Leveraging Text Mining for Analyzing Students' Preferences in Computer Science and Language Courses
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
In an increasingly competitive, globalized world, educational institutions must strategically offer courses that align with the skill-acquisition needs of their target audience.As such, the application of text mining techniques to extract valuable insights and patterns from structured data across various knowledge domains becomes paramount.This study employed text mining to scrutinize students' preferences for course offerings at the Computer and Language Center of the National University of Jaen.The analysis was based on data collected from a Google Forms survey of 315 students.The employed methodology facilitated the unearthing of patterns, trends, and semantic relationships within a large corpus of students' opinions.Frequency distributions and word clouds were generated using R programming language.Furthermore, the WEKA software and Python were utilized for cluster analysis, enabling the detection of groupings and trends within the data.Although other methods such as sentiment analysis and statistical methodologies exist, text mining was deemed most suitable for identifying patterns and relationships within students' opinions.The study revealed that students predominantly favored advanced Excel, AutoCAD, ArcGIS, Nutrition, and Revit courses, which appeared to correlate with their professional aspirations and prevailing course trends.Therefore, the application of text mining tools to analyze structured institutional data can significantly contribute to informed decision-making processes.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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