Abstract
Academic performance is a substantial element in determining students’ achievement. The foremost goal of this research was to generate a model that best fits the academic performance of the students. Three exogenous variables were utilized to generate the best-fit model: attitude, learning style, and academic commitment. Data were gathered from the 145 fourth-year BTVTED students. Descriptive-causal and structural equation models were utilized as the design of this study. This study adjusted its tools and used online platforms to collect data in order to account for the pandemic’s challenges. Initially, analysis using descriptive statistical tools namely mean, the standard deviation was utilized followed by the inferential analysis of data such as Pearson r, multiple regression analysis, and structural equation modeling (SEM). Results revealed that attitude towards research, learning style and commitment among the BTVTED students is oftentimes observed. While academic performance, students’ grades revealed that it is very satisfactory. Moreover, a positive and significant correlation was revealed between the exogenous and endogenous variables. The test of influence result revealed that all the exogenous variables significantly influence academic performance. The best-fit model reveals that attitude and commitment are significantly influencing academic performance. Additionally, research usefulness, research anxiety, positive attitude, and research difficulty are the remaining domains of attitude. Commitment retains the following as its indicators: investment and meaningfulness. While the endogenous variable academic performance maintains its indicators: Technology for Research 1 and Technology for Research 2. Hence, in improving the academic performance among students, it should be accompanied by attitude and commitment.
Keywords: attitude, learning style, commitment, academic performance, research, structural equation modeling, Philippines
*This paper is presented at the 4th International Conference on Multidisciplinary Industry and Academic Research (ICMIAR) 2023
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