Muh. Arief Muhsin; Muhyddin M. Hayat; Baharuddin; Wahyuddin; Hartati Binti Maskur; Muhammad Faisal

AI-Based Macro Expression Analysis to Enhance Engagement in English Learning for Hyperactive Students

PDF (866KB), PP.106-120

Views: 0 Downloads: 0

Author(s)

Muh. Arief Muhsin ^1,* Muhyddin M. Hayat ² Baharuddin ³ Wahyuddin ⁴ Hartati Binti Maskur ⁵ Muhammad Faisal ⁶

1. Department of English Education, University of Muhammadiyah Makassar, South Sulawesi, Indonesia

2. Department of Informatics, University of Muhammadiyah Makassar, South Sulawesi, Indonesia

3. Department of Education Management, University of Muhammadiyah Indonesia, Bekasi, Indonesia

4. Department of Education Technology, University of Muhammadiyah Makassar, Indonesia

5. Department of Information Engineering, Polytechnic Besut Terengganu, Malaysia

6. Department of Informatics, Universitas Muhammadiyah Makassar, Indonenesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2026.03.07

Received: 28 Jan. 2026 / Revised: 18 Feb. 2026 / Accepted: 15 Mar. 2026 / Published: 8 Jun. 2026

Index Terms

AI, English Language, Hyperactive, Inclusive, Macro Expression

Abstract

This study investigates the use of AI-driven macro expression analysis to enhance the engagement of hyperactive students in English language learning. By utilizing Convolutional Neural Networks (CNN) and K-Nearest Neighbors (K-NN), this research aims to detect and analyze students' macro facial expressions, as well as their correlation with engagement levels. Data was obtained from 24 learning videos, consisting of 13,263 frames, analyzed to identify expressions of boredom, sadness, and happiness. The analysis results show that boredom and sadness dominate, while happiness is recorded at a lower frequency, indicating the need for a more varied and responsive teaching approach. This study also finds that AI-driven emotion detection can provide more adaptive feedback for hyperactive students, allowing teachers to adjust teaching methods in real-time according to the students' emotional responses. The findings contribute new insights into the field of inclusive education by integrating AI technology to monitor and tailor learning for students with special needs. Theoretically, this research enriches the understanding of the role of macro expressions in student engagement, particularly in the context of ADHD. Practically, the results offer technology-based solutions to support more adaptive and responsive teaching that aligns with students' emotional changes. This research contributes to the development of more holistic and interactive learning methods, which can improve learning outcomes for students with special needs, especially in English language education.

Cite This Paper

Muh. Arief Muhsin, Muhyddin M. Hayat, Baharuddin, Wahyuddin, Hartati Binti Maskur, Muhammad Faisal, "AI-Based Macro Expression Analysis to Enhance Engagement in English Learning for Hyperactive Students", International Journal of Modern Education and Computer Science(IJMECS), Vol.18, No.3, pp. 106-120, 2026. DOI:10.5815/ijmecs.2026.03.07

Reference

[1]R. BR, H. Baharun, and D. Asiya, “Inclusive Education Management in the Development of Cognitive Intelligence of Children,” AL-ISHLAH: Jurnal Pendidikan, vol. 13, no. 2, pp. 1057–1067, Aug. 2021, doi: 10.35445/alishlah.v13i2.538.
[2]T. Effendi, A. Thurston, and A. MacKenzie, “A systematic evidence synthesis of disability and inclusive pedagogy in initial teacher training in English as a Foreign Language,” International Journal of Educational Research Open, vol. 7, no. January, p. 100350, 2024, doi: 10.1016/j.ijedro.2024.100350.
[3]B. Vigo-Arrazola and C. Moreno-Pinillos, “Creative and inclusive teaching practices in multigrade schools. An ethnographic study on the use of digital media,” Int. J. Educ. Res., vol. 131, Jan. 2025, doi: 10.1016/j.ijer.2025.102596.
[4]N. Yao and Q. Wang, “Factors influencing pre-service special education teachers’ intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness,” Heliyon, vol. 10, no. 14, Jul. 2024, doi: 10.1016/j.heliyon.2024.e34894.
[5]Y. Yuxiu, “Application of translation technology based on AI in translation teaching,” Systems and Soft Computing, vol. 6, no. January, p. 200072, 2024, doi: 10.1016/j.sasc.2024.200072.
[6]Y. Zhao and H. Zhao, “A hybrid machine learning framework by incorporating categorical boosting and manifold learning for financial analysis,” Intelligent Systems with Applications, vol. 25, Mar. 2025, doi: 10.1016/j.iswa.2024.200473.
[7]A. F. Aldakhil, “Investigating the impact of an AI-based play activities intervention on the quality of life of school-aged children with ADHD,” Res. Dev. Disabil., vol. 154, Nov. 2024, doi: 10.1016/j.ridd.2024.104858.
[8]G. Andreou and A. Argatzopoulou, “A systematic review on the use of technology to enhance the academic achievements of children with attention deficit hyperactivity disorder in language learning,” Feb. 01, 2024, Elsevier Inc. doi: 10.1016/j.ridd.2023.104666.
[9]S. Khanahmadi, H. Sourtiji, Z. Khanahmadi, and A. Sheikhtaheri, “Effect of a sensory diet smartphone application on the symptoms of children with attention deficit hyperactivity disorder (ADHD): A feasibility study,” Heliyon, vol. 9, no. 8, Aug. 2023, doi: 10.1016/j.heliyon.2023.e19086.
[10]H. Ichikawa et al., “Hemodynamic response of children with attention-deficit and hyperactive disorder (ADHD) to emotional facial expressions,” Neuropsychologia, vol. 63, no. 1, pp. 51–58, Oct. 2014, doi: 10.1016/j.neuropsychologia.2014.08.010.
[11]X. Li, L. Jia, Q. Sun, G. Guo, and W. Li, “A deep multi-task learning framework leveraging multi-modal behavioral signals: Real-time, fine-grained assessment of IIVIS-induced driver distraction,” Expert Syst. Appl., vol. 297, Feb. 2026, doi: 10.1016/j.eswa.2025.129317.
[12]P. Utami, R. Hartanto, and I. Soesanti, “A study on facial expression recognition in assessing teaching skills: Datasets and methods,” in Procedia Computer Science, Elsevier B.V., 2019, pp. 544–552. doi: 10.1016/j.procs.2019.11.154.
[13]K. Alnowaiser, A. A. Alarfaj, E. A. Alabdulqader, M. Umer, L. Cascone, and B. Alankar, “IoT based smart framework to predict air quality in congested traffic areas using SV-CNN ensemble and KNN imputation model,” Computers and Electrical Engineering, vol. 118, Aug. 2024, doi: 10.1016/j.compeleceng.2024.109311.
[14]E. Yücesoy, “Gender Recognition from Speech Signal Using CNN, KNN, SVM and RF,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 2251–2257. doi: 10.1016/j.procs.2024.04.213.
[15]J. A. Teixeira da Silva and Y. Yamada, “An extended state of uncertainty: A snap-shot of expressions of concern in neuroscience,” Current Research in Behavioral Sciences, vol. 2, no. May, p. 100045, 2021, doi: 10.1016/j.crbeha.2021.100045.
[16]M. A. Muhsin, “Authentic-based assessment in english language learning: a study on non-english major students’ descriptions of daily activities,” IJEE (Indonesian Journal of English Education), vol. 11, no. 2, pp. 275–290, 2024, doi: 10.15408/ijee.v11i2.38750.
[17]P. Bustos-Valenzuela et al., “Atypical cognitive vergence responses in children with attention deficit hyperactivity disorder but not with autism spectrum disorder in a facial emotion recognition task,” Psychiatry Research Communications, vol. 2, no. 2, Jun. 2022, doi: 10.1016/j.psycom.2022.100045.
[18]F. Kirschner, L. Kester, and G. Corbalan, “Cognitive load theory and multimedia learning, task characteristics and learning engagement: The Current State of the Art,” 2011, Elsevier Ltd. doi: 10.1016/j.chb.2010.05.003.
[19]M. S. Resnick, “Teachers’ presentation of higher-order thinking questions and student engagement: Missing out on HOT opportunities,” Think. Skills Creat., vol. 50, no. May, p. 101412, 2023, doi: 10.1016/j.tsc.2023.101412.
[20]D. Elleuch, “The transformative role of Positive Psychology in fostering inclusive language education for SEN students,” Dec. 01, 2024, Elsevier Ltd. doi: 10.1016/j.system.2024.103545.
[21]J. Kim and Y. J. Chung, “A single case study of digital art therapy for a child with ADHD using the metaverse platform,” Arts in Psychotherapy, vol. 89, Jul. 2024, doi: 10.1016/j.aip.2024.102146.
[22]F. Köder, C. Rummelhoff, and M. Garraffa, “Learning and using multiple languages: Experiences of adults with ADHD,” Ampersand, vol. 13, Dec. 2024, doi: 10.1016/j.amper.2024.100191.
[23]L. Henrickson, B. Hall, and T. Procter, “On constructive bewilderment: Using special collections material for teaching digital practices,” ., vol. 50, no. 2, Mar. 2024, doi: 10.1016/j.acalib.2024.102852.
[24]E. Akib, M. A. Muhsin, S. M. Hamid, and N. Irawan, “Critical Thinking in Authentic Assessment: An Exploration into Argumentative Writing Non-English Department in Higher Education,” International Journal of Language Education, vol. 8, no. 4, pp. 854–869, 2024, doi: 10.26858/ijole.v8i4.70051.
[25]C. Kooli and R. Chakraoui, “AI-driven assistive technologies in inclusive education: benefits, challenges, and policy recommendations,” Sustainable Futures, vol. 10, Dec. 2025, doi: 10.1016/j.sftr.2025.101042.
[26]X. Fan and X. Zhong, “Artificial intelligence-based creative thinking skill analysis model using human–computer interaction in art design teaching,” Computers and Electrical Engineering, vol. 100, May 2022, doi: 10.1016/j.compeleceng.2022.107957.
[27]A. Havnes, B. Christiansen, I. Torunn, and E. Hessevaagbakke, “Learning , Culture and Social Interaction Peer learning in higher education : Patterns of talk and interaction in skills centre simulation,” LCSI, vol. 8, pp. 75–87, 2016, doi: 10.1016/j.lcsi.2015.12.004.
[28]Y. Wang, “Effects of troubleshooting robotics learning on students’ engagement, computational thinking, and programming skills,” Think. Skills Creat., vol. 60, p. 102068, Jun. 2026, doi: 10.1016/j.tsc.2025.102068.
[29]M. Wang and Y. Wang, “A structural equation modeling approach in examining EFL students’ foreign language enjoyment, trait emotional intelligence, and classroom climate,” Learn. Motiv., vol. 86, no. 122, p. 101981, 2024, doi: 10.1016/j.lmot.2024.101981.
[30]N. El Bahri, Z. Itahriouan, and M. Ouazzani Jamil, “Emotion-Aware Education Through Affective Computing and Learning Analytics: Insights from a Moroccan University Case Study,” Digital, vol. 5, no. 3, p. 45, Sep. 2025, doi: 10.3390/digital5030045.
[31]A.-M. Soler-Gutiérrez et al., “Emotion processing difficulties in ADHD: a Bayesian meta-analysis study.,” Eur. Child Adolesc. Psychiatry, vol. 34, no. 8, pp. 2369–2390, Aug. 2025, doi: 10.1007/s00787-025-02647-3.
[32]Z. Xu, F. Liu, G. Xia, Y. Duan, and L. Yu, “A scoping review of inclusive and adaptive human–AI interaction design for neurodivergent users,” Disabil. Rehabil. Assist. Technol., pp. 1–19, Nov. 2025, doi: 10.1080/17483107.2025.2579822.
[33]P. R. Shekar, A. Mathew, P. V. Yeswanth, and S. Deivalakshmi, “A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India,” Artificial Intelligence in Geosciences, vol. 5, Dec. 2024, doi: 10.1016/j.aiig.2024.100073.
[34]A. O. Skliar et al., “Using the Set Equality Method ( SEM ) to Determine the Validity of Categorical Syllogisms,” ScienceOpen Preprints, vol. 17, no. 2, pp. 1–23, 2022, doi: 10.14293/S2199-1006.1.SOR-.PP8EZJC.v1.
[35]Y. Li, X. Nian, C. Gu, P. Deng, S. He, and B. Hong, “Assessing children’s outdoor thermal comfort with facial expression recognition: An efficient approach using machine learning,” Build. Environ., vol. 258, Jun. 2024, doi: 10.1016/j.buildenv.2024.111556.
[36]W. Maqableh, F. Y. Alzyoud, and J. Zraqou, “The use of facial expressions in measuring students’ interaction with distance learning environments during the COVID-19 crisis,” Visual Informatics, vol. 7, no. 1, pp. 1–17, Mar. 2023, doi: 10.1016/j.visinf.2022.10.001.
[37]S. Cui, S. Song, J. Si, M. Wu, and J. Feng, “The influence of mouth opening and closing degrees on processing in NimStim facial expressions: An ERP study from Chinese college students,” International Journal of Psychophysiology, vol. 162, pp. 157–165, Apr. 2021, doi: 10.1016/j.ijpsycho.2021.01.013.
[38]S. Hopkyns and C. Gkonou, “Sites of belonging: Fluctuating and entangled emotions at a UAE English-medium university,” Linguistics and Education, vol. 75, p. 101148, 2023, doi: 10.1016/j.linged.2023.101148.
[39]E. Namaziandost, A. Rezai, T. Heydarnejad, and M. Kruk, “Emotion and cognition are two wings of the same bird: Insights into academic emotion regulation, critical thinking, self-efficacy beliefs, academic resilience, and academic engagement in Iranian EFL context,” Think. Skills Creat., vol. 50, no. August, p. 101409, 2023, doi: 10.1016/j.tsc.2023.101409.
[40]Y. Ma and Z. Liu, “Emotion regulation and well-being as factors contributing to lessening burnout among Chinese EFL teachers,” Acta Psychol. (Amst)., vol. 245, no. March, p. 104219, 2024, doi: 10.1016/j.actpsy.2024.104219.
[41]A. F. Theiyab Alazemi, T. Heydarnejad, S. M. Ismail, and A. Gheisari, “A model of academic buoyancy, L2 grit, academic emotion regulation, and personal best: An evidence from EFL context,” Heliyon, vol. 9, no. 2, p. e13149, 2023, doi: 10.1016/j.heliyon.2023.e13149.
[42]D. Wu, M. Chen, X. Chen, and X. Liu, “Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy,” Computers and Education: Artificial Intelligence, vol. 7, p. 100295, Dec. 2024, doi: 10.1016/j.caeai.2024.100295.
[43]D. Elleuch, “The Role of Stress Management in the Acquisition of English as a Foreign Language for Children with ADHD: A Longitudinal Case Study,” Heliyon, p. e43060, Mar. 2025, doi: 10.1016/j.heliyon.2025.e43060.
[44]A. Mills, M. Bali, and L. Eaton, “How do we respond to generative AI in education? Open educational practices give us a framework for an ongoing process,” Journal of Applied Learning and Teaching, vol. 6, no. 1, pp. 16–30, Jan. 2023, doi: 10.37074/jalt.2023.6.1.34.
[45]S. Shamsuddinova, P. Heryani, and M. A. Naval, “Evolution to revolution: Critical exploration of educators’ perceptions of the impact of Artificial Intelligence (AI) on the teaching and learning process in the GCC region,” Int. J. Educ. Res., vol. 125, Jan. 2024, doi: 10.1016/j.ijer.2024.102326.
[46]K. H. Joo and N. H. Park, “Teaching and Learning Model for Artificial Intelligence Education,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 226–233. doi: 10.1016/j.procs.2024.06.166.
[47]J. Connors, P. Good, and T. Gollery, “Using innovative teaching strategies to improve nursing student competence and confidence in providing spiritual care,” Nurse Educ., vol. 42, no. 2, pp. 62–64, 2017, doi: 10.1097/NNE.0000000000000301.

International Journal of Modern Education and Computer Science (IJMECS)