TY - JOUR
T1 - Identifying Factors that Promote Students’ Learning in Thermodynamics
AU - Akangah, Paul
AU - Davis, Francisi
PY - 2020
Y1 - 2020
N2 - Thermodynamics is an important subject in engineering training and forms the basis of pure engineering sciences. However, the performance of students in thermodynamics nationwide is poor. Not all students with high GPAs (>3.0) are able to pass thermodynamics on the first attempt. This study seeks to understand the correlation between the success rate in the Fundamentals of Thermodynamics (MEEN241) and the following dependent variables: General Physics (PHYS241), combined quizzes and reading quizzes, homework, tests, midterm examination, final examination, and prior GPA. We also designed assignments and assessments to capture acquired skills. These items test high-level thinking skills such as applying a thermodynamic principle to illuminate a problem. The research question this study tries to answer is, “How does success in quizzes and reading quizzes and prior knowledge in thermodynamics (PHYS241) impact the success rate in MEEN241?” To answer this question, we designed a machine-learning algorithm that is made up of decision trees, random forest ensemble, and Naïve Bayes classifiers that take as input the academic data of students (N=111) enrolled in MEEN241. The machine-learning algorithm makes the prediction by popular vote. The machine-learning model has an accuracy of 86.49%. The class recall is, respectively, 90.48 and 81.25%, for true pass and true failed. The class precision is, respectively, 86.36 and 86.67%, for predicated pass and predicted fail. Combined quizzes and reading quizzes is the root node in six out of seven classifiers, while PHYS241 was eliminated because the information content was less than the 0.1 threshold. These results show that success in RRQ impacts positively on the success rate in MEEN241 while also showing that prior knowledge in the form of PHYS241 has no influence on the success rate in MEEN241. This study suggests that students’ success depends on developing and constantly improving good pedagogy and good study habits.
AB - Thermodynamics is an important subject in engineering training and forms the basis of pure engineering sciences. However, the performance of students in thermodynamics nationwide is poor. Not all students with high GPAs (>3.0) are able to pass thermodynamics on the first attempt. This study seeks to understand the correlation between the success rate in the Fundamentals of Thermodynamics (MEEN241) and the following dependent variables: General Physics (PHYS241), combined quizzes and reading quizzes, homework, tests, midterm examination, final examination, and prior GPA. We also designed assignments and assessments to capture acquired skills. These items test high-level thinking skills such as applying a thermodynamic principle to illuminate a problem. The research question this study tries to answer is, “How does success in quizzes and reading quizzes and prior knowledge in thermodynamics (PHYS241) impact the success rate in MEEN241?” To answer this question, we designed a machine-learning algorithm that is made up of decision trees, random forest ensemble, and Naïve Bayes classifiers that take as input the academic data of students (N=111) enrolled in MEEN241. The machine-learning algorithm makes the prediction by popular vote. The machine-learning model has an accuracy of 86.49%. The class recall is, respectively, 90.48 and 81.25%, for true pass and true failed. The class precision is, respectively, 86.36 and 86.67%, for predicated pass and predicted fail. Combined quizzes and reading quizzes is the root node in six out of seven classifiers, while PHYS241 was eliminated because the information content was less than the 0.1 threshold. These results show that success in RRQ impacts positively on the success rate in MEEN241 while also showing that prior knowledge in the form of PHYS241 has no influence on the success rate in MEEN241. This study suggests that students’ success depends on developing and constantly improving good pedagogy and good study habits.
M3 - Article
JO - Technology Interface International Journal
JF - Technology Interface International Journal
ER -