Open the Black Box of Autonomous Learning: A Sustainable Approach to Language Learning
Bibliographic record
Abstract
Autonomous learning is a fundamental aspect of education that builds important skills, like critical thinking, problem-solving, and adaptability, which are important for achievement in a constantly changing professional environment. Cultivating it promotes lifelong learning, self-improvement, and knowledge beyond traditional educational settings. The objective of this study is to demonstrate the impact of the investigated autonomous learning approach to learners, and assess their ability to sustain the learning process, hence fostering lifelong learning within the framework of formal education. An Autonomous Learning Model (ALM) based 14-week qualitative study examined learners' work and reflections. Theme-based analysis was conducted with 62 fourth-semester English-language learners. A six-stage thematic analysis discovered coded responses' themes. The ALM examined the following key aspects of individual development: personal responsibility, positive self-esteem, decision-making, problem-solving, interpersonal skills, critical and creative thinking abilities, and a strong enthusiasm for learning. The results showed that autonomous learning is achievable, and that instructors’ support and institutional collaboration will improve new curriculum and courses. This study aims to enhance semester-end evaluations, leading to significant improvements for future language-programed learners and ensuring the sustainability of their learning.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".