Attention Classification and Lecture Video Recommendation Based on Captured EEG Signal in Flipped Learning Pedagogy

References

• Afzaal, M., Nouri, J., Zia, A., Papapetrou, P., Fors, U., Wu, Y., Li, X., & Weegar, R. (2021). Automatic and intelligent recommendations to support students’ self-regulation. In Proceedings of the 2021 International Conference on Advanced Learning Technologies (ICALT) (pp. 336–338). IEEE. https://doi.org/10.1109/ICALT52272.2021.00107
   Google Scholar
• Arapakis, I., Moshfeghi, Y., Joho, H., Ren, R., Hannah, D., & Jose, J. M. (2009). Enriching user profiling with affective features for the improvement of a multimodal recommender system. In Proceedings of the ACM International Conference on Image and Video Retrieval (pp. 1–8). ACM.
   Google Scholar
• Balakrishnan, G., & Coetzee, D. (2013). Predicting student retention in massive open online courses using hidden Markov models. Electrical Engineering and Computer Sciences University of California at Berkeley, (UCB/EECS-2013-109). http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-109.html.
   Google Scholar
• Basu, S., Yu, Y., Singh, V. K., Zimmermann, R. (2016). Videopedia: Lecture video recommendation for educational blogs using topic modeling. In Q. Tian, N. Sebe, G.-J. Qi, B. Huet, R. Hong, & X. Liu (Eds.), Proceedings of the MultiMedia Modeling (pp. 238–250). Springer.
   Google Scholar
• Chao, C.-Y., Chen, Y.-T., & Chuang, K.-Y. (2015). Exploring students’ learning attitude and achievement in flipped learning supported computer aided design curriculum: A study in high school engineering education. Computer Applications in Engineering Education, 23(4), 514–526. https://doi.org/10.1002/cae.21622
   Web of Science ®Google Scholar
• Chen, W., Cai, F., Chen, H., & Rijke, M. D. (2019). Joint neural collaborative filtering for recommender systems. ACM Transactions on Information Systems, 37(4), 1–30. https://doi.org/10.1145/3343117
   Web of Science ®Google Scholar
• Chen, Z., Cao, J., Song, Y., Zhang, Y., & Li, J. (2010). Web video categorization based on Wikipedia categories and content-duplicated open resources. In Proceedings of the 18th ACM International Conference on Multimedia (pp. 1107–1110). ACM.
   Google Scholar
• Chen, Z., Feng, B., Xie, H., Zheng, R., & Xu, B. (2014). Video to article hyperlinking by multiple tag property exploration. In C. Gurrin, F. Hopfgartner, W. Hurst, H. Johansen, H. Lee, & N. O’Connor (Eds.), MMM 2014, Part I. LNCS (Vol. 8325, pp. 62–73). Springer.
   Google Scholar
• Dacrema, M. F., Cremonesi, P., & Jannach, D. (2019). Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 101–109). ACM.
   Google Scholar
• Dai, M., Zheng, D., Na, R., Wang, S., & Zhang, S. (2019). EEG classification of motor imagery using a novel deep learning framework. Sensors, 19(3), 551. https://doi.org/10.3390/s19030551
   PubMed Web of Science ®Google Scholar
• Drugman, T., Pylkkonen, J., Kneser, R. (2019). Active and semi-supervised learning in ASR: Benefits on the acoustic and language models. https://arxiv.org/abs/1903.02852,
   Google Scholar
• Fenu, G., Marras, M., & Boratto, L. (2018). A multi-biometric system for continuous student authentication in e-learning platforms. Pattern Recognition Letters, 113(13), 83–92. https://doi.org/10.1016/j.patrec.2017.03.027
   Web of Science ®Google Scholar
• Giannakos, M. N., Jaccheri, L., & Krogstie, J. (2016). Exploring the relationship between video lecture usage patterns and students’ attitudes. British Journal of Educational Technology, 47(6), 1259–1275. https://doi.org/10.1111/bjet.12313
   Web of Science ®Google Scholar
• Giannakos, M. N., Krogstie, J., & Aalberg, T. (2016). Video-based learning ecosystem to support active learning: Application to an introductory computer science course. Smart Learning Environments, 3(1), 1–13. https://doi.org/10.1186/s40561-016-0036-0
   Google Scholar
• Gnana Rajesh, D. (2016). Analysis of MFCC features for EEG signal classification. International Journal of Advances in Signal and Image Sciences, 2(2), 14–20. https://doi.org/10.29284/ijasis.2.2.2016.14-20.
   Google Scholar
• Gong, J., Wang, S., Wang, J., Feng, W., Peng, H., Tang, J., & Yu, P. S. (2020). Attentional graph convolutional networks for knowledge concept recommendation in MOOCs in a heterogeneous view. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 79–88). ACM. https://doi.org/10.1145/3397271.3401057
   Google Scholar
• Hao, Y. (2016). Exploring undergraduates’ perspectives and flipped learning readiness in their flipped classrooms. Computers in Human Behavior, 59(7), 82–92. https://doi.org/10.1016/j.chb.2016.01.032
   Google Scholar
• Heil, C. E., & Walnut, D. F. (1989). Continuous and discrete wavelet transforms. SIAM Review, 31(4), 628–666. https://doi.org/10.1137/1031129
   Web of Science ®Google Scholar
• Hogervorst, M. A., Brouwer, A.-M., & van Erp, J. B. F. (2014). Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload. Frontiers in Neuroscience, 8, 1–14. https://doi.org/10.3389/fnins.2014.00322
   PubMed Web of Science ®Google Scholar
• Hung, H.-C., Liu, I.-F., Liang, C.-T., & Su, Y.-S. (2020). Applying educational data mining to explore students’ learning patterns in the flipped learning approach for coding education. Symmetry, 12(2), 213–214. https://doi.org/10.3390/sym12020213
   Google Scholar
• Ibrahim, N. J., Razak, Z., Yakub, M., Yusoff, Z. M., Idris, M. Y. I., & Tamil, E. (2008). Quranic verse recitation feature extraction using mel-frequency cepstral coefficients (MFCC). In Proceedings of the 4th IEEE Int. Colloquium on Signal Processing and its Application (CSPA), Kuala Lumpur, Malaysia (pp. 13–18). IEEE.
   Google Scholar
• Jiang, H., Dykstra, K., & Whitehill, J. (2018). Predicting when teachers look at their students in 1-on-1 tutoring sessions. In Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG) (pp. 593–598). IEEE. https://doi.org/10.1109/FG.2018.00094
   Google Scholar
• Khalid, H., Woo, S. S. (2020). Oc-fakedect: Classifying deepfakes using one-class variational autoencoder. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 656–657). IEEE.
   Google Scholar
• Kim, Y., Soyata, T., & Behnagh, R. F. (2018). Towards emotionally aware AI smart classroom: Current issues and directions for engineering and education. IEEE Access, 6(1), 5308–5331. https://doi.org/10.1109/ACCESS.2018.2791861
   Google Scholar
• Kopparapu, S. K., Laxminarayana, M. (2010). Choice of mel filter bank in computing MFCC of a resampled speech. In Proceedings of the 10th International Conference on Information Science, Signal Processing and Their Applications (ISSPA 2010) (pp. 121–124). IEEE.
   Google Scholar
• Kumar, N., Alam, K., & Siddiqi, A. H. (2017). Wavelet transform for classification of EEG signal using SVM and ANN. Biomedical and Pharmacology Journal, 10(4), 2061–2069. https://doi.org/10.13005/bpj/1328
   Google Scholar
• Lang, F., Liang, L., Huang, K., Chen, T., & Zhu, S. (2021). Movie recommendation system for educational purposes based on field-aware factorization machine. Mobile Networks and Applications, 26(5), 2199–2205. https://doi.org/10.1007/s11036-021-01775-9
   Web of Science ®Google Scholar
• Lee, D.-Y., Lee, M., & Lee, S.-W. (2020). Classification of imagined speech using siamese neural network. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2979–2984). IEEE. https://doi.org/10.1109/SMC42975.2020.9282982
   Google Scholar
• Li, L., Yan, J., Wang, H., & Jin, Y. (2021). Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder. IEEE Transactions on Neural Networks and Learning Systems, 32(3), 1177–1191. https://doi.org/10.1109/TNNLS.2020.2980749
   PubMed Web of Science ®Google Scholar
• Li, Z., Hu, H., Xia, Z., Zhang, J., Li, X., Shi, J., Li, H., & Li, X. (2021). Exercise recommendation algorithm based on improved collaborative filtering. In Proceedings of the 2021 International Conference on Advanced Learning Technologies (ICALT) (pp. 47–49). IEEE. https://doi.org/10.1109/ICALT52272.2021.00022
   Google Scholar
• Li, Z., Hu, H., Xia, Z., Zhang, J., Li, X., Wang, Z., Huang, X., Zeng, S., & Qiu, B. (2021). Exercise recommendation method based on machine learning. In Proceedings of the 2021 International Conference on Advanced Learning Technologies (ICALT) (pp. 50–52). IEEE. https://doi.org/10.1109/ICALT52272.2021.00023
   Google Scholar
• Liang, N.-Y., Saratchandran, P., Huang, G.-B., & Sundararajan, N. (2006). Classification of mental tasks from EEG signals using extreme learning machine. International Journal of Neural Systems, 16(1), 29–38. https://doi.org/10.1142/S0129065706000482
   PubMed Web of Science ®Google Scholar
• Ling, G., Lyu, M. R., & King, I. (2014). Ratings meet reviews, a combined approach to recommend. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 105–112). ACM.
   Google Scholar
• Luo, Y., Fu, Q., Xie, J., Qin, Y., Wu, G., Liu, J., Jiang, F., Cao, Y., & Ding, X. (2020). EEG-based emotion classification using spiking neural networks. IEEE Access, 8(1), 46007–46016. https://doi.org/10.1109/ACCESS.2020.2978163
   Google Scholar
• Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. https://doi.org/10.1207/S15326985EP3801_6
   Web of Science ®Google Scholar
• Mühl, C., Jeunet, C., & Lotte, F. (2014). EEG-based workload estimation across affective contexts. Frontiers in Neuroscience, 8(114), 114–115. https://doi.org/10.3389/fnins.2014.00114
   PubMedGoogle Scholar
• Muda, L., Begam, M., & Elamvazuthi, I. (2010). Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. arXiv preprint arXiv:1003.4083.
   Google Scholar
• Nakao, T., Hanaoka, S., Nomura, Y., Murata, M., Takenaga, T., Miki, S., Watadani, T., Yoshikawa, T., Hayashi, N., & Abe, O. (2021). Unsupervised deep anomaly detection in chest radiographs. Journal of Digital Imaging, 34(2), 418–427. https://doi.org/10.1007/s10278-020-00413-2
   PubMed Web of Science ®Google Scholar
• Nguyen, P., Tran, D., Huang, X., & Ma, W. (2013). Age and gender classification using EEG paralinguistic features. In Proceedings of the 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 1295–1298). IEEE. https://doi.org/10.1109/NER.2013.6696178
   Google Scholar
• Pappas, I. O., Giannakos, M. N., & Mikalef, P. (2017). Investigating students’ use and adoption of with-video assignments: Lessons learnt for video-based open educational resources. Journal of Computing in Higher Education, 29(1), 160–177. https://doi.org/10.1007/s12528-017-9132-6
   Web of Science ®Google Scholar
• Repp, S., Linckels, S., & Meinel, C. (2008). Question answering from lecture videos based on an automatic semantic annotation. ACM SIGCSE Bulletin, 40(3), 17–21. https://doi.org/10.1145/1597849.1384278
   Google Scholar
• Roach, T. (2014). Student perceptions toward flipped learning: New methods to increase interaction and active learning in economics. International Review of Economics Education, 17(4), 74–84. https://doi.org/10.1016/j.iree.2014.08.003
   Google Scholar
• Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’02) (pp. 253–260). ACM.
   Google Scholar
• Shah, R. Y., Zimmermann, Y. S. A., Trace, R. (2015). Linguistic-based approach for automatic lecture video segmentation leveraging Wikipedia texts. In Proceedings of the IEEE International Symposium on Multimedia (ISM) (pp. 217–220). IEEE.
   Google Scholar
• Sharma, K., Papamitsiou, Z., & Giannakos, M. (2019). Building pipelines for educational data using ai and multimodal analytics: A “grey-box” approach. British Journal of Educational Technology, 50(6), 3004–3031. https://doi.org/10.1111/bjet.12854
   Web of Science ®Google Scholar
• Sun, Z., & Chang, C. (2002). Structural damage assessment based on wavelet packet transform. Journal of Structural Engineering, 128(10), 1354–1361. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:10(1354)
   Web of Science ®Google Scholar
• Szafir, D., & Mutlu, B. (2013). Artful: Adaptive review technology for flipped learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1001–1010). ACM.
   Google Scholar
• Tallapally, D., Sreepada, R. S., Patra, B. K., & Babu, K. S. (2018). User preference learning in multi-criteria recommendations using stacked auto encoders. In Proceedings of the 12th ACM Conference on Recommender Systems (pp. 475–479). ACM.
   Google Scholar
• Wang, M., Abdelfattah, S., Moustafa, N., & Hu, J. (2018). Deep Gaussian mixture-hidden Markov model for classification of EEG signals. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(4), 278–287. https://doi.org/10.1109/TETCI.2018.2829981
   Google Scholar
• Wei, X., Cheng, I.-L., Chen, N.-S., Yang, X., Liu, Y., Dong, Y., & Zhai, X. (2020). Effect of the flipped classroom on the mathematics performance of middle school students. Educational Technology Research and Development, 68(3), 1–24. https://doi.org/10.1007/s11423-020-09752-x.
   Web of Science ®Google Scholar
• Wu, E. Q., Deng, P.-Y., Qiu, X.-Y., Tang, Z., Zhang, W.-M., Zhu, L.-M., Ren, H., Zhou, G.-R., & Sheng, R. S. F. (2021). Detecting fatigue status of pilots based on deep learning network using EEG signals. IEEE Transactions on Cognitive and Developmental Systems, 13(3), 575–585. https://doi.org/10.1109/TCDS.2019.2963476
   Web of Science ®Google Scholar
• Wu, Z., Li, M., Tang, Y., & Liang, Q. (2020). Exercise recommendation based on knowledge concept prediction. Knowledge-Based Systems, 210(3), 106481. https://doi.org/10.1016/j.knosys.2020.106481
   Google Scholar
• Xiao, X., & Wang, J. (2015). Towards attentive, bi-directional MOOC learning on mobile devices. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 163–170). ACM.
   Google Scholar
• Yan, Y., Hara, K., Kazuma, T., & He, A. (2017). A method for personalized c programming learning contents recommendation to enhance traditional instruction. In Proceedings of the 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA) (pp. 320–327). IEEE. https://doi.org/10.1109/AINA.2017.13
   Google Scholar
• Yang, H., & Meinel, C. (2014). Content based lecture video retrieval using speech and video text information. IEEE Transactions on Learning Technologies, 7(2), 142–154. https://doi.org/10.1109/TLT.2014.2307305
   Web of Science ®Google Scholar
• Zhao, X., Zhang, J., Li, W., Kahn, K., Lu, Y., & Winters, N. (2021). Learners’ non-cognitive skills and behavioral patterns of programming: A sequential analysis. In Proceedings of the 2021 International Conference on Advanced Learning Technologies (ICALT) (pp. 168–172). IEEE.
   Google Scholar