paper.md
In lumenlearning/rise: Conduct RISE Analysis
title: 'RISE: An R package for RISE analysis'
tags:
- R
- learning analytics
- open educational resources
- continuous improvement
authors:
- name: David Wiley
orcid: 0000-0001-6722-4744
affiliation: "1, 2" # (Multiple affiliations must be quoted)
affiliations:
- name: Lumen Learning
index: 1
- name: Brigham Young University
index: 2
date: 20 July 2018
bibliography: paper.bib
Summary
Learning analytics provide designers of educational materials with the
ability to ask and answer questions about the effectiveness of those
materials in supporting student learning. However, copyright typically
prevents designers from engaging in continuous improvement activities.
Open educational resources provide designers of educational materials
with copyright permission to revise and remix those materials, but
they do not provide insights into where designers might most
productively invest their continuous improvement efforts. Combining
learning analytics with open educational resources opens the door to
data-driven continuous improvement of educational materials.
However, no open source tools exist to support designers in this
time-intensive process.
rise is an R package that implements the resource inspection,
selection, and evaluation (RISE) technique described in [@Bodily:2017].
rise combines information about course learning outcomes, student
use of learning materials aligned with learning outcomes, and student
performance on assessments aligned with learning outcomes in order to
automatically identify places where learning materials are not
effectively supporting student learning. Automating the process of
identifying areas for improvement allows designers to spend less of
their time determining which learning materials need to be improved
and more time actually making improvements to learning materials.
The source code for rise has been archived to Zenodo with the
linked DOI: [@zenodo]
Acknowledgements
I gratefully acknowledge contributions to the model implemented in
rise made by my co-authors of the original paper, Bob Bodily and
Rob Nyland.
References
lumenlearning/rise documentation built on May 13, 2019, 11:55 p.m.
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title: 'RISE: An R package for RISE analysis' tags: - R - learning analytics - open educational resources - continuous improvement authors: - name: David Wiley orcid: 0000-0001-6722-4744 affiliation: "1, 2" # (Multiple affiliations must be quoted) affiliations: - name: Lumen Learning index: 1 - name: Brigham Young University index: 2 date: 20 July 2018 bibliography: paper.bib
Summary
Learning analytics provide designers of educational materials with the ability to ask and answer questions about the effectiveness of those materials in supporting student learning. However, copyright typically prevents designers from engaging in continuous improvement activities. Open educational resources provide designers of educational materials with copyright permission to revise and remix those materials, but they do not provide insights into where designers might most productively invest their continuous improvement efforts. Combining learning analytics with open educational resources opens the door to data-driven continuous improvement of educational materials. However, no open source tools exist to support designers in this time-intensive process.
rise is an R package that implements the resource inspection,
selection, and evaluation (RISE) technique described in [@Bodily:2017].
rise combines information about course learning outcomes, student
use of learning materials aligned with learning outcomes, and student
performance on assessments aligned with learning outcomes in order to
automatically identify places where learning materials are not
effectively supporting student learning. Automating the process of
identifying areas for improvement allows designers to spend less of
their time determining which learning materials need to be improved
and more time actually making improvements to learning materials.
The source code for rise has been archived to Zenodo with the
linked DOI: [@zenodo]
Acknowledgements
I gratefully acknowledge contributions to the model implemented in
rise made by my co-authors of the original paper, Bob Bodily and
Rob Nyland.
References
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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