Math Snacks: Using Animations and Games to Fill the Gaps in Mathematics
Bibliographic record
Abstract
Math Snacks animations and support materials were developed for use on the web and mobile technologies to teachratio, proportion, scale factor, and number line concepts using a multi-modal approach. Included in Math Snacks are:Animations which promote the visualization of a concept image; written lessons which provide cognitive complexityfor understanding; and active, situated learning activities to facilitate memorable experiences to deepencomprehension. This pilot study compared pre-post test gains for 460 sixth and seventh grade students enrolled innine different classrooms. In five of the nine classrooms, teachers utilized the Teacher Guide that corresponded withthe five Math Snacks animations and one game and in four classrooms teachers used the same Math Snacksanimations and one game, but were free to develop their own lessons using available online resources. Resultsshowed moderate and significant pre-post test gains for all six grade students. However, significant gains for seventhgrade students were shown only for classrooms where the teacher Guide was used. While it appears that the use ofTeacher Guide is useful only for seventh grade, such a conclusion is premature given the small number of classroomsand the exploratory nature of this investigation. Further analyses of moderator variables (e.g., instructional fidelity,learner characteristics) are certainly necessary.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".