Last week, HESA published their latest data on student continuation rates.. An important set of figures for a number of reasons: non-continuation is something that directly affects the finance of universities; non-continuation is potentially a failure for the individual as well as the institution, and finally this data is used in some league tables.
A concern is that overall, the non continuation rate has risen across the sector (and indeed for us at Staffordshire University), with the national figure rising from 5.7% to 6.0% of students who entered in 2013-14 not progressing to the second year.The headline statistics are
- 6.0% of UK domiciled, young, full-time, first degree entrants in 2013/14 did not continue in higher education in 2014/15.
- 10.2% of UK domiciled, full-time, first degree starters in 2013/14 were projected to leave higher education without gaining a qualification
Usefully, HESA provides breakdowns of the data by both age of students as well as POLAR3 low participation indicator. This doesn’t necessarily provide any greater detail than that already held by any individual institution, but it does allow for comparisons to be made against comparators.
Looking at the data for Staffordshire University we can see that :
| Percentage no longer in HE (%) | Benchmark (%) | |
| young entrants | 12.2 | 10.1 |
| mature entrants | 14.1 | 13.8 |
| all entrants | 12.8 | 11.4 |
| young entrants from low participation neighbourhoods | 15.7 | 11.2 |
| young from all other neighbourghoods | 11.2 | 9.7 |
So, no surprises there, but it does add to weight to the argument that we should revise the way in which we look at the necessary interventions to support retention. If, as is evidenced here, there are groups of students who are more likely to withdraw than others, then a “one size fits all” approach to student retention will not deliver all the necessary outcomes.
In addition, HESA provide data on non continuation rates based on subject studied as well as entry tariff and types of qualifications. The rates compared to entry are summarised as:
| Entry qualifications | All subjects |
| 01 A level/VCE/Advanced Higher grades AAAA or Scottish Highers grades AAAAAA | 1.4% |
| 02 A level/VCE/Advanced Higher grades at least AAA or Scottish Highers grades at least AAAAA | 1.8% |
| 03 A level/VCE/Advanced Higher grades at least AAB or Scottish Highers grades at least AAAAB or AAAAC or AAABB | 2.5% |
| 04 A level/VCE/Advanced Higher grades at least AAC | 3.1% |
| 05 A level/VCE/Advanced Higher grades at least ABB or Scottish Highers grades at least AAABC or AAACC or AABBB or AABBC | 3.1% |
| 06 A level/VCE/Advanced Higher grades at least ABC or BBB or Scottish Highers grades at least AABCC or ABBBC or ABBBCC or ABBBB or BBBBB | 3.9% |
| 07 A level/VCE/Advanced Higher grades at least ACC or BBC or Scottish Highers grades at least AACCC or ABCCC or BBBBC or BBBCC | 3.9% |
| 08 A level/VCE/Advanced Higher grades at least BCC or CCC or Scottish Highers grades at least ACCCC or BBCCC or BCCCC or CCCCC | 4.2% |
| 09 Tariff points > 290 | 4.8% |
| 10 Tariff points > 260 | 5.3% |
| 11 Tariff points > 230 | 6.6% |
| 12 Tariff points > 200 | 7.4% |
| 13 Tariff points > 160 | 9.2% |
| 14 Tariff points > 100 | 11.3% |
| 15 Tariff points > 0 | 12.9% |
| 17 Level 3 and A level equivalent qualifications with unknown points | 13.9% |
| 19 International Baccalaureate | 3.4% |
| 20 HE level foundation course | 6.1% |
| 21 Access course | 11.1% |
| 22 BTEC | 11.5% |
| 23 Higher education qualification – Postgraduate | 7.1% |
| 24 Higher education qualification – First degree | 7.6% |
| 25 Higher education qualification – Other undergraduate | 8.1% |
| 26 No previous qualification | 24.1% |
| 27 Other qualifications not given elsewhere | 17.0% |
| 28 Unknown qualification | 32.6% |
| All qualifications | 6.0% |
Or looking at this graphically:
Important lessons from this data? As A level tariff points decrease, then the likelihood of non-continuation increases. Also, for institutions or courses that recruit significant numbers of students with BTEC qualifications, then higher withdrawal rates might be expected
Putting these factors together: age, POLAR3 neighbourhood, subject and entry grades, we can use better data analytics, linked to market segmentation and enhanced personal tutoring, to identify how to provide right support to all students, but in a way that is tailored to their needs and expectations. The key part of this will not be the identification of possible at risk students – the more difficult work will be in deciding what are the interventions needed to support an increasingly diverse range of students, and how to deliver this.
Ultimately, we want all of our students to succeed, and if we have decided that these are the people that we want to educate, then we have to provide the best opportunities for that success.