1. Oliver, 2010 July

2.  Define target audience  Decide on stastistics  Make Data Requests  Chase data requests  Collate Data  Check the data  Run analysis  Produce report  Deliver 1 st draft of report

3.  Internal to Southampton Solent University  External  Dean and Assistant Dean  Subject lecturers  Public relations and Marketing

4.  Mode, Median, Mean  Discreet or continuous variables  Normal distribution  Chi squared, null hypothesis

5.  Discuss suitable format for the reports with faculty managers and course leaders  Which statistics would be appropriate ?

6.  Drop out rate  Degree results 1sts, 2.1, 2.2, 3rds  Masters results : number of distinctions  Vocational courses

7.  Request the data extract or access to database from IT  Construct one or more queries. Maybe link data from different tables / systems.  Manipulate the data to obtain a data set suitable to run the statistics on.  Use a stats package or do by hand or using Excel functions.

8.  Use a student id rather than a name, maybe generate a new key field  So that students can't be individually identified by the student id If the data is grouped need an audit trail back to starting point

9.  Show initial reports to faculty managers and course leaders and request feedback, customisation  Add / Remove columns, further analysis

10.  Are all the sections of the data complete ?  Are they consistent, do parts of the data contradict each other ?  How are nulls treated, no data, N/A, no shows for exam

11.  Comma delimited, xml, flat file, excel,.mdb, blogs, Wikis  Research : Wikipaedia versus journals  Tools for mapping data sets, eg Apatar forge

12.  Co-worker, line manager  Sanity check, totals etc.  Agile techniques, code peer review  Investigate anomalies

13.  Hard copy,  Access report, email, web page,  Interactive web page linked to data, mySql, PHP, Java  Confidence limits 95%

14.  For subject lecturers  5 Technology Subject Groups:  Communications Engineering,  Computing,  Design,  Engineering and Construction,  Maritime and Coastal Studies  Faculty as a whole

15.  Architecture  Business IT  Civil Engineering  Computer Networking  Computing  Construction  Electrical/Electronic Engineering  Engineering (General)  Interior Design  Mechanical Engineering  Media Technology  Music and Music Technology  Outside Broadcasting  Surveying

16.  What are the key performance indicators ?  Career outcomes, did the course help job prospects ?  Meeting learning objectives 360 students, lecturers, exam boards, employers  Qualitative data - stories of students

17.  Quantitative data - pass rate, number of distinctions  Complaints - modular choice, overlap  Level - too technical, not technical enough  Drop out rates  Raising status of the organisation,

18.  Helping to meet social or research objectives  Helping to meet staff development objectives  Time spent online to student portal ?  Amount of portal interaction between students and teachers ?

19.  Social presence, tweets, webcam, video phone, skype  Mandatory number of postings per week ?  In online coursed, terms such as communities of inquiry, communities of learners, and knowledge- building communities have evolved.  Improvement from knowledge / educational level at course entry  Endowments and annual giving from past students

20.  Define target audience  Decide on stastitistics  Make Data Requests  Chase data requests  Collate Data  Check the data  Run analysis  Produce report  Deliver 1 st draft of report

21.  Any questions welcome  Thanks Olly  http://obromley.deoss.net/web/index/course _performance.htm