Evidence map›Paper›PMID 28913703›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2019

Using Technology and Assessment to Personalize Instruction: Preventing Reading Problems.

Carol McDonald Connor

Abstract read
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Moving Toward a Precision-Based, Personalized Framework for Prevention Science: Introduction to the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2019
    Article
  3. Precision Strategies as a Timely and Unifying Framework for Ongoing Prevention Science Advances.Prevention science : the official journal of the Society for Prevention Research · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Carol McDonald ConnorUniversity of California, Irvine, CA, USA. connorcm@uci.edu.

Funding

THE FLORIDA LONGITUDINAL STUDYP50HD052120 · NICHD · FLORIDA STATE UNIVERSITY · PI TIFFANY P HOGAN · 2006 to 2026
$31.7M
Child-Instruction Interactions Causal Effects on ReadingR01HD048539 · NICHD · FLORIDA STATE UNIVERSITY · PI CONNOR, CAROL MCDONALD · 2005 to 2010
$2.4M
Developing Dynamic Forecasting Intervention Models for Upper Elementary StudentsR21HD062834 · NICHD · FLORIDA STATE UNIVERSITY · PI CONNOR, CAROL MCDONALD · 2010 to 2011
$356k
NICHD NIH HHS P50 HD052120NICHD NIH HHS R01 HD048539NICHD NIH HHS R21 HD062834
6 · The paper itself

Abstract

Children who fail to learn to read proficiently are at serious risk of referral to special education, grade retention, dropping out of high school, and entering the juvenile justice system. Accumulating research suggests that instruction regimes that rely on assessment to inform instruction are effective in improving the implementation of personalized instruction and, in turn, student learning. However, teachers find it difficult to interpret assessment results in a way that optimizes learning opportunities for all of the students in their classrooms. This article focuses on the use of language, decoding, and comprehension assessments to develop personalized plans of literacy instruction for students from kindergarten through third grade, and A2i technology designed to support teachers' use of assessment to guide instruction. Results of seven randomized controlled trials demonstrate that personalized literacy instruction is more effective than traditional instruction, and that sustained implementation of personalized literacy instruction first through third grade may prevent the development of serious reading problems. We found effect sizes from .2 to .4 per school year, which translates into about a 2-month advantage. These effects accumulated from first through third grade with a large effect size (d = .7) equivalent to a full grade-equivalent advantage on standardize tests of literacy. These results demonstrate the efficacy of technology-supported personalized data-driven literacy instruction to prevent serious reading difficulties. Implications for translational prevention research in education and healthcare are discussed.

Indexed as

Education, SpecialReadingTechnologyChildChild, PreschoolHumansLanguageLearningLiteracyRandomized Controlled Trials as TopicStudentsTeachingAcademicIndividualized instructionInstructionInterventionLiteracyPrecision interventionReadingWriting

Identifiers

PMID28913703
PMCPMC5854500

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.