The Driving Force for Sustaining Future Competencies for University Students in the Digital Era: Learning Strategies
Bibliographic record
Abstract
This study explores how learning strategies act as key drivers in strengthening sustainable competencies among university students in the digital age. As issues such as misinformation, cyberbullying, and online fraud increasingly threaten personal and societal well-being, digital literacy has become a critical competency. Learning strategies, particularly metacognitive self-regulation and resource management, play a crucial role in enhancing digital literacy by facilitating the critical evaluation of information and ethical decision-making. These strategies interact with digital literacy to foster core competencies—encompassing cognitive, emotional, and social abilities—which enable students to navigate complex digital environments responsibly and ethically. In this way, students not only enhance their capacity for critical thinking and problem-solving but also contribute to societal progress and pursue personal well-being in an increasingly digital world. This study employs a quantitative research design, using surveys with university students and random sampling to examine the relationship between learning strategies and digital literacy. The findings reveal that while cognitive strategies alone had limited effects, metacognitive self-regulation and resource management strategies were essential in translating digital literacy into practical skills. These skills empower students to critically assess information, identify credible sources, and navigate ethical challenges in both academic and real-world contexts. The results underscore the importance of embedding targeted learning strategies, particularly metacognitive self-regulation and resource management, within educational frameworks. These strategies help cultivate resilient, responsible digital citizens who are capable of addressing the ethical and practical challenges posed by the digital era. By equipping students with the necessary tools to engage responsibly and ethically in digital spaces, higher education institutions can foster the development of autonomous, reflective learners prepared for the complexities of the digital world.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".