We’ve looked at a classification of evaluators that matches our understanding of the complexity of the assessment tasks we could ask students to perform. If we want to look at this from an aesthetic framing then, as Dewey notes:
“By common consent, the Parthenon is a great work of art. Yet it has aesthetic standing only as the work becomes an experience for a human being.”
John Dewey, Art as Experience, Chapter 1, The Live Creature.
Having a classification of evaluators cannot be appreciated aesthetically unless we provide a way for it to be experienced. Our aesthetic framing demands an implementation that makes use of such an evaluator classification, applies to a problem where we can apply a pedagogical lens and then, finally, we can start to ask how aesthetically pleasing it is.
And this is what brings us to beauty.
A systematic allocation of tasks to these different evaluators should provide valid and reliable marking, assuming we’ve carried out our design phase correctly. But what about fairness, motivation or relevancy, the three points that we did not address previously? To be able to satisfy these aesthetic constraints, and to confirm the others, it now matters how we handle these evaluation phases because it’s not enough to be aware that some things are going to need different approaches, we have to create a learning environment to provide fairness, motivation and relevancy.
I’ve already argued that arbitrary deadlines are unfair, that extrinsic motivational factors are grossly inferior to those found within, and, in even earlier articles, that we too insist on the relevancy of the measurements that we have, rather than designing for relevancy and insisting on the measurements that we need.
To achieve all of this and to provide a framework that we can use to develop a sense of aesthetic satisfaction (and hence beauty), here is a brief description of a four-tier, penalty free, assessment.
Let’s say that, as part of our course design, we develop an assessment item, A1, that is one of the elements to provide evaluation coverage of one of the knowledge areas. (Thus, we can assume that A1 is not required to be achieved by itself to show mastery but I will come back to this in a later post.)
Recall that the marking groups are: E1, expert human markers; E2, trained or guided human markers; E3, complex automated marking; and E4, simple and mechanical automated marking.
A1 has four, inbuilt, course deadlines but rather than these being arbitrary reductions of mark, these reflect the availability of evaluation resource, a real limitation as we’ve already discussed. When the teacher sets these courses up, she develops an evaluation scheme for the most advanced aspects (E1, which is her in this case), an evaluation scheme that could be used by other markers or her (E2), an E3 acceptance test suite and some E4 tests for simplicity. She matches the aspects of the assignment to these evaluation groups, building from simple to complex, concrete to abstract, definite to ambiguous.
The overall assessment of work consists of the evaluation of four separate areas, associated with each of the evaluators. Individual components of the assessment build up towards the most complex but, for example, a student should usually have had to complete at least some of E4-evaluated work to be able to attempt E3.
Here’s a diagram of the overall pattern for evaluation and assessment.
The first deadline for the assignment is where all evaluation is available. If students provide their work by this time, the E1 will look at the work, after executing the automated mechanisms, first E4 then E3, and applying the E2 rubrics. If the student has actually answered some E1-level items, then the “top tier” E1 evaluator will look at that work and evaluate it. Regardless of whether there is E1 work or not, human-written feedback from the lecturer on everything will be provided if students get their work in at that point. This includes things that would be of help for all other levels. This is the richest form of feedback, it is the most useful to the students and, if we are going to use measures of performance, this is the point at which the most opportunities to demonstrate performance can occur.
This feedback will be provided in enough time that the students can modify their work to meet the next deadline, which is the availability of E2 markers. Now TAs or casuals are marking instead or the lecturer is now doing easier evaluation from a simpler rubric. These human markers still start by running the automated scripts, E4 then E3, to make sure that they can mark something in E2. They also provide feedback on everything in E2 to E4, sent out in time for students to make changes for the next deadline.
Now note carefully what’s going on here. Students will get useful feedback, which is great, but because we have these staggered deadlines, we can pass on important messages as we identify problems. If the class is struggling with key complex or more abstract elements, harder to fix and requiring more thought, we know about it quickly because we have front-loaded our labour.
Once we move down to the fully automated systems, we’re losing opportunities for rich and human feedback to students who have not yet submitted. However, we have a list of students who haven’t submitted, which is where we can allocate human labour, and we can encourage them to get work in, in time for the E3 “complicated” script. This E3 marking script remains open for the rest of the semester, to encourage students to do the work sometime ahead of the exam. At this point, the discretionary allocation of labour for feedback is possible, because the lecturer has done most of the hard work in E1 and E2 and should, with any luck, have far fewer evaluation activities for this particular assignment. (Other things may intrude, including other assignments, but we have time bounds on this one, which is better than we often have!)
Finally, at the end of the teaching time (in our parlance, a semester’s teaching will end then we will move to exams), we move the assessment to E4 marking only, giving students the ability (if required) to test their work to meet any “minimum performance” requirements you may have for their eligibility to sit the exam. Eventually, the requirement to enter a record of student performance in this course forces us to declare the assessment item closed.
This is totally transparent and it’s based on real resource limitations. Our restrictions have been put in place to improve student feedback opportunities and give them more guidance. We have also improved our own ability to predict our workload and to guide our resource requests, as well as allowing us to reuse some elements of automated scripts between assignments, without forcing us to regurgitate entire assignments. These deadlines are not arbitrary. They are not punitive. We have improved feedback and provided supportive approaches to encourage more work on assignments. We are able to get better insight into what our students are achieving, against our design, in a timely fashion. We can now see fairness, intrinsic motivation and relevance.
I’m not saying this is beautiful yet (I think I have more to prove to you) but I think this is much closer than many solutions that we are currently using. It’s not hiding anything, so it’s true. It does many things we know are great for students so it looks pretty good.
Tomorrow, we’ll look at whether such a complicated system is necessary for early years and, spoilers, I’ll explain a system for first year that uses peer assessment to provide a similar, but easier to scale, solution.