HLM in cluster-randomised trials - measuring efficacy across diverse populations of learners

HLM in cluster-randomised trials - measuring efficacy across diverse populations of learners

We describe the application of Hierarchical Linear Modelling (HLM) in a cluster-randomised study to examine learning algebraic concepts and procedures in an innovative, technology-rich environment in the US. HLM is applied to measure the impact of such treatment on learning and on contextual variabl...

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Veröffentlicht in:Research in mathematics education 2013-07, Vol.15 (2), p.177-188
Hauptverfasser: Hegedus, Stephen, Tapper, John, Dalton, Sara, Sloane, Finbarr
Format: Artikel
Sprache:eng
Schlagworte:
Algebra
algebra and learning
Classrooms
Clinical trials
cluster-randomised trials
Control Groups
Effect Size
Efficacy
Experimental Groups
Generalized linear models
Group Membership
Hierarchical Linear Modeling
HLM
Honors Curriculum
Learning
Mathematical Concepts
Mathematics Achievement
Modelling
Outcome Measures
Predictor Variables
Preservice Teacher Education
Pretests Posttests
Randomized Controlled Trials
SimCalc
Student Diversity
Teaching Methods
Technology Integration
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Zusammenfassung:We describe the application of Hierarchical Linear Modelling (HLM) in a cluster-randomised study to examine learning algebraic concepts and procedures in an innovative, technology-rich environment in the US. HLM is applied to measure the impact of such treatment on learning and on contextual variables. We provide a detailed description of such methods, methodically analysing nested classroom data with respect to various outcome measures through HLM.
ISSN:1479-4802
1754-0178
DOI:10.1080/14794802.2013.797749