Bibliographic record
Abstract
Scholarly research has indicated that technology adoption to facilitate blended learning promotes the academic success of many different types of students and improves the quality of existing educational offerings. To understand how technology enhances learning, surveys queried the faculty and students of a statewide community college system. The results indicated widespread technology use among the faculty and students. The faculty survey revealed details of technology tools employed and the motivations for their use or discontinued use. Details regarding faculty use of learning management systems, textbooks, and other media characterized the current technology adoption climate. The student survey collected information about students’ perceptions of how technology influenced their learning, their preferences for specific technology tools, and their student progress. Ninety-three percent of student respondents indicated that technology enhanced their learning. Alignment between the faculty use and student preference for technology tools suggested that students are actively engaged in the technology resources used by faculty to enhance learning. Students described how technology facilitated multimodal learning. They also noted that technology increased communication, access, and inclusion in learning. Successful technology use and integration, accompanied by ongoing scholarly debate and monitoring, has the potential to provide more access, promote learning outcomes, and preserve the investment of technology for the institution. The surveys employed here, when used semi-annually, may provide a low-cost model for technology integration monitoring and evaluation. The responses to the surveys also have the potential to provide technology use and integration data that informs strategic planning processes and institutional learning outcome development.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".