I look forward to the return of lectures

I look forward to the return of lectures because they have always been so much more than the imparting of information.

They have been part of university education for many decades. In the current climate, with university teaching forced to be online, their value has been questioned, e.g. Guardian columnist Simon Jenkins writing “Who needs a lecture hall when you can sit in Starbucks with a book and a laptop?”.

I gave my first on-line classes soon after the Easter break. I found them to be miserable affairs. I sat in our spare room (a quiet, pleasant and productive place to work) and spoke to my slides on my computer. The only evidence of my students was the participant list on the right hand side of my screen. I received a small number of questions on the on-line chat. In the class, we ran computer practicals – also remotely – with a few more questions and some useful screen sharing with postgraduate demonstrators to sort out problems. And I got good feedback from the students. But I found them deeply unsatisfactory

The truth is that much of the purpose of the lecture is not to impart information. The question “who needs a lecture hall” could have been asked long before the advent of multi-person video conferencing and mobile network technology. We already had books: technologies can impart information from one person to many others in different places and different times, synchronously or asynchronously. In fact, when I started my first job as a university lecturer, I did ask exactly that question: who needs lectures when you can get the same information from reading a book – and the answer is pretty obvious. Human contact.

The lecture puts people directly in touch with each other. Students have direct contact with their lecturer, and the lecturer has direct contact with the students. They (we) ask each other questions. As a lecturer, I look at my students as I teach – I can see the light in their eyes if they are understanding what I am saying – or the blank looks when they are not – and I need to give a different explanation. Sometimes I get them to the front to help show complex ideas – my favourite being explaining the minus sign in the advection equation by getting a line of students to do the Mexican wave. And students have direct contact with each other. They ask each other questions and give each other explanations. In my computer practicals, they work together (informally), supporting each other, and ideally learning more from each other than they do from me.

And it is even deeper than that. The act of going to a lecture course is a shared learning journey. Attending the same time and place is a powerful experience in of itself that binds the experience of the people, the place, and the content together, so that the knowledge and experience of learning sits so much deeper than if someone reads a book (or follow online content) alone.

These human elements are very hard to reproduce on line, although I will try my best if/when I have to teach on-line next term (I have some ideas).

So what about the future when we can return to face-to-face? One analysis I heard (in relation to primary school but just as relevant to universities) was from the educationalist Professor Roy Pea. He produced a quadrant map of formal vs informal learning in formal vs informal spaces.

Traditional lectures fit into formal learning in formal spaces. In fact, much of what we have traditionally produced in university frameworks is about ‘formal leaning in formal spaces’: lectures, seminars, workshops, laboratory practicals, computer practicals etc.

Recorded lectures and our new on-line content is about formal learning in informal spaces. Even before digital technologies and online content, we would produce problem sheets or reading lists; these were just as valuable, if less celebrated and talked about.

The physicality of universities also allow for informal learning in formal spaces. Groups of students sitting in libraries, common rooms or halls of residence discussing their work, or what they have just heard.

Informal learning in informal spaces is perhaps least acknowledged: reading a popular science book outside of your main discipline just because it is interesting, or being in a pub or cafe with some university friends studying different subjects and talking about different ideas.

Formal LearningInformal Learning
Formal Spaceslectures, practicals, workshops, laboratoriesDiscussions in libraries, common rooms, student halls
Informal SpacesOn-line content, problem sheets, reading listsOutside reading, chats in pubs and cafes
Lectures and online content are complementary and only a small part of a rich and rounded university education experience

What is clear is that these are all complementary – and that a good university educational experience will encompass elements of all four. I hope that one of the things that shakes out as we return to a new normal at universities is the value of all these elements: new on-line methods of teaching will be a valuable complement to, but never a replacement of, face-to-face teaching. Remote teaching provides access to different tutors and student groups, and I hope will stay, but will never provide the richness of experience of gathering together students and teachers into a bricks-and-mortar university and its informal surrounding spaces.

Covid models: wrong? useful?

I’ve never seen mathematical modelling so much in the news. As a mathematical modeller myself, who has worked in all sorts of infectious and non-infectious diseases, it is, in some ways, an interesting time. But what is a model? What makes a good model? One of my friends asked “why don’t you do a better job of it” but what would that even mean?

A mathematical model describes something in the world using numbers. The model then has a set of rules that describe how the numbers change in time. Mathematical models can be used for pretty much anything that can be described in these ways: examples include the spread of epidemics, or air flow over a new car design, or the weather.  Weather forecasting is one we are quite used to: we check weather forecasting apps regularly, and make decisions about what to wear outside on the basis of these apps – and they are mainly quite reliable.

What makes a model good? Well, in order to be accurate, the model needs to include a sensible set of processes, but also needs to have correct estimates of special numbers called ‘parameters’: these are numbers that the model needs to help with its rules. A parameter might be “how long is somebody infectious for before developing symptoms” or “how much heat does one hour of sunshine provide”. In order to get good estimates of parameters, the model needs lots of good quality data. The more data, and the better the quality of the data, the more reliable the model. Where a model is calibrated with mainly incomplete, or inaccurate (and especially biased) data, its predictions are not reliable. The simple adage is “garbage in, garbage out”.

This means that good practice involves a number of things:

1. Carefully calibrating models on existing data.

2. Quantifying (putting numbers on) the uncertainty (doubt) of the model predictions.

There are methods that can do (1) and (2) together (Bayesian statistics) so that we can put a measure on how good our predictions are that also takes into account how good the underlying data and other assumptions are.

[there is a big difference between ‘imprecise’ data and ‘inaccurate’ data: ‘imprecise’ data are data that are not measured especially well, but on average are about right; ‘inaccurate’ data are data that are biased or consistently wrong: the average of a lot of inaccurate data is still rubbish]

3. Carrying out what are known as sensitivity analyses: that is, to find out which factors the model predictions are most sensitive to. This can tell is what is and is not important – to measure or to control – in order to avoid bad outcomes.

Weather forecasting models provide reliable results because they have been calibrated on large volumes of high quality data. They also express uncertainty in their predictions: we get a percentage chance of rain rather than just rain/sun.

However, for Covid-19 models, there is a number that is both unknown and on which the predicted outcomes depend considerably: what proportion of the population have (already) contracted the virus. This can only be known through mass testing. If only a small proportion of people in a country have had the virus, then the epidemic may still have a long way to go, and the situation may become far worse than it already is. If a lot of people in a country have already contracted the virus, and we do not know, because they have had minor (or no) symptoms and have not (yet) been tested, then the epidemic might be closer to its peak, and the final outcome may be less bad. In the absence of reliable data, we simply do not know.

This means that any model we build at this stage can only provide highly unreliable results. And that will be the case until we have good quality data. While I appreciate that governments need /some/ modelling to make decisions, the outcomes of those models have to be treated with great care.

I’ll finish with a famous quote from the statistician George Box, which I alluded to in my Blog title: “All models are wrong but some are useful”. Current Covid-19 models are certainly wrong. Whether or not they are useful remains to be seen.

Don’t be too hard on the REF – save your anger for FEC

As we count-down to the next Research Excellence Framework (REF2021), universities across the UK are now in full swing of trying to get their best submission. A vast effort is now being made – reviewing and rating research papers, writing and reviewing impact case studies and environment templates, recruiting staff from rival universities – and it is all very expensive – REF2014 cost nearly £250M. Unsurprisingly, academic staff across the UK complain about this – after all, we are busy enough, and would prefer to be getting on with our core jobs of research and teaching.

However, I am going to argue here that we are misdirecting our protests. Flawed as it is, REF attempts to measure the right things (even if we might argue that they are not measurable or should not be measured). But perversely we do not protest about the Full Economic Cost (FEC) model for UKRI (UK Research and Innovation) funding, but meekly accept it and its appalling consequences. We should protest. Strongly. FEC is quietly and insidiously destroying our time, health and research culture – and needs to be abolished in favour of alternative models for distribution of that money to research institutions.

British universities receive public money for research in two ways: through the REF, as a block grant to each university on the basis of its research outcomes (mainly research papers; also impact on wider society; and a smaller amount for research environment); and through FEC, each time an academic in the university is awarded a research grant from UKRI (and some other agencies). When the grant is awarded, some money (directly incurred costs) actually supports the research done – researcher staff time, consumables, equipment, travel and so forth – this is all well and good. The problems lie in the other costs, especially directly allocated costs and indirect costs, which together lead to a financial gain referred to as margin. Directly allocated costs sort of pay for the academics who were awarded the grant – but in an intrinsically unfair way. It is not a direct contract out of our time. Rather, universities are not given enough money to support their research staff, and academics have to, essentially, make up the rest of their salaries through directly allocated costs. Indirect costs are supposed to pay for other activities of the university: finance, HR, IT etc, but are calculated in an opaque way, and vary considerably between institutions. Margin is very valuable to research-intensive universities: the funding system is structured so that these universities need this money. So research-intensive universities need its academics to win research grants, in order to bring in this money, so that universities can operate. This leads to considerable pressure on academics to bring in research income through writing (and winning) grants.

However, this model has so many flaws that it needs urgent change. Here are some.

1. Disrespect of many academic fields. I happen to work in a field of research where it makes sense to win grants and hire postdoctoral staff to do research. But in many fields this makes no sense at all: what counts is the scholarship and individual brilliance of the academic in question, and really they are the person who should be free to be that scholar. Such fields range from pure maths and theoretical physics to history and music.  It is wasteful disrespectful to prevent brilliant scholars from doing what they do best and force them instead to spend their time writing grants to employ others who may be less brilliant than themselves.

2. Misdirection of effort. We academics spend an inordinate amount of time and effort writing, reviewing and panelling  grants that are not funded. I don’t know if someone has estimated the cost in the UK, but this Australian study and also this one both show that the cost can be quantified and the waste is enormous. That time could be put to better use actually doing research. Put it another way – how much time did Einstein spend writing grants in 1905?

3. Impact on our mental health. It is well reported that academic staff are suffering from an epidemic of mental health. The pressures of academic work are manifold – workloads are known to be extreme – and both the processes of publishing papers and of winning grants are beset with continuous and repeated rejection, with one’s ideas and work often facing extreme criticism through anonymous peer review. These pressures lead to poor mental health, and in the most tragic cases, suicide, in some cases, directly attributable to the pressure of obtaining research grant funding.

4. Universities start measuring the wrong thing. Just as REF at least attempts to measure the right things, FEC leads to a measurement of the wrong thing. Grant income is not scholarship. And yet universities obsess about how much income individuals bring in – with statements like “Professor X brings in £yM per year” as some kind of indication of their excellence. When I was a student, the discourse was different – and indeed much more scholarly –  “Professor Z is famous for their discovery of W”. Universities have ended in a strange place where one might think that the point of research is to bring in money. Which of course is the wrong way round – the point of grant funding is to be able to do research – and if someone can do research without bringing in grant funding (good reasons are in points 1-3 above), then that is a good thing! I have a fabulous colleague who produces incredible research, with PhD students from all over the world who seeks this person out as a supervisor, but who has little UKRI funding, and who is then vilified for not bringing in “margin”, despite producing exceptional research papers.  That is not what universities are supposed to be about.

5. It leads to a bizarre culture of research. What should we be discussing at universities? I personally would like to spend my time discussing scientific ideas and discoveries. And yet we spend more time discussing how we get money than the actual research that we do. I remember one particularly depressing meeting of the academic group to which I belong in which we were all asked to update each other on our research. Everyone (myself included) talked about the grants we were writing or that were under review – we didn’t actually discuss any ideas, or findings, or work of our PhD students – just how to get money. And I have heard these discussions on all of my travels. One of my colleagues who recently took early retirement said: “I’ve had enough; I came here to do and teach science, not to chase money.”

So what do we do about FEC? The universities need the money – that is no question – so really it is about finding a way to distribute the money that respects academic research, isn’t ludicrously expensive, and doesn’t damage our mental health or research culture. One way might be to distribute the money through some form of block grant to university departments for research. This could be done in several ways – to balance the need for equity (on the one hand) with promoting centres of research excellence (on the other). For example, some FEC money could be directly allocated to all research active university departments on a per capita basis, while other money could be redirected in proportion to REF outcomes.  I am sure that there are other ways that the money could be distributed that does not lead to the terrible outcomes for the current system. As for the research grant process itself? I have ideas about that and will follow up in a future post.

I would like to end this post the way I started: if you moan about the REF – I understand – but please save your moaning for FEC – it is much worse – and we should stop being silent about it and raise our voices in protest and anger.

Disclaimer: I am writing this post (and indeed my whole Blog) in a personal capacity. I have both REF and FEC roles: for REF I am our school’s coordinator of our Environment Statement, and for FEC I have twice served on BBSRC committee C.

On strike yet again: surely university leaders must know that our greatest assets are our people?

I’m on strike. Again. I’ve actually lost count of the numbers of times I have been on strike in the 15.5 years I have worked in the university sector. This is the third time in the last 12 months. Like (almost) all university academics I know, I am totally committed both to my students and to my research. The thought of letting my students and research group down makes me sick to the bone. But I will continue to strike until senior leaders at our universities actually get the message. In fact, I think it is particularly important that people in my position (permanent full time job and professorial salary) do take action, as we are the people who can most afford the pay hit. It is much harder for people on hourly paid or fixed term contracts to take this action.

Universities are not factories. We do not rely on expensive machines to produce our outcomes. Rather, we are a human endeavour.  Our success, whether in teaching or research, comes from the scholarship, creativity, commitment and hard graft of our staff. Without happy and healthy staff who are able and willing to give of their best, universities cannot function at all.

Therefore, the single most important work of university leaders is to foster an environment in which staff (and students) can flourish – are happy, healthy, committed, motivated, and give of their absolute best. The fact that we are on strike so often, despite our total commitment to our students and our research, is a clear indication that vice chancellors and university executive boards, up and down this country, do not seem to understand or act upon this basic point.

Yes, of course there are constraints: financial constraints will always be there, whatever the level of university income; market-based approaches have been imposed by government; as have been the many performance metrics (REF, TEF, FEC, NSS, you name it). And senior leaders need to operate within these constraints. But it should be patently obvious that the best way to deliver on them is with well-motivated staff, not demotivated staff. Staff with proper jobs (not fixed term or zero hour teaching contracts); staff who feel valued for the work they do (fair pay commensurate with the level of expertise needed, irrespective of their gender or ethnicity); staff who are not over-worked (and not suffering from mental health problems); staff who do not need to worry about their retirement.

These matters are a choice for senior leaders. University executives in this country have chosen to reduce the proportion of turnover that is spent on pay. University executives have chosen to build many fancy buildings, to spend large sums on large research centres that benefit only a small number of people, and to massively increase their own pay, while cutting the real-terms wages and pensions for the majority of staff.

So I am on strike. And I will strike again if necessary, until senior leaders get the message: value your staff.

 

Making conferences, meetings and panels friendly for highly sensitive introverts will be good for all sorts of people

I first started thinking about this a couple of years ago at a small meeting on mathematical models of virus dynamics. In the middle of a talk, a fire alarm went off in the hotel where the meeting was held. As we evacuated the building, about 1/3 of the delegates had to cover their ears because the sound of the alarm was too loud. I am highly sensitive to sound myself – and I was OK – so I know that these people must have been even more sensitive than me!

Of course, many research staff are highly sensitive, or introverts, or on the autistic spectrum, or some combination of the three. This is especially the case in my cluster of fields (computational biology / mathematical biology / bioinformatics). Yet many of our communal activities often don’t make allowances for our needs – and I will argue that if they did it will actually make things better for all sorts of people.

A second anecdote. I was at another meeting last year. This was an antimicrobial resistance (AMR) conference, with lots of microbiologists and vets, and a few modellers/bioinformaticians. The sessions were long and the coffee room noisy. At some point I wanted to find my PhD student to talk about something that had come up – and he wasn’t to be seen. In the corner of the corridor just outside the coffee room was screened off by some unused poster boards. I looked behind them (I knew!) and there he was – he had found a calm, quiet place and was happily using his laptop.

So, here are some important ideas for making meetings friendly; they can apply to some other activities too (advisory boards; grant panels etc):

1. Never make sessions too long. 90 minutes is long enough. 80 minutes is better as that allows for over-run. There is a limit to what we (all of us) can take in, how long we can be in an auditorium, how many voices we can hear. Once we are saturated, there is no point listening to more. Instead, we need time to think about and digest what we have heard. I understand that organizers of meetings want to give as many people opportunities to talk as possible – but other things are also important – long informal chats in coffee / lunch breaks, or plenty of time with the posters – so ensure that programmes give plenty of time for these. And over-long sessions are bad for lots of people, not just highly sensitive types: older people; pregnant people; people managing chronic health conditions etc. Over-long sessions are bad for everyone. Longer breaks are better.

2. Run a video stream of the main auditorium in a smaller room. I saw this once in a mathematical biology conference (they actually screened it in two rooms!). It was great – people who were finding the main auditorium a bit overwhelming could be in one of the smaller rooms and still hear the talks. There was a really nice and calm atmosphere in the room I spent some time in. It also helps people who might have been engaged in a very long coffee conversation to be able to then re-enter the talks without having to disturb the main auditorium.

3. Ensure some quieter break-out spaces at the coffee/tea breaks. It’s just a matter of geography and signposting, or even booking a couple of extra rooms if necessary (and again with sign-posting), so people have a place to go for a quieter chat or space. People don’t have to use them if they don’t want to, but some people might need some quieter break, especially at larger meetings where the coffee breaks can be very noisy.

4. If there is a big dinner, give people the option of room service. I know that sounds crazy – but there have been occasions where that has been exactly what I or others I know have needed. For many people, the dinner is an important part of the conference – an opportunity to network, for scientific discussions , or just some good company. But some people – having listened to talks all day, spoken with people during coffee and lunch breaks – just need some time away from people to re-charge and be ready to be their best the next day. The dinner is being paid for anyway – so give people the option to take a dinner in their room. It gives people permission to attend to their needs rather than feeling that they have to go to something that may not be good for them.

 

Reflections on BBSRC panel second time round: PIs, please just answer the reviwers’ questions

I have now attended my second BBSRC grant panel. It is a different experience doing this for a second time: a better understanding of what is required from me as an IM and a better understanding of the scoring culture of the panel definitely help. I feel more attuned to identifying weaknesses in grants – or make better arguments for their strengths where appropriate. One of the great skills that it is nice to develop is in calm and respectful yet swift decision making where there may be disagreements between introducing members.

However, I have been really struck by some of the poor quality responses to reviewers: PIs should really know better. So here are a few pointers.

As a principle, be kind to your IMs. Preparing for a grant panel is hard work: we have 10-12 grants to read – cases for support, justification for resources, data management plans, reviews, responses to reviews etc – often  in areas that may be at the boundaries of our expertise. You want us to be on your side – so make things clear and easy for us. This means:

1. If reviewer A says that Objective 1.2 is not clear, please clarify it! Don’t just say “well reviewer B thought is was clear” – that doesn’t help us. You don’t know whether we agree with reviewer A or reviewer B, so err on the safe side and clarify.

2. If reviewer C asks for more experimental details, please do not just say “they are in reference 99”. Please just give more details. We may or may not be able to access reference 99: we might be in our office and able to access it; or we might be at home and unable to get through a journal pay wall, or on the train with poor WiFi signal. Please, make it easy for us, and provide the extra details asked for.

3. If reviewer D asks a question that needs a certain expertise to answer, please, speak to an expert (a co-I or collaborator), and let them help with your answer. Please, don’t waffle about things outside your expertise – we can tell.

4. If reviewer E asks a question that you don’t like, because you think it is stupid, or unfair, or just plain wrong, please, just answer the question. Don’t have a rant or be impolite. All you need to do is refute the question, calmly and rationally, with facts: citations or preliminary data or quotes from your CfS. You don’t know whether the IM thinks it is a good or bad question. If you need to let out negative emotions (we’ve all been there), please, find a different outlet, and leave them out of the response to reviewers. Having a rant or making impolite comments runs the risk of annoying IMs which will not achieve the result you want – for us to be on your side.

 

We are recruiting: Research Associate/Fellow in Mathematical Modelling in Biology

We are recruiting!

Research Associate/Fellow in Mathematical Modelling in Biology (Fixed term)

Agricultural & Environmental Sciences

Location:  Sutton Bonington
Salary:  £27,511 to £40,322 per annum (pro rata if applicable) depending on skills and experience (minimum £30942 with relevant PhD). Salary progression beyond this scale is subject to performance
Closing Date:  Thursday 05 March 2020
Reference:  SCI011520

We are looking for a mathematical modeller to work in the area of modelling the spread of antimicrobial resistance in bacteria from chicken (broiler) litter,  and to use the model to inform improved agricultural practise to minimise risk. The work will include development of mathematical models, fitting models to experimental data derived by project partners and from other sources, carrying out sensitivity analyses on the model in order to identify risk factors, and carrying out Monte Carlo simulations of different farm and manure management scenarios with results to inform policy and practise. The role will involve working effectively with experimental scientists in Nottingham, elsewhere in the UK, and abroad.

The role holder should have PhD awarded or near successful completion in relevant mathematical, statistical or computational approaches in the biological sciences; research experience in microbiology or antimicrobial resistance would be an advantage. The role holder must be highly proficient in: the development and analysis of ordinary differential equation models of biological systems; method for fitting models to data (e.g. nonlinear least squares, MCMC or genetic algorithms); and in a relevant programming environment (e.g. R, Matlab or Python). Excellent English language oral and written communication skills are also essential, including the ability to author research articles, and to communicate complex ideas to non-specialists. The person we appoint will have an outstanding publication record commensurate with career stage and must be able to demonstrate commitment to aims of the project in line with longer term career development. Experience of working effectively in a team with experimental scientists would be an advantage.

This role is available on a fixed term basis until 31st May 2022. Hours of work are full time (36.25 hours). Job share arrangements may be considered.

Informal enquiries may be addressed to Dov Stekel, email dov.stekel@nottingham.ac.uk. Please note that applications sent directly to this email address will not be accepted.

For further information, or to apply for the job, please follow this link to the University of Nottingham recruitment site.