S7-PRO7-2 - How to Improve Student Understanding of Professional And Ethical Responsibility?

2. Research-to-Practice Full Paper
wen cheng1 , Bengang Li1, Menglu Cao1, Edward Clay1, Mankirat Singh1
1 California state polytechnic university, Pomona

The full paper focuses on the professional code of ethics which sets a standard for which each member of the profession can be expected to meet. It is a promise to act in a manner that protects the public's well-being. In workplace, if we use shoddy materials or workmanship on the job, we can jeopardize the safety of others. Therefore, it is essential that all students shall fully understand the professional and ethical responsibility before they graduate for career development.

Unfortunately, there is a consistent lack of data measuring students’ capabilities to understand their professional ethics due to the unmeasurable nature of moral reasoning. To address this issue and equip faculty with  more tools to enhance students’ comprehension of ethical engineering practice, the present research proposes a quantitative approach to measuring ethical behavior and exploring its contributing factors based on a long-duration (years 2013-2019) of senior exit survey data consisting of more than 1000 survey responses. The senior exit survey questionnaire is made up of a set of questions to gauge the students self-rated capabilities of student outcomes (e.g., ethical responsibility, communication skills, life-long learning etc.), collect background information (e.g., admission year, transfer or first time freshman, etc.) and quantity the level of extracurricular participation (e.g., number of student clubs participated, the amount of engagement for part time work, etc.).

There are a broad range of statistical tools to deal with the multiple categorical responses (1- Poor; 2 – Fair; 3 – Average; 4 – Good; 5 – Excellent) to the understanding of professional ethics as implemented in the survey. Given the limitations of the multinomial logistic models which intend to ignore the ordering of the categories and treat the variable as nominal, the authors utilized the ordinal logistics regression models which describe the relationship between the categorical response (dependent) variable and other explanatory (independent) variables while explicitly accounting for the ordering of the categories. Distinct data preparation tasks such as data-centering, scaling, outlier identification, and covariate correlation analysis, were used prior to modeling development to ensure result accuracy. The findings illustrate various statically influential factors to enhance students’ professional ethics and therefore shed more insight to faculty who aims at improving student outcomes especially from the fact of ethics and morals.

Keywords: Senior exit survey, ordinal logistic regression model, Professional and ethical responsibility.