Bibliographic record
Abstract
Examples of multimedia learning situations using technology are described from the perspective of enhancing cognition. Implications for instructional designers are provided along with some future directions for this field of research. We see a need for multimedia research to foster higher degrees of interactivity with more varied types of media. More importantly, such research requires the inclusion of more scaffolding of learners, more attention to assisting learners in self-regulation, and perhaps media that serves in a pedagogical manner through coaching, pedagogical agents, and realistic environments that may include virtual reality dimensions. This new generation of multimedia will focus more on active knowledge construction through performing or doing some task with guidance. Introduction to Multimedia Learning of Cognitive Skills In examining the mores of today's society one cannot help but notice that higher demands are placed on an individuals' processing capability. In fact, daily expectations exist for people to attend and respond to multiple forms of information efficiently. Multimedia learning refers to the ability to learn from multiple representations, in particular, verbal and visual representations that are used to present an instructional message (Mayer, 2003). Verbal representations are defined as text, spoken or written, and visual representations as pictures, static or dynamic. Other researchers have extended this definition to include descriptions of the different functions multiple representations play in learning, that is, complementing or constraining learning, or helping learners to construct new knowledge (Ainsworth, 1999).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".