The Use of Natural Language Processing in Learning 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
Abstract In the educational context, the usage of written text is as equally important as the usage of numerical components for teachers to make informed decisions in their pedagogical strategies. This chapter explores the practical implementation of natural language processing (NLP) within the context of learning analytics, specifically focusing on text mining and information extraction of textual data sourced from LA environments, including student feedback, forum posts, online discussions, and course materials. This application could provide insights into student behaviors, learning patterns, and educational content. This chapter introduces foundational NLP concepts and techniques, including text preprocessing, TF-IDF analysis, topic modeling, and text summarization. We demonstrate these techniques using functions from R packages such as tm, tidytext, topicmodels, and lexRankr to students’ comments on a course evaluation survey. Finally, we explore the application of sentiment analysis in learning analytics to gain insights into student perceptions. In summary, this chapter serves as a comprehensive guide for leveraging NLP techniques in learning analytics using R. This guideline provides readers with the knowledge and tools necessary to analyze and derive insights from textual data in educational contexts.
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 | 0.000 | 0.000 |
| 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