On the joint use of Artificial Intelligence and Brain-Imaging Techniques in Technology-enhanced Learning Environments: A Systematic Literature Review

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.

[1]  Chih-Chien Wang,et al.  An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction , 2014, Inf. Manag..

[2]  G. Fontaine,et al.  Using a Wireless Electroencephalography Device to Evaluate E-Health and E-Learning Interventions , 2018, Nursing research.

[3]  Luisa M. Regueras,et al.  An analysis of the research on adaptive learning: the next generation of e-learning , 2008 .

[4]  Fabio Paternò,et al.  Dynamic user interface adaptation driven by physiological parameters to support learning , 2015, EICS.

[5]  William H. Sanders,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2014 .

[6]  Wolfgang Rosenstiel,et al.  EEG-based prediction of cognitive workload induced by arithmetic: a step towards online adaptation in numerical learning , 2016 .

[7]  Hector Rodriguez Rangel,et al.  Hyperparameter optimization in CNN for learning-centered emotion recognition for intelligent tutoring systems , 2019, Soft Computing.

[8]  Zoran Budimac,et al.  E-Learning personalization based on hybrid recommendation strategy and learning style identification , 2011, Comput. Educ..

[9]  Anke Meyer-Bäse,et al.  An Evolutionary Online Framework for MOOC Performance Using EEG Data , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[10]  Minho Lee,et al.  Development of Intelligent Learning Tool for Improving Foreign Language Skills Based on EEG and Eye tracker , 2015, HAI.

[11]  Muneera Bano,et al.  Mobile learning for science and mathematics school education: A systematic review of empirical evidence , 2018, Comput. Educ..

[12]  Ig Ibert Bittencourt,et al.  Flow Theory to Promote Learning in Educational Systems: Is it Really Relevant? , 2018, Revista Brasileira de Informática na Educ..

[13]  Aamir Saeed Malik,et al.  EEG in classroom: EMD features to detect situational interest of students during learning , 2018, Multimedia Tools and Applications.

[14]  Pipatsarun Phobun,et al.  Adaptive intelligent tutoring systems for e-learning systems , 2010 .

[15]  Fu-Ren Lin,et al.  Mental effort detection using EEG data in E-learning contexts , 2018, Comput. Educ..

[16]  H. Cooper,et al.  A meta-analysis of the effectiveness of intelligent tutoring systems on K–12 students’ mathematical learning. , 2013 .

[17]  Andrew Luxton-Reilly,et al.  Gamification of student peer review in education: A systematic literature review , 2020, Education and Information Technologies.

[18]  Claude Frasson,et al.  Modeling mental workload using EEG features for intelligent systems , 2011, UMAP'11.

[19]  Olusola O. Adesope,et al.  Intelligent tutoring systems and learning outcomes: A meta-analysis , 2014 .

[20]  Muhammad Ghulam,et al.  A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals , 2015, Comput. Intell. Neurosci..

[21]  Gwo-Jen Hwang,et al.  Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017 , 2019, Comput. Educ..

[22]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[23]  Edgard Morya,et al.  A Serious Game to Build a Database for ErrP Signal Recognition , 2019, IWANN.

[24]  Vimala Balakrishnan,et al.  Identification of personal traits in adaptive learning environment: Systematic literature review , 2019, Comput. Educ..

[25]  John R. Anderson,et al.  Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System , 2010, EDM.

[26]  Ashraf H. Badawi,et al.  Facilitating Classroom Orchestration Using EEG to Detect the Cognitive States of Learners , 2019, AMLTA.

[27]  Yavuz Akbulut,et al.  Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011 , 2012, Comput. Educ..

[28]  A. Graesser,et al.  A meta-analysis of the effectiveness of ALEKS on learning , 2018, Educational Psychology.

[29]  R. Bosker,et al.  Effective differentiation Practices:A systematic review and meta-analysis of studies on the cognitive effects of differentiation practices in primary education , 2018, Educational Research Review.

[30]  M. Helms-Lorenz,et al.  Differentiated Instruction in Secondary Education: A Systematic Review of Research Evidence , 2019, Front. Psychol..

[31]  Ruifeng Shi,et al.  Beyond engagement: an EEG-based methodology for assessing user’s confusion in an educational game , 2019, Universal Access in the Information Society.

[32]  Melissa A. Gallagher,et al.  Teachers’ Instructional Adaptations: A Research Synthesis , 2017 .

[33]  Quanying Liu,et al.  A real-time EEG-based BCI system for attention recognition in ubiquitous environment , 2011, UAAII '11.

[34]  Debatri Chatterjee,et al.  Unsupervised approach for measurement of cognitive load using EEG signals , 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.

[35]  Merlin Teodosia Suarez,et al.  Recognizing Student Emotions using Brainwaves and Mouse Behavior Data , 2013, Int. J. Distance Educ. Technol..

[36]  Joachim M. Buhmann,et al.  Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains , 2013, Int. J. Artif. Intell. Educ..

[37]  K. VanLehn.  The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems , 2011 .

[38]  Bimal Aklesh Kumar,et al.  Mobile learning adoption: A systematic review , 2018, Education and Information Technologies.

[39]  K. Kaszuba,et al.  Employing a Biofeedback Method Based on Hemispheric Synchronization in Effective Learning , 2012 .

[40]  John Hattie,et al.  Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement , 2008 .

[41]  Vandana Naik,et al.  Adaptive and Gamified Learning Environment (AGLE) , 2015, 2015 IEEE Seventh International Conference on Technology for Education (T4E).

[42]  Heidi Krömker,et al.  Application of Wearable Technology for the Acquisition of Learning Motivation in an Adaptive E-Learning Platform , 2018, Advances in Human Factors in Wearable Technologies and Game Design.

[43]  H. Cooper,et al.  A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning , 2014 .

[44]  Xiaobo Sharon Hu,et al.  Using EEG to Improve Massive Open Online Courses Feedback Interaction , 2013, AIED Workshops.

[45]  Bin Hu,et al.  Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[46]  Marcin Grzegorzek,et al.  [Regular Paper] Biomedical Data Acquisition and Processing to Recognize Emotions for Affective Learning , 2018, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE).

[47]  L. Liu,et al.  Improve Affective Learning with EEG Approach , 2010, Comput. Informatics.

[48]  Lei Xie,et al.  Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks , 2017, BCB.