Clustering and Classification Models For Student's Grit Detection in E-Learning
Renato R. Maaliw
a
,
Karen Anne C. Quing
b
,
Julie Ann B. Susa
a
,
Jed Frank S. Marqueses
b
,
Ace C. Lagman
c
,
Rossana T. Adao
d
,
Ma. Corazon G. Fernando
c
,
Ranie B. Canlas
e
a College of Engineering Southern Luzon State University, Lucban, Quezon, Philippines
b College of Arts & Sciences Southern Luzon State University, Lucban, Quezon, Philippines
c Information Technology Dept., FEU Institute of Technology, Manila, Philippines
d College of Computer Studies FEU Institute of Technology, Manila, Philippines
e College of Computer Studies, Don Honorio Ventura State University, Bacolor, Pampanga, Philippines
Abstract: Grit plays a crucial role in determining high individual success more than intellectual talent alone. However, there is no existing literature that ventured into the trait identification in an e-learning environment. This study presents a comprehensive computational-driven strategy for detecting a learner's grit using machine learning. Empirical results show that DBSCAN and Random Forest models produce average accurate prediction consistency of 92.67% against the questionnaire method. Knowledge interpretation using feature importance and association mining quantifies perseverance and sustained interest as the most pressing component of grit. Correlational analysis reveals that grit has a weak connection with course grades (short-term goal) but demonstrates a strong positive association with professional achievement (long-term goal) and maturation. Collectively, our findings substantiate that breakthrough accomplishment is contingent not solely on cognitive ability but on constant interests and resilience.