Factors Influencing STM Teachers' Acceptance of Mobile Learning in South-West Nigeria

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.