Skip to main content
SpringerLink
Log in

A Novel Model to Predict the Effects of Enhanced Students’ Computer Interaction on Their Health in COVID-19 Pandemics

  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

During the COVID-19 pandemic time, educational institutions have really played a good role in imparting online education to students. Their career and academic tenure were not affected as contrary to the past pandemics throughout world history. All this has been possible through long sessions of classes, quizzes, assignments, discussions, chat interactions, and examinations through online video-based learning using computer interactive measures. The students were privileged to utilize digital technologies for longer durations for learning purposes. However, these long stretches have adversely affected their body postures, and physical and mental health as they majorly remain confined to chairs with restricted levels of physical activities. Thus, there is a need to have a model which can act as an insight for parents, doctors (pediatricians), and academic policymakers to decide on maximum hours for online teaching and related activities during future pandemics. The novel model proposed in this work helps to predict the impact of enhanced students’ computer interactions on their physical and mental health. The method proposed uses a novel model which is advanced and computationally strong. The model follows a two-step methodology, where at the first level, a variant of already existing machine learning algorithm is proposed and at the next level, it is optimized further using a hybrid bio-inspired optimization algorithm. The model consists of proposing a variant of XGBoost model (step1 optimization) followed by a hybrid bio-inspired algorithm (step2 optimization). The work considers a humongous dataset with varied age groups of students with more than 10 attributes. The proposed model is highly efficient in making predictions with 98.07% accuracy level and 98.43% F1-score. The time complexity of the model obtained is also of order of “n” where “n” depicts the number of input variables. Strong empirical results for other parameters also like specificity (95.63%) and sensitivity (96.74%) ascertain the enhanced predictive power generated using the proposed model. An extensive comparative study with other machine learning models ascertains the elevated accuracy and predictive power using the proposed model. Till now none of the researchers have proposed any such pioneering tool for parents, doctors, and academicians using advanced machine learning algorithms.

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

Access this article

Log in via an institution

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

Data available on demand.

References

  1. Abdous, M. (2019). Influence of satisfaction and preparedness on online students’ feelings of anxiety. The Internet and Higher Education, 41,34–44.https://doi.org/10.1016/j.iheduc.2019.01.001

  2. Agarwal, N., Jain, A., Gupta, A., & Tayal, D. K. (2021, November). Applying XGBoost Machine Learning Model to Succor Astronomers Detect Exoplanets in Distant Galaxies. In International Conference on Artificial Intelligence and Speech

  3. Agarwal, N., Tayal, D.K.: FFT based ensembled model to predict ranks of higher educational institutions. Multimedia Tools and Applications 81(23), 34129–34162 (2022)

    Article  Google Scholar 

  4. Agarwal, N. and Tayal, D.K. (2023) A new model based on the extended COPRAS method for improving performance during the accreditation process of Indian Higher Educational Institutions. Computer Applications in Engineering Education.

  5. Al-Asadi, M.A., Tasdemír, S.: Empirical comparisons for combining balancing and feature selection strategies for characterizing football players using FIFA video game system. IEEE Access 9, 149266–149286 (2021)

    Article  Google Scholar 

  6. Al-Asadi, M.A., Tasdemır, S.: Predict the value of football players using FIFA video game data and machine learning techniques. IEEE Access 10, 22631–22645 (2022)

    Article  Google Scholar 

  7. Alhadreti, O.: Assessing academics’ perceptions of blackboard usability using SUS and CSUQ: a case study during the COVID-19 pandemic. International Journal of Human-Computer Interaction 37(11), 1003–1015 (2021). https://doi.org/10.1080/10447318.2020.1861766

    Article  Google Scholar 

  8. Alonso, F., Manrique, D., Martinez, L., & Vines, J. M. (2011). How blended learning reduce sunder achievement in higher education: An experience in teaching computer sciences. IEEE Transactions on Education, 54(3), 471–478. https://doi.org/10.1109/TE.2010.2083665

  9. Anthony, B., Kamaludin, A., Romli, A., Raffei, A.F.M., Nincarean ALEhPhon, D., Abdullah, A., Ming, G.L., Shukor, N.A., Nordin, M.S., & Baba, S. (2019). Exploring the role of blended learning for teaching and learning effect iveness in institution so fhigher learning: An empirical investigation. Education and Information Technologies, 24(6), 3433–3466. https://doi.org/10.1007/s10639-019-09941-z

  10. Banik, D., Bhattacharjee, D.: Mitigating data imbalance issues in medical image analysis. In: Rana, D.P., Mehta, R.G. (eds.) Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance, pp. 66–89. IGI Global (2021)

  11. Beaunoyer, E., Dupéré, S., & Guitton, M.J. (2020). COVID19 and digital in equalities: Reciprocal impacts and mitigation strategies. Computers in Human Behavior, 111, 106424. https://doi.org/10.1016/j.chb.2020.106424.

  12. Bhagat, R.S., Krishnan, B., Nelson, T.A., Leonard, K.M., Ford, D.L., Billing, T.K.: Organizational stress, psychological strain, and work outcomes in six national contexts. IEEE Eng. Manage. Rev. 38(4), 39–57 (2010)

    Article  Google Scholar 

  13. Biner, P. M., Welsh, K. D., Barone, N. M., Summers, M., & Dean, R. S. (1997). The impact of remote–site group size on student satisfaction and relative performance in interactive tele courses. Int. J. Phytoremediation, 11(1), 23–33. https://doi.org/10.1080/08923649709526949

  14. Brooks, S., Longstreet, P., &Califf, C. (2017). Social media induced techno stress and its impact on internet addiction: Adistraction-conflict theory perspective. AIS Trans. Hum.-Comput. Interaction, 9(2), 99–122.https://doi.org/10.17705/1thci.00091

  15. Cabero-Almenara, J., Fernández-Batanero, J.M., & Barroso Osuna, J. (2019). Adoption of augmented reality technology by university students. Heliyon, 5(5), e01597. https://doi.org/10.1016/j.heliyon.2019.e01597

  16. Cao, X., Masood, A., Luqman, A., Ali, A.: Excessive use of mobile social networking sites and poor academic performance: antecedents and consequences from stressor-strain-out come perspective. Comput. Hum. Behav. 85, 163–174 (2018). https://doi.org/10.1016/j.chb.2018.03.023

    Article  Google Scholar 

  17. Chatterjee, S., Maity, S., Bhattacharjee, M., et al.: Variational autoencoder based imbalanced COVID-19 detection using chest X-ray images. New Gener. Comput. (2022). https://doi.org/10.1007/s00354-022-00194-y

    Article  Google Scholar 

  18. Chawla, N.V., Bowyer, K.W., Hall, L.O., Philip Kegelmeyer, W.: Smote synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  MATH  Google Scholar 

  19. Chin, C. (2020). Learning must n’t stop with Covid19. The Star Online. https://www.thestar.com.my/news/education/2020/03/29/learning-mustnt-stop-with-covid-19

  20. Chiu, C. M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information and Management, 45(3), 194–201.

  21. Etherington, C. (2017). Selfmotivation is essential to elearning. Elearning Inside. https://news.elearninginside.com/self-motivation-essential-elearning/

  22. Fozdar, B.I., & Kumar, L.S. (2007). Mobile learning and student retention. International Review of Research in Open and Distance Learning, 8(2), 1–18. https://files.eric.ed.gov/fulltext/EJ800952.pdf

  23. Garrison, D.R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001

  24. Güğerçin, U. (2020). Does tech no-stress justify cybers lacking? An empirical study based on the neutralisation theory. Behaviour & Information Technology, 39(7), 824–836. https://doi.org/10.1080/0144929X.2019.1617350.

  25. Gupta, A., Sharma, S., Goyal, S., Rashid, M. (2020). Novel XGBoost Tuned Machine Learning Model for Software Bug Prediction, 2020 International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 376–380. https://doi.org/10.1109/ICIEM48762.2020.9160152.

  26. Hung, W.H., Chen, K., Lin, C.P.: Does the proactive personality mitigate the adverse effect of technostress on productivity in the mobile environment? Telematics Inform. 32(1), 143–157 (2015)

    Article  Google Scholar 

  27. Johnson, D.A., & Christensen, J. (2011). A comparison of simplified-visually rich and traditional presentation styles. Teaching of Psychology, 38(4), 293–297. https://doi.org/10.1177/0098628311421333

  28. Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and youth services review, 116, 105194.

  29. Lee, D.Y., Ryu, H.: Learner acceptance of a multimedia-based learning system. Int. J. Hum.-Comput. Interaction 29(6), 419–437 (2013). https://doi.org/10.1080/10447318.2012.715278

    Article  Google Scholar 

  30. Lee, D.Y., Shin, D.-H.: Effects of spatial ability and richness of motion cue on learning in mechanically complex domain. Comput. Hum. Behav. 27(5), 1665–1674 (2011). https://doi.org/10.1016/j.chb.2011.02.005

    Article  Google Scholar 

  31. Iivari, N., Sharma, S., & Ventä-Olkkonen, L. (2020). Digital transformation of everyday life – How COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? Int. J. Inform. Manag., 55, 102183. https://doi.org/10.1016/j.ijinfomgt.2020.102183

  32. Li, L.-Y. (2019). Effect of prior knowledge on attitudes, behavior, and learning performance in video lecture viewing. Int. J. Hum.–Comput. Interaction, 35(4–5), 415–426. https://doi.org/10.1080/10447318.2018.1543086

  33. Lwoga, E.T., Komba, M.: Antecedents of continued usage intentions of web-based learning management system in Tanzania. Educ. Train. 57(7), 738–756 (2015). https://doi.org/10.1108/ET-02-2014-0014

    Article  Google Scholar 

  34. New Straits Times. (2020). Online classes lack student-teacher engage- ment: Study. New Straits Times. https://www.nst.com.my/world/region/2020/05/589963/online-classes-lack-student-teacher- engagement-study.

  35. Nguyen, Q.N., Ta, A., Prybutok, V.: An integrated model of voice-user interface continuance intention: the gender effect. International Journal of Human-Computer Interaction 35(15), 1362–1377 (2019). https://doi.org/10.1080/10447318.2018.1525023

    Article  Google Scholar 

  36. O’Callaghan, F.V., Neumann, D.L., Jones, L., Creed, P.A.: The use of lecture recordings in higher education: a review of institutional, student, and lecturer issues. Educ. Inf. Technol. 22(1), 399–415 (2017). https://doi.org/10.1007/s10639-015-9451-z

    Article  Google Scholar 

  37. P., N. P., Rajani, M., Georg, G., Lynnea, E., & Raghu, R. (2018). Towards an inclusive digital literacy framework for digital India. Education + Training, 60(6), 516–528. https://doi.org/10.1108/ET-03-2018-0061

  38. Pal, D., Patra, S.: University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective. International Journal of Human-Computer Interaction 37(10), 903–921 (2021). https://doi.org/10.1080/10447318.2020.1848164

    Article  Google Scholar 

  39. Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and Youth Services Review, 119, 105535. https://doi.org/10.1016/j.childyouth.2020.105535

  40. Park, C., Kim, D., Cho, S., Han, H.-J.: Adoption of multimedia technology for learning and gender difference. Comput. Hum. Behav. 92, 288–296 (2019). https://doi.org/10.1016/j.chb.2018.11.029

    Article  Google Scholar 

  41. Saade, R. G., Kira, D., Mak, T., & Nebebe, F. (2017). Anxiety and performance in online learning. Informing science and information technology education conference (Vietnam: Informing Science Institute) (pp. 147–157).

  42. Sarstedt, M., Henseler, J., Ringle, C.M.: Multigroup analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. Adv Int Mark 2011(22), 195–218 (2011)

    Article  Google Scholar 

  43. Shi, C., Yu, L., Wang, N., Cheng, B., & Cao, X. (2020). Effects of social media overload on academic performance: A stressor–strain–outcome perspective. Asian Journal of Communication, 30(2), 179–197. https://doi.org/10.1080/01292986.2020.1748073

  44. Shen, R., Wang, M., Gao, W., Novak, D., Tang, L.: Mobile learning in a large, blended computer science classroom: system function, pedagogies, and their impact on learning. IEEE Trans. Educ. 52(4), 538–546 (2009). https://doi.org/10.1109/TE.2008.930794

    Article  Google Scholar 

  45. Shu, Q., Tu, Q., Wang, K.: The impact of computer self-efficacy and technology dependence on computer-related technostress: a social cognitive theory perspective. Int. J. Hum.- Comput. Interaction 27(10), 923–939 (2011). https://doi.org/10.1080/10447318.2011.555313

    Article  Google Scholar 

  46. Tamm, S. (2019). Disadvantages of e-learning. E-Student.Org. https://estudent.org/disadvantages-of-e-learning/

  47. Ulrich, F., Helms, N.H., Frandsen, U.P., Rafn, A.V.: Learning effectiveness of 360° video: Experiences from a controlled experiment in healthcare education. Interact. Learn. Environ. 26(1), 1–14 (2019). https://doi.org/10.1080/10494820.2019.1579234

    Article  Google Scholar 

  48. NESCO. (2020). Quality education. In COVID-19 educational disruption and response. https://en.unesco.org/news/covid-19-educational-disruption-and-response

  49. UNESCO. (n.d.). No title. COVID-19 Educational Disruption and Response. Retrieved June 30, 2020, from https://en.unesco.org/news/covid-19-educational-disruption-and-response.

  50. Xu, D., Huang, W. W., Wang, H., & Heales, J. (2014). Enhancing e-learning effectiveness using an intelligent agent-supported personlized personalized virtual learning environment: An empirical investigation. Information and Management, 51(4), 430–440. https://doi.org/10.1016/j.im.2014.02.009

  51. Zhang, D., Zhou, L., Briggs, R.O., Nunamaker, J.F.: Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Inform. Manag. 43(1), 15–27 (2006). https://doi.org/10.1016/j.im.2005.01.004

    Article  Google Scholar 

  52. Zheng, X., Lee, M.K.O.: Excessive use of mobile social networking sites: Negative consequences on individuals. Comput. Hum. Behav. 65, 65–76 (2016). https://doi.org/10.1016/j.chb.2016.08

    Article  Google Scholar 

  53. Zhou, J., Rau, P.-L. P., & Salvendy, G. (2014). Older adults’ text entry on smartphones and tablets: Investigating effects of display size and input method on acceptance and performance. International Journal of Human–Computer Interaction, 30(9), 727–739. https://doi.org/10.1080/10447318.2014.924348

  54. Zimmerman, B.J., Bandura, A., Martinez-Pons, M.: Self- motivation for academic attainment: the role of self-efficacy beliefs and personal goal setting. Am. Educ. Res. J. 29(3), 663–676 (1992). https://doi.org/10.3102/0002831202.9003663

    Article  Google Scholar 

Download references

Funding

This work is not funded by any funding agencies.

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India

    Nidhi Agarwal

  2. Department of Computer Science Engineering, Galgotias University, Greater Noida, India

    Nidhi Agarwal

  3. Department of Computer Science Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, Telengana, India

    Sachi Nandan Mohanty

  4. Department of Computer Science, Maitreyi College (University of Delhi), Bapu Dham, Chanakyapuri, New Delhi, India

    Shweta Sankhwar

  5. Department of Computer Science & Engineering, SRM University-AP, Amaravati, Andhra Pradesh, India

    Jatindra Kumar Dash

Authors
  1. Nidhi Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sachi Nandan Mohanty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shweta Sankhwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jatindra Kumar Dash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jatindra Kumar Dash.

Ethics declarations

Ethical standards

No humans and/or animals are involved in this study.

Additional information

Publisher's Note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agarwal, N., Mohanty, S.N., Sankhwar, S. et al. A Novel Model to Predict the Effects of Enhanced Students’ Computer Interaction on Their Health in COVID-19 Pandemics. New Gener. Comput. 41, 635–668 (2023). https://doi.org/10.1007/s00354-023-00224-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00354-023-00224-3

Keywords

Search

Navigation