GASPRO International Journal of Eminent Scholars

ABSTRACT

This study examined the use of Big Data in promoting motivational teaching methods as a strategic approach to improving pupils’ academic performance in contemporary educational settings. In carrying out this study, a descriptive survey design was adopted. The study was conducted in Uyo Metropolis. The target population consisted of all students and teachers in Uyo Metropolis. A simple random sampling technique was employed to select 50 students and 10 teachers from each of the five selected schools in the study area, giving a total sample size of 300 respondents. Data were collected using a structured questionnaire entitled “Big Data and Motivational Teaching Methods Questionnaire” (BDMTMQ). The instrument was validated by an expert in Test, Measurement, and Evaluation to ensure its suitability and clarity. A reliability coefficient of 0.92 was obtained, confirming the reliability of the instrument. Data collected were analyzed using descriptive statistics to answer the research questions. The findings revealed that the most commonly used motivational teaching strategy was Mastery-Oriented Strategies (17.33%), while Gamification and Interactive Learning and Self-Regulated Learning Strategies recorded the least percentage (12.67%). The findings further showed that Personalized Learning Pathways (27.33%) constituted the most significant role of Big Data in promoting motivational teaching strategies, while Tailored Professional Development recorded the least percentage (23.00%). The study concluded that integrating Big Data into teaching not only enhances motivation through personalized and data-driven instructional approaches but also offering a pathway to more engaging, personalized, and effective learning experiences for pupils. One of the recommendations made was that Schools should encourage teachers to integrate big data analytics into lesson planning so that instructional strategies are aligned with pupils’ learning patterns, strengths, and weaknesses.