Factors Influencing STM Teachers' Acceptance of Mobile Learning in South-West Nigeria
DOI:
https://doi.org/10.37134/jictie.vol11.2.9.2024Keywords:
Reception, mobile learning, STM, teachers, Teachnology Acceptance ModelAbstract
Science, Technology, and Mathematics (STM) education in Nigeria faces numerous challenges, including declining student performance, reduced enrolment rates, and limited integration of technology into teaching practices. Nevertheless, former investigations have proven the effectiveness of mobile learning (m-learning) in mitigating the difficulties encountered in STM education. Notwithstanding the paybacks accrue to m-learning STM teaching space, its espousal and implementations are still lower than the projected rates. The reception of m-learning is a function of the disposition of its handlers. While many studies focused on students’ reception of m-learning, very few studies have examined STM teachers’ reception of m-learning with little or no studies conducted in Nigeria. With the Technology Acceptance Model, this study examined the determinants of STM teachers’ behavioural intention (BI) to use m-learning. 280 participants were selected through purposive sampling technique from 60 senior secondary schools in south-west Nigeria and this constituted the sample of the study. A quantitative approach within the blueprint of descriptive survey design of a correlational type was implemented and numerical data collected with a valid and reliable questionnaire were analysed at 5% level of significance using multiple regression analysis and Pearson product-moment correlation coefficient. The study outcomes showed that 85.2% of the variance in STM teachers’ behavioural intention (BI) to use m-learning was accounted for by the joint of perceived attitude (ATT), perceived ease of use (PEOU), perceived usefulness (PU), perceived resources (PR) and perceived social influence (PSI) with a significant F(5, 274)= 811.348. There was a significantly positive and meaningful relationship between the STM teachers’ BI and PSI (r=.512, p<.01), PR (r=.446, p<.01), PEOU (r=.425, p<.01), ATT (r=.414, p<.01), and PU (r=.502, p<.01). PU was the best meaningful predictor of BI to use m-learning among the STM teachers (β = 1.656, t = 18.445, p=.000), followed by ATT (β = 1.246, t = 14.002, p=.000), followed by PR (β =1.112, t = 12.224, p=.000) and followed by PEOU (β = 1.086, t = 10.005, p=.000). PSI was the least predictor of BI to use m-learning among STM teachers (β = .886, t = 8.896, p=.000). The regression equation that satisfied the model is displayed by BI to use m-learningpredicted=9.62 + 2.564 PR + 3.814 PU + 2.845 ATT + 1.089 PEOU + 1.004 PSI towards m-learning. The present study did not use a mixed-methods approach so future studies could combine quantitative and qualitative data, to gain a more comprehensive understanding of mobile learning acceptance and its influencing factors. Nevertheless, this study concluded that more seminars and workshops should be conducted for STM teachers to enhance their reception of m-learning for pedagogical transactions in the classrooms in Nigeria.
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References
Ajao, E. A., & Awofala, A. O. A. (2024). Developing problem-solving skills in mathematics at primary, secondary, and tertiary levels. Nigerian Online Journal of Educational Sciences and Technology, 6(1), 136-152.
Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and a mixed-method study. Education and Information Technologies, 24(3), 2127-2144.
Al-dheleai, Y. M., Baki, R., Tasir, Z., & Al-rahmi, W. M. (2019). What hinders the use of ICT among academic staff at Yemen’s public universities? International Journal of Humanities and Innovation, 2(1), 7-12.
Al-Emran, M., & Salloum, S. A. (2017). Students’ attitudes towards the use of mobile technologies in e-evaluation. International Journal of Interactive Mobile Technologies, 11(5), 195-202.
Almaiah, M. A., Al-lozi, E. M., Al-Khasawneh, A., Shishakly, R., & Nachouki, M. (2021). Factors affecting students’ acceptance of mobile learning application in higher education during COVID-19 using ANN-SEM modelling technique. Electronics, 10, 3121. https://doi.org/10.3390/electronics10243121
AlMarwani, M. (2021). Acceptance and use of mobile technologies in learning and teaching of EFL: An economic perspective. The EuroCALL Review, 28(2), 39-49. https://doi.org/10.4995/eurocall.2020.12388
Alrajawy, I., Daud, N. M., Isaac, O., & Mutahar, A. M. (2017). Examine factors influencing the intention to use mobile learning in Yemen public universities. Asian Journal of Information Technology, 16(2), 287-297.
Alrajawy, I., Isaac, O., Ghosh, A., Nusari, M., Al-Shibami, A. H., & Ameen, A. (2018). Determinants of student’s intention to use mobile learning in Yemeni public universities: Extending the technology acceptance model with anxiety. International Journal of Management and Human Science, 2.
Alshmrany, S., & Wilkinson, B. (2017). Factors influencing the adoption of ICT by teachers in primary schools in Saudi Arabia. Education (Mohe), 27, 143-156.
Awofala, A. O. A., Olafare, F. O., Awofala, A. A., Ojo, O. T., Fatade, A. O., Arigbabu, A. A., & Udeani, U. N. (2021). Prevalence of COVID-19 in Nigeria: Doorway for digital learning of mathematics amid senior secondary science and mathematics students. International Journal of Innovative Technology Integration in Education, 5(1), 9-16.
Awofala, A. O. A., & Oladipo, A. J. (2023). A simulation study of preservice STM teachers’ technostress as related to supposed utility, attitudes towards portable technology and continuance intents to use portable technology. Digital Education Review, 44, 23-29.
Awofala, A. O. A., & Fatade, A. O. (2023). Nigerian students’ poor performance in mathematics: Who do we blame? Nigerian Online Journal of Educational Sciences and Technology, 5(1), 80-91.
Awofala, A. O. A., & Lawal, R. F. (2022). The relationship between critical thinking skills and quantitative reasoning among junior secondary school students in Nigeria. Jurnal Pendidikan Matematika (Kudus), 5(1), 1-16.
Awofala, A. O. A., & Lawani, A. O. (2020a). Examining the efficacy of co-operative learning strategy on undergraduate students’ achievement in mathematics. International Journal of Pedagogy and Teacher Education, 4(1), 59-82.
Awofala, A. O. A., & Lawani, A. O. (2020b). Increasing mathematics achievement of senior secondary school students through differentiated instruction. Journal of Educational Sciences, 4(1), 1-19.
Awofala, A. O. A., Akinoso, S. O., & Fatade, A. O. (2017). Attitudes towards computer and computer self-efficacy as predictors of preservice mathematics teachers’ computer anxiety. Acta Didactica Napocensia, 10(3), 91-108.
Awofala, A. O. A., Arigbabu, A. A., & Awofala, A. A. (2013). Effects of framing and team-assisted individualized instructional strategies on senior secondary school students’ attitudes toward mathematics. Acta Didactica Napocensia, 6(1), 1-22.
Awofala, A. O. A., Ojo, O. T., Okunuga, R. O., Babajide, V. F. T., Olabiyi, O. S., & Adenle, S. O. (2019). Investigating perception of the 21st century skills among Nigerian pre-service science, technology and mathematics teachers. International Online Journal of Educational Sciences, 11(5), 1-12.
Awofala, A. O. A., Olabiyi, O. S., Awofala, A. A., Arigbabu, A. A., Fatade, A. O., & Udeani, U. N. (2019). Attitudes toward computer, computer anxiety and gender as determinants of pre-service science, technology, and mathematics teachers’ computer self-efficacy. Digital Education Review, 36, 51-67.
Awofala, A. O. A., Olabiyi, O. S., Ogunleye, A., Udeani, U. N., & Fatade, A. O. (2017). School administrators’ perceptions of the employability of preservice science, technology, and mathematics teachers through teaching practice in Nigeria. International Journal of Research in Education and Science, 3(1), 42-55.
Awofala, A. O., Lawal, R. F., Arigbabu, A. A., & Fatade, A. O. (2022). Mathematics productive disposition as a correlate of senior secondary school students’ achievement in mathematics in Nigeria. International Journal of Mathematical Education in Science and Technology, 53(6), 1326-1342.
Blut, M., Chong, A. Y., Loong, T. Z., & Venkatesh, V. (2022). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): Challenging its validity and charting a research agenda in the red ocean. Journal of the Association for Information Systems, 23(1), 13-95. https://doi.org/10.17705/1jais.00719
Camilleri, M. A., & Camilleri, A. C. (2019). The acceptance and use of mobile learning applications in higher education. In Pfennig, A. & Chen, K. C. (Eds.), 3rd International Conference on Education and eLearning (ICEEL2019), Barcelona, Spain.
Dahri, N. A., Al-Rahmi, W. M., Almogren, A. S., Yahaya, N., Vighio, M. S., Al-maatuok, Q., Al-Rahmi, A. M., & Al-Adwan, A. S. (2023). Acceptance of mobile learning technology by teachers: Influencing mobile self-efficacy and 21st-century skills-based training. Sustainability, 15, 8514. https://doi.org/10.3390/su15118514
Edumadze, J., Ditlhokwa, G., & Demuyakor, J. (2022). Students’ acceptance and perceptions of perceived usefulness of mobile learning devices in higher educational institutions. Online Journal of Communication and Media Technologies, 12(2), e202209. https://doi.org/10.30935/ojcmt/11539
Estriegana, R., Medina, J. A., & Barchino, R. P. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education, 135, 1-14.
Fagan, M. H. (2019). Factors influencing student acceptance of mobile learning in higher education. Computers in the Schools, 36(2), 105-121. https://doi.org/10.1080/07380569.2019.1603051
Gómez-Ramirez, I., Valencia-Arias, A., & Laura Duque, L. (2019). Approach to m-learning acceptance among university students: An integrated model of TPB and TAM. International Review of Research in Open and Distributed Learning, 20(3), 141-164.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Leys, C., Delacre, M., Mora, Y. L., Lakens, D., & Ley, C. (2019). How to classify, detect, and manage univariate and multivariate outliers. With emphasis on pre-registration. International Review of Social Psychology, 32(1), 1–10. https://doi.org/10.5334/irsp.289
Lin, E. Y.-C., Hsu, H.-T., & Chen, K. T.-C. (2023). Factors that influence students’ acceptance of mobile learning for EFL in higher education. EURASIA Journal of Mathematics, Science and Technology Education, 19(6), em2279. https://doi.org/10.29333/ejmste/13219
Mahasneh, O. (2021). Factors that affect university college students’ acceptance and use of mobile learning (ML). International Journal of Instruction, 14(3), 861-872. https://doi.org/10.29333/iji.2021.14350a
Makgato, M. (2019). STEM for sustainable skills for the fourth industrial revolution: Snapshot at some TVET colleges in South Africa. In Theorizing STEM education in the 21st century. IntechOpen.
Moradi, R., & Fazeli, M. (2022). Identifying effective factors on acceptance and establishment of mobile learning in virtual training center for academics. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 13(3), 190-201. https://doi.org/10.30476/IJVLMS.2022.94924.1147
Mutambara, D., & Bayaga, A. (2020). Predicting rural STEM teachers’ acceptance of mobile learning in the fourth industrial revolution. Journal of Construction Project Management and Innovation, 10(2), 14-29.
Nakarmi, S. S. (2024). Multi-collinearity in research and the way forward. Kaladarpan, 4(1), 85-91.
Nikou, S. A., & Economides, A. A. (2018). Mobile‐based micro‐learning and assessment: Impact on learning performance and motivation of high school students. Journal of Computer Assisted Learning, 34(3), 269-278.
Odiakaosa, O. J., Dlodlo, N., & Jere, N. (2017). Teacher and learner perceptions on mobile learning technology: A case of Namibian high schools from the Hardap region. Higher Educator-An International Journal, 1(1), 13-41.
Okunuga, R. O., Awofala, A. O. A., & Osarenren, U. (2020). Critical thinking acquisition of senior secondary school science students in Lagos State, Nigeria: A predictor of academic achievement. Journal of Curriculum and Instruction, 13(1), 44-56.
Samad, M. R. A., Iksan, Z. H., & Khalid, F. (2019). Acceptance and readiness to use m-learning among primary school science teachers. Creative Education, 10, 3003-3011. https://doi.org/10.4236/ce.2019.1012224
Sarkingobir, Y., Egbebi, L. F., & Awofala, A. O. A. (2023). Bibliometric analysis of the thinking styles in math and its implication on science learning. International Journal of Essential Competencies in Education, 2(1), 75-87.
Sivo, S. A., Ku, C. H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Educational Technology, 34(4).
Siyam, N. (2019). Factors impacting special education teachers’ acceptance and actual use of technology. Education and Information Technologies, 24(3), 2035-2057.
Yıldız, G., Yıldırım, A., Akça, B. A., Kök, A., Özer, A., & Karataş, S. (2020). Research trends in mobile learning. International Review of Research in Open and Distributed Learning, 21(3), 175-96.
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Copyright (c) 2024 Adeneye Olarewaju A. Awofala, Michael A. Adewusi, Peter A. Betiang, Ruth F. Lawal, Oladiran S. Olabiyi, Abayomi A. Arigbabu, Alfred O. Fatade
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