The level of self-learning ability among university students in the light of dealing with innovative technologies
Bibliographic record
Abstract
Introduction. A crucial topic of investigation in modern education is the study of self-learning abilities among college students, especially in using innovative technologies. In order to improve educational practices and student results, it is crucial to understand how students adapt to and use digital tools and online resources for self-directed learning. This is because these resources are becoming increasingly important to academic performance. The purpose of this study is to explore how college students utilize digital tools and online resources for self-learning. Study participants and methods. This investigation, which involved 500 pupils, set out to answer four primary questions: (1) the confidence that college students have in their abilities to learn independently with the help of digital tools; (2) how students' use and familiarity with cutting-edge tools change as they progress through college; (3) whether there are any gender variations in students' ability to learn on their own using modern technology; and (4) whether or not there are any relationships between students' grades and their use of cutting-edge study tools, using a Likert-scale survey. Results. The results demonstrated that out of 500 college students, 60% had faith in self-directed learning through innovative technology, with 200 agreeing and 100 strongly agreeing. On the other side, nearly a quarter were uneasy, with seventeen percent remaining indifferent and sixty-five percent strongly opposing. Just 10% of first-year students reported often utilizing tools, indicating reduced tool utilization and comfort. Whereas half of the fourth-year students regularly used them, the other half used them more frequently and were more comfortable with them. The study did not find any notable difference in the usage of technology for self-learning based on gender. There was an association between grade point average and technology use; students whose GPAs were between 3.5 and 4.0 were more likely to use technology frequently (4.8 out of 5.0) and were more comfortable using it (4.5 out of 5.0). Practical significance. This study has the ability to shed light on current educational procedures and strategies, which is where its practical significance lies. Teachers can gain a better grasp of how students make use of and adjust to digital resources for independent study in order to incorporate these technologies into lessons in a way that may improve students' learning experiences and outcomes. Insights from this study regarding the link between tech use and higher GPAs can help schools design better online classrooms. More personalized and efficient methods of higher education instruction can be a result of the study's impact on educational technology policy decisions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".