Implementation of Data Mining in a Model for Generating Enthusiasm for Learning in Schools

Jhon Frenky, Seyum Betey

Abstract


Different from conventional educational paradigms, online education lacks the direct interplay between instructors and learners, particularly in the sphere of virtual physical education. Regrettably, extant research seldom directs its focus toward the intricacies of emotional arousal within the teacher-student course dynamic. The formulation of an emotion generation model exhibits constraints necessitating refinement tailored to distinct educational cohorts, disciplines, and instructional contexts. This study proffers an emotion generation model rooted in data mining of teacher-student course interactions to refine emotional discourse and enhance learning outcomes in the realm of online physical education. This model includes techniques for data preprocessing and augmentation, a multimodal dialogue text emotion recognition model, and a topic-expanding emotional dialogue generation model based on joint decoding. The encoder assimilates the input sentence into a fixed-length vector, culminating in the final state, wherein the vector produced by the context recurrent neural network is conjoined with the preceding word’s vector and employed as the decoder’s input. Leveraging the long-short-term memory neural network facilitates the modeling of emotional fluctuations across multiple rounds of dialogue, thus fulfilling the mandate of emotion prediction. The evaluation of the model against the DailyDialog dataset demonstrates its superiority over the conventional end-to-end model in terms of loss and confusion values. Achieving an accuracy rate of 84.4%.

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References


Asghar N, Poupart P, Hoey J, Jiang X, Mou L. 2018. Affective neural response generation.

Bishop JM. 2021. Artificial intelligence is stupid and causal reasoning will not fix it. Frontiers in Psychology 11:2603

Caldarini G, Jaf S, McGarry K. 2022. A literature survey of recent advances in chatbots. Information 13(1):41

Chen H, Liu X, Yin D, Tang J. 2017. A survey on dialogue systems: recent advances and new frontiers. ACM SIGKDD Explorations Newsletter 19(2):25-35

Feng Y, Cheng Y. 2021. Short text sentiment analysis based on multi-channel CNN with multi-head attention mechanism. IEEE Access 9:19854-19863

Gamazo A, Martínez-Abad F. 2020. An exploration of factors linked to academic performance in PISA 2018 through data mining techniques. Frontiers in Psychology 11:575167

Gao J, Galley M, Li L. 2018. Neural approaches to conversational AI.

Hu Z, Yang Z, Liang X, Salakhutdinov R, Xing EP. 2017. Toward controlled generation of text.

Huang C, Han Z, Li M, Wang X, Zhao W. 2021. Sentiment evolution with interaction levels in blended learning environments: using learning analytics and epistemic network analysis. Australasian Journal of Educational Technology 37(2):81-95

Huang C, Zaiane OR, Trabelsi A, Dziri N. 2018. Automatic dialogue generation with expressed emotions.

Karagöz S, Dinç H, Kaya DG. 2022. Self-leadership and leisure management of sports science students in the online education process. International Journal of Technology in Education 5(2):206-220

Karthikeyan VG, Thangaraj P, Karthik S. 2020. Towards developing hybrid educational data mining model (HEDM) for efficient and accurate student performance evaluation. Soft Computing 24(24):18477-18487

Kragel PA, LaBar KS. 2015. Multivariate neural biomarkers of emotional states are categorically distinct. Social Cognitive and Affective Neuroscience 10(11):1437-1448

Li D, Ortegas KD, White M. 2023. Exploring the computational effects of advanced deep neural networks on logical and activity learning for enhanced thinking skills. Systems 11(7):319

Li T, Xia T, Wang H, Tu Z, Tarkoma S, Han Z, Hui P. 2022. Smartphone app usage analysis: datasets, methods, and applications. IEEE Communications Surveys & Tutorials 24(2):937-966

Ling Y, Cai F, Hu X, Liu J, Chen W, Chen H. 2021. Context-controlled topic-aware neural response generation for open-domain dialog systems. Information Processing & Management 58(1):102392

Liu Z, Wen C, Su Z, Liu S, Sun J, Kong W, Yang Z. 2023. Emotion-semantic-aware dual contrastive learning for epistemic emotion identification of learner-generated reviews in MOOCs. IEEE Transactions on Neural Networks and Learning Systems 37486839:1-14

Nie W, Bao Y, Zhao Y, Liu A. 2023. Long dialogue emotion detection based on commonsense knowledge graph guidance. IEEE Transactions on Multimedia 3267295:1-15

Roy B, Das S. 2022. Perceptible sentiment analysis of students’ WhatsApp group chats in valence, arousal, and dominance space. Social Network Analysis and Mining 13(1):9

Shao L, Gouws S, Britz D, Goldie A, Strope B, Kurzweil R. 2017. Generating long and diverse responses with neural conversation models. ArXiv preprint. 1-11

Shaukat K, Nawaz I, Aslam S, Shaukat U. 2016. Student’s performance in the context of data mining.

Shum H-Y, He X-D, Li D. 2018. From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering 19(1):10-26

Shutova TN, Andryushchenko LB. 2020. Digitalization of physical education and sports educational process at university. Theory and Practice of Physical Culture 9:68-70

Song Z, Zheng X, Liu L, Xu M, Huang X. 2019. Generating responses with a specific emotion INDIALOG.

Sun X, Chen X, Pei Z, Ding S. 2018. Emotional human machine conversation generationbased on SegGAN.

Sutskever I, Vinyals O, Le QV. 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 27:1-9

Viscione I, D’Elia F. 2019. Augmented reality for learning in distance education: the case of e-sports. Journal of Physical Education and Sport 19:2047-2050

Wang R. 2021. Exploration of data mining algorithms of an online learning behaviour log based on cloud computing. International Journal of Continuing Engineering Education and Life Long Learning 31(3):371-380

Xing C, Wu W, Wu Y, Liu J, Huang Y, Zhou M, Ma W-Y. 2017. Topic awareneural response generation.

Xiong Z, Liu Q, Huang X. 2022. The influence of digital educational games on preschool Children’s creative thinking. Computers & Education 189(3):104578

Yang F, Zhang J, Kim H. 2022. Traditional Chinese sports under China’s health strategy. Journal of Environmental and Public Health 2022(11):138

Zhang R, Wang Z, Yin K, Huang Z. 2019. Emotional text generation based on cross-domain sentiment transfer. IEEE Access 7 100081–100089

Zhou H, Huang M, Zhang T, Liu B. 2018. Emotional chatting machine: emotional conversation generation with internal and external memory. Proceedings of the AAAI Conference on Artificial Intelligence 32(1):1-9


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