The Evolving Textbook: The Development And Marketing Of New-Media Products In Educational Publishing
Bibliographic record
Abstract
Educational institutions, instructors and students are increasingly embracing information technologies in their teaching and learning. Educational publishers, meanwhile, aim to develop and market new-media educational materials that meet and satisfy the needs of institutions, instructors and students, as well as to increase their competitive advantage in the educational-publishing industry. To help it reach its goal, an educational publisher formulates a new-media strategy based on information gathered through market research. Rapid development of information technologies, the high costs of their implementation, as well as limited technical skills among instructors and students, however, are causing uncertainty in how to best integrate information technologies into education. Consequently, a company's development of a new-media strategy is a slow process. This report brings together market research in four areas to better understand the uncertainty surrounding information technologies in education. It presents ? an evaluation of delivery systems for educational materials, ? a comparative analysis of educational publishers? web sites and textbook sites, ? an analysis of the competitive environment, ? an analysis of trends and issues in both education and information technologies. The first three areas are based on market research conducted at McGraw-Hill Ryerson in 1999. The fourth area is primarily a review of secondary sources that explore trends in education and information technologies. Based on analysis of these four areas, this report concludes with four opportunities that an educational publisher might pursue. These opportunities are 1. employing flexible delivery systems for content that respond to the changing needs of instructors and students, the changing capabilities of information technology and the constantly shifting priorities of the educational marketplace, 2. forging partnerships with companies with core competencies that can increase the value of an educational publisher?s products, 3. investing in customer support as a means of training instructors in the use of an educational publisher?s products, 4. developing customer loyalty through customer support and other marketing 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".