Implementation of Efficient Online English Learning System and Student Performance Prediction Using Linear K-Nearest Neighbors (L-Knn) Method
Bibliographic record
Abstract
Technical assistance for the establishment of a distance learning environment for learning English is provided by the advancement of information technology and the educational information process. People are still getting used to online teaching methods, and it is becoming more widely accepted. E-learning and online education have advanced significantly in recent years. The teaching paradigm has moved from traditional classroom learning to dynamic web-based learning. As a result, instead of static information, learners have received dynamic learning material tailored to their abilities, requirements, and preferences. To improve the English learning material efficiency, this paper implements an online English learning system based on efficient learning material selection. The English learning materials are preprocessed using normalization. The dimensionality reduction of the data is done using the Kernel-based-Independent Component Analysis (K-ICA). Data classification is performed using the Hypothetical Naïve Bayes Algorithm (HNBA). The student performance like learning efficiency, interactive accuracy rate, and artistic skills are predicted using the linear k-Nearest Neighbors (L-KNN). The proposed system can be simulated by employing the MATLAB tool and the performance is compared with other conventional methodologies. The findings of this study reveal that the presented online learning method may significantly increase students' oral and written skills.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".