What Do We Think We Think We Are Doing?: Metacognition and Self-Regulation in Programming

Metacognition and self-regulation are popular areas of interest in programming education, and they have been extensively researched outside of computing. While computing education researchers should draw upon this prior work, programming education is unique enough that we should explore the extent to which prior work applies to our context. The goal of this systematic review is to support research on metacognition and self-regulation in programming education by synthesizing relevant theories, measurements, and prior work on these topics. By reviewing papers that mention metacognition or self-regulation in the context of programming, we aim to provide a benchmark of our current progress towards understanding these topics and recommendations for future research. In our results, we discuss eight common theories that are widely used outside of computing education research, half of which are commonly used in computing education research. We also highlight 11 theories on related constructs (e.g., self-efficacy) that have been used successfully to understand programming education. Towards measuring metacognition and self-regulation in learners, we discuss seven instruments and protocols that have been used and highlight their strengths and weaknesses. To benchmark the current state of research, we examined papers that primarily studied metacognition and self-regulation in programming education and synthesize the reported interventions used and results from that research. While the primary intended contribution of this paper is to support research, readers will also learn about developing and supporting metacognition and self-regulation of students in programming courses

Reference curves for pediatric endocrinology: leveraging biomarker z-scores for clinical classifications

Context: Hormone reference intervals in pediatric endocrinology are traditionally partitioned by age and lack the framework for benchmarking individual blood test results as normalized z-scores and plotting sequential measurements onto a chart. Reference curve modeling is applicable to endocrine variables and represents a standardized method to account for variation with gender and age. Objective: We aimed to establish gender-specifc biomarker reference curves for clinical use and benchmark associations between hormones, pubertal phenotype, and body mass index (BMI). Methods: Using cross-sectional population sample data from 2139 healthy Norwegian children and adolescents, we analyzed the pubertal status, ultrasound measures of glandular breast tissue (girls) and testicular volume (boys), BMI, and laboratory measurements of 17 clinical biomarkers modeled using the established “LMS” growth chart algorithm in R. Results: Reference curves for puberty hormones and pertinent biomarkers were modeled to adjust for age and gender. Z-score equivalents of biomarker levels and anthropometric measurements were compiled in a comprehensive beta coeffcient matrix for each gender. Excerpted from this analysis and independently of age, BMI was positively associated with female glandular breast volume (β = 0.5, P < 0.001) and leptin (β = 0.6, P < 0.001), and inversely correlated with serum levels of sex hormone-binding globulin (SHBG) (β = −0.4, P < 0.001). Biomarker z-score profles differed signifcantly between cohort subgroups stratifed by puberty phenotype and BMI weight class. <p<Conclusion: Biomarker reference curves and corresponding z-scores provide an intuitive framework for clinical implementation in pediatric endocrinology and facilitate the application of machine learning classifcation and covariate precision medicine for pediatric patients

Learning by Constructing Collaborative Representations: An Empirical Comparison of Three Alternatives.

: Given the explosive growth in the use of computer media for learning and the wide range of choices available to designers of online learning tools, it is crucial to understand how these design choices may influence learning. This study evaluated the influence of tools for constructing representations of evidential models on collaborative learning processes and outcomes. Pairs of participants worked with one of three representations while investigating complex science and public health problems. Dependent variables included quantity of discourse about evidential relations (&quot;for&quot; and &quot;against&quot;) and two learning outcome measures. Significant effects of tools on learning processes were found, although there appears to have been insufficient time for these process differences to influence learning outcomes. Keywords: collaborative representations, representational guidance 1. Introduction The importance of social processes to learning, including the potential utility of collaborative l..

The Effects of Representation on Students' Elaborations in Collaborative Inquiry.

In order to better understand how software design choices may influence students ’ collaborative learning, we conducted a study of the influence of tools for constructing representations of evidential models on collaborative learning processes and outcomes. Pairs of participants worked with one of three representations (matrix, graph, text) while investigating a complex public health problem. Focusing on students ’ collaborative investigative processes and post-hoc essays, we present several analyses that assess the impact of representation type on students ’ elaborations of their emerging knowledge. Our analyses indicate significant impacts on the extent to which students revisit knowledge and the likelihood that they will use that knowledge later