Design and practice of ideological and political education of computer application basic course in sports colleges and universities based on the introduction of CNN models
Bibliographic record
Abstract
As society progresses and science and technology evolve, the need for skilled professionals in the country continues to rise.Ideological and political education (IPE) in courses, crucial for fostering students' overall development, has shifted from theoretical exploration to practical implementation.Sports colleges should incorporate ideological and political elements into professional courses like sports and computers based on their unique characteristics, aiming to develop high-quality, multidisciplinary talents that align with national requirements.Professional courses are often disconnected from IPE in traditional sports college teaching methods.There is excessive focus on knowledge and technology instruction, while insuf icient attention is given to guiding students in developing correct values through the courses.Additionally, there is a lack of well-targeted course design and a comprehensive evaluation system.In response to the above problems, this paper studies and constructs a framework that integrates IPE with basic computer application knowledge, and designs a variety of course designs and teaching methods.In addition, a progressive assessment is designed to introduce the Convolutional Neural Networks (CNN) model and the Bidirectional Encoder Representations from Transformers (BERT) model in the early stage of teaching to conduct preliminary assessments of students' basic computer skills and ideological and political qualities.In the later stage of the course, the Long Short-Term Memory (LSTM) network model is introduced to analyze student learning behavior and assess overall student quality based on prior evaluations.The experiment shows that the course design studied in this paper can enable students of the School of Physical Education to learn basic computer knowledge while also receiving certain ideological and political quality education.The designed teaching method is better than traditional of line teaching, online teaching and online and of line mixed teaching.When conducting comprehensive quality assessment, a 40% excellent rate can be achieved.Students are randomly selected to track their growth analysis.After the application of this teaching design, 90% of the students' comprehensive quality assessment remains the same or improves.The indings suggest that the teaching model discussed in this paper not only improves students' computer skills but also plays a crucial role in their ✉Corresponding author.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".