ERIC KWAME AUSTRO GOZAH - Academia.edu (original) (raw)

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Papers by ERIC KWAME AUSTRO GOZAH

Kumah et al., 2023

This study investigates the demographic factors, including gender, age, program of study, and fin... more 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.

International Journal For Multidisciplinary Research

This study investigates the demographic factors, including gender, age, program of study, and fin... more 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 over...

American Journal of Mathematical and Computer Modelling, 2020

The study was conducted to identify the performing stocks as well as examine the portfolio optimi... more The study was conducted to identify the performing stocks as well as examine the portfolio optimization with

Kumah et al., 2023

This study investigates the demographic factors, including gender, age, program of study, and fin... more 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.

International Journal For Multidisciplinary Research

This study investigates the demographic factors, including gender, age, program of study, and fin... more 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 over...

American Journal of Mathematical and Computer Modelling, 2020

The study was conducted to identify the performing stocks as well as examine the portfolio optimi... more The study was conducted to identify the performing stocks as well as examine the portfolio optimization with