Transformation of Social Studies Pedagogy through Artificial Intelligence –Driving Technologies and Students’ Motivation in Calabar Metropolis of Cross River State, Nigeria
Opoh Frederick Awhen PhD, EKPOTO, Ekpoto Agbor, Hilary Ejim Egan
Abstract
This study examined the transformation of Social Studies pedagogy through artificial intelligence –driving technologies and students’ motivation in Calabar Metropolis of Cross River State, Nigeria. The research design adopted for the study was the pre-test post-test quasi- experimental design. The population of the study consisted of all the JSS2 students in the 15 public secondary schools in Calabar Metropolis. Through simple random sampling technique, 92 students were randomly selected from two secondary schools in the study area and they formed the sample size of the experiment (40 for the exp grp and 52 for the control group). Pre and post-test were administered in the two randomly selected schools which represent both the experimental and control groups. The instrument used for data collection was Social Studies Motivation Scale (SOSMS). The researcher prepared a note of lesson for the experimental group teachers while the control group teachers were allowed to teach conventionally with their notes. A twenty-item questionnaires were designed by the researcher to measure the motivation of students in the course of the experiment using Likert Scale. The instrument underwent face and content validity by two experts in the Department of Educational Foundation, University of Calabar. A justifiable reliability coefficient of 0.78 and 0.81 were established using Cronbach’ Alpha. Analysis of Co- variance (ANCOVA) statistics was used to test the null hypotheses at 0.05 level of significance. All analyses were carried out using the SPSS application (Statistical package for social sciences). In order to arrive at statistical decision, the P-value (probability value) obtained from SPSS application was compared with the significance level of 0.05. The findings revealed that the mean motivation of students taught Social Studies with intelligence tutoring systems and automated grading systems instructional methods were significantly h
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References
of acceptance and use of technology model. Indonesian Journal of Educational Research
and Technology, 3(1), 79–90. https://doi.org/10.17509/ijert.v3i1.47183
Ali, T. Q. A., Ahmed, Y. F., Basma, J. S., Nadia, M. A., & Lamees, A. (2019). The effect of the
intelligent tutoring systems on the education.
Bishara, S. (2023). Humor, motivation and achievements in mathematics in students with learning
disabilities.
Cogent
Education,
10(1),
https://doi.org/10.1080/2331186X.2022.2162694
Casinillo, L. F. (2023). Modeling students’ self-efficacy in mathematics during the Covid-19
pandemic. Canadian Journal of Family and Youth/Le Journal Canadien de Famille et de
la Jeunesse, 15(1), 77–89. https://doi.org/10.29173/cjfy29902
Churi, P. P., Joshi, S., Elhoseny, M., & Omrane, A. (Eds.). (2022). Artificial intelligence in higher
education: A practical approach (1st ed.). CRC Press. https:// doi. org/ 10. 1201/ 97810
03184 157
Davis, B. (2021). How would you describe the 21st century? Retrieved from MV Organizing:
https://www.mvorganizing.org
Egan, H. E., Adie, H. I., Bisong, K. B., Ekpoto, A. E., Orji, E. I., Jelman, B. G. & Anyebe, M.
(2025). Diversification of funding and students learning outcomes in economics in Calabar
education zone, Cross River State. Irish Journal of Educational Practice, 8(3), 102-118.
Egan, H. E., Ariya, D. A., Oti, G. O. & Umaru, R. J. (2024). Assessment type and students’
academic achievement in Social Studies. Advance Journal of Education and Social
Sciences, 8(9), 6-12
Erickson, J. A., Botelho, A. F., McAteer, S., Varatharaj, A. & Heffernan, N. T. (2020). The
automated grading of student open responses in social studies. In Proceedings of the 10th
International Conference on Learning Analytics and Knowledge (LAK ’20), March 23–27,
2020, Frankfurt, Germany. ACM, New York, NY, USA, 53-63. https://doi.org/10.1145/
3375462.3375523
Erika, B. (2023). Automated Grading Systems: How AI is Revolutionizing Exam Evaluation. Data
Science Central; a community of big data practitioners.
Glaze, A. R. (2019). Students' conceptions of Social Studies and intelligent tutoring system use.
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 7539.
https://digitalcommons.usu.edu/etd/7539.
Hettinger, K., Lazarides, R., & Schiefele, U. (2023). Motivational climate in mathematics
classrooms: Teacher self-efficacy for student engagement, student-and teacher-reported
emotional support and student interest. ZDM–Mathematics Education, 55(2), 413–426.
https://doi.org/10.1007/s11858-022-01430-x
Hofer, S. I., Reinhold, F., & Koch, M. (2023). Students home alone—Profiles of internal and
external conditions associated with mathematics learning from home. European Journal of
Psychology of Education, 38(1), 333–366. https://doi.org/10.1007/s11858-022-01430-x
Matthew, K., Janicki, T., He, L. & Patterson, L. (2012). Implementation of an automated grading
system with an adaptive learning component to affect student feedback and response time.
Journal of Information System, 23(1), 71-84.
Manjang, A. J., Umaru, R. J. & Egan, H. E. (2024). Effects of visual metaphor on pupils’
achievement in Social Studies in Bassa, Plateau State. International Journal of Education
and Evaluation, 10(3), 243-249.
Meremikwu, A. N., Ekwueme, C. O. & Opoh, D.A. (2023). Modern learning strategies and
mathematics academic achievement among junior secondary school students in Post–
Covid–19 Era in Calabar Education Zone, Cross River State, Nigeria. Global Journal of
Educational Research, 22(1),1-9.
National Council of Students of Social Studies. (2000). Principles and standards for school Social
Studies. Reston, VA: Author.
Nour, N. A. (2017). Intelligent tutoring system for Social Studies. International Journal of
Advanced Scientific Research, 2(1), 11-16. www.allscientificjournal.com
Ogbaji, D. I., Opoh, F. A., & Onnoghen, U. N. (2019). Principal leadership behaviour and Social
Studies teachers’ contribution to nation building in Cross River State. Journal of Social
Studies and Civic Education Association of Nigeria, 10(3), 267-275
Orim, R. E., Opoh, F. A. & Igwe, B. I. (2018). Information and communication technology: The
leading way for teaching and learning in Nigeria. Journal of Environmental and Tourism
Education, 1(1), 240-246
Rach, S. (2023). Motivational states in an undergraduate mathematics course: Relations between
facets of individual interest, task values, basic needs, and effort. ZDM–Mathematics
Education, 55(2), 461–476. https://doi.org/10.1007/s11858-022-01406-x
Sabandar, G. N., Supit, N. R., & Suryana, H. E. (2018). Bring the fun into classroom using kahoot.
Indonesian Journal of Informatics Education, 127-134.
Shute, V., & Zapata-Rivera, D. (2007). Adaptive technologies (No. RR-07-05). Princeton, NJ:
Educational Testing Service.
Sirhan, N. N., Heileman, G. L., & Lamb, C. C. (2015). Traffic offloading impact on the
performance of channel-aware/QoS-aware scheduling algorithms for video applications
over LTE-A HetNets using carrier aggregation. International Journal of Computer
Networks & Communications, 7(3), 75–90. https://doi.org/10.5121/ijcnc.2015.7306
Sottilare, R. A., Graesser, A., Hu, X., & Holden, H. (2013). Design recommendations for
intelligent tutoring systems [Research Report]. Orlando, FL: U.S. Army Research
Laboratory.
Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent
tutoring systems on K-12 student’s mathematical learning. Journal of Educational
Psychology, 105(4), 970-987.
Weiyi, B., Omar, A. & Jörg, K. (2020). Is Automated Grading of Models Effective? Assessing
Automated Grading of Class Diagrams. In ACM/IEEE 23rd International Conference on
Model Driven Engineering Languages and Systems (MODELS ’20), October 18–23, 2020,
Montreal,
QC,
Canada.
ACM,
New
York,
NY,
USA,
12
pages.
https://doi.org/10.1145/3365438. 3410944
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of
research on artificial intelligence applications in higher education–where are the learnerss?
International Journal of Educational Technology in Higher Education, 16(1), 1–27.
https:// doi. org/ 10. 1186/s41239- 019- 0171-0