Discovering Computer-Assisted Learning Processes based on Objective Exam Score Transformations

Wessa, Patrick

doi:10.1016/j.sbspro.2009.01.457

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…On the other hand. Reproducible Computing and objective measurements, empower us to research actual learning processes and reveal important guidelines for the improvement of the effectiveness of learning through improved software usability ( [22], [23]) and a series of cognitive, behavioral, and constructivist incentives [24], [25].…”

Section: Measurement Errors In Self Reported Datamentioning

confidence: 99%

“…For obvious reasons, Reproducible Computing allows one to collect such measurements of high quality even if the student population is very large. The learning outcomes however, need to subjected to critical analysis too, as is explained in a recent paper [25] in which the effect of objective exam score transformations is studied. The paper includes a mathematical description of an easy-to-use methodology and an illustrated case in which the effect of exam score transformations is clearly demonstrated.…”

Section: Effectiveness Of Constructivism and Computer Assited Learningmentioning

confidence: 99%

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Reproducible Computing: a new Technology for Statistics Education and Educational Research

Wessa¹,

Ao²

2009

AIP Conference Proceedings

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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.

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Section: Measurement Errors In Self Reported Datamentioning

confidence: 99%

Section: Effectiveness Of Constructivism and Computer Assited Learningmentioning

confidence: 99%

Reproducible Computing: a new Technology for Statistics Education and Educational Research

Wessa¹,

Ao²

2009

AIP Conference Proceedings

Self Cite

View full text Add to dashboard Cite

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“…The methodology that allows us to do this is based on a mathematical model which is described in [40] and relates the learning outcomes to objectively measured activities and reported experiences.…”

Section: Quality Controlmentioning

confidence: 99%

“…The third model (M 3 ) simply combines M 1 and M 2 by relating^ y to X in the regression model^ y = X f+ u. The estimator for f can be shown to be^ f = (X X) 40]). M 3 is likely to yield different results from M 1 unless the estimated parameters M 2 are (nearly) equal to the original weights^ c = (…”

Section: Modelmentioning

confidence: 99%

Quality Control of Statistical Learning Environments and Prediction of Learning Outcomes through Reproducible Computing

Wessa

2009

INT J COMPUT COMMUN

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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.

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Discovering Computer-Assisted Learning Processes based on Objective Exam Score Transformations

Cited by 2 publications

References 7 publications

Reproducible Computing: a new Technology for Statistics Education and Educational Research

Reproducible Computing: a new Technology for Statistics Education and Educational Research

Quality Control of Statistical Learning Environments and Prediction of Learning Outcomes through Reproducible Computing

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