A study of undergraduate computer science students examined changes in major aft

Question

A study of undergraduate computer science students examined changes in major after the first year. The study examined the fates of 256 students who enrolled as first-year students in the same fall semester. The students were classified according to gender and their declared major at the beginning of the second year. The students studied were enrolled at a large Midwestern university several years ago. Discuss how you would conduct a similar study at a college or university of your choice today. Include a description of all variables that you would collect for your study.

Explanation / Answer

So my approach would be,

I find out the reason why student changes their major at the end of the first semester. The historic data which is mentioned in the question has happened long time ago. First, check how long the date differs from today's date. If the date difference is too long then I would build the logistic model based on that data and cross validate it present data. If my prediction is correct, then I start to use the model. In case if it wrong, I start from the scratch by the question what make the difference between the date. Research the student by finding out the reason why students change by conducting the survey.

Question the student who wants to changes the major at the end of the first semester. My guess for possible answers will be like,

1) They found the subject very interesting.

2) They have chosen the major by misunderstanding it initially.

3) The unemployment rate is higher in current major.

4) The sudden requirement in industry for their second-semester major.

5) Current major is tougher than what the student think initially.

6) professor lecture would be more understandable than their current major.

7) Senior recommendation of their current major.

8) Their friends would have moved to that major.

How we collect the data above reasons,

1) Library subject book category: Based on the book issued to student segment.

2) Class attendance: if the student is interested in the subject they would happy attend the session with the lecture.

3) Job portal unemployment data: This can be identified by analyzing the local job portal unemployment rate for that particular subject.

4) Job Portal requirement rank: By analyzing the recruiter job invitation.

5) Marks or Points attained by the student.

6) College website: Student engagement on the webinar of that particular professor in college website

7) Feedback from alumni or second-semester student satisfaction on the major they chosen.

8) Find association of students on specific major compared to others.

I again build the model, based the feedback given by the current student. This time, the model would much better than the previous one for sure. To improve it better need to research much more data collection and evaluation.

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