The Impact of Using Online Learning Platforms on Student Learning Motivation
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
Background. The rapid growth of online learning platforms has significantly impacted educational practices globally, particularly in enhancing student learning motivation. Purpose. This study explores the effect of utilizing online learning platforms on students’ motivation to learn, considering their engagement, learning strategies, and academic performance. The primary aim of this research is to analyze how the use of such platforms influences students’ intrinsic and extrinsic motivation within the context of various educational settings. Method. This study adopts a quantitative research approach, using surveys and questionnaires administered to a sample of students from different educational institutions. Data collected were analyzed using descriptive statistics and inferential analysis to determine the relationship between online learning platform usage and students’ motivation levels. Results. The findings reveal a positive correlation between online learning platform usage and increased motivation, particularly in terms of fostering self-regulation, engagement, and a greater sense of autonomy in learning. Students reported higher motivation to participate in lessons and complete assignments when using these platforms. Conclusion. In conclusion, integrating online learning platforms into traditional education methods can significantly enhance students’ learning motivation, supporting both their academic success and personal growth. Future studies should focus on long-term effects and the comparative benefits of different platforms.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| 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 it