Construction of a Machine Learning-based Model for Analyzing English Teaching and Learner Behavior
Bibliographic record
Abstract
Behavior of different types of English learners tends to follow different patterns and characteristics, and the analysis of behavioral data is one of the directions for improving English learning and teaching.This study designs a set of behavioral analysis methods based on machine learning for English teaching and learners.The learners' behaviors are firstly operated with feature extraction and quantification, and the behavioral data are clustered by using the systematic clustering method (HCM) to improve the SOM model.1DCNN is used to process the learning time-series data and enhance the data mining and performance prediction ability by BiLSTM and attention mechanism, respectively.This paper distinguishes five categories of English learners, such as excellent, diligent, average, procrastinating and negative, and filters out the factors that are highly correlated with English performance, such as the download of learning resources and the number of times of teaching viewing.Comparison experiments show that the ACC of this paper's achievement prediction model = 0.53, which is better than other comparison methods.Therefore, the idea of this paper based on machine learning methods to analyze the behavior of English teaching and learners has feasibility.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".