Is a Phone‐Based Language and Literacy Assessment a Reliable and Valid Measure of Children's Reading Skills in Low‐Resource 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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".