Achievement Motivation for Introductory College Biology

Johnson, Marcus Lee

doi:10.5296/jse.v4i1.4306

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…However, the performance of students in the subject remains low [32,33]. Research in Biology education point to a nexus between motivation and achievement in Biology [34]. Reviewed literature indicates that research in gender differences in motivation is inconclusive, mixed and equivocal.…”

Section: Justification and Purpose Of The Studymentioning

confidence: 99%

Motivation to Learn Biology: Gender and School Type Differences in Co-Educational Schools in Siaya County, Kenya

Owino

2018

JESBS

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Aims:The purpose of the study was to investigate gender and school type differences in motivational orientations among grade 10 students in co-educational schools of Siaya County, Kenya. Study Design: The study adopted a concurrent mixed methods design. Place and Duration of Study:The study was carried out in Siaya County, Kenya during the second term of the year 2018 in June. Methodology: The sample consisted of 680 students (380 boys, 300 girls) from a population of 6800 students (3800 boys, 3000 girls) using multi-stage cluster sampling and simple random sampling. The study used Biology Motivation Questionnaire (BMQ) adopted and modified to suit the study from Tuan, Chin and Shieh (2005) and Biology Interview Guide (BIG). To test gender and school type differences in motivation, independent sample t-tests were used. The hypotheses were accepted at a significance level of α=0.05. Results:The findings indicate statistically significant gender differences in Self-efficacy (SE), Active Learning strategies (ALS) and Learning Environment Stimulation (LES) in favour of boys.

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Section: Justification and Purpose Of The Studymentioning

confidence: 99%

Motivation to Learn Biology: Gender and School Type Differences in Co-Educational Schools in Siaya County, Kenya

Owino

2018

JESBS

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Considerations for assessing model averaging of regression coefficients

Banner

Higgs

2016

Ecological Applications

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Model choice is usually an inevitable source of uncertainty in model-based statistical analyses. While the focus of model choice was traditionally on methods for choosing a single model, methods to formally account for multiple models within a single analysis are now accessible to many researchers. The specific technique of model averaging was developed to improve predictive ability by combining predictions from a set of models. However, it is now often used to average regression coefficients across multiple models with the ultimate goal of capturing a variable's overall effect. This use of model averaging implicitly assumes the same parameter exists across models so that averaging is sensible. While this assumption may initially seem tenable, regression coefficients associated with particular explanatory variables may not hold equivalent interpretations across all of the models in which they appear, making explanatory inference about covariates challenging. Accessibility to easily implementable software, concerns about being criticized for ignoring model uncertainty, and the chance to avoid having to justify choice of a final model have all led to the increasing popularity of model averaging in practice. We see a gap between the theoretical development of model averaging and its current use in practice, potentially leaving well-intentioned researchers with unclear inferences or difficulties justifying reasons for using (or not using) model averaging. We attempt to narrow this gap by revisiting some relevant foundations of regression modeling, suggesting more explicit notation and graphical tools, and discussing how individual model results are combined to obtain a model averaged result. Our goal is to help researchers make informed decisions about model averaging and to encourage question-focused modeling over method-focused modeling.

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The predictive Lasso

2011

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We propose a shrinkage procedure for simultaneous variable selection and estimation in generalized linear models (GLMs) with an explicit predictive motivation. The procedure estimates the coefficients by minimizing the Kullback-Leibler divergence of a set of predictive distributions to the corresponding predictive distributions for the full model, subject to an l1 constraint on the coefficient vector. This results in selection of a parsimonious model with similar predictive performance to the full model. Thanks to its similar form to the original lasso problem for GLMs, our procedure can benefit from available l1-regularization path algorithms. Simulation studies and real-data examples confirm the efficiency of our method in terms of predictive performance on future observations.

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Achievement Motivation for Introductory College Biology

Cited by 6 publications

References 19 publications

Motivation to Learn Biology: Gender and School Type Differences in Co-Educational Schools in Siaya County, Kenya

Motivation to Learn Biology: Gender and School Type Differences in Co-Educational Schools in Siaya County, Kenya

Considerations for assessing model averaging of regression coefficients

The predictive Lasso

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