Unveiling Uncertainty: Supporting Learners Through NLP-Driven Confusion Identification
Bibliographic record
Abstract
Online learning has increased significantly in popularity over the past several years, driven by global events such as the pandemic and the accessibility offered by educational platforms such as Moodle, Brightspace and so on. However, online learning platforms present challenges, including limited access to support and a sense of disconnection among students. This research works to mitigate these challenges by identifying confusion in learners in online learning platforms by analyzing their posts in course discussion forums. We utilized the Stanford MOOCPosts dataset, evaluated the performance of various ma-chine learning (ML) models, and explored the effectiveness of a custom classification embedding model on the Cohere. This Artificial Intelligence (AI) platform provides access to Large Language Models (LLM) and natural language processing (NLP) tools through an application programming interface (API). Our findings highlight the utility of AI platforms and LLMs in identifying and classifying confusion in online learners. With a substantial potential for the classification task to be dealt with by a custom model running on a third-party platform, researchers can focus on developing conversational agents to support learners with their confusion in courses in online learning platforms.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".