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Käser, Tanja; Schwartz, Daniel L. – International Educational Data Mining Society, 2019
Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and effective exploration. Guidance in the form of targeted interventions or feedback therefore has…
Descriptors: Educational Environment, Interaction, Cluster Grouping, Models
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Karagiannakis, Giannis N.; Baccaglini-Frank, Anna E.; Roussos, Petros – Australian Journal of Learning Difficulties, 2016
Through a review of the literature on mathematical learning disabilities (MLD) and low achievement in mathematics (LA) we have proposed a model classifying mathematical skills involved in learning mathematics into four domains (Core number, Memory, Reasoning, and Visual-spatial). In this paper we present a new experimental computer-based battery…
Descriptors: Mathematics Skills, Mathematical Aptitude, Skill Analysis, Learning Disabilities
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Sherin, Bruce – Journal of the Learning Sciences, 2013
A large body of research in the learning sciences has focused on students' commonsense science knowledge--the everyday knowledge of the natural world that is gained outside of formal instruction. Although researchers studying commonsense science have employed a variety of methods, 1-on-1 clinical interviews have played a unique role. The data…
Descriptors: Informal Education, Computational Linguistics, Transcripts (Written Records), Interviews
Dillon, Robert – Principal Leadership, 2009
The teacher leaders and school administrators at Nipher Middle School in Kirkwood, Missouri, have recently embraced a new set of strategies for supporting learning for all students. During the weekly problem-solving conversations that are a part of their response-to-intervention model, it became evident that clusters of students with unique and…
Descriptors: Obesity, Mental Disorders, Grade 9, Teacher Leadership
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Hedges, Larry V. – Journal of Educational and Behavioral Statistics, 2007
A common mistake in analysis of cluster randomized trials is to ignore the effect of clustering and analyze the data as if each treatment group were a simple random sample. This typically leads to an overstatement of the precision of results and anticonservative conclusions about precision and statistical significance of treatment effects. This…
Descriptors: Statistical Significance, Computation, Cluster Grouping, Statistics