A Study on the Metricized Assessment of Foreign Language Talent Cultivation Goal Achievement in Applied Colleges and Universities under the Concept of OBE Based on Decision Tree Algorithm
Bibliographic record
Abstract
The evaluation of English course goal attainment is an important basis for colleges and universities to judge whether the goal of cultivating foreign language talents has been achieved.This paper proposes a method for quantitative assessment of course goal attainment according to the OBE concept.Calculating the importance of attributes about classification, the decision tree algorithm based on rough set is proposed, combined with association rules for deep mining of educational data.Collect quantitative educational data and questionnaire data of a university, modeling relying on SPSS Modeler 14.2, and outputting decision tree of influencing factors.Using the evaluation of course goal achievement to analyze the achievement of A4 course goals, and exploring the association rules of influencing factors based on the decision tree.The traditional decision tree algorithm is introduced as a control group to evaluate the performance of the rough set-based decision tree algorithm.The results show that the achievement degree of each sub-objective of A4 course is higher than 0.70, and students who have the achievement degree of A4 course objective greater than 0.7, the nature of their major is foreign language and they have passed the Grade 4 test have a higher possibility of achieving the final foreign language talent cultivation goal of the university.The precision of the assessment method based on rough set decision tree is maintained at about 88%, and the accuracy rate is basically maintained at about 90%.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".