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All Journal JPMS (Jurnal Pendidikan Matematika dan Sains) AMAR (Andalas Management Review) Indonesian Journal of Pedagogy and Teacher Education
Asanre, Akorede Ayoola
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Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Social Sciences

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Teachers' Perception Towards the Integration of Artificial Intelligence in the Teaching of Mathematics in Senior Secondary School Asanre, Akorede Ayoola; Taiwo, Taiwo Oluwadayo; Odupe, Toyin Alaba
Jurnal Pendidikan Matematika dan Sains Vol. 12 No. 2 (2024): December 2024
Publisher : Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpms.v12i2.77349

Abstract

In many societies today, Artificial intelligence (AI) has developed into a disruptive force, and the system of education is only one example of how this technology is being used. Therefore, this study looked at the teachers' perceptions in terms of perception, attitude, and experience towards incorporating AI into mathematics education in senior secondary school in Remo, Ogun State, Nigeria. Three research questions were raised to gather data from the respondents. A descriptive survey research design was used in this study with a sample comprised of 60 respondents, with 30 teachers from private and public senior secondary schools each selected from 20 schools. The instrument used was the Teacher Perception Towards AI Questionnaire (TPTAQ) with a reliability coefficient of 0.77. The results revealed that the perceptive level of the teachers towards AI integration was found to be high in terms of perception, attitude, and experience, showing that teachers embrace the use of AI in mathematics education in schools. Therefore, educators are advised to be well-trained in the application of AI technology to mathematics education.
Investigating the Interplay of Social and Biological Factors on Mathematics Anxiety among Senior Secondary Students Asanre, Akorede Ayoola; Sondlo, Aviwe; Adekunle, Ridwan Ayomide
Jurnal Pendidikan Matematika dan Sains Vol. 13 No. 2 (2025): December 2025
Publisher : Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpms.v13i2.84085

Abstract

This study investigates the social and biological factors influencing mathematics anxiety among senior secondary school students in Sagamu Local Government Area, Ogun State, Nigeria. Data were gathered using a descriptive survey methodology, 500 students across five secondary schools using simple random sampling techniques. A standardized questionnaires Social Factor Questionnaire (SFQ, r=0.82), Biological Factors Questionnaire (BFQ, r=0.73) and Abbreviated Math Anxiety Scale (AMAS, r=0.84). Descriptive statistics (mean and standard deviation) and Pearson correlation analysis were used to analyze the data. Results reveal that family pressure, inappropriate teaching methods, the school environment, neurological responses to stress, and age and developmental stage are significantly related to mathematics anxiety among senior secondary school students. Conversely, learners' behaviour, genetic predispositions, and gender do not significantly affect mathematics anxiety. Therefore, the study recommends that schools s implement parent-teacher programs and counselling service to reduce family pressure and foster a supportive home environment for students. 
Exploring Principal Identity Models for Effective School Management in uMgungundlovu District, South Africa Asanre, Akorede Ayoola; Mchunu, N. V.; Buthelezi, Alan B.; Mhlongo, H. R.
AMAR (Andalas Management Review) Vol. 9 No. 2 (2025)
Publisher : Management Institute Faculty of Economics Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/amar.9.2.145-173.2025

Abstract

This study explores effective models of principal identity in relation to school management and leadership within the uMgungundlovu District of KwaZulu-Natal, South Africa. It examines how principals perceive and develop their professional identities, and how these evolving identities impact effective school management and administration. Therefore, the study identifies both the challenges and potential solutions for improving school leadership practices. The central argument is that a principal's influence and effectiveness stem from the continuous evolution of their professional identity from their experiences as teachers to their eventual roles as school leaders. Grounded in the theory of intersectionality, as a lens to uncover the identity of principals for effective management. The study employs an interpretivist qualitative paradigm to explain principals’ identities. Data was collected through semi-structured interviews, focus group discussions, and document analysis. Thematic analysis was used to interpret emerging themes and provide nuanced insights into the participants’ experiences. Findings reveal that understanding a principal’s identity is fundamental to enhancing school management and leadership effectiveness. The study concludes that collaboration and open communication among principals are critical to organizational success. Furthermore, sharing professional experiences, resources, and best practices fosters a deeper appreciation of how identity development contributes to effective school leadership.
The Relationship between Students’ Personality Traits and Their Achievement in Mathematics in Ogun State Nigeria Taiwo, Abiodun Oluwadayo; Asanre, Akorede Ayoola; Arigbabu, Abimbola Ismail
Jurnal Pendidikan Matematika dan Sains Vol. 14 No. 1 (2026): February 2026
Publisher : Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpms.v14i1.90467

Abstract

This study examines the relationship between students’ personality traits and their achievement in mathematics in Ogun State, Nigeria. A descriptive survey design was employed in the study. Three research questions guided the study. The study's population consisted of all students at Senior Secondary School Two (SSS2) in Ogun Central Senatorial District, Ogun State, Nigeria. Using a random sample method, ten (10) secondary schools in Ogun Central Senatorial District of the State were selected. From each school, forty-two (42) responders were chosen. This method was selected in accordance with particular standards, including the schools' size, type, location, and demographic makeup. The study's sample consisted of seven hundred and twenty (420) respondents from the Senior Secondary School II (SSSII) class.  The instruments used for data collection are: NEO Personality Inventory (r= 0.79) using Cronbach's alpha technique and Mathematics Achievement Test (MAT) with reliability, r = 0.86 using Kuder Richardson formula 20 (K-R20). Multiple Regression Analysis (MRA) and Pearson Product-Moment Correlation (PPMC) were used as inferential statistics to analyze the data at the 0.05 level of significance. The results of the findings showed that conscientiousness, extroversion, and agreeableness have positive and significant influence on students’ academic performance in a public senior secondary school in Ogun State.
Early Prediction of Student Academic Performance Using Machine Learning Adedeji, Oluwaseun Bukonla; Asanre, Akorede Ayoola; Omilabu, Ademola Abiodun; Odulaja, Godwin Oluseyi; Abimbola, Bolanle Lateefat; Nosiru, Sulaimon Olawale; Osofuye, Odunayo Damilola
Indonesian Journal of Pedagogy and Teacher Education Vol. 4 No. 1 (2026): Indonesian Journal of Pedagogy and Teacher Education (April 2026)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijopate.v4i1.625

Abstract

Background:As the database grows, predicting students' academic performance becomes more difficult. Traditional methods often overlook students with exceptional achievements and fail to fully track their progress. Although traditional assessments like exams and assignments provide valuable insights, they may not consider all factors affecting performance, such as socioeconomic status and engagement rates. Aims: This study develops a predictive model aimed at classifying students' academic performance in higher education. Methods: Using a combination of machine learning algorithms. Data collected from the Department of Computer Science and the Department of Mathematics at Tai Solarin University of Education was analyzed through the mutual information method to identify important factors. The model was created and tested using Google CoLaboratory, employing two algorithms: Support Vector Machines (SVM) and Decision Trees (DT). The accuracy of the models was measured using important indicators, including accuracy, precision, and the F-measure. Results:This study shows that machine learning techniques can effectively identify student performance early, with SVM achieving 100% accuracy, enabling quicker involvements and better resource allocation. Conclusion: Additionally, it supports evidence-based decision-making in educational institutions, which helps improve student encounter and enhances learners retention.
Co-Authors Abimbola, Bolanle Lateefat Adedeji, Oluwaseun Bukonla Adekunle, Ridwan Ayomide Arigbabu, Abimbola Ismail Buthelezi, Alan B. Mchunu, N. V. Mhlongo, H. R. Nosiru, Sulaimon Olawale Odulaja, Godwin Oluseyi Odupe, Toyin Alaba Omilabu, Ademola Abiodun Osofuye, Odunayo Damilola Sondlo, Aviwe Taiwo, Abiodun Oluwadayo Taiwo, Taiwo Oluwadayo