Teachers' Perception on Integration of Machine Learning for Algebraic Instruction in Secondary Schools

Abstract This study examines mathematics teachers' perceptions regarding the integration of machine learning (ML) for algebra instruction in secondary schools within Nkanu West, Enugu State, Nigeria. It addresses critical challenges such as persistently low pass rates in algebra, a significant shortage of qualified mathematics teachers, and apprehensions surrounding the adoption of ML technologies. Employing an innovative research design that combines qualitative and quantitative methodologies, the study was guided by two research questions and one hypothesis. The target population comprised 36 mathematics teachers in public secondary schools within the local government area. Data were collected using a structured 4-point Likert-scale questionnaire, titled "Teachers’ Perception of Machine Learning Integration for Algebra Instruction (TPMLIAI)," which demonstrated a reliability index of 0.79 (Cronbach's Alpha). Findings revealed predominantly negative perceptions of ML integration, primarily driven by concerns about job security, insufficient training, and doubts about its long-term efficacy and sustainability. Male teachers exhibited greater confidence in ML adoption compared to their female counterparts, who expressed heightened skepticism and concerns. Recommendations include implementing robust training programs, practical workshops, and continuous support to enhance teacher proficiency in ML tools, formulating policies that address job security and encourage teacher involvement in technology adoption, and designing gender-specific interventions to address perceptual differences through targeted professional development initiatives for both male and female teachers.