Contextual Embeddings and Graph Convolutional Networks for Concept Prerequisite Learning
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
Concept prerequisite learning (CPL) plays a crucial role in education. The objective of CPL is to predict prerequisite relations between different concepts. In this paper, we present a new approach for CPL using Sentence Transformers and Relational Graph Convolutional Networks (R-GCNs). This approach creates concept embeddings from single-sentence definitions extracted from Wikipedia using a Sentence Transformer. These embeddings are then used as an input feature matrix for the R-GCN, in addition to a graph structure that distinguishes prerequisites and non-prerequisites as distinct link types. Furthermore, the R-GCN is optimized simultaneously on CPL and concept domain classification to enhance prerequisite prediction generalization for unseen domains. Extensive experiments on the AL-CPL dataset show the effectiveness of our approach for the in-domain and cross-domain settings, as it outperforms the State-Of-The-Art (SOTA) methods on this dataset. Finally, we introduce a novel data split algorithm for this task to address a methodological issue found in previous studies. The new data split algorithm makes CPL more challenging to solve, but also more realistic as it excludes simple inferences by transitivity.
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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 | 0.000 |
| 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 | 0.001 |
| 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