| ou-analyse |
organization |
ou-analyse |
| ou-analyse |
organization |
ou-analyse |
| ou-analyse |
organization |
ou-analyse |
| ou-analyse |
organization |
ou-analyse |
| ou-analyse |
organization |
ou-analyse |
| ou-analyse |
organization |
ou-analyse |
| ou-analyse |
organization |
ou-analyse |
| ou-analyse |
endDate |
2015-07-31 |
| ou-analyse |
hasPrincipalInvestigator |
b8d521a6173aa941c32cd5686f640bfa |
| ou-analyse |
hasPrincipalInvestigator |
c79cc1129e553cdca30fa856443ac46e |
| ou-analyse |
startDate |
2013-08-01 |
| ou-analyse |
type |
Project |
| ou-analyse |
comment |
The OU Analyse project is piloting new machine learning based methods for early identification
of students who are at risk of failing. |
| ou-analyse |
label |
OU Analyse |
| ou-analyse |
depiction |
ouanalyse.png |
| ou-analyse |
homepage |
analyse.kmi.open.ac.uk |
| ou-analyse |
name |
OU Analyse |
| ou-analyse |
page |
ou-analyse |
| ou-analyse |
Description |
A list of such students is communicated weekly to the module and Student Support teams
to help them consider appropriate support. The overall objective is to significantly
improve the retention of OU students. This is 'research-led' as the project builds
on previous experience from the Jisc funded Retain in 2010/2011 and the joint OU-Microsoft
Research Cambridge project in 2012/2013.
The work is innovative in that it is applying machine learning techniques to two types
of data: student demographic data and dynamic data represented by their VLE activities.
Records of previous presentations are used to build and validate predictive models,
which are then applied to the data of the presentation currently running. |
| ou-analyse |
Description |
A list of such students is communicated weekly to the module and Student Support teams
to help them consider appropriate support. The overall objective is to significantly
improve the retention of OU students. This is ‘research-led’ as the project builds
on previous experience from the Jisc funded Retain in 2010/2011 and the joint OU-Microsoft
Research Cambridge project in 2012/2013.
The work is innovative in that it is applying machine learning techniques to two types
of data: student demographic data and dynamic data represented by their VLE activities.
Records of previous presentations are used to build and validate predictive models,
which are then applied to the data of the presentation currently running. |
| ou-analyse |
in dataset |
kmifoaf |
