Information Literacy Development Path for Teachers of Finance and Economics in Higher Vocational Colleges and Universities in the Era of Big Data Based on Dynamic Planning
Why this work is in the frame
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Bibliographic record
Abstract
Teachers' information literacy is related to the quality and efficiency of education and teaching in higher vocational colleges and universities.In this paper, a dynamic planning-based scheduling method is constructed to improve teachers' time allocation efficiency and information literacy.First of all, according to the factors and constraints involved in the scheduling problem to determine the goal of solving the scheduling problem, mathematical model, and then the constraints involved in the scheduling of classes, converted into a dynamic planning of the mutually independent and related stages, with 1, 0 indicates whether to meet the constraints.By solving each stage and analyzing the solution of each stage, the optimal value function is summarized, and ACAA is used to traverse all the optimal solutions for each set of constraints.Examples are selected for scheduling test to verify the effectiveness of the algorithm, and the teacher information literacy assessment scale is designed.Applying the class scheduling algorithm to a higher vocational college, the mean value of the overall information literacy scores of the surveyed teachers is 0.15 points higher than the standard reference value, and the effectiveness of the class scheduling algorithm in this paper is verified.Practical experience (58.27%), teaching philosophy (50.19%), and subject requirements (33.36%) are the top three factors affecting teachers' information literacy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it