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Student Course Feedback Dataset
Reviews & Ratings
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides course-level reviews from the University of Waterloo, meticulously sourced to facilitate the training of a classifier. The primary purpose is to infer whether a reviewer enjoyed a particular course based on their submitted feedback. It is an ideal resource for Natural Language Processing (NLP) and text mining applications, especially those focused on educational content and sentiment analysis. Each entry represents a review associated with a specific course, making it valuable for understanding student perceptions and course effectiveness.
Columns
- course_code: The unique identifier for each course at the University of Waterloo, as listed on uwflow.com.
- course_title: The official title of the respective course.
- num_ratings: The total count of times users provided feedback for a course, which may include non-textual ratings like "useful" or "easy".
- useful: The percentage of reviewers who indicated that the course was useful.
- easy: The percentage of reviewers who indicated that the course was easy.
- liked: The percentage of reviewers who indicated that they liked the course.
- num_reviews: The number of reviews for a given course that specifically included a text review.
- reviews: The actual text content of the course reviews.
- course_rating: A categorical indicator of whether the reviewer liked the course ("liked course" or "disliked course").
- course_rating_int: The numerical representation of the 'course_rating', typically an integer value.
Distribution
The dataset is structured at the course-review level, with each row corresponding to an individual review for a particular course. While a specific total number of rows or records is not available in the provided details, the file is typically supplied in CSV format. It should be noted that not all reviews necessarily contain text, and not all include a "liked course" rating, although the vast majority do.
Usage
This dataset is well-suited for several analytical and machine learning applications, including:
- Developing and training classification models to predict reviewer sentiment or enjoyment from text reviews.
- Conducting Natural Language Processing (NLP) tasks such as sentiment analysis, topic modelling, and text summarisation on educational feedback.
- Performing text mining operations to extract insights and patterns from student course reviews.
- Researching student satisfaction and identifying factors contributing to positive or negative course experiences.
Coverage
The data specifically covers course reviews from the University of Waterloo. No explicit time range or demographic breakdown for the reviews is provided. It is important to note that while most reviews contain text, some may only include numerical feedback. Similarly, not every review includes a clear "liked course" or "disliked course" rating.
License
CC0
Who Can Use It
This dataset is primarily intended for:
- Data Scientists and Machine Learning Engineers aiming to build and evaluate models for sentiment analysis or text classification in an educational context.
- Researchers in fields such as education, computational linguistics, or data science, who are studying student feedback, course effectiveness, or NLP methodologies.
- Developers looking to integrate sentiment analysis capabilities into educational technology platforms.
- Academics interested in analysing qualitative feedback for institutional improvement or pedagogical research.
Dataset Name Suggestions
- University of Waterloo Course Reviews
- UWFLOW Course Feedback Dataset
- Student Course Experience Reviews
- Waterloo Course Sentiment Data
Attributes
Original Data Source: Course Reviews University of Waterloo
QUALITY