Analysis on Factors for Prior Evaluation of PBL Class Using IPMA

Abstract

Purpose This study examines how students value each of the factors represented in a matrix reflecting the following importance-performance matrix analysis (IPMA). This IPMA was used as the primary survey (Kim, 2019), and each evaluation factor, the average value of the latent variable, and the estimated path coefficient were used Methods SmartPLS 3.0 was used for the numerical calculations and SPSS 19.0 was used to create the graph. Analysis occurs prior to student participation in the class to form a consensus among students at the beginning of the semester. Results Attempts are made to derive the analysis results and apply them to actual problem-based learning classes to improve class evaluation through feedback. The results show that prioritizing the evaluation factors such as learning outcomes and self-directed learning is meaningful in terms of lecture efficiency. Conclusion This approach is particularly meaningful, as it attempts an integrated model (importance-performance matrix-analytical hierarchy process) and is a continuation of a previous study (Kim, 2019)

Keywords

• problem-based learning
• evaluation
• importance-performance matrix analysis
• partial least squares-structural equation modeling