THE MOTIVATIONAL EFFICACY OF SELF-REGULATED ICT RESOURCES ON INTEREST IN BIOLOGY AMONG HIGH, MEDIUM, AND LOW ACHIEVING SECONDARY SCHOOL STUDENTS
DOI:
https://doi.org/10.33003/fjs-2024-0806-2821Keywords:
Information and Communication Technological (ICT), Interactive hypermedia, Student Achievement, Student Interest, Achievement level, Score levelAbstract
This study examined the impact of technological software on learning from an interactive hypermedia. It explores participants’ learning interest and engagement, analyzing variations based on their score levels. The mixed method study adopted a pretest, posttest, quasi-experimental design using 3x2x3 factorial matrix, a Semi-Structured interview guide, and a Students’ interest inventory, entitled Questionnaire on the effect of ICT Resources on the Interest of Students in Biology (QEIISB) utilized to elicit responses on students’ interest. Post-hoc analysis was used to determine the direction of difference and findings revealed were discussed in the context of perfect understanding across achievement levels, because participants showed better interest and engagement more in the group of medium Scorers. The medium score level students showed significant interest and benefitted most from the software. By evaluating the efficiency of Information and Communication Technological (ICT) tools, influence students with varying performance or achievement levels, the study aims to provide insights into the effective these resources influence achievement and foster better interest in teaching and learning.
References
Ahad, N. A., Okwonu, F. Z., & Siong, P. Y. (2020). COVID-19 Outbreak in Malaysia: Investigation on Fatality Cases. Journal of Advanced Research in Applied Sciences and Engineering Technology, In Press.
Ahmad, N. A., & Okwonu, F. Z. (2011). Least square problem for adaptive filtering. Australian Journal of Basic and Applied Sciences, 5, 69–74.
Al-Najjar, H., & Al-Rousan, N. (2020). A classifier prediction model to predict the status of Coronavirus CoVID-19 patients in South Korea. European Review for Medical and Pharmacological Sciences, 24(6). https://doi.org/10.26355/eurrev_202003_20709
Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020). COVID-19 outbreak prediction with machine learning. Algorithms, 13(10). https://doi.org/10.3390/a13100249
Basu, S., & Campbell, R. H. (2020). Going by the numbers: Learning and modeling COVID-19 disease dynamics. Chaos, Solitons and Fractals, 138. https://doi.org/10.1016/j.chaos.2020.110140
Borovkov, A. I., Bolsunovskaya, M. v., & Gintciak, A. M. (2022). Intelligent Data Analysis for Infection Spread Prediction. Sustainability (Switzerland), 14(4). https://doi.org/10.3390/su14041995
Branas, C. C., Rundle, A., Pei, S., Yang, W., Carr, B. G., Sims, S., Zebrowski, A., Doorley, R., Schluger, N., Quinn, J. W., & Shaman, J. (2020). Flattening the curve before it flattens us: hospital critical care capacity limits and mortality from novel coronavirus ({SARS-CoV2}) cases in {US} counties. MeRxiv.
Calafiore, G. C., Novara, C., & Possieri, C. (2020). A time-varying SIRD model for the COVID-19 contagion in Italy. Annual Reviews in Control, 50. https://doi.org/10.1016/j.arcontrol.2020.10.005
el Asnaoui, K., Chawki, Y., & Idri, A. (2021). Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning. https://doi.org/10.1007/978-3-030-74575-2_14
Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons and Fractals, 134. https://doi.org/10.1016/j.chaos.2020.109761
Ferguson, N. M., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z., Cuomo-Dannenburg, G., Dighe, A., Dorigatti, I., Fu, H., Gaythorpe, K., Green, W., Hamlet, A., Hinsley, W., Okell, L. C., Elsland, S. van, … Ghani., A. C. (2020). Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand Neil.
García-García, J. A., Enríquez, J. G., Ruiz, M., Arévalo, C., & Jiménez-Ramírez, A. (2020). Software Process Simulation Modeling: Systematic literature review. In Computer Standards and Interfaces (Vol. 70). https://doi.org/10.1016/j.csi.2020.103425
Hirschprung, R. S., & Hajaj, C. (2021). Prediction model for the spread of the COVID-19 outbreak in the global environment. Heliyon, 7(7). https://doi.org/10.1016/j.heliyon.2021.e07416
https://www.nodehealth.org/covid-resource/covid-19-hospital-impact-model-for-epidemics-chime/. (2021). COVID-19 Hospital Impact Model for Epidemics (CHIME). Https://Www.Nodehealth.Org/Covid-Resource/Covid-19-Hospital-Impact-Model-for-Epidemics-Chime/.
Hu, Z., Ge, Q., Li, S., & Xiong, M. (2020). Artificial Intelligence Forecasting of Covid-19 in China. International Journal of Educational Excellence, 6(1). https://doi.org/10.18562/ijee.054
John A. Rice. (2007). Mathematical statistics and data analysis (3rd ed.). Duxbury Press.
Long, J. B., & Ehrenfeld, J. M. (2020). The Role of Augmented Intelligence (AI) in Detecting and Preventing the Spread of Novel Coronavirus. Journal of Medical Systems, 44(3), 59. https://doi.org/10.1007/s10916-020-1536-6
Ma, W., Song, M., & Takeuchi, Y. (2004). Global stability of an SIR epidemic model with time delay. Applied Mathematics Letters, 17(10). https://doi.org/10.1016/j.aml.2003.11.005
Ma, Y., Xu, Z., Wu, Z., & Bai, Y. (2020). COVID-19 Spreading Prediction with Enhanced SEIR Model. Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020. https://doi.org/10.1109/ICAICE51518.2020.00080
Mohammed, M. B., Salsabil, L., Tanaaz, S. S., Shahriar, M., & Fahmin, A. (2020). An extensive analysis of the effect of social distancing in transmission of COVID-19 in Bangladesh by the aid of a modified SEIRD Model. 2020 2nd International Conference on Advanced Information and Communication Technology, ICAICT 2020. https://doi.org/10.1109/ICAICT51780.2020.9333517
Okwonu, F. Z. , Arunaye, F. I. , & Ahad, N. A. ,. (2020). MATHEMATICAL MODEL FOR SOCIAL DISTANCING IN MITIGATING THE SPREAD OF COVID-19. Nigerian Journal of Science and Environment, 18(1), 173–182.
Okwonu, F. Z., Ahad, N. A., Apanapudor, J. S., & Arunaye, F. I. (2021). Covid-19 prediction model (Covid-19-PM) for social distancing: The height perspective. Proceedings of the Pakistan Academy of Sciences: Part A, 57(4).
Okwonu, F. Z., Ahad, N. A., Apanapudor, J. S., Arunaye, F. I., Ekiyor, F. E., & Okoloko, I. E. (2021). REVIEW OF COVID-19 112 DAYS OF GLOBAL EXPLORATION IN 212 COUNTRIES OUTSIDE CHINA: A COMPREHENSIVE REVIEW. JOURNAL OF HARBIN INSTITUTE OF TECHNOLOGY, 53(9), 1–26.
Okwonu, F. Z., Arunaye, F. I., & Ahad, N. A. (2020). Mathematical model for social distancing in mitigating the spread of covid-19. Nigerian Journal of Science and Environment , 18(1), 173–182.
Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., & Coelho, L. dos S. (2020). Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons and Fractals, 135. https://doi.org/10.1016/j.chaos.2020.109853
Santosh, K. C. (2020a). AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. Journal of Medical Systems, 44(5), 93. https://doi.org/10.1007/s10916-020-01562-1
Santosh, K. C. (2020b). COVID-19 Prediction Models and Unexploited Data. Journal of Medical Systems, 44(9), 170. https://doi.org/10.1007/s10916-020-01645-z
Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020). Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things (Netherlands), 11. https://doi.org/10.1016/j.iot.2020.100222
Wang, Z., & Tang, K. (2020). Combating COVID-19: health equity matters. In Nature Medicine (Vol. 26, Issue 4). https://doi.org/10.1038/s41591-020-0823-6
Wynants, L., van Calster, B., Collins, G. S., Riley, R. D., Heinze, G., Schuit, E., Bonten, M. M. J., Damen, J. A. A., Debray, T. P. A., de Vos, M., Dhiman, P., Haller, M. C., Harhay, M. O., Henckaerts, L., Kreuzberger, N., Lohmann, A., Luijken, K., Ma, J., Andaur Navarro, C. L., … van Smeden, M. (2020). Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. The BMJ, 369. https://doi.org/10.1136/bmj.m1328
Yakovyna, V., & Shakhovska, N. (2021). Modelling and predicting the spread of COVID-19 cases depending on restriction policy based on mined recommendation rules. Mathematical Biosciences and Engineering, 18(3). https://doi.org/10.3934/MBE.2021142
Yang, Z., Zeng, Z., Wang, K., Wong, S. S., Liang, W., Zanin, M., Liu, P., Cao, X., Gao, Z., Mai, Z., Liang, J., Liu, X., Li, S., Li, Y., Ye, F., Guan, W., Yang, Y., Li, F., Luo, S., … He, J. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3). https://doi.org/10.21037/jtd.2020.02.64
Zhao, H., Merchant, N. N., McNulty, A., Radcliff, T. A., Cote, M. J., Fischer, R. S. B., Sang, H., & Ory, M. G. (2021). COVID-19: Short term prediction model using daily incidence data. PLoS ONE, 16(4 April). https://doi.org/10.1371/journal.pone.0250110
Zheng, N., Du, S., Wang, J., Zhang, H., Cui, W., Kang, Z., Yang, T., Lou, B., Chi, Y., Long, H., Ma, M., Yuan, Q., Zhang, S., Zhang, D., Ye, F., & Xin, J. (2020). Predicting COVID-19 in China Using Hybrid AI Model. IEEE Transactions on Cybernetics, 50(7). https://doi.org/10.1109/TCYB.2020.2990162
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences
