Teachers' Grow use is strongly influenced by technology professional development, their own use of technology, and principal support for Grow¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2004 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? In HLM models, professional development in technology (coefficient 0.36, p < 0.000) and teacher use of technology (0.44, p < 0.000) predict Teacher Use of and Belief in Grow; bar graphs show large differences in Grow scores across quartiles of these factors. → John Q. Easton 2004

Evidence¶

John Q. Easton 2004¶

John Q. Easton, Stuart Luppescu. (2004). Teacher and Principal Responses to the Grow Network: A Preliminary Evaluation of Use in Chicago Public Elementary Schools. Consortium on Chicago School Research. https://consortium.uchicago.edu/publications/teacher-and-principal-responses-grow-network-preliminary-evaluation-use-chicago-public

q2 · i? · associational · r2

HLM analysis of the 2003 survey; the printed table reports professional development in technology at 0.36 (SE 0.05, p < 0.000) and teacher use of technology at 0.44 (SE 0.06, p < 0.000). Bar graphs of Grow scores by quartile of each factor show, in the brief's words, "large differences" — descriptive, untested visual contrasts. No standardized effect size is printed.

"We find that teachers' Grow use is strongly influenced by three important factors: Professional Development in Technology , Teachers' Own Use of Technology, and Principal Support for Grow."

Discussion¶

Related Claims¶

• Teachers receiving 11 or more hours of training on integrating digital content report almost double the reliance on software and the Internet compared with untrained teachers — the general claim this finding bears on, with the studies behind it
• Principal support for Grow predicts teacher Grow use above and beyond teacher demographics and technology factors — related
• Teacher technology use and school culture explain an additional 18 percent of school-by-school differences in high school student technology use — related
• Teacher characteristics from the will-skill-tool model — data literacy, availability of data technologies, and positive beliefs — predict teachers' pedagogical use of digital data, with data literacy the strongest predictor — related
• Grow use is consistently greater in predominantly African-American schools across Chicago — related
• No readily accessible sources of rigorous evidence exist for most ed-tech products — related
• Only 18 percent of schools strongly agree teachers are sufficiently trained to use technology for instruction — related
• Between schools, principal leadership has small positive indirect relationships with test-score gains and GPA through the learning climate and classroom instruction — related
• Within schools, principal leadership relates to individual teachers' instruction through multiple pathways, with the strongest via program quality (professional development and coherence) — related
• School-related factors influence teachers' digital data use only indirectly, mediated by teacher will-skill-tool characteristics, in a model explaining 50% of variance — related
• Computer availability explains almost half of between-school differences in student computer use, with the strongest relationship in schools with insufficient access — related