Name
Title | ||
|---|---|---|
Enhancing STEM Motivation through Personal and Communal Values: NLP for Assessment of Utility Value in Student Writing. |
Abstract | ||
|---|---|---|
We present, to our knowledge, the first experiments on using NLP to measure the extent to which a writing sample expresses the writer’s utility value from studying a STEM subject. Studies in social psychology have shown that a writing intervention where a STEM student is asked to reflect on the value of the STEM subject in their personal and social life is effective for improving motivation and retention of students in STEM in college. Automated assessment of UV in student writing would allow scaling the intervention up, opening access to its benefits to multitudes of college students. Our results on biology data suggest that expression of utility value can be measured with reasonable accuracy using automated means, especially in personal essays. |
Year | Venue | Field |
|---|---|---|
2016 | BEA@NAACL-HLT | Natural language processing,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beata Beigman Klebanov | 1 | 137 | 19.49 |
| Jill Burstein | 2 | 327 | 43.64 |
| Judith Harackiewicz | 3 | 1 | 1.02 |
| Stacy Priniski | 4 | 1 | 0.68 |
| Matthew Mulholland | 5 | 24 | 6.79 |