Student Engagement: Enhancing Students’ Appreciation for Learning and Their Achievement in High Schools
Bibliographic record
Abstract
Students’ perspectives and ideas related to classroom learning seem to be mostly ignored in high schools. Not only does this issue result in both teachers and students struggling in the process of teaching and learning, but students also fail to appreciate the intrinsic value of the curriculum content. It is therefore important to explore the significance of student engagement on their appreciation of learning as well as any positive effects that it might have on their success. This paper has two main aims. First, it provides an overview of the consequence of student engagement and why attending to students’ points of view and their engagement in the process of learning might improve their content learning and achievement. Second, it provides a sketch of the attempts made toward the use of technology and social media to motivate and engage students in content learning. Consequently, the paper has three main sections. The first gives succinct descriptions of student engagement in high school. The second part alongside with my own teaching experiences traces the ways that students are helped to develop an appreciation for learning and highlights the importance of the impact of student engagement in learning. The third section interweaves students’ interest and engagement with digital media and an appreciation of content learning. In so doing, the paper suggests that social media could be an aid for students to learn the content in the subjects being studied, which connects their in-school context and experience to out of school.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".