Using the SECTIONS Framework to Evaluate Flash Media
Bibliographic record
Abstract
It is easy to feel overwhelmed with the tantalizing array of instructional technologies available. How do educators choose the ones to use? We are part of a distributed team of instructional designers, evaluation consultants, and new media specialists within the University of Alberta (Canada). Until recently, we worked together as instructional developers in Academic Technologies for Learning (ATL). Instructors often ask us what will be the best technology for teaching. The problem is that there are many factors to consider, and there typically is not one best answer to any educational situation. Without clear guidelines, popular trends can drive decisions rather than the educational appropriateness of the technology or media. A model for the analysis and selection of technologies is provided by Bates and Poole (2003) in their book Effective Teaching with Technology in Higher Education. Their SECTIONS framework is particularly appealing as it considers broader organizational implications, student needs, and subject matter concerns. At ATL, we have found that instructional designers, technical specialists, and professional educators sometimes have difficulty communicating effectively about the multitude of factors that influence the design of educational resources. The SECTIONS framework provides a unified approach for individuals who have widely varying perspectives, backgrounds, and expertise. Project teams will find that the framework is both flexible and comprehensive and may be used to guide decision-making in a variety of educational contexts. The SECTIONS framework is based on an acronym representing the criteria that should be considered when selecting instructional technologies: Students, Ease of use,
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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 | 0.053 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".