HEAR and Recruitment: Using rich achievement data in recruitment processes

Last week I met with representatives from a range of companies and organisations involved in graduate recruitment to discuss potential value from the UK HEAR initiative in a meeting organised by CRA and Critical Thinking.

The Higher Education Achievement Report (HEAR) is a new way for UK degrees to be issued, and includes both a rich data format as well as the printable certificate. Each HEAR includes detailed module information and results, and also information about non-formal achievements such as additional awards, prizes, and Student Union responsibilities alongside academic achievement – all verified by the issuing institution. At the behest of JISC and HEAR initiative, CETIS and APS Ltd developed the technical specification for HEAR with input from suppliers, based on XCRI and BS EN 15981.

As an exploratory meeting there aren’t really hard outcomes from this, but we explored some scenarios of how HEAR might affect future recruitment systems and processes. Here’s my take on them:

Mass Filtering

Currently the largest employers of graduates use bulk filtering methods to get potential candidates down to reasonable numbers for processing, based on high-level data such as degree class, subject and (sometimes) UCAS points. Partly this is because there is relatively sparse data available from which to process. While there is nothing directly in the HEAR that can enhance this process in advance, there was an interesting discussion about using the greater level of detail in the HEAR to do some post-hoc analysis to emerge useful indicators or engrams that could feed into future candidate filtering systems.

Profile Creation

Something common to both campaign-based recruitment and online recruiting sites is the need for graduates to enter information about their course and achievements. This is pretty time consuming and error-prone, and so the idea of using HEAR to pre-fill these with verified information is quite attractive, though would require a fair amount of development.

In terms of implementation, something along the lines of the oAuth-based flows used by Facebook and Twitter would make a lot of sense, so that recruiters could have a “Get my university profile” button that would start a standard authentication process using a model familiar already to students. This would also make it easier for students to customise the data returned, for example making a decision whether or not to include the additional information section of the HEAR for a particular application.

Even using fairly stable specifications this would still require a few development cycles from most recruitment sites, so it would likely follow HEAR adoption rather than drive it, depending on how much pain the profile-filling process was causing.

The main barrier for this use of the HEAR is potential fragmentation – if recruiters have to use a different API for each university, or even the same APIs but need to contact individual universities directly, this creates an expensive variety problem. So shared services such as DARE look  attractive from a recruiter perspective, and so recruiters may want to get involved with the consortium to make sure their use cases are met, and that as many institutions as possible sign up for it.

Profile Matching

Because HEAR provides much more detailed information not just on the achievements but also at a micro level on individual units and modules there is far greater potential for smart matching of candidates and opportunities, for example using latent semantic analysis of module descriptions and learning outcomes. Another opportunity is processing detailed data on assessment types to match particular profiles. For example, some professions such as accountancy require timed exams to progress, and so evidence of this ability at undergraduate level could be a useful indicator, and again could feed back into the teaching and assessment process. One of the barriers to this however is if the information being provided by HEAR is either too diverse (i.e. some Universities not recording sufficient information in some areas) or too homogeneous (i.e. Universities filling the HEAR up with boilerplate text saying how good they and their candidates are  – what we might call achievement spam). This is likely something that will emerge from practice rather than something baked into the HEAR technical specification, and I can see a useful dialogue emerging between universities, students and recruiters about what a good level of detail might be.

Verification

A quick win for the HEAR and recruiters is rapid online verification of achievement information, something which is being provided by HEDD and the HEAR. Even better, this verification can be done at any time – for example, on submitting a HEAR as part of profile creation or application the information is already verified. However, where there are predicted rather than actual grades, this can be updated and verified after graduation. Likewise, information can still be verified years later.

Also, its not just grades and dates that can be potentially verified, but any information added to a HEAR, such as module descriptions enabling checking details such as “did that module really cover combinatorics?”.

Focus on non-formal achievements

One of the more interesting aspects of HEAR is not really part of the HEAR data itself, but more a change of process emerging from it, which is focussing of attention of both students and institutions on recording and evidencing a far wider range of achievements and activities, and also for institutions to recognise a wider range of non-formal activities students are engaged in. So as well as providing more detailed information about what graduates have achieved now, HEAR may also influence how students think about their time at university and how they value what they have achieved and communicate that with potential employers. The HEAR provides a profile that students and staff can look at and stimulate ideas for activities to engage in to round it out. Perhaps as a result we’ll also see an increase in the status of non-formal learning, and an increasing role for Universities in recognising and verifying it.

I think this could be a very positive outcome, certainly in my own personal experience I found that editing the Student Union newspaper was one of my main achievements at University, and something all recruiters were very interested in once I’d got through the first stage – however in future that kind of experience would automatically be part of the recognised and verified achievements of a students time at University and feed into the recruitment process at an earlier stage than the CV and interview. Recruiters are certainly interested in the wider picture of graduates, and this may be particularly useful for SMEs who often need to recruit graduates with a broad range of skills and qualities rather than specialists.

This entry was posted in cetis, open education. Bookmark the permalink.

2 Responses to HEAR and Recruitment: Using rich achievement data in recruitment processes

  1. arc12 says:

    Scott – thanks for sharing this.

    I think the matter of process surrounding the hear (penultimate para) is an important one. Indeed, I anticipate that those institutions that make the most of the HEAR will actively change process. Is there a parallel here with evidence that IT as a capital investment only really works with complementary changes?

    Achievement spam is inevitable but probably self-defeating. Certainly naive. Maybe we need Turnitin for the HEAR?

    I also wonder whether there is a potential for feedback here too. If we (the education suppliers) get a feel for what is being seached for or even selected-for in pre-filtering, does that give us evidence of demand (and a risk of more spamming, I suppose)? Anecdote is that what employers say they want and what they actually select by are not identical; I wonder if inferences drawn on how recruiters/employers interact with HEAR (or other) achievement information would actually be more reliable for course design and in deciding what kind of other experiences to promote and actively support. Did this kind of thing come up at the meeting?

    Cheers, Adam

  2. scottbw says:

    Drawing inferences from HEAR was certainly discussed, so the idea of not using the HEAR a priori for filtering, but to adopt algorithms that emerge from selection activity, however feeding some of that back to providers would make sense too – I don’t think that idea came up at the meeting.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s