Oliver, 2010 July.  Define target audience  Decide on stastistics  Make Data Requests  Chase data requests  Collate Data  Check the data  Run analysis.

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Presentation transcript:

Oliver, 2010 July

 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

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

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

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

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

 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.

 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

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

 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

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

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

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

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

 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

 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

 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,

 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 ?

 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

 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

 Any questions welcome  Thanks Olly  _performance.htm