Closing the Word Gap with Big Word Club: Evaluating the Impact of a Tech-Based Early Childhood Vocabulary Program
Bibliographic record
Abstract
Vocabulary is an important part of literacy skills development, and understanding the effectiveness of various types of programs can help parents, schools, and states decide what to invest in. Programs that use entertainment to engage students to learn (“Edutainment” programs) are popular, but experimental evidence on their effectiveness at teaching vocabulary is sparse. In this paper, we use an RCT to evaluate the effectiveness of a program that uses technology and entertainment to teach vocabulary to children. The program, Big Word Club (BWC), is a web-based platform consisting of animated book videos, dance videos, and music videos intended to help children learn one new word per day. Our field experiment was conducted with 818 Pre-K and Kindergarten students in 47 schools across 3 U.S. states. We randomly assigned schools to a control condition or to use the BWC platform in their curriculum. We find that the program was effective in teaching the vocabulary words that were targeted by the program (0.30 SD) after four months of use. Further, this effect persisted in a follow-up test 2 months later, but only for girls. Treated students also scored higher on a standardized vocabulary assessment (PPVT), but this difference was not statistically significant.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".