Structural Equation Modeling Based Partial Least Square of Student Misconceptions in Estimating Probability Distribution Parameters - Wardhani Utami Dewi; Tri Cahyo Wahyudi; Miftahul Irfan (JTAM (Jurnal Teori dan Aplikasi Matematika), 2026)
Students' misconceptions in estimating the parameters of probability distribution are still an important problem in learning statistics in universities. Most previous studies have examined partial misconceptions from cognitive aspects, so the structural relationship between concept understanding, learning experience, learning motivation, and problem-solving skills in explaining misconceptions has not been widely analyzed in an integrated manner. This study aims to develop and validate a structural model that explains the factors that influence student misconceptions in estimating probability distribution parameters using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. This study uses an explanatory quantitative design involving 200 students who have studied probability distribution and parameter estimation in several universities in Lampung Province. Data was collected through a Likert scale questionnaire that measured five latent constructs, namely concept comprehension, learning experience, learning motivation, problem-solving skills, and student misconceptions. The analysis was carried out through the evaluation of the measurement model (loading factor, composite reliability, and average variance extracted), structural model testing using the bootstrapping technique, and evaluation of the overall suitability of the model. The results showed that concept comprehension (β = 0.74) and learning experience (β = 0.82) had a significant effect on problem-solving skills. Problem-solving skills further affect learning motivation (β = 1.92), while learning motivation affects the level of student misconception (β = 0.67). The developed model was able to explain 65% of the variation in problem-solving skills and 88% of the variation in student misconceptions. This research contributes in the form of a SEM-PLS model that integrates cognitive and affective factors in explaining the emergence of statistical misconceptions, as well as providing an empirical basis for the development of more effective statistical learning strategies to reduce student misconceptions.