RSS Centre for Statistical Education The University of Nottingham Nottingham NG7 2RD Phone: +44 (0) 115 951 4911 Fax: +44 (0) 115 951 4951

Contents.

•  Summary
•  The  MEANS Web pages
•  MEANS Regional Conferences at Woking & Nottingham
•  Survey of employers of statisticians - a summary
•  Survey of undergraduate courses - a  summary
•  What could we learn from job advertisements?
•  Review of post-university courses

Summary This has been an active year for the MEANS project. An outline of most of these activities is given in this newsletter, further details can be found elsewhere on our Web pages.

At the beginning of the year we had the last of our regional workshops. This was held in Glasgow University and had the usual mixture of academics, employers and recently employed graduates. Many of the lessons learned from previous workshops were reinforced. There was again a suggestion that SAS should be used in teaching so we have asked Keith Jones to write an article for this newsletter based on the experience at Sheffield Hallam University. The problems of assessing group project work were discussed and Ed Redfern writes later in this newsletter about the experience at Leeds University.

Throughout the year we have carried out surveys of employers needs, of university provision, of commercial courses being run for employees and of job advertisements for statisticians.

A major part of our brief is to encourage communication between employers, employees and universities. As part of this we have set up a mailbase discussion group - the MEANS group. To join this send the message ‘'join means firstname surname" (inserting your own first name and surname) to mailbase@mailbase.ac.uk. The more of you who join and the more messages you send, the more successful this network will be. There has been a little discussion on the implications of the Dearing Report and we expect more when the White Paper is published. We have also spent quite a lot of time building up our Web pages and trying to develop useful cross-links.

Both Anne Hawkins and Peter Holmes have given talks about the MEANS project. Our current major initiative is the two important Regional Conferences to be held in the University of Nottingham on 12 February 1998 and in the SPSS Offices in Woking on 26 February 1998. We have an impressive line-up of speakers and hope to get many people to come from industry business commerce and education. Make a note in your diaries, tell others and book to come!

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The MEANS Web Pages

 As work is done on the project - e.g. surveys, reports, examples of courses, planned conferences - we incorporate the information into our Web pages.

From the homepage you can get to the following pages:
The Project describes the background to the project and asks employees, employers and university statisticians to get in touch with us.

Who’s Who gives details of our Steering Committee and Core Partners.

Newsletter leads to the two newsletters, published in December 1996 and December 1997..

The Conferences and Workshops section gives details of the planned conferences to be held in Nottingham and Woking and reports of the workshops held in Glasgow, London, Woking and Nottingham.

The Activities section gives the programme for the four workshops held in 1996/1997; reports on visits to companies made; a report on the survey of undergraduate statistics courses made this year together with the questionnaire used and a report on the survey of employers made this year together with the questionnaire used. It also includes reference to a large number of commercial courses in statistics being run for graduates. These give an indication of perceived need by employers and employees. This section will also include the report on the survey of job advertisements.

In the link to other sites section we point to sites with interesting material on assessment; to the Mathskills homepage; to the Shell Centre for Mathematical Education home page; to pages with interesting teaching initiatives and to appropriate teaching material and courses that we have been able to find.

The more information that we can include under courses, assessment, teaching initiatives then the more useful these pages will be. If you have any good information please let us know.

Peter Holmes
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Regional Conferences in Nottingham and Woking 12 February 1998 (Nottingham) and 26 February (Woking)

The aim of these one-day conferences is to bring together employers, employees and university staff to discuss the findings of the MEANS project and to consider how best to improve university teaching of statistics in the light of the findings of this project and the recommendations of the Dearing Committee.

Click here for Full details of the Nottingham or Woking conference or for
an application form
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Survey of employers of statisticians A summary of findings

There were 21 employers who replied to this survey. They included large and small companies; pharmaceutical and medical and others.

Initially the questionnaire defined three levels of expertise at which employers might be employing people to do statistics. These were:
elementary e.g. entry of data into a database, cleaning up data, graphical representation - perhaps using a package, elementary statistical calculations, simple reporting;
general e.g. carrying out standard surveys or experiments, drawing inferences and conclusions, working in a team on decision making, reporting;
specialist e.g. involvement with new surveys or design of experiments, consultancy work, specialist analysis of data, modelling unusual situations, recommending courses of action.

In general the drug companies said they were looking for specialist statisticians but the two non-pharmaceutical companies with the largest number of statisticians claimed to employ at the elementary and general levels only. The indications are that many companies expect employers to develop to become specialist statisticians.

Employers are generally looking for enthusiasm, ability to communicate, independence, an aptitude for IT, accuracy, ability to work in a team and ability to solve real problems as well as for qualifications.

All employers used interviews as part of their first recruitment procedure; some specifically asked potential employers to discuss a practical statistical situation. Some larger companies used a variety of activities over a one- or two-day period.

In general the medically oriented companies expected a higher level of theoretical knowledge than did the other companies. Many said they required little specific content knowledge but required a thorough understanding of the basic concepts of probability, statistics and inference. Some of the smaller companies were clearly not now recruiting people with only a first degree but were requiring an MSc.

Many companies were looking for the ability to apply statistics to real problems and to present the results clearly. Most expected people to have IT skills and to be able to use computer packages.

Some specifically mentioned the need to work with SAS. All wanted people who could think and who would not just be button pushers.

The specific content required related very much to the nature of the company. Generally the smaller companies required a broader base of skills. These included the ability to analyse large data sets, use of chi-squared and t-tests, means, percentages and confidence intervals.

The questionnaire gave ten possible non-statistical characteristics that companies might require. Many said they wanted all ten!! Those highlighted were the ability to: work in teams, write reports, work to deadlines, think logically and communicate well. Flexibility and commercial awareness were also mentioned.

The most common personal characteristics looked for were enthusiasm, communication, ability to work in a team and motivation, but many other qualities were also mentioned.

Generally employers assess their employers early in their posts. A wide range of assessment methods was reported.

Nearly all companies recognised that they had to train their employees. The smaller companies were more likely to use internal methods. The emphasis is often not on developing technical skills, except in the use of software, but on general aspects such as good report writing and management training. Some training is in-house but the majority use external courses. There is a general feeling that the employee should take some initiative in suggesting appropriate courses.

Very few employers think they have any influence on university course content. The main direct link with university staff was through having placement students. Several companies use university staff as expert consultants and they sometimes collaborate with them in specific projects.

(A longer version of this report may be found elsewhere on our web pages  or obtained from the project offices.)

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Survey of undergraduate courses A summary of findings Only a small minority of universities report that they have developed undergraduate courses in general consultation with employers. What contact there is with employers is usually informal, and largely through consultancy work or placement students.

Very few universities reported any specific change in content of their courses because of information from employers.
A minority of universities offer courses aimed at specific areas of employment - for example in medical science, and for the pharmaceutical industry. One university, at least, has a Statistics in Industry course. About half the universities replying to our survey reported the placement of students in industry.

Most of the universities replying said they used data and real problems from employers as part of their teaching. Often this was linked to projects or case studies. The use of visiting lecturers from employment was very rare.

Very few universities reported available access to courses through the Internet, and one of these said it was only for their registered students.

Most universities offer courses involving practical work. Many universities said they gave opportunity for transferable skills, such as communication and problem solving, to be developed through project and coursework. Very few said that they specifically taught these skills.

Courses aimed at developing skills in statistical consultancy were usually within Masters degrees, not part of undergraduate courses.

The bulk of assessment is based on standard examinations. Most courses include some assessment of coursework and projects. A small number of universities use posters and presentations to help assess communication skills - sometimes this is done by peer assessment.

(A fuller version of this report may be found elsewhere on our web pages  or obtained from the project offices.)

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What Could We Learn From Job Advertisements? A database of advertisements for statistical posts was compiled between March 1996 and May 1997, relating to 444 distinct vacancies. These ranged from ‘Graduate opportunities and studentships’ through ‘Research Assistant’, ‘Statistician’ ‘Researcher’ and ‘Lecturer’ to ‘Senior Statistical Post’. The more senior posts were included to give some indication of upper limits to the pre-requisites and job requirements for new and recent graduates.

Iterative qualitative data analysis (QDA) techniques yielded 25 variables describing the content of the advertisements, plus others relating to the recruitment process;
• Job description; Type of job, Application area, Location, Salary
• Applicant description; Qualifications, Subject area, Years of experience, Type of experience (Varied, In field of application, Of particular statistical methods, In computing and IT)
• Requirements of post; Collaboration, Communication, Teaching and consultancy, Analysis, Research, Report and give presentations, Leadership, Initiate projects, Speed and efficiency, Accuracy, Enthusiasm, Work under pressure, Independent work, Critically appraise other work.

The posts spanned 11 main application areas. Entry-level posts in pharmaceutical, social/behavioural sciences, business or government contexts did tend to be open to first degree graduates. A postgraduate qualification (not necessarily a Doctorate), however, was generally required in order to enter employment in the medical, as distinct from biostatistics, field.

Computational statistics and science/engineering contexts seemed to have two rather distinct levels of recruitment; one for candidates with a first degree only, and the other for candidates with a Doctorate.
The required qualifications were classified as ‘first degree’, ‘post-graduate (Diploma, Masters or Doctorate), and ‘Doctorate’. In fact, only one advertisement mentioned a Diploma as being a possible pre-requisite post-graduate qualification. Some advertisements stated that the posts were for ‘professionally qualified’ statisticians, but only three specifically referred to CStat (Chartered Statistician) status.

Four main types of experience were identified. The first one, ‘varied experience’ was noticeable by its absence. Only three of the advertisements mentioned this. In contrast, approximately one third of the advertisements specified experience with particular types of statistical procedures. A (different) third demanded IT or computing skills, or experience of using a particular piece of statistical software, e.g. SAS. Finally, a third of the advertisements asked for applicants with experience of the field of application. The tendency to supplement first degree courses with postgraduate experience (and qualifications) of a more applied kind is the conventional route to many employment opportunities. Expectations that this will be the case take the pressure off first degree courses to prepare students for employment. Rather, they tend to be seen as preparation for further training.

Members of the project team had expected that ‘good communication skills’ would feature prominently, because this was what employers and educators were telling them. In fact, relatively few of the advertisements specified this requirement. Failure to draw attention to such a key aspect of most jobs involving statistical duties may serve to de-emphasise the need for training in relevant skills, making it even less safe for advertisers to ‘take it as read’ that potential recruits will possess this quality.

There was a general under-emphasis of the required personal characteristics. People differ in what they have to offer, and different vacancies may well suit different people. For example, some posts need people who are good ‘parallel processors’, who can operate on several projects at once, prioritising their work to meet appropriate deadlines. Others suit ‘serial processors’ who prefer to concentrate on one task to its completion, and do not cope well with the pressure of many competing deadlines. Both may be admirable employees, but not necessarily in the same work context. It might be expected that universities would have a part to play in helping students to identify and develop their personal work-styles, and yet there seemed to be no real incentive for them to do so based on what was said in the advertisements. It is clearly important for employers and employees to understand what is required for a particular post if the ‘square peg in a round hole’ syndrome is to be avoided. Moreover, precise job specifications can provide telling insights into how universities can make their graduates more employable. For their part, universities would do well to take heed if they wish to continue to attract students, especially when those students will increasingly be looking for good returns for their tuition fees.

[This item gives a brief introduction to a report that is to be published elsewhere under the title Graduate Recruitment and Employment Destinations.]

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Review of Post-University Courses Content and approaches of post-university top-up courses are barometers of employment needs. The provision of such courses, workshops and seminars is increasingly governed by supply and demand. The existence of post-university courses that can be successfully (and repeatedly) marketed by universities, commercial concerns and other organisations may therefore indicate ways in which undergraduate training is failing to satisfy graduate’ subsequent employment needs.
Clearly the statistical training of some undergraduates is more comprehensive than that of others, or will correspond more exactly to their subsequent employment needs, irrespective of whether the graduates are specialists in statistics or in other disciplines. The objectives, content and approaches of undergraduate courses are all subject to variability, as are workplace requirements.
Details of post-university top-up courses (as opposed to postgraduate taught courses and research degrees) were collected over a period of fifteen months spanning 1996 to 1997. These top-up courses supplement and/or complement undergraduate training. Their content can be classified into four categories;
    • Established statistical methods
    • Applications of statistics
    • Emergent and more specialised statistical methods
    • Computing and statistics

Often the content and approaches, of top-up courses are entirely appropriate for post-university continuing education. Training and development should not end with the award of a degree certificate. However, a number of training opportunities have been identified which cast doubt on the adequacy of current levels of undergraduate training in statistics, for both specialists and non-specialists. Some raise questions about how to ensure enough flexibility in the content and methods of such training to allow undergraduate courses to anticipate, and/or develop in step with, statistical developments of the future. By implication, some of the approaches adopted in some undergraduate courses may not be appropriate, e.g. they may be too theoretical. Finally, one outstanding problem area remains – that of ensuring that teachers in Higher Education are taught to teach. Otherwise, how are they to know how best to respond to the needs of their students, as well as to the demands of their subject?

[A fuller version of this report has been submitted for publication by Anne Hawkins to JRSS (Series D) The Statistician.]

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SAS in Teaching Keith Jones and William Wallace (School of Computing and Management Sciences, Sheffield Hallam University)

Introduction
The Undergraduate Management Sciences Programme at Sheffield Hallam University currently incorporates degree courses in Applied Statistics, Business Systems Modelling and Computing and Management Sciences. All three courses include extensive teaching on the SAS package in a common first year. Our intention here is to explain why we feel it important to include SAS in our undergraduate curriculum, and to outline the major benefits and drawbacks to both staff and students. We also have some suggestions for overcoming some of the potential problems.

What is SAS?
SAS is an extremely powerful, flexible and comprehensive data management and statistical package. It runs on a wide range of different platforms, from individual and networked PC’s, through UNIX workstations, to mini and mainframe computers.
Unlike many other statistical packages, SAS is structured in such a way that data manipulation and management functions are separated from statistical analysis of the data. This has proved to be a very effective marketing strategy - many customers of SAS use only its data management features.

Instructions in SAS are entered as programs - sequences of SAS statements. Groups of SAS statements are organised into Data or Procedure steps . SAS derives its power and flexibility from its program-based structure. Also, all functions performed in SAS can be audited - the SAS program forms a permanent record of what has been done. The Data Steps allow inputting and editing; the Procedures are used to process and analyse the data and produce reports.

Why Use SAS?
There are many reasons for employing SAS on our undergraduate Management Sciences courses. The most important are as follows:
 

Benefits and Drawbacks
The major advantages of SAS have been summarised in the preceding section. Staff benefit from having a comprehensive, flexible and truly professional standard tool for statistical analysis. This is also of great benefit to our students.  Furthermore, the sheer “marketability” of SAS skills cannot be understated. We find that knowledge of SAS gives our students a definite advantage when competing for professional experience placements and jobs.

Perhaps the most serious drawback associated with SAS derives from its sheer size and complexity, coupled with its program-based structure. As a consequence, learning to use SAS requires a major investment of effort from both teaching staff and students. Further effort is then required to maintain these skills once developed.

Our experience suggests that an effective teaching vehicle is to encourage students to gain “hands-on” experience, using student-centred learning exercises. A typical class would involve the use of a handout, summarising the material to be learned. The handout would also include illustrative examples for the student to input or download and run for himself, followed by exercises in which the student is expected to write his own programs and run them.

It is important that the exercises that the student is asked to complete are relatively straightforward and appear relevant, especially in the early stages of any SAS course. Thus, where possible, realistic data sets are employed, and students make use of statistical procedures which reflect their current state of statistical knowledge. Some time is be devoted to investigating and interpreting the output of such procedures. The idea here is quickly to build confidence in using the package together with an appreciation of its benefits.

SAS is capable of performing some very complicated manipulations of data. In order to achieve such results, some very complex SAS programming is often required. It is strongly recommended that such exercises are avoided in the early stages of any SAS course! It is a very disappointing experience to have otherwise good, well-motivated students avoiding the use of SAS because it is “too difficult”.

Following the first year introductory course, we maintain students’ SAS skills by integrating the package fully into all subsequent teaching. Where appropriate, SAS programs and output are introduced alongside more traditional methods, and the results compared and discussed. Students are encouraged to “customise” these programs to find SAS solutions to the various exercises that they are set.

Furthermore, continuous assessment is largely based on 3- to 4- hour practical classes, in which students work on realistic, complex “real-world” problems. These practicals often form an ideal vehicle for the use of SAS. Finally, students undertake a substantial individual project in their final year. Many of these projects are based on research problems brought back from placement employers. Virtually all Applied Statistics projects, and a substantial number on the other routes, make extensive use of SAS.

SAS documentation is another potential problem. SAS manuals are extremely detailed, but this makes them bulky and expensive, and there is no convenient way to make everything available to everyone who may wish to use it. SAS have recognised this problem, and are addressing it in two ways. Firstly, it is planned to produce a CD ROM holding all SAS documentation. Secondly, there are plans to introduce a service whereby “customised” manuals can be supplied, including only those aspects of SAS specified by the client.

To conclude, it will be recalled that we stated earlier that SAS has reached a large market by separating data management from statistical analysis. This has certainly been true in the past, but seems set to change in the future. The SAS Institute is now placing great emphasis on providing integrated business solutions, marrying together the two facets of SAS in application areas such as data warehousing and data mining. This augers extremely well for the career prospects of graduate statisticians with well developed SAS skills.

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Group Working & Peer Assessment - a useful contribution? Edwin J Redfern  (University of Leeds)

The current vogue in transferable skills requires students to experience working in groups, making presentations and learning about assessment.  Can this be achieved in a statistics undergraduate course and is it desirable?

At Leeds University we have experimented with task-focused group project work as a component of a first year statistics module and in a full modelling and investigations module encompassing pure mathematics, applied mathematics and statistics.   The first of these involved two three-week projects while the second consisted of three two-week projects followed by a four week project.   In the latter case at least one project was done in each of the three subject areas.   Assessment was on the basis of group reports, individual reports, oral presentations or posters. Peer assessment was also included, of other groups and individuals in the group.

Groups of 4 to 5 students worked on open ended projects designed to allow reasonable scope for originality in choice of methods, analysis and selection of data.  The skills developed included problem formulation, data design and selection, organisation and co-ordinating of work, evaluating their own and fellow group members contribution, meeting deadlines (a well organised group had several of these self imposed over the period of the project), use of information technology (word processors, spreadsheets and statistical packages) and identifying appropriate mathematical and statistical concepts needed to solve the problem.

Success has been varied.  Some groups are well organised; all members contribute and the work generated on the project stretches beyond the bounds of the syllabus.  Other groups struggle to progress; work is duplicated or not completed, students fail to turn up for meetings and the better students in the group become disillusioned.  The standard of the final report or presentation usually reflects the degree of co-operation and organisation, as much as the ability of the students in the group.   Groups have been constructed both by students choosing partners and by random selection, with no noticeable difference in results.

For many students the freedom to become involved and to express themselves results in them pursuing ideas with a determination that is not often generated within the constraints of the traditional lecture course. Typical comments by students included “Working in a group is more fun”, “It was nice to have a change from normal lectures.  Has improved my statistics being more practical” and “Good experience working with people I didn’t know but our group didn’t get on that well and the project suffered as a result”   On the other hand, others consider it is a complete waste of time.  “More like Blue Peter than mathematics or statistics”.   For some students it was an eye-opening experience into the ways in which other people worked and were willing to contribute, “I always assumed everyone worked equally as hard”.

Each year we have found that about one third of the students are enthusiastic, asking for more courses involving work of this type.   At the other end of the spectrum another third either hate it and cannot see the point, or didn’t enjoy it but could see that it was worthwhile.

The method of allocating marks is difficult.  Equal marks for all group members is the easy solution but does not reflect the differing efforts of individuals in the group.   Students making the greater contributions need to be rewarded, while those who feel they can let others do the work and still get the reward, need to feel the cold wind of the harsher side of life.

Each group was asked to divide a hundred points between the group members to reflect the relative contribution of each to the whole project.  The scores were then used to weight the marks.  Many students admitted that at first they did not find this easy.  Slackers were allowed a far higher weighting than deserved while the hard workers in the group did not claim the greater credit their contribution merited.  However many learnt in later projects, particularly when they realised the effect.

Many interesting, sometimes heated, discussions occurred as students attempted to justify their efforts to fellow group members.  Allocations on subsequent projects often changed, as students, who realised that not contributing meant no rewards, made a greater effort while others held out for a fairer reflection of their efforts.   Equal weightings were usually agreed in about 50% of the groups, with only 25% remaining so for all projects.

At the end of each of the projects the students produced written reports (either individually or as a group), made oral presentations or prepared a poster which was displayed in the department.  Orals and posters were assessed both by the student and the staff.

The student’s assessment of others, in the oral presentations, was perhaps the most difficult part of the exercise.  Many students found it difficult, or were unwilling, for a variety of reasons, to make a rational assessment.    Despite this fact, the mean marks for presentations awarded by the staff and the students were in close agreement and the ordering of marks also  generally agreed.   However, the variability in the staff marks was much greater.  Thus, good presentations were often marked lower by the students than the staff, and poor presentations higher.  So to a certain extent quality was recognised by the students but perhaps not distinguished on a wide enough scale.  We experimented with various marking schemes. The one that worked best was the simplest one in which each student was asked to grade the performance of each group on a five point scale for both quality of presentation and the mathematical/statistical content.

Views on the effort required varied. In the case of the statistics module, in which we tried to place the work alongside other material, the students found it interfered with their other work.  “Group projects disrupt the rest of the work in the module and make you behind in the other work” was a typical comment.   As a consequence of this type of comment we reduced the number of projects from two to one.   The difficulty with this is that it removes the opportunity for the students to implement the lessons learnt from the first project.   The use of a full module dedicated to this type of work was more successful.   It allowed time for the students to concentrate on the project work without feeling they were neglecting other material.

The amount of mathematical or statistical content will not be as extensive, nor as controlled, as in a more traditional form of teaching.   However the extent of what can be achieved goes  beyond the constraints of a syllabus outlining a traditional module.  The ideas and concepts covered are only constrained by the ability and ingenuity of the student  and many are prepared, successfully, to read up on topics not yet covered in other courses.   The availability of computer assisted learning material such as STEPS also proved a useful supporting medium.

Regular feedback on performance, group organisation, and presentation methods proved as important as offering guidance on the proposed methods of analysis or the type and amount of data to be gathered.  Subsequent projects often proved to be better in content, extent, and presentation as organisational and communication skills improved.

Despite the many problems, courses such as these are perceived by many students to be generally beneficial.   The course tutor’s role is more a consultant/advisor, guiding the students rather than teaching, steering them between the impossible and the trivial, and arbitrating when disagreements arise.  The advantages are the variety of interpersonal skills that can be practised and developed, the freedom of students to express themselves, asking questions and interaction. Discussion of statistical concepts appears to be more readily achieved than in a tutorial environment and generates more interaction between the students.  The disadvantages are the time spent on non-mathematical or statistical aspects and the loose degree of control over the type and extent of the mathematics or statistics. The overall conclusion from both the students and the staff involved is that, despite a few reservations over the time required that some feel could be better used elsewhere, courses of this type are useful  for allowing students to experience and practice some of the non-mathematical skills needed to prepare them for employment. We await feed-back from students who subsequently enter employment, but positive comments from students who have had to make presentations and work in groups in interview situations are encouraging.

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Career Development Michele Lundy  (CCN)

Career preparation and development for graduates is a serious business in which educational establishments and employers have each a significant role to play.  In this article Michele Lundy examines the issues by exploring ways in which universities and employers can work together to ensure that an individual’s abilities and career aspirations are best matched with an employer’s requirements and expectations.

From a university viewpoint, determining the most appropriate syllabus for a varied group of undergraduate students heading for a varied range of careers in ‘statistics’ is by no means an easy task.

It’s about getting the balance right between a myriad of factors and about resolving questions such as:
 

 
If we manage to resolve all or at least most of these issues then the undergraduates should emerge fully qualified to enter their chosen career………..but do they?

For example, switching to the employer’s perspective, how qualified do employers find new graduate recruits to be and what can the employer legitimately expect of a new graduate from the outset?  Perhaps the most important issue of all is what actions can the employer take to ensure that a smooth and valuable career progression develops from the academic foundation with which the graduate starts employment.

The first step in this process is in recruitment itself right from the content of the initial advertisement through to the format of the interview.  If the applicant response rate is low, or if few applicants pass the interview stage, then it may well be a reflection of the content, positioning and timing of the advertisement or a reflection of the appropriateness of the interviewing content and format, rather than an indication of the quality of the applicant base.
 
Of course the recruitment process, no matter how good, can only ever hope to contribute to the assessment of a candidate’s ability and appropriateness for a role. The real ‘test’ begins once the graduate has been employed.

In my personal experience as an employer of graduates for over twelve years, there is a large difference between the early performance of first time graduates and that of those graduates with a further degree.  While the individuals with further degrees are in many cases only 1-2 years older than their single degree counterparts, the increase in maturity is nonetheless remarkable.  This manifests itself in the second degree graduates showing a greater readiness to use their own initiative rather than expecting to be spoon-fed and also in having increased confidence in the value of their own ideas.  The latter is particularly true of PhD graduates who, unlike first time graduates, seem to have a greater belief that their own ideas and interpretation of techniques have their own intrinsic value.

However regardless of the quality of the recruitment process or of the quality of the individual graduate, as time progresses, the details of techniques which are not in daily use can be forgotten and issues will inevitably arise concerning the application of these techniques in the specific solution context of the employer.

In my experience it has been useful therefore to forge links with Universities who have the expertise to:
 

 
 By working together in this way, universities and employers can ensure that the expectations of graduates and employers are not only satisfied but also continually enhanced.

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Please email ntu with any comments or corrections.