Bibliographic record
Abstract
With the introduction of screen media and 1:1 devices in the classroom, educators are finding themselves in a unique position; navigating new technological platforms, changing their teaching methods and pedagogy to adapt, and oftentimes, competing for their students’ attention. Some important factors for classroom implementation and practice are the need for learner preference, differentiation, high quality applications, and a complementary balance between traditional methods of learning and the usage of screen media. Many teachers have observed the benefits of adopting new technology, but have concerns with its integration. Classrooms in the United States have undergone a significant change because of the use of screen media, including laptop computers (i.e. Chromebooks), and digital textbooks. While using technology in the classroom is undoubtedly not a novel concept, utilizing technology in place of traditional textbooks is relatively new. The motivation for this article was my personal experience and interest in technology for learning purposes. I have taught middle school students for 16 years, and throughout this time I have seen technology substitute traditional textbooks in various subject areas. Additionally, I have seen the effects of reading from a paper source and from screens, as well as the various strategies learners apply while using both to process the information. As a lifelong learner, I remain abreast of the most recent studies and advice on literacy for children, as well as technology use with adolescents. I incorporate best practices in my classroom. This article will provide ideas that have proven successful, not only in my classroom, but also in empirical research. Keywords: Screen media, differentiation, pedagogy, Chromebooks
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".