Can Seventh Graders Learn Fractions from a Web-Based Pedagogical Agent? Using Comparison Groups Thre Times Over Several Weeks
Bibliographic record
Abstract
In this study, a Web-based pedagogical agent presented 7th grade students (n = 91) with examples and practice questions involving the multiplication and division of fractions. Pedagogical agents are animated, talking characters that can be made to introduce, guide or otherwise enhance educational Web sites. It was expected that school-age students using moving and talking pedagogical agents would retain more and find more creative solutions to problems than students in the other treatment conditions. A repeated measures-by-occasion research design was used to determine if the movement and or talking by the agent helped them learn to multiply and divide fractions. Results of the analyses showed that students learned from the pre-test to immediate post-test. But there were no effects for either modality (speech vs. text) or agent animation (movement vs. no movement). Consistent with a previous study with 7th grade students using educational multimedia (Mann, Newhouse, Pagram, Campbell, & Schulz, 2002) positive findings from using speech in educational multimedia may only be generalizable to adults and older adolescents. Implications are discussed regarding the instructional design of educational Web sites.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".