Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Human emotion recognition for enhanced performance evaluation in e-learning

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

In interpersonal relationships, the recognition of human emotion plays an important role. Speech, hand and gestures of the body and facial expressions reflect emotions. Therefore, the interaction between human and machine communication plays a high role in extracting and understanding emotion. The challenging characteristic in e-learning includes lack of learners' motivation, the belief that e-learning offers no support, and the learners' busy schedules are considered essential. In this paper, heuristic multimodal real-time emotion recognition approach (HMR-TER) has been proposed to provide timely and appropriate online feedback based on learners' vocal intonations and facial expressions to foster their learning. Hybrid validation dynamic analysis is introduced that e-learning professionals must overcome an overall lack of learner motivation. e-Learning requires greater consciousness, encouragement and autonomy than academic learning. The Hybrid validation dynamic analysis provides a template for students to become and continue to stay motivated with attention, trust and satisfaction. Adequate gesture recognition Analysis is presented to take an e-learning course because they think that they would not be able to go at their own pace or require a great deal of time. E-learning achievement focuses significantly on the development and amount of asynchronous online communications. To decrease the feeling of alienation in E-learning, it should be adequately supervised. This exclusion is one of the significant causes of e-learning efficiency and common blockages. The gesture recognition analysis to be carried out in this field seeks to bring consolidation remedies to grasp and recognize manual gestures from an intimate picture. The simulation analysis proves how to enhance the quality and efficiency of e-learning by including the learner's emotional states. Emotion significantly impacts human brain functions, such as perceived notion, alertness, acquiring knowledge, cognition, thinking and problem-solving. A feeling has a heavy impact on enhancing the learning process, significantly modifying attention specificity and encouraging activity and behaviors. The final results are obtained as the face detection ratio to 84.25%, the hand gestures ratio to 92.70%, the voice recognition ratio to 82.26%, the reduction of the emotion problems ratio to 84.5% and the efficiency of the e-learning ratio to 93.85%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Tuncer, T., et al.: A new fractal pattern feature generation function-based emotion recognition method using EEG. Chaos, Solitons Fractals. 144, 110671 (2021)

    Article  MathSciNet  Google Scholar 

  2. Kayapinar, U., Spathopoulou, F., Safieddine, F., Nakhoul, I., Kadry, S.: Tablet use in teaching: a study on developing an attitude scale for academics. Eurasian J. Educ. Res. 78, 219–234 (2018)

    Google Scholar 

  3. Elazab, S., & Alazab, M.: The effectiveness of the flipped classroom in higher education. In 2015 Fifth International Conference on e-Learning (econf) (pp. 207–211). (2015) IEEE

  4. Deng, J., et al.: A survey of textual emotion recognition and its challenges. IEEE Transactions on Affective Computing. (2021)

  5. Yassine, S., Kadry, S., & Sicilia, M. A.: A framework for learning analytics in Moodle for assessing course outcomes. In 2016 IEEE Global Engineering Education Conference (EDUCON) (pp. 261–266). (2016) IEEE

  6. Elhoseny, M., Metawa, N., & Hassanien, A. E.: An automated information system to ensure quality in higher education institutions. In 2016 12th International Computer Engineering Conference (ICENCO) (pp. 196–201). (2016) IEEE

  7. Tan, C., et al.: Short-term emotion recognition and understanding based on spiking neural network modeling of Spatio-temporal EEG patterns. Neurocomputing 28(434), 137–148 (2021)

    Article  Google Scholar 

  8. Elhoseny, M., Metawa, N., Darwish, A., Hassanien, A.E.: Intelligent information system to ensure quality in higher education institutions, towards an automated e-university. Int. J. Comput. Intell. Stud. 6(2–3), 115–149 (2017)

    Google Scholar 

  9. Kumar, P.M., Pandey, H.M., Srivastava, G.: Call for special issue papers: Multimedia big data analytics for engineering education. Big data 8(3), 165–166 (2020)

    Article  Google Scholar 

  10. Giannakos, M.N., et al.: Systematic literature review of e-learning capabilities to enhance organizational learning. Inf. Syst. Front. 1, 1–7 (2021)

    Google Scholar 

  11. Saravanan, V., Alagan, A., Naik, K.: Computational biology as a compelling pedagogical tool in computer science education. J. Comput. Sci. 11(1), 45–52 (2020)

    Google Scholar 

  12. Nieto, Y., García-Díaz, V., Montenegro, C., Crespo, R.G.: Supporting academic decision-making at higher educational institutions using machine learning-based algorithms. Soft. Comput. 23(12), 4145–4153 (2019)

    Article  Google Scholar 

  13. Meemansha, Y.: Application of emotion detection using facial expression recognition. Advances in systems engineering (pp. 409–417). Springer, Singapore (2021)

  14. Nieto, Y., Gacía-Díaz, V., Montenegro, C., González, C.C., Crespo, R.G.: Usage of machine learning for strategic decision making at higher educational institutions. IEEE Access 7, 75007–75017 (2019)

    Article  Google Scholar 

  15. Kadry, S., El Hami, A.: Flipped classroom model in calculus II. Education 4(4), 103–107 (2014)

    Google Scholar 

  16. Santamaria-Granados, L., et al.: Tourist recommender systems based on emotion recognition—a scientometric review. Future Internet. 13(1), 2 (2021)

    Article  Google Scholar 

  17. Nieto, Y.V., García-Díaz, V., Montenegro, C.E.: Decision-making model at higher educational institutions based on machine learning. J. UCS 25(10), 1301–1322 (2019)

    Google Scholar 

  18. Tai, D.W., Zhang, R.C., Chang, S.H., Chen, C.P., Chen, J.L.: A meta-analytic path analysis of e-learning acceptance model. Int. J. Edu. Pedagogical Sci. 6(5), 760–763 (2012)

    Google Scholar 

  19. Tang, K.Y., et al.: Trends in artificial intelligence-supported e-learning: a systematic review and co-citation network analysis (1998–2019). Interact. Learn. Environ. 20, 1–9 (2021)

    Article  Google Scholar 

  20. Li, B., et al.: Facial expression recognition via ResNet-50. Int. J. Cogn. Comput. Eng. 2021(2), 57–64 (2021)

    MathSciNet  Google Scholar 

  21. Saravanan, V.: Impact of intelligence methodologies on education and training process. Journal of Intelligent & Fuzzy Systems, (Preprint), 1–2

  22. Alam, M.M., et al.: E-learning services to achieve sustainable learning and academic performance: an empirical study. Sustainability 13, 2653 (2021)

    Article  Google Scholar 

  23. Rasheed, F., et al.: Learning style detection in E-learning systems using machine learning techniques. Expert Syst. Appl. 174, 114774 (2021)

    Article  Google Scholar 

  24. Daultani, Y. et al.: Perceived outcomes of e-learning: identifying key attributes affecting user satisfaction in higher education institutes. Measuring Business Excellence 11 (2021)

  25. Alam, M. M., et al.: E-learning services to achieve sustainable learning and academic performance: an empirical study. Sustainability. 13(5), 2653 (2021)

    Article  Google Scholar 

  26. De Carolis, B. et al.: Cognitive emotions recognition in e-learning: Exploring the role of age differences and personality traits. In International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning (pp. 97–104). Springer, Cham (2019)

  27. Pise, A., et al.: Facial emotion recognition using temporal relational network: an application to E-learning. Multimedia Tools Appl. 14, 1–21 (2020)

    Google Scholar 

  28. Lalitha, S.D. et al.: Micro-facial expression recognition in video based on optimal convolutional neural Network (MFEOCNN) algorithm. arXiv preprint. 2020

  29. Akputu, O. K., et al.: Emotion recognition using multiple kernels learning toward E-learning applications. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) (2018) 14(1), 1–20

  30. Chu, H.C., et al.: Facial emotion recognition with transition detection for students with high-functioning autism in adaptive e-learning. Soft Comput. 22(9), 2973–2999 (2019)

    Article  Google Scholar 

  31. Nandi, A. et al.: A survey on multimodal data stream mining for e-learner's emotion recognition. In 2020 International Conference on Omni-layer Intelligent Systems (COINS) (pp. 1–6). (2020) IEEE

  32. Feng, K., et al.: Transfer learning and generalizability in automatic emotion recognition. Frontiers in Computer Science. 2020(2), 9 (2020)

    Article  Google Scholar 

  33. Wani, T.M., et al.: A comprehensive review of speech emotion recognition systems. IEEE Access. 9, 47795–47814 (2021)

    Article  Google Scholar 

  34. Jiang, D., et al.: A probability and integrated learning-based classification algorithm for high-level human emotion recognition problems. Measurement 150, 107049 (2020)

    Article  Google Scholar 

  35. Imani, M., et al.: A survey of emotion recognition methods with emphasis on E-Learning environments. J. Netw. Comput. Appl. 147, 102423 (2019)

    Article  Google Scholar 

  36. Plaza-del-Arco, F.M., et al.: Improved emotion recognition in Spanish social media by incorporating lexical knowledge. Futur. Gener. Comput. Syst. 1(110), 1000–1008 (2020)

    Article  Google Scholar 

  37. Zhang, J., et al.: Emotion recognition using multimodal data and machine learning techniques: A tutorial and review. Inform. Fusion. 59, 103–126 (2020)

    Article  Google Scholar 

  38. https://www.kaggle.com/iwilldoit/emotions-sensor-data-set

  39. https://www.kaggle.com/sasanj/human-activity-smart-devices

Download references

Author information

Authors and Affiliations

  1. College of Communication Information, Yunnan Communications Polytechnic, Kunming, 650500, Yunnan, China

    Yu Du

  2. Computer Science Department, School of Engineering and Technology, Universidad Internacional de La Rioja, 26006, Logroño, Spain

    Rubén González Crespo

  3. Universidad Internacional de La Rioja, Logroño, Spain

    Oscar Sanjuán Martínez

Authors
  1. Yu Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rubén González Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oscar Sanjuán Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Du.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, Y., Crespo, R.G. & Martínez, O.S. Human emotion recognition for enhanced performance evaluation in e-learning. Prog Artif Intell 12, 199–211 (2023). https://doi.org/10.1007/s13748-022-00278-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-022-00278-2

Keywords

Search

Navigation