Prediction of students performance using decision tree towards improvement of FOMA course content/
Real, Olivia D.
Prediction of students performance using decision tree towards improvement of FOMA course content/ Olivia D. Real .-- - Manila: Technological University of the Philippines, 2023. - xiii, 171pages: 29cm.
Master's thesis
College of Science .--
Includes bibliographic references and index.
The study aimed to predict the performance of the students in Fundamentals of Mathematics Analysis (FOMA) using Decision Tree. It involved 348 students from the Computer Studies Department, College of Science, who are officially enrolled in Bachelor of Science in Computer Science (BSCS), Bachelor of Science in Information Systems (BSIS), and Bachelor of Science in Information Technology (BSIT). Based on the gathered data, the findings of the study are the following: the students have an average performance in the four (4) assessments (formative and summative) that include seatwork, assignments, long quizzes, and final examination; significant positive marked relationship exists between formative and students’ final grade in FOMA, marked relationship between summative assessment and their final grade in FOMA, and substantial relationship between formative assessment and summative assessment, the appropriate predictive modeling is CHAID on the ten-level category, but in the binary pass/fail category C & RT Model performance is better because of its high sensitivity and overall accuracy; CHAID model performed well if the dataset is a multi-level classification and C&RT performed better if the dataset is a binary level category, hence the best algorithm for the prediction of the performance of the students in FOMA among the Decision Tree models is based on the dataset and its classification.
Keywords: Student Performance, Decision Tree, C5.0, C & RT, and CHAID
Mathematics--study and teaching
Decision tree
FOMA course content
THE QA 14 / R43 2023 c.2.
Prediction of students performance using decision tree towards improvement of FOMA course content/ Olivia D. Real .-- - Manila: Technological University of the Philippines, 2023. - xiii, 171pages: 29cm.
Master's thesis
College of Science .--
Includes bibliographic references and index.
The study aimed to predict the performance of the students in Fundamentals of Mathematics Analysis (FOMA) using Decision Tree. It involved 348 students from the Computer Studies Department, College of Science, who are officially enrolled in Bachelor of Science in Computer Science (BSCS), Bachelor of Science in Information Systems (BSIS), and Bachelor of Science in Information Technology (BSIT). Based on the gathered data, the findings of the study are the following: the students have an average performance in the four (4) assessments (formative and summative) that include seatwork, assignments, long quizzes, and final examination; significant positive marked relationship exists between formative and students’ final grade in FOMA, marked relationship between summative assessment and their final grade in FOMA, and substantial relationship between formative assessment and summative assessment, the appropriate predictive modeling is CHAID on the ten-level category, but in the binary pass/fail category C & RT Model performance is better because of its high sensitivity and overall accuracy; CHAID model performed well if the dataset is a multi-level classification and C&RT performed better if the dataset is a binary level category, hence the best algorithm for the prediction of the performance of the students in FOMA among the Decision Tree models is based on the dataset and its classification.
Keywords: Student Performance, Decision Tree, C5.0, C & RT, and CHAID
Mathematics--study and teaching
Decision tree
FOMA course content
THE QA 14 / R43 2023 c.2.