Big Data Set from RateMyProfessor.com for Professors' Teaching Evaluation

This dataset is shared by Dr. Jibo HE, founder of the USEE Eye Tracking Inc. and professor of Tsinghua University. Contact me at [e-mail] if you need to get the full dataset in over 5G size. This is the dataset from RateMyProfessor.com for professors' teaching evaluation. The dataset is provided in two format. The zip file contains many single .csv files, with each file for a single professor's webpage. The RateMyProfessor_Sample data.csv is the combined version with many professors' information in a single csv file. We have crawled almost one million professors' information. The total dataset is over 5. The dataset crawled and extracted from RMP has 18 variables. This part briefly describes each variable that needs to be analyzed.  Professor name: name of the professor who is rated  School name: university where the professor is currently teaching  Department name: currently working there  Local name: university’s locally known as  State name: state which the university is located in  Year since first review: the professor's teaching age, from the first student evaluation to the time when we did the analysis in year 2019.  Star rating: the star rating of the professor's overall quality, the point 3.5-5.0 is good, 2.5-3.4 is average and 1.0-2.4 is poor according to RMP’s official standard. This star rating is the average score given to professors by all student comments;  Take again: percentage of students who want to choose this course again;  Difficulty index: The difficulty level of a course. Point 1 is easiest, and point 5 is hardest. The difficulty index is the average score given to professors by all students;  Tags: the tag students chose to describe a professor;  Post date: the date when the student posted an evaluation of a course;  Student star: each student gives a star rating to a professor;  Student-rated difficulty: every student gives difficulty index to a professor;  Attendance: whether a course is mandatory or not;  For credit: whether students chose a course for credit (yes or no);  Would take again: whether students would like to choose a course again (yes or no)  Grade: student’s final score of a course, such as A+, A, A-, B+, B, B-, C+, C, C-, D+, D, D-, F, WD, INC, Not, Audit/No. “WD” is Drop/Withdrawal. “INC” means Incomplete. “Not” is Not sure yet, and “Audit/No” is Audit/No Grade.  Comment: comments that students gave for professors.

搜集汇总

背景与挑战

背景概述

该数据集是从RateMyProfessor.com爬取的教授教学评价大数据集，包含近百万教授的教学评价信息，涵盖18个关键变量如评分、课程难度和学生评论等，数据以CSV格式提供，总大小超过5GB。