based methodology for assessing user’s confusion in an educational game. Universal Access in the Information Society, 18(3), 551–563. doi: 10.1007/s10209-019-00678-7 [GS Search]
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