What’s your approach to learning and teaching?

For a series of VR workshops that we are running next week, we want the participants to give us a very brief view of their approach to learning and teaching. This is an example of the kind of thing we are looking for, to put their responses to VR into context. The prompting questions are:

Briefly explain what you aim to get out of the teaching and/or learning that you do. What matters most about the design and implementation of teaching and learning? What values are important in guiding the choices you make in what you do and how you do it?

And an example response (by me as a student, although it could easily be recast as being about my approach to teaching):

I study what might be called “the philosophy of design” and “designerly practices applied to everyday life”. I’m very much motivated by wanting to improve the world, through helping people to work more effectively together in understanding their collective interests and shaping the things that they do. So I’m not a particularly career-minded or instrumental kind of learner. But I carefully choose what I engage in, so as to use my precious time and energy to find ideas and practices that will help me with what I do. I like some lectures – but only when they are really engaging and social. I don’t really like seminars, as I have always found them to be too short and too contrived. I like to formulate my ideas through writing, but am increasingly experimenting with other media, including diagrams, photography and video.

A problem with assessment in super-selective institutions

“As argued in Chapter 1, good teaching narrows the initial gap between Robert and Susan therefore producing a smaller spread of final grades than that predicted by the initial spread of ability. The distribution of results after good teaching should not be bell shaped but skewed, with high scores more frequent than low scores. At university level there is therefore every reason not to expect a bell curve distribution of assessment results in our classes.” Biggs & Tang (2011) Teaching for Quality Learning at University (4th edition), p.200

In a super-selective university this is even more so. If we assume a high quality intake, with very narrow spread of capabilities, then the eventual attainment spread should be extremely narrow. When we look at a student who achieved 65% (student 1) and compare them to a student who achieved 80% (student 2) in reality that difference might mean very little. The difference might simply be the product of entirely extraneous variables, random events (student 1 having a cold during exam week).

Unless we can demonstrate a difference in kind between the high achiever and the slightly lower achiever, this is meaningless. It might be (and I think I see this happening) that academics invest much into the identification and application of those differences in kind – “student 2 really got it, they have become a proper philosopher/physicist/economist”.

“The categories of honours (first class, upper second, lower second) originally suggested qualities that students’ work should manifest: a first was qualitatively different from an upper second, it was not simply that the first got more sums right.” ibid. p.210

But that then is also open to subjective biases. Biggs and Tang don’t really seem to have an answer to this. But they are very much entrapped by their strict adherence to definitive “intended learning outcomes” within the system of constructive alignment. Hussey and Smith’s alternative combination of ILOs and “emergent learning outcomes” within an “articulated curriculum” leaves room for student creative input, risk taking, genuine innovation, individuation and other (possibly) less determinate characteristics of learning as research/innovation/creativity. As such, the curriculum offers opportunities for more significant and transformative student input, and consequently aspects of student transformation-through-learning that can be meaningfully assessed and reported upon. Having experienced such learning activities, and achieved unforeseeable outcomes, the student is more likely to value and build upon their success. Thus the learning itself, and the transformation being evaluated, is a more reliable indicator of the student’s future capabilities. And that IS what we are looking for when we assess students in the university.

“The extent to which emergent learning outcomes (ELOs) contribute to the achievement of intended learning outcomes (ILOs) varies. Some emergent outcomes are relatively close to the intended learning outcomes and can be perceived to contribute directly towards their achievement. The contribution of others is less direct, being capable of inclusion on the basis of their contribution to the student’s knowledge of the subject in general, whilst the contribution of other emergent learning outcomes is to the field of studies in general and might be included on those terms. Yet other ELOs contribute to the overall development of the students as autonomous, self-managing learners, far beyond the field of study.” Hussey & Smith (2003) “The Uses of Learning Outcomes”, Teaching in Higher Education, Vol. 8, No. 3, 2003, pp. 357–368.

Understanding and shaping student engagement live and online

This lecture was originally created as part of the LDC APP PGR course, introducing postgraduate research students to teaching. I’ve done it in various forms now (from 30 minutes to 2 hours) and it is always really good. It is all about designing to gauge and shape behavioural, emotional and cognitive engagement. The longer version includes some advice about using online tools. This is put into the Warwick context, where most use of online is to “sustain and amplify” good class teaching – the Extended Classroom approach.

Peer-learning methods are used in the lecture, with ResponseWare, to illustrate how we can understand and shape student engagement. Simple MCQs are used in some cases, with and without set answers. Text based responses to some questions are also used, with results presented as a word clouds. There is also a numeric response question (about how often one should stop and prompt student thinking and discussion during a lecture) with a range specified as correct.

I’ve added notes to the slides to explain what I was doing and how I used ResponseWare.

Method: the VRIP matrix for analysing current and ideal tech adoption

Over the last few days i’ve been involved in yet another one of the long winding discussions that occur in the academic tech business concerning how a particular requirement is best satisfied, and what part institutional IT should play in providing a solution. In this case it is “video and audio streaming live events” (yes, that one again). The debate is messy in two dimensions:

  1. “Live event” in an academic context covers a wide range of quite different things, from lectures through to physical theatre workshops.
  2. There’s a huge range of possible technology elements, including cameras, mics, encoders, streaming platforms, ad-ons to common tools (YouTube, Facebook) with a super complex matrix of features and quality levels.

Fortunately we now have analytical tools that can help us – so long as they are used systematically. And here are some notes I have written to explain it to colleagues.

The VRIP matrix (as I’ve decided to call it so as to sound more impressive) emerged out of work done on digital capabilities, and the visitor/resident model (Dave White, Helen Beetham, Alison Le Cornu, Lawrie Phipps, Donna Lanclos, James Clay and others). This is what a matrix looks like. The idea is that a tool/technique can be placed onto the matrix to indicate the type of adoption and integration into institutional services. This can be used to show the situation as it is or the ideal we think we should design and work towards. We can also indicate the extent of adoption by using a colour coding schema. Typically we map out the situation regarding a specific group (e.g. undergraduates).

The matrix can be used to graph the current situation – e.g. where most people are at with a tool or technique now. It may also be used to show the ideal degree/type of adoption and support.

At the institutional end of the matrix we place things that are or should be chosen, provided and supported by the institution. At the other end, we put personally (or communally) chosen, owned and supported.

On the visitor end of the visitor/resident axis we put tools and techniques that people use less often and which they don’t become familiar (they often have to relearn each time they visit). And at the resident end are the tools and techniques that they live and breathe all the time.

So where is “streaming a lecture” on the matrix, for specific groups of people? That’s an empirical question for which we haven’t really got an answer.

And where should it be? That’s a complex strategy question.

And how do we get to the point at which a tool/technique is in the right place on the matrix for the right people?

So this is the kind of sophisticated analysis, that needs to be backed up by an investigation (of the kind I’m doing for VR at the moment).


Workshop: Design Thinking techniques for effective participation in the design process

I’ll be running this workshop on 14th March 2017 at 12.30, Oculus Building OC1.01, University of Warwick.

The University is a design rich environment: we design all kinds of aspects of the educational process, courses, tools, technologies, spaces, organisations, publications. Across all of these fields there is a desire to do designing more collaboratively. Effective participation by staff and students should ensure outcomes that fit more neatly with needs and capabilities, stick in use for longer, spread more widely and grow our capabilities for further development.

Professional designers have developed a broad repertoire of techniques for ensuring effective participation. In this workshop, we will learn about and try out techniques that focus upon the language and dialog used by design collaborations throughout the lifecycle of a design (right through to supporting its use once implemented).

Design Language

Designs are typically described and recognised using a remarkably limited and un-examined vocabulary. Expanding and critically assessing a richer shared vocabulary is essential for successful designing. This may counter unhelpful assumptions and cognitive biases, and is especially important when aiming for inclusive and universally accessible designs. Finding just the right words to describe an actual or possible aspect of a design may also unlock new possibilities or new pathways for investigation. Working with language in this way, bringing together all of the perspectives involved in the design process (including users), helps to ensure a higher level of engagement and a sense of ownership.

How do you and your collaborators describe your designs? How might that language be refined and enriched?

Design Patterns

A design pattern is a statement of a problem plus a pattern of actions and interactions that addresses a problem with links to related patterns. It is elaborated with information on its originating context and the concerns, values and problems out of which it arose and advice on implementation and customisation. The pattern is usually headed with a catchy and meaningful title. In some disciplines the inclusion of diagrams and images is considered essential. In education, this might be best achieved with a storyboard or even a video. All of these elements are intended to act as a guide to design activity and a prompt for thinking and prototyping.

Could you benefit from stating your design patterns more explicitly? Could that enable more objective and precise collaborative designing?

Extended Classroom Pedagogy-First Cards

This is a H5P interactive presentation introducing a second set of Extended Classroom cards. This set has been produced by Sara Hattersley and Emma King of LDC (academic staff training) at Warwick. Our first (green) set looked at 12 technologies and their application to enhance learning, teaching and the student experience. This (blue) set starts from the other side, considering a set of themes or ambitions in TEL and considering how technology may be used to help.

Why I gave my EU referendum vote to my 11 year old son

I’m not usually a political blogger. But this country is making me very angry. Here’s why.

I’m 45 years old and I am (on paper at least) very highly educated. I got there the hard way – working class etc but still got into a Russell Group University and got an excellent education. Just the kind of achievement that UK families aspire to.

I now work with young people. I work at a University. Here is a simple fact for our predominately old electorate: they are smarter than us; they are smarter than we will ever be; they’ve been through an increasingly sophisticated and challenging education system; and they are very hard working – alcohol consumption amongst the young is dropping dramatically. We have to trust the young. And most importantly we are responsible for creating a political system that they care about, that they will engage with.

Yes, let’s face it. We have screwed up.

I have two sons – 5 and 11. The 11 year old is already doing school work at a level that I did when I was 16. Yes 16. He’s not unusual. The education system has changed. The expectations have changed. I gave him my vote because he is disenfranchised and will remain effectively disenfranchised by an elderly electorate who are not qualified to make big decisions that will have a massive impact on his life chances.

Old people – have some humility. Know when to give way.