Presentation Title

Investigating Significant Factors of Student Attributes that Predict Engagement in Lifelong Learning

Faculty Mentor

Wen Cheng

Start Date

23-11-2019 8:45 AM

End Date

23-11-2019 9:30 AM

Location

178

Session

poster 2

Type of Presentation

Poster

Subject Area

engineering_computer_science

Abstract

The research presented in this paper is intended to demonstrate the quantitative relationship between specific personal attributes of engineering students and the chance that they will actively engage in life-long learning. With the use of senior exit survey data provided by the Civil Engineering Department at Cal Poly Pomona, student responses were collected and analyzed using an advanced statistical model. The statistical methodology that was incorporated with the data was a multinomial logistic regression model. The data consists of categorical responses to the departments 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.

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Nov 23rd, 8:45 AM Nov 23rd, 9:30 AM

Investigating Significant Factors of Student Attributes that Predict Engagement in Lifelong Learning

178

The research presented in this paper is intended to demonstrate the quantitative relationship between specific personal attributes of engineering students and the chance that they will actively engage in life-long learning. With the use of senior exit survey data provided by the Civil Engineering Department at Cal Poly Pomona, student responses were collected and analyzed using an advanced statistical model. The statistical methodology that was incorporated with the data was a multinomial logistic regression model. The data consists of categorical responses to the departments 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.