| Autor: |
Susan Mossing, Kathryn F. Gates, Sumali Conlon, Dawn Wilkins, Maurice R. Eftink |
| Rok vydání: |
2013 |
| Předmět: |
|
| Zdroj: |
Educational Data Mining ISBN: 9783319027371 |
| DOI: |
10.1007/978-3-319-02738-8_14 |
| Popis: |
Student ratings of instruction are an important means of assessment within universities and have been the focus of much study over the last 50 years. Until very recently it has been difficult to perform meaningful analysis of student narrative comments given that most universities collected them as hand-written notes. This work uses statistical and text mining techniques to analyze a data set consisting of over 1 million student comments that were collected using an online process. The methodology makes use of positive and negative “category vectors” representing instructor characteristics and a domain-specific lexicon. Sentiment analysis is used to detect and gauge attitudes embedded in comments about each category. The methodology is validated using three approaches, two quantitative and one qualitative. While useful to individual instructors and administrators, it is only through data mining that student perceptions of teaching can be analyzed en masse to inform and influence the educational process. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
