Diversity workshop Exploration of how/whether factors of class, race, gender can be used to identify students who may need extra support and an intervention strategy for academic and support staff to offer assistance Peter Chalk, Undergraduate Centre TB2 LondonMet Learning & Teaching Conference (11 July 2006)

Background to this discussion “drop-out rates were higher at UNL than at LGU, and these differences were not accounted for by differences in the nature of the student body. The study then looked to see if there were particular groups of student at UNL whose drop-out rates were particularly high” Non-completion at the University of North London and London Guildhall University: a case study, Libby Aston & Bahram Bekhradnia, January 2005, Higher Education Policy Institute [HEPI05]

HEPI’s higher drop-out rate groups “Older ‘young’ entrants and mature entrants aged Some major subjects such as Business Studies, Business Administration, English, Applied Psychology, Computing, Environmental and Social Studies Young GNVQ entrants, entrants whose highest qualification recorded is GCSEs and mature entrants with an existing HE qualification

HEPI’s higher drop-out rate groups Students on foundation years White and Black females and females from ‘other’ ethnic groups Male entrants, in particular Black males and males from ‘other’ ethnic groups Direct entrants, in particular mature direct entrants“ [HEPI05]

Possible explanations? “UNL more likely to admit students –Without formal qualifications –By direct application –From surrounding postcodes A higher proportion of entrants who might not have applied elsewhere – ‘open access’” [HEPI05]

At risk intervention Current indicators that trigger contact (by / letter/ phone call): –non-attendance, –module choice non-participation, –course work non-submission, –poor results.

‘Predictive indicators’? ‘Predictive indicators’ using the [HEPI05] report as a guide: –Postcode (class) –Race –Gender –Mature entrants –Non A level

Support strategy Centre – PAA meeting, monitor Department – tutors made aware, extra tutorials, HEO assessment LDU – skill support, referral system PASS sessions – module support

Problems with the strategy Is this a strategy that would be welcomed? Is it self-defeating? Are there other perhaps more effective components? –eg mentoring, using a PASS system? –student services referrals for financial advice?

Course leader anecdote “It is possible that a major reason for the disparity is the age and gender of the majority of the students on the two routes. It has been reported to me ( by … staff that teach on both routes) that a majority of students on the xxx route are mature, i.e. over 21 years old, and female students. The majority of students on the yyy route are male and mostly between 18 and 21….”

Course leader anecdote (cont.) “… It is from this age group that I think most of the dropouts have occurred although I do not have the statistics to hand. It is also mostly male students who are excluded for tuition fee debt and young males again who have been excluded for much of the year for hall fee debt. The mature students we do have on the yyy route this year, both male and female have mostly been excellent attenders and I also expect their results to be excellent.” CCTM & HHS – which is which?