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Understanding Technology Use Using Multilevel Modeling

This study explores the use of multilevel modeling techniques to understand the factors influencing technology use in education. It examines the effects of teacher and school characteristics on technology use, highlighting the importance of considering nested data in modeling educational outcomes.

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Understanding Technology Use Using Multilevel Modeling

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  1. Understanding Technology Use Using Multilevel Modeling Laura M. O’Dwyer inTASC Boston College April 21st, 2003

  2. Technology Use and Previous Research • Becker, Ravitz & Anderson et al (1998) found that teacher technology use was influenced by: • Teachers’ pedagogy and philosophy • Subject area and grade level • Access to technology and the location of the technology • School Climate/Culture • Lemke – school/district leadership effect use

  3. Vision Leadership Community Resources Prof. Development. District Level Physical Infra. Support/Personnel Curriculum &Instructional Model Policies and Standards School Leadership Principal. Beliefs School School Culture Principal Preparedness Teacher Beliefs Resources Teacher Preparedness Education Class Students Use Home Resource

  4. District Characteristics Teachers’ Use of Technology Principal Characteristics School Characteristics

  5. What are Multilevel Techniques? • Ideal for use with data that are nested • Teachers are nested within schools or districts • Generalization of OLS regression in which a regression equation is created for each level of data • Regression coefficients refer to specific levels in the data • Total variance in the outcome is partitioned into within group and between group components • Add predictors that explain the available variance • Available software such as HLM, MLwin etc.

  6. Why use Multilevel Techniques? • Provide increased power in the prediction of outcomes • Can avoid within group dependency • Can avoid aggregation bias • Allows for the heterogeneity of regression slopes

  7. Modeling Outcomes Using Multilevel Techniques • Partition the variance into its within and between group components • the unconditional model • Add predictors that explain some of the available variance

  8. Example: Modeling Teacher Use of Technology for Preparation • USEIT Study conducted by inTASC • Outcome variable: • Teacher use of technology for preparation • 4 Item scale • Mean = 0, s.d. = 1

  9. Unconditional Model Models • Level 1 Equation • Level 2 Equation

  10. Variance Structure – Teacher Technology Use for Preparation

  11. Statistical Models • Level 1 Equation • Level 2 Equation

  12. Predictors • Teacher Characteristics • Teacher pedagogical beliefs • Teacher beliefs about technology • Teacher confidence using technology • School Characteristics • School mean student ease of access to technology at home • School mean perception of obstacles to technology use • School mean principals’ emphasis on technology • District Characteristics • Professional development focuses on technology • District leaders discuss technology • Variety of available tech-related professional development

  13. OLS Solution Multilevel Solution Teachers Use Technology For Coefficient p - value Coefficient p - value Preparation Student ease of access to technology at home (school mean) 0.334 0.098 0.462 0.015 Teachers report that technology quality is an obstacle (school mean) - 0.147 0.053 - 0.181 0.010 Teachers report that poor professional development is an obstacle (school mean) 0.245 0.013 0.184 0.445 Teachers report on principals' emphasis on technology (school mean) 0.134 0.165 0.258 0.014 District leaders discuss technology 0.185 0.001 0.223 0.001 Relative emphasis on technology - related professional development - 0.062 0.022 - 0.047 0.096 Variety of available technology - related professional development 0.006 0.920 0.081 0.253 *Teacher believes in student - centered instruction 0.133 0.000 0.084 0.011 *Issues with the quality of technology obstructs use - 0.098 0.007 - 0.061 0.042 *Teacher believes that computers help students 0.122 0.000 0.110 0.000 *Teacher confidence using technology 0.245 0.000 OLS v Multilevel Solution 0.273 0.000 *Teacher believes in teacher - centered instruction - 0.057 0.077 - 0.088 0.001

  14. Conclusions • Multilevel modeling provides better representation of the effects of school and district characteristics • Can model different relationships within schools • Ideal for modeling technology use where school, district and policies may have powerful effects

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