Month: June 2017

Feedback on feedback

Feedback on students’ assignments is a challenge for many in higher education.  Students appear to be increasingly dissatisfied with it and academics are frustrated by its apparent ineffectiveness, especially when set against the effort required for its provision.  In the UK, the National Student Survey results show that satisfaction with assessment and feedback is increasing but it remains the lowest ranked category in the survey [1].  My own recent experience has been of the students’ insatiable hunger for feedback on a continuing professional development (CPD) programme, despite receiving detailed written feedback and one-to-one oral discussion of their assignments.

So, what is going wrong?  I am aware that many of my academic colleagues in engineering do not invest much time in reading the education research literature; perhaps because, like the engineering research literature, much of it is written in a language that is readily appreciated only by those immersed in the subject.  So, here is an accessible digest of research on effective feedback that meets students’ expectations and realises the potential improvement in their performance.

It is widely accepted that feedback is an essential component [2] in the learning cycle and there is evidence that feedback is the single most powerful influence on student achievement [3, 4].  However, we often fail to realise this potential because our feedback is too generic or vague, not sufficiently timely [5], and transmission-focussed rather than student-centered or participatory [6].  In addition, our students tend not to be ‘assessment literate’, meaning they are unfamiliar with assessment and feedback approaches and they do not interpret assessment expectations in the same way as their tutors [5, 7].  Student reaction to feedback is strongly related to their emotional maturity, self-efficacy and motivation [1]; so that for a student with low self-esteem, negative feedback can be annihilating [8].  Emotional immaturity and assessment illiteracy, such as is typically found amongst first year students, is a toxic mix that in the absence of a supportive tutorial system leads to student dissatisfaction with the feedback process [1].

So, how should we provide feedback?  I provide copious detailed comments on students’ written work following the example of my own university tutor, who I suspect was following example of his tutor, and so on.  I found these comments helpful but at times overwhelming.  I also remember a college tutor who made, what seemed to me, devastatingly negative comments about my writing skills, which destroyed my confidence in my writing ability for decades.  It was only restored by a Professor of English who recently complimented me on my writing; although I still harbour a suspicion that she was just being kind to me.  So, neither of my tutors got it right; although one was clearly worse than the other.  Students tend to find negative feedback unfair and unhelpful, even when it is carefully and politely worded [8].

Students like clear, unambiguous, instructional and direction feedback [8].  Feedback should provide a statement of student performance and suggestions for improvement [9], i.e. identify the gap between actual and expected performance and provide instructive advice on closing the gap.  This implies that specific assessment criteria are required that explicitly define the expectation [2].  The table below lists some of the positive and negative attributes of feedback based on the literature [1,2].  However, deploying the appropriate attributes does not guarantee that students will engage with feedback; sometimes students fail to recognise that feedback is being provided, for example in informal discussion and dialogic teaching; and hence, it is important to identify the nature and purpose of feedback every time it is provided.  We should reduce our over-emphasis on written feedback and make more use of oral feedback and one-to-one, or small group, discussion.  We need to take care that the receipt of grades or marks does not obscure the feedback, perhaps by delaying the release of marks.  You could ask students about the mark they would expect in the light of the feedback; and, you could require students to show in future work how they have used the feedback – both of these actions are likely to improve the effectiveness of feedback [5].

In summary, feedback that is content rather than process-driven is unlikely to engage students [10].  We need to strike a better balance between positive and negative comments, which includes a focus on appropriate guidance and motivation rather than justifying marks and diagnosing short-comings [2].  For most of us, this means learning a new way of providing feedback, which is difficult and potentially arduous; however, the likely rewards are more engaged, higher achieving students who might appreciate their tutors more.

References

[1] Pitt E & Norton L, ‘Now that’s the feedback that I want!’ Students reactions to feedback on graded work and what they do with it. Assessment & Evaluation in HE, 42(4):499-516, 2017.

[2] Weaver MR, Do students value feedback? Student perceptions of tutors’ written responses.  Assessment & Evaluation in HE, 31(3):379-394, 2006.

[3] Hattie JA, Identifying the salient facets of a model of student learning: a synthesis of meta-analyses.  IJ Educational Research, 11(2):187-212, 1987.

[4] Black P & Wiliam D, Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1):7-74, 1998.

[5] O’Donovan B, Rust C & Price M, A scholarly approach to solving the feedback dilemma in practice. Assessment & Evaluation in HE, 41(6):938-949, 2016.

[6] Nicol D & MacFarlane-Dick D, Formative assessment and self-regulatory learning: a model and seven principles of good feedback practice. Studies in HE, 31(2):199-218, 2006.

[7] Price M, Rust C, O’Donovan B, Handley K & Bryant R, Assessment literacy: the foundation for improving student learning. Oxford: Oxford Centre for Staff and Learning Development, 2012.

[8] Sellbjer S, “Have you read my comment? It is not noticeable. Change!” An analysis of feedback given to students who have failed examinations.  Assessment & Evaluation in HE, DOI: 10.1080/02602938.2017.1310801, 2017.

[9] Saddler R, Beyond feedback: developing student capability in complex appraisal. Assessment & Evaluation in HE, 35(5):535-550, 2010.

[10] Hounsell D, Essay writing and the quality of feedback. In J Richardson, M. Eysenck & D. Piper (eds) Student learning: research in education and cognitive psychology. Milton Keynes: Open University Press, 1987.

Getting smarter

A350 XWB passes Maximum Wing Bending test [from: http://www.airbus.com/galleries/photo-gallery%5D

Garbage in, garbage out (GIGO) is a perennial problem in computational simulations of engineering structures.  If the description of the geometry of the structure, the material behaviour, the loading conditions or the boundary conditions are incorrect (garbage in), then the simulation generates predictions that are wrong (garbage out), or least an unreliable representation of reality.  It is not easy to describe precisely the geometry, material, loading and environment of a complex structure, such as an aircraft or a powerstation; because, the complete description is either unavailable or too complicated.  Hence, modellers make assumptions about the unknown information and, or to simplify the description.  This means the predictions from the simulation have to be tested against reality in order to establish confidence in them – a process known as model validation [see my post entitled ‘Model validation‘ on September 18th, 2012].

It is good practice to design experiments specifically to generate data for model validation but it is expensive, especially when your structure is a huge passenger aircraft.  So naturally, you would like to extract as much information from each experiment as possible and to perform as few experiments as possible, whilst both ensuring predictions are reliable and providing confidence in them.  In other words, you have to be very smart about designing and conducting the experiments as well as performing the validation process.

Together with researchers at Empa in Zurich, the Industrial Systems Institute of the Athena Research Centre in Athens and Dantec Dynamics in Ulm, I am embarking on a new EU Horizon 2020 project to try and make us smarter about experiments and validation.  The project, known as MOTIVATE [Matrix Optimization for Testing by Interaction of Virtual and Test Environments (Grant Nr. 754660)], is funded through the Clean Sky 2 Joint Undertaking with Airbus acting as our topic manager to guide us towards an outcome that will be applicable in industry.  We held our kick-off meeting in Liverpool last week, which is why it is uppermost in my mind at the moment.  We have 36-months to get smarter on an industrial scale and demonstrate it in a full-scale test on an aircraft structure.  So, some sleepness nights ahead…

Bibliography:

ASME V&V 10-2006, Guide for verification & validation in computational solid mechanics, American Society of Mech. Engineers, New York, 2006.

European Committee for Standardisation (CEN), Validation of computational solid mechanics models, CEN Workshop Agreement, CWA 16799:2014 E.

Hack E & Lampeas G (Guest Editors) & Patterson EA (Editor), Special issue on advances in validation of computational mechanics models, J. Strain Analysis, 51 (1), 2016.

http://www.engineeringvalidation.org/

Clueless on leadership style

Sunset from Peppercombe beachStrategic leadership is widely defined as the ability to influence others to voluntarily make decisions that enhance the prospects of the organisation’s success.  In learning and teaching, you could substitute or supplement organisation’s success with the students’ success.   I believe that this is achieved by creating an environment in which your colleagues can thrive and contribute; so, I see leadership of an academic community as being primarily a service involving the creation and maintenance of a culture of scholarship and excellence.

I have led academic departments on both sides of the Atlantic, university-industrial research programmes and various other organisations and initiatives.  However, the standard interview question about my leadership style still tends to stump me – I struggle to identify a consistent approach to my leadership and I am nervous that too much analysis could undermine my ability to lead.  However, by chance, I recently came across Daniel Goleman’s work.  His research has shown that the use of a collection of leadership styles (he identifies six styles), each at the right time and in the right amount, produces the most effective outcomes.  In other words, effective leadership is about being pragmatic and adjusting your approach to suit the circumstances. What’s more, Goleman found that most successful business leaders who followed this pragmatic approach had no idea how they selected the right style for the right time.

Goleman’s work implies that you do not have to conform to one leadership model.  Instead, you can roam across a number of leadership styles and select the right one, for the right situation and use it in just the right amount.  It sounds straightforward but this flexibility is tough to put into action.  Of course, that’s not easy to teach because most of us don’t know how or why we make those decisions but it is related to emotional intelligence and leadership competencies, which we do know how to teach.

Bibliography:

Goleman D, Boyatzis R & McKee, The new leaders: transforming the art of leadership into the science of results, London: Sphere, 2002.

Goleman D, Leadership that get results, Harvard Business Review, 78(2):4-17, 2000.

 

Uncertainty about Bayesian methods

I have written before about why people find thermodynamics so hard [see my post entitled ‘Why is thermodynamics so hard?’ on February 11th, 2015] so I think it is time to mention another subject that causes difficulty: statistics.  I am worried that just mentioning the word ‘statistics’ will cause people to stop reading, such is its reputation.  Statistics is used to describe phenomena that do not have single values, like the height or weight of my readers.  I would expect the weights of my readers to be a normal distribution, that is they form a bell-shaped graph when the number of readers at each value of weight is plotted as a vertical bar from a horizontal axis representing weight.  In other words, plotting weight along the x-axis and frequency on the y-axis as in the diagram.

The normal distribution has dominated statistical practice and theory since its equation was first published by De Moivre in 1733.  The mean or average value corresponds to the peak in the bell-shaped curve and the standard deviation describes the shape of the bell, basically how fat the bell is.  That’s why we learn to calculate the mean and standard deviation in elementary statistics classes, although often no one tells us this or we quickly forget it.

If all of you told me your weight then I could plot the frequency distribution described above.  And, if I divided the y-axis, the frequency values, by the total number of readers who sent me weight information then the graph would become a probability density distribution [see my post entitled ‘Wind power‘ on August 7th, 2013].  It would tell me the probability that the reader I met last week had a weight of 70.2kg – the probability would be the height of the bell-shaped curve at 70.2kg.  The most likely weight would correspond to the peak value in the curve.

However, I don’t need any of you to send me your weights to be reasonably confident that the weight of the reader I talked to last week was 70.2kg!  I cannot be certain about it but the probability is high.  The reader was female and lived in the UK and according to the Office of National Statistics (ONS) the average weight of women in the UK is 70.2kg – so it is reasonable to assume that the peak in the bell-shaped curve for my female UK readers will coincide with the national distribution, which makes 70.2kg the most probable weight of the reader I met last week.

However, guessing the weight of a reader becomes more difficult if I don’t know where they live or I can’t access national statistics.  The Reverend Thomas Baye (1701-1761) comes to the rescue with the rule named after him.  In Bayesian statistics, I can assume that the probability density distribution of readers’ weight is the same as for the UK population and when I receive some information about your weights then I can update this probability distribution to better describe the true distribution.  I can update as often as I like and use information about the quality of the new data to control its influence on the updated distribution.  If you have got this far then we have both done well; and, I am not going lose you now by expressing Baye’s law in terms of probability, or talking about prior (that’s my initial guess using national statistics) or posterior (that’s the updated one) distributions; because I think the opaque language is one of the reasons that the use of Bayesian statistics has not become widespread.

By the way, I can never be certain about your weight; even if you tell me directly, because I don’t know whether your scales are accurate and whether you are telling the truth!  But that’s a whole different issue!