This paper explains how the R Framework (http : //www. wessa .net) and a newly developed Compendium Platform (http : //www. freestatistics. org) allow us to create, use, and maintain documents that contain empirical research results which can be recomputed and reused in derived work. It is illustrated that this technological innovation can be used to create educational applications that can be shown to support effective learning of statistics and associated analytical skills. It is explained how a Compendium can be created by anyone, without the need to understand the technicalities of scientific word processing (MgX) or statistical computing (R code). The propo&sd Reproducible Computing system allows educational researchers to objectively measure key aspects of the actual learning process based on individual and constructivist activities such as: peer review, collaboration in research, computational experimentation, etc. The system was implemented and tested in three statistics courses in which the use of Compendia was used to create an interactive e-leaming environment that simulated the real-world process of empirical scientific research.

In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and group assignments, immerses students in a novel blended e-learning experience. Based on the Delone and McLean framework, we tested an explanatory success model with a sample of 607 business students, collected during three consecutive academic years. The results indicate that system quality and teacher support are the most important success factors, directly or indirectly contributing to a higher degree of relative advantage and satisfaction, both of which strongly determine continuous intention to use. The findings ascertain the usability and acceptance of the CP and promote a more radical constructivist approach to the teaching of statistics, but also other subjects.

R, Statistical software, Compendium, Business model,

BackgroundWe introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific–purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology.ObjectivesThe main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience.MethodsBoth VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively.ResultsThe effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content–based design outperforms the traditional VLE–based design.

This article introduces a new approach to statistics education that allows us to accurately measure and control key aspects of the computations and communication processes that are involved in non-rote learning within the pedagogical paradigm of Constructivism. The solution that is presented relies on a newly developed technology (hosted at www.freestatistics.org) and computing framework (hosted at www.wessa.net) that supports reproducibility and reusability of statistical research results that are presented in a so-called Compendium. Reproducible computing leads to responsible learning behaviour, and a stream of high-quality communications that emerges when students are engaged in peer review activities. More importantly, the proposed solution provides a series of objective measurements of actual learning processes that are otherwise unobservable. A comparison between actual and reported data, demonstrates that reported learning process measurements are highly misleading in unexpected ways. However, reproducible computing and objective measurements of actual learning behaviour, reveal important guidelines that allow us to improve the effectiveness of learning and the e-learning system.

This paper focuses on a newly developed method to detect fraud in empirical papers that are submitted by students. The proposed solution is based

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Background Combination antiretroviral therapy (ART) suppresses viral replication in HIV-infected children. The growth of virologically suppressed children on ART has not been well documented. We aimed to develop dynamic reference curves for weight-for-age z scores (WAZ) and height-for-age z scores (HAZ). Methods Children aged <11 years at ART initiation with continuously undetectable viral loads (<400 copies/ml) treated at seven South African ART programs with routine viral load monitoring were included. We used multilevel models to define trajectories of WAZ and HAZ up to 3 years and developed a web application to monitor trajectories in individual children. Results A total of 4,876 children were followed for 7,407 person-years. Analyses were stratified by baseline z-scores and age, which were the most important predictors of growth response. The youngest children showed the most pronounced increase in weight and height initially but catch-up growth stagnated after 1–2 years. Three years after starting ART, WAZ ranged from −2.2 (95% Prediction interval −5.6 to 0.8) in children with baseline age >5 years and z-score <−3 to 0.0 (−2.7 to 2.4) in children with baseline age <2 years and WAZ >−1. For HAZ the corresponding range was −2.3 (−4.9 to 0.3) in children with baseline age>5 years and z-score <−3 to 0.3 (−3.1 to 3.4) in children with baseline age 2–5 years and HAZ >−1. Conclusions We have developed an online tool to calculate reference trajectories in fully suppressed children. The web application could help to define ‘optimal’ growth response and identify children with treatment failure.