The ICAP Active Learning Framework Predicts the Learning Gains Observed in Intensely Active Classroom Experiences

Wiggins, Benjamin; Eddy, Sarah L.; Grunspan, Daniel Z.; Crowe, Alison J.

doi:10.1177/2332858417708567

Cited by 51 publications

(48 citation statements)

References 37 publications

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“…Moderator analyses did not provide evidence that the social nature of learning activities influenced learning gains from flipped classroom interventions. This is somewhat unexpected given the seemingly convincing evidence that collaborative, active teaching approaches generally are effective (Burgess et al, 2014;Foldnes, 2016;Johnson et al, 2014;Liu & Beaujean, 2017;Wiggins, Eddy, Grunspan, & Crowe, 2017). How the social activities are designed may influence the social interaction and its effect on learning outcomes.…”

Section: Are Any Effects Of Flipped Classroom Interventionsmentioning

confidence: 99%

Does the Flipped Classroom Improve Student Learning and Satisfaction? A Systematic Review and Meta-Analysis

2019

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We searched and meta-analyzed studies comparing flipped classroom teaching with traditional, lecture-based teaching to evaluate the evidence for the flipped classroom’s influence on continuous-learning measures, pass/fail rates, and student evaluations of teaching. Eight electronic reference databases were searched to retrieve relevant studies. Our results indicate a small effect in favor of the flipped classroom on learning (Hedges’ g = 0.35, 95% confidence interval [CI] [0.31, 0.40], k = 272). However, analyses restricted to studies with sufficient power resulted in an estimate of 0.24 (95% CI [0.18, 0.31], k = 90). Effects on pass rates (odds ratio = 1.55, 95% CI [1.34, 1.78], k = 45) and student satisfaction (Hedges’ g = 0.16, 95% CI [0.06, 0.26], k = 69) were small and also likely influenced by publication bias. There is some support for the notion that the positive impact on learning may increase slightly if testing student preparation is part of the implementation.

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Section: Are Any Effects Of Flipped Classroom Interventionsmentioning

confidence: 99%

Does the Flipped Classroom Improve Student Learning and Satisfaction? A Systematic Review and Meta-Analysis

2019

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“…In addition, several DBER papers have implemented random effects well and have detailed their justification and implementation. These papers include studies that use random effects to account for repeated measures, typically students (Eddy et al , 2014; Linton et al , 2014; Wright et al , 2016; Theobald et al , 2017; Wiggins et al , 2017), and studies that use random effects to account for clustering, either by class, quarter, or instructor (Freeman et al , 2011; Eddy et al , 2014; Wright et al , 2016). …”

Section: A Case For Multilevel Modelingmentioning

confidence: 99%

Students Are Rarely Independent: When, Why, and How to Use Random Effects in Discipline-Based Education Research

Theobald

2018

LSE

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Discipline-based education researchers have a natural laboratory—classrooms, programs, colleges, and universities. Studies that administer treatments to multiple sections, in multiple years, or at multiple institutions are particularly compelling for two reasons: first, the sample sizes increase, and second, the implementation of the treatments can be intentionally designed and carefully monitored, potentially negating the need for additional control variables. However, when studies are implemented in this way, the observations on students are not completely independent; rather, students are clustered in sections, terms, years, or other factors. Here, I demonstrate why this clustering can be problematic in regression analysis. Fortunately, nonindependence of sampling can often be accounted for with random effects in multilevel regression models. Using several examples, including an extended example with R code, this paper illustrates why and how to implement random effects in multilevel modeling. It also provides resources to promote implementation of analyses that control for the nonindependence inherent in many quasi-random sampling designs.

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“…Another common type of categorical ordinal data come from quizzes, concept inventories, or tests that have few questions (for few possible points). For example, Wiggins, Eddy, Grunspan, and Crowe [76] tested whether an intervention improved student learning on an 8-item post-test. The distribution of test scores was both highly skewed and tightly bounded, so treating them as linear and analyzing them with a linear regression would violate the assumption of linearity.…”

Section: Proportional Odds Logistic Regression 1 When To Use Propmentioning

confidence: 99%

Beyond linear regression: A reference for analyzing common data types in discipline based education research

Theobald

Aikens

Eddy

et al. 2019

Phys. Rev. Phys. Educ. Res.

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This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In quasirandom designs, subjects are not randomly assigned to treatments. For example, when treatment is assigned by classroom, and observations are made on students, the design is quasirandom because treatment is assigned to classroom, not subject (students).) However, many types of outcome data cannot be appropriately analyzed with linear regression. In these instances, researchers must move beyond linear regression and implement alternative regression techniques. For example, student outcomes can be measured on binary scales (e.g., pass or fail), tightly bound scales (e.g., strongly agree to strongly disagree), or nominal scales (i.e., different discrete choices for example multiple tracks within a physics major), each necessitating alternative regression techniques. Here, we review extensions of linear modeling-generalized linear models (glms)-and specifically compare five glms that are useful for analyzing DBER data: logistic, binomial, proportional odds (also called ordinal; including censored regression), multinomial, and Poisson (including negative binomial, hurdle, and zero-inflated) regression. We introduce a diagnostic tool to facilitate a researcher's identification of the most appropriate glm for their own data. For each model type, we explain when, why, and how to implement the regression approach. When: we provide examples of the types of research questions and outcome data that would motivate this regression approach, including citations to articles in the DBER literature. Why: we name which linear regression assumption is violated by the data type. How: we detail implementation and interpretation of this modeling approach in R, including R syntax and code, and how to discuss the regression output in research papers. Code accompanying each analysis can be found in the online github repository that is associated with this paper (https://github.com/ejtheobald/BeyondLinearRegression). This paper is not an exhaustive review of regression techniques, nor does it review nonregression-based analyses. Rather, it aims to compile and summarize regression techniques useful for the most common types of DBER data and provide examples, citations, and heavily annotated R code so that researchers can easily implement the technique in their work.

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The ICAP Active Learning Framework Predicts the Learning Gains Observed in Intensely Active Classroom Experiences

Cited by 51 publications

References 37 publications

Does the Flipped Classroom Improve Student Learning and Satisfaction? A Systematic Review and Meta-Analysis

Does the Flipped Classroom Improve Student Learning and Satisfaction? A Systematic Review and Meta-Analysis

Students Are Rarely Independent: When, Why, and How to Use Random Effects in Discipline-Based Education Research

Beyond linear regression: A reference for analyzing common data types in discipline based education research

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