Paper Info
Title
Quality Control Of Statistical Learning Environments And Prediction Of Learning Outcomes Through Reproducible Computing
Abstract
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.
Year
DOI
Venue
2009
10.15837/ijccc.2009.2.2426
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
Keywords
Field
DocType
Reproducible Computing, Learning Environment, Quality Control, Statistics Education, Psychometrics
Constructivism (philosophy of education),Compendium,Statistics education,Computer science,Statistical learning,Artificial intelligence,Learning environment,Unobservable,Machine learning,Reusability
Journal
Volume
Issue
ISSN
4
2
1841-9836
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
Patrick Wessa111.07