Understanding Intention to Use Multimedia Information Systems for Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The challenge today for educators interested in online teaching and learning is how to create, use and assess multimedia technologies for enhanced learning. Unlike hypertext and web-based instruction, the reliance on reading blocks of text is minimized. There still remains little evidence supporting multimedia to enhance learning. From the author’s perspectives, the challenge is to better understand the processes involved in developing effective multimedia tools and to establish appropriate assessment methodologies that may be used to guide standards and ‘good educational multimedia design practice’. This paper aims at sharing the author’s experiences with the development of a multimedia tool and its assessment. A multimedia learning system (MMLS) is presented and the Technology Acceptance Model (TAM) is used to explain user acceptance. We investigate and discuss the TAM results involving a different technology used in a different context. As an initial attempt to understand students’ beliefs, perceptions, attitudes and intentions (and their inter-relationships) our results show that TAM performs well in explaining them.
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it