Key Factors that Predict Students’ Mathematics Competence in a College of Education in Hohoe, Volta region of Ghana

Maxwell Seyram Kwame Kumah; Wilson Kofi Fiakumah; Eric  Kwame Austro Gozah; Leonard Kwame Edekor

doi:10.36948/ijfmr.2023.v05i04.5711

Key Factors that Predict Students’ Mathematics Competence in a College of Education in Hohoe, Volta region of Ghana

Author(s)	Maxwell Seyram Kwame Kumah, Wilson Kofi Fiakumah, Eric Kwame Austro Gozah, Leonard Kwame Edekor
Country	Ghana
Abstract	This study investigates the demographic factors, including gender, age, program of study, and financial background, that predict students' mathematics competence in a College of Education in Hohoe, Ghana. A quantitative research predictive design was employed to examine the relationship between these demographic factors and mathematics competence. The study population consisted of 80 Science and Mathematics major students enrolled in a College of Education during the 2023 academic year. A simple random sampling technique was used to obtain 69 participants who successfully filled their questionnaire. The collected data were analyzed using several predictive models. Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²) were utilized to compare the performance of these models. Among the models compared, Boosting Regression demonstrated the best overall predictive performance. Random Forest Regression ranked as the second-best model, while RLR, KNNR, and NNR had poorer performance. The findings indicate that gender and program of study are consistently important factors in predicting students' mathematics competence. Additionally, age showed a weak positive association with mathematics competence, while financial status was inversely associated with performance. The results provide valuable insights for educators and policymakers, facilitating the development of targeted interventions to enhance students' mathematics competence.
Keywords	Demographic factors, Mathematics, Boosting Regression, Random Forest Regression, Regularized Linear Regression, K-Nearest Neighbors Regression and Neural Network Regression
Field	Sociology > Education
Published In	Volume 5, Issue 4, July-August 2023
Published On	2023-08-30
Cite This	Key Factors that Predict Students’ Mathematics Competence in a College of Education in Hohoe, Volta region of Ghana - Maxwell Seyram Kwame Kumah, Wilson Kofi Fiakumah, Eric Kwame Austro Gozah, Leonard Kwame Edekor - IJFMR Volume 5, Issue 4, July-August 2023. DOI 10.36948/ijfmr.2023.v05i04.5711
DOI	https://doi.org/10.36948/ijfmr.2023.v05i04.5711
Short DOI	https://doi.org/gspcgk

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Key Factors that Predict Students’ Mathematics Competence in a College of Education in Hohoe, Volta region of Ghana

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