Exploration of Influential Factors of Student Attributes On Lifelong Learning

The research to practice full paper aims to demonstrate the quantitative relationship between specific attributes of engineering students and the likelihood that they will engage in life-long learning. With the utilization of senior exit survey data provided by the Civil Engineering Department at Cal Poly Pomona, student responses were analyzed using a multinomial logistic regression model. The senior exit survey data consist of the categorical responses to the department's questions from the graduating class from the years 2013-2018. The collected data contain specific information such as student's name, the year they started, career outlook, and individual capabilities. The data were then utilized for implementing a multinomial logistic regression model because of the many benefits it could provide. 'The benefits include the precise identification of data anomalies and individual effects of each covariate in the research, which can result in the optimal independent. Although several other research papers have explored this topic, little research involving the use of statistical models have been conducted to develop a better understanding of factors that may influence engagement in life-long learning. Past research has mostly investigated ways in which the practice of life-long learning can be enhanced or more efficiently practiced. The multinomial logistic regression model was produced using the statistical software package R. The results demonstrate the student attributes which play a role in determining engagement in life-long learning. Among the eleven student attributes tested in the study, only three are statistically significant; these attributes consist of cognizance of current engineering issues, confidence in applying engineering fundamentals, and confidence in being an effective team leader.