SIGCSE 2014: Research: Concept Inventories and Neo-Piagetian Theory, Thursday 1:45-3:00pm (#SIGCSE2014)

The first talk was “Developing a Pre- and Post- Course Concept Inventory to Gauge Operating Systems Learning” presented by Kevin Webb.

Kevin opened by talking about the difficulties we have in sharing our comparison of student learning behaviour and performance. Assessment should be practical, technical, comprehensive, and, most critically, comparable so you can compare these results across instructors, courses and institutions. It is, as we know, difficult to compare homework and lab assignments, student surveys and exam results, for a wide range of reasons. Concept inventories, according to Kevin, give us a mechanism for combining the technical and comparable aspects.

Concept inventories are short, standardised exempts to deal with high-levbe conceptual take-awaks to reveal systematic misconceptions, MCQ format, deployed before and after courses. You can supplement your courses with the small exam to see how student learning is progressing and you can use this to compare performance and learning between classes. The one you’ve probably heard of is the Physics Force Concept Inventory, which Mazur talks about a lot as it was the big motivator for Peer Instruction to address shallow conceptual learning.

There are two Concept Inventories for CS but they’re not publicly available or even maintained anymore but, when they were run, students were less successful than expected – 40-60% of the course was concepts were successfully learned AFTER the course. If your students were struggling with 40% of the key concepts, wouldn’t you like to know?

This work hopes to democratise CI development, using open source principles. (There is an ITiCSE paper coming soon, apparently.) This work has some preliminary development of a CI for Operating Systems.

Goals and challenges included dealing with the diversity of OS courses and trading off which aspects would best fit into the CI. The researchers also wanted it to be transparent and flexible to make questions available immediately and provide a path (via GitHub) for collaboration and iteration. From an accessibility perspective, developing questions for a universal pre-test is hard, and the work is based in the real world where possible.

An example of this is paging/caching replacement, because of the limited capacity of some of these storage mechanism, so the key concept is locality, with an “evict oldest” policy. What happens if the students don’t have the vocabulary of a page table or staleness yet? How about an example of books on your desk, via books on a shelf, via books in the library? (We used similar examples in our new course to explain memory structures in C++ with a supermarket and the various shelves.)

Results so far indicate that taking the OS course improved performance (good) but not all concepts showed an equal increase – some concepts appear to be less intuitive than others. Student confidence increased, even where they weren’t getting the right answers. Scenario “word problems” appear to be challenging to students and opted for similar, less efficient solutions. (This may be related to the “long document hard to read” problem that we’ve observed locally.)

The next example was on indirection with pointers where simplifying the pointer chain was something students intuitively did, even where the resulting solution was sub-optimal. This was tested by asking two similar questions on the exam, where the first was neutrally stated as a “should we” and the second asked them to justify the complexity of something, which gave them a tip as to where the correct answer lay.

Another example, using input/output and polling, presenting the device without a name deprived the students of the ability to use a common pattern. When, in an exam, the device was named (as a disk) then the correct answer was chosen, but the reasoning behind the answer was still lacking – so they appear to be pattern matching, rather than thinking to the answer. From some more discussion, students unsurprisingly appear to choose solutions that match what they have already seen – so they will apply mutexes even in applications where it’s not needed because we drown them in locks. Presenting the same problem without “constricting” names as a code examples, the students could then solve the problem correctly, without locks, despite almost all of them wanting to use locks earlier.

Interesting talk with a fair bit to think about. I need to read the paper! The concept inventory can be found at https://github.com/osconceptinventory” and the group welcome collaboration so go and … what’s the verb for “concept inventory” – inventorise? Anyway, go and do it! (There was a good reminder in question time to mine your TAs for knowledge about what students come to talk to them about – those areas of uncertainty might be ripe for redevelopment!)

The next talk was “Misconceptions and Concept Inventory Questions for Hash Tables and Binary Search Trees” presented by Kuba Karpierz ( a senior Computer Science student at the University of British Columbia). Kuba reviewed the concept inventory concept for newcomers to the room. (Poor Kuba was slightly interrupted by a machine shutdown that nearly broke his presentation but carried on with little evidence of problem and recovered it well.) The core properties of concept inventories are that they must be brief and multiple choice at least.

Students found hash table resizing to be difficult so this was nominated as a CI question. Students would sketch the wrong graph for resizing, ignoring the resize cost and exaggerating the curve shape of what should be a linear increase.The team used think aloud exercises to explain why students picked the wrong solution. Regrettably, the technical problems continued and made it harder to follow the presentation.

A large number of students had no idea how to resize the hash table (for reasons I won’t explain) but this was immediately obvious after the concept inventory exam, rather than having to dig it out of the exams. The next example was on Binary Search Trees and the misconception that they are are always balanced. (It turns out that students are conflating them with heaps.) Looking at the CI MCQs for this, it’s apparent that we were teaching with these exemplars in lectures, but not as an MCQ short exam. Food for thought. The example shown did make me think because it was deliberately ambiguous. I wondered if it would be better if it were slightly less challenging and the students could pick the right answer. Apparently they are looking at this in a different question.

The final talk was “Neo-Piagetian Theory as a Guide to Curriculum Analysis”, presented by Claudia Szabo, from our Computer Science Education Research group. This is the work that we’re using as the basis for the course redesign of our local Object Oriented Programming course so I know this work quite well! (It’s nice to see theory being put into practice, though, isn’t it?)

Claudia started with a discussion of curriculum analyse – the systematic processes that we use to guide teachers to identify instructional goals and learning objectives. We develop, we teach, we observe and we refine, but this refinement may lead to diversion from the originally stated goals. The course loses focus and structure, and possibly even lose its scaffolding. Claudia’s paper has lots of good references for the various theory areas so I won’t reproduce it here but, to get back to the talk, Claudia covered the Piagetian stages of cognitive development in the child: sensorimotor, pre-operational, concrete operational and formal operational. In short, you can handle concepts in pre-, can perform logic and solve for specific situations in concrete but only get to abstract thought and true problem-sovling in the formal operational mode. (Pre-operations is ages 2-7, concrete is 7-11 and formal is 11-15 by the time it is achieved. This is not a short process but also explains why we teach things differently at different age groups.)

Fundamentally, Neo-Piagetian theory starts from the premise that the cognitive developmental stages that humans go through during childhood are seen again as we learn very new and different concepts in new contexts, including mathematics and computer science, exhibited in the same stages. Ultimately, this means places limitations on the amount of abstraction versus concrete reasoning that students can apply. (Without trying to start an Internet battle, neo-Piagetian theory is one of the theories in this space, with the other two that I generally associate being Threshold Concepts and Learning Edge Momentum – we’re going to hold a workshop in Australia shortly to talk about how these intersect, conflict and agree, but I digress.)

So this peer is looking to analyse learning and teaching activities to determine the level at which we are teaching it and the level at which we are assessing it – this should allow us to determine prerequisite concepts (concept is tested before being taught) and assessment leaps (concept is assessed at a level higher than we taught it). The approach uses an ACM CS curriculum basis, combined with course-secific materials, and a neo-Piaget taxonomy to classify teaching activities to work out if we have not provided the correct pre-requisite material or whether we are assessing at a higher level than we taught students (or we provided a learning environment for them to reach that level, if we’re being precise). There’s a really good write-up in the paper to show you how conceptual handling and abstraction changes over the developmental stages.

For example, in representational systems a concrete explanation of memory allocation is “memory allocation is when you use the keyword new to create a variable”. In a familiar Single Abstraction, we could rely upon knowledge of the programming language and the framework to build upon the memory allocation knowledge to explain how memory allocation dynamically requests memory from the free store, initialises it and returns a pointer to the allocated space. If the student was able to carry out Single Abstraction on the global level, they would be able to map their knowledge of memory allocation in C++ into a new language such as Java. As the student developed, they can map abstractions to a global level, so class hierarchies in C++ can be mapped into similar understanding in Java, for example.

The course that was analysed, Object Oriented Programming, had a high failure rate, and students were struggling in the downstream course with fundamental concepts that we thought we had covered in OOP. So a concept definition document was produced to give a laundry list of concepts (Pro tip: concept inventories get big quickly. Be ruthless in your trimming.) For the selected concepts, the authors looked to see where it was taught, how it was taught and then how it was assessed. This quickly identified problems that needed to be fixed. One example is that the important C++ concept of Strings, assessment had been carried out before the concrete operational teaching had taken place! We start to see why the failure rate had been creeping up over time.

As the developer, in association with the speaker, of the new OOP course, this framework is REALLY handy because you are aways thinking “How am I teaching this? Can I assess it at this level yet?” If you do this up front then you can design a much better course, in my opinion, as you can move around the course to get things in the right order at the right time and have enough time to rewrite materials to match the levels. It doesn’t actually take that long to run over the course and it clearly visualises where our pitfalls are.

Next on the table is looking at second and third year courses and improving the visualisation – but I suspect I may have to get involved in that one, personally.

Good session! Lots of great information. Seriously, if you’re not at SIGCSE, why aren’t you here?



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