Is a <scp>Phone‐Based</scp> Language and Literacy Assessment a Reliable and Valid Measure of Children's Reading Skills in <scp>Low‐Resource</scp> Settings?
Bibliographic record
Abstract
Abstract Technology‐based remote research methods are increasingly widespread, including learning assessments in child development and education research. However, little is known about whether technology‐based remote assessments remain as valid and reliable as in‐person assessments. We developed a low‐cost phone‐based language and literacy assessment for primary‐school children in low‐resource communities in rural Côte d'Ivoire using voice calls and SMS. We compared the reliability and validity of this phone‐based assessment to an established in‐person assessment. A total of 685 5th grade children completed language (phonological awareness, vocabulary, language comprehension) and literacy (letter, word, pseudoword, passage reading, and comprehension) tasks in‐person and by phone. Reliability (internal consistency) and predictive validity were high across in‐person and phone‐based tasks. Children's performance across in‐person and phone‐based assessments was moderately to strongly correlated. Phonological awareness and vocabulary skills measured in‐person and by phone significantly predicted in‐person and phone‐based letter, word, and pseudoword reading. Oral language and decoding skills measured in‐person and by phone significantly predicted in‐person and phone‐based passage reading and comprehension. Our phone‐based assessment was a reliable and valid measure of language and reading and feasible for low‐resource settings. Low‐cost technologies offer significant potential to measure children's learning remotely, increasing the inclusion of remote and low‐resource populations in education research.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".