Comparison and Analysis of Multiple Machine Learning Algorithms for Predicting Student Adaptation Levels in Online Education
Bibliographic record
Abstract
With the rapid development and popularization of Internet technology, online education has become a new way of education. Compared with traditional classroom teaching, online education has a more flexible learning mode, a more convenient learning environment and a wider range of learning resources. However, at the same time, online education also faces some challenges, one of the most important challenges is the adaptability of students to online education. In this paper, we use machine learning techniques to predict students' adaptability in online classrooms. After using logistic regression model, k-neighborhood algorithm model, random forest model, XGBoost model and Cat Boost model to make predictions, it is found that random forest model is the best in predicting students' adaptability to online classroom, with a prediction accuracy of 89.6%. The XGBoost model and CatBoost model were also better in prediction, with prediction accuracies of 89.1% and 88.6%, respectively. In contrast, the logistic regression and KNN models have poorer prediction accuracy with 68.8% and 77.1%, respectively. The research in this article has important implications for the online education industry. By using machine learning techniques to predict students' adaptability in an online classroom, it can help educational institutions better understand students' learning and improve teaching effectiveness. Meanwhile, for students, knowing their adaptive ability in online classroom also helps them to better plan their study programs and improve their learning efficiency. This study uses machine learning techniques to predict students' adaptive ability in online classrooms, and the results show that the random forest model performs the best in terms of predictive effectiveness. This study provides a useful reference for the online education industry and also provides some ideas for future research.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".