Research on the Current Status and Improvement Strategies of College Students' Mobile Learning Ability under the Background of AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the continuous popularization of mobile learning, in order to meet the development needs of the Internet and a learning society, it is particularly important to improve college students' mobile learning ability. This article takes college students at Zhaoqing University as the research object and conducts a questionnaire survey on the current status of college students' mobile learning ability under the Internet environment. It is found that college students have problems such as weak awareness of mobile learning, poor resource management ability, low self-monitoring ability, weak awareness of cooperation and exchange, and insufficient information practice ability. Therefore, strategies such as strengthening the promotion and construction of mobile learning, providing effective learning guidance for college students, enhancing their self-monitoring ability, strengthening their awareness of cooperation and exchange, and improving their information practice ability are proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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