Using Social Media in the Language Arts Classroom to Enhance Motivation and Engagement of Reading in Middle School Students
Bibliographic record
Abstract
The purpose of this project was to create a website by which teachers could readily seek strategies to motivate their middle school readers and in doing so, increase their reading comprehension. The accompanying paper provides a brief review of the literature and a website user guide, with a focus on using blogging as a strategy to increase motivation and reading comprehension in middle school classrooms. The website is an avenue where knowledge accumulated through peer-reviewed research and classroom experience (both personal and that of others) is shared. Research has shown that motivation to read in and out of school decreases as students progress through middle school (Kelley & Decker, 2009). Furthermore, results from grade ten students in Ontario on EQAO assessments have shown a decrease in achievement for literacy across subject areas over the last five years. This new website, Engaging Middle School Readers, is a combination of blog posts, educational book summaries, video modules, and suggested online resources suitable for teachers, students, and parents – all made accessible in hopes to counteract the decrease in motivation, and encourage the frequent use of comprehension reading strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".