New Solutions, New Barriers: Current Uses of the Web in Higher Education
Bibliographic record
Abstract
This paper examines the use of network technologies in higher education and explores how adaptable network tools are for particular learning and teaching environments. The study included a review of the literature describing World Wide Web use to support learning in postsecondary courses in the United States and Canada. Additionally, interviews were conducted with 16 students in three classes at two institutions in which Web technology was used, as well as with four instructors. Data examined included student profiles, class topic, network tool, pedagogical tool, context, adaptable system components, educational purpose, and evaluation. The study found Web-based learning at all levels (undergraduate, graduate, professional) and in many different kinds of institutions, with the majority of courses concentrated in scientific and technical subject areas, which do not include many students with identified special needs. Instructors most often use the Web to enhance communication with students; to provide links to outside resources; and to post course information. Teachers struggle to meet students' differing needs because development of Web-based material is so time-consuming. Appended are a list of the literature reviewed, questions applied to the literature review, and interview protocols. (Contains 35 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document. New Solutions, New Barriers: Current Uses of the Web in Higher Education
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".