An Empirical Study on Learning Path Optimization by Data Collection and Computational Analysis of English Learners’ Behavior in the Era of Big Data
Bibliographic record
Abstract
Learning path optimization aims to generate and optimize a knowledge learning sequence for learners that best meets their knowledge needs.This study focuses on the important role of online learner behavior in personalized path planning.By constructing a knowledge point difficulty model and a learning behavior prediction model based on online learning behavior, together with a user-based collaborative filtering recommendation algorithm, a personalized learning path is proposed comprehensively.The MOOC websites "College English 1" and "Xuedang Online" are selected as sample data to analyze the online learning behavior of English learners and verify the learning effect of the learning path proposed in the article through the change of students' online time.The personalized teaching model based on the learning path is investigated in practice by taking the college English course in school A as an example.Compared with the traditional teaching mode, the optimized learning path shows a significant difference of 0.01% in the dimensions of learners' "knowledge and skills", "process and method" and "affective attitude".The mean values of the optimized blended teaching mode are 4.12, 4.33 and 4.07 respectively, which are all better than the traditional teaching mode.It shows that the English learning path proposed in this paper is conducive to enhancing students' personalized learning needs and provides a reference for promoting the effective implementation of personalized learning in the information technology environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".