INTERPRETING STUDENT RESPONSES USING SENTIMENT ANALYSIS AND TEXT-ANALYTICS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper discusses exploratory research which computationally examines over one and a half million words presented by first-year students as part of weekly online assignments over the Fall 2020 academic term. This work aims to explore whether computational analyses of first-year engineering student vocabulary can be employed to understand the levels of student motivation when learning engineering in an online environment. The investigation uses NVivo 12 Plus (NVivo), a data analysis software, to track the overall sentiment of weekly student discussion board responses and apply text queries to determine the number of responses that include words related to the expectancy-value theory. Applying this theory reveals trends in overall student motivation, with weeks four to six and eight to ten having an overall positive sentiment. This positive sentiment reveals higher levels of student motivation during those weeks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it