ITiCSE 2014, Day 3, Final Session, “CS Ed Research”, #ITiCSE2014 #ITiCSE

The first paper, in the final session, was the “Effect of a 2-week Scratch Intervention in CS1 on Learners with Varying Prior Knowledge”, presented by Shitanshu Mirha, from IIT Bombay. The CS1 course context is a single programming course for all freshmen engineer students, thus it has to work for novice and advanced learners. It’s the usual problem: novices get daunted and advanced learners get bored. (We had this problem in the past.) The proposed solution is to use Scratch, because it’s low-floor (easy to get started), high-ceiling (can build complex projects) and wide-walls (applies to a wide variety of topics and themes). Thus it should work for both novice and advanced learners.

The theoretical underpinning is that novice learners reach cognitive overload while trying to learn techniques for programming and a language at the same time. One way to reduce cognitive load is to use visual programming environments such as Scratch. For advanced learners, Scratch can provide a sufficiently challenging set of learning material. From the perspective of Flow theory, students need to reach equilibrium between challenge level and perceived skill.

The research goal was to investigate the impact of a two-week intervention in a college course that will transition to C++. What would novices learn in terms of concepts and C++ transition? What would advanced students learn? What was the overall impact on students?

The cohort was 450 students, no CS majors, with a variety of advanced and novice learners, with a course objective of teaching programming in C++ across 14 weeks. The Scratch intervention took place over the first four weeks in terms of teaching and assessment. Novice scaffolding was achieved by ramping up over the teaching time. Engagement for advanced learners was achieved by starting the project early (second week). Students were assessed by quizzes, midterms and project production, with very high quality projects being demonstrated as Hall of Fame projects.

Students were also asked to generate questions on what they learned and these could be used for other students to practice with. A survey was given to determine student perception of usefulness of the Scratch approach.

The results for Novices were presented. While the Novices were able to catch up in basic Scratch comprehension (predict output and debug code), this didn’t translate into writing code in Scratch or debugging programs in C++. For question generation, Novices were comparable to advanced learners in terms of number of questions generated on sequences, conditionals and data. For threads, events and operators, Novices generated more questions – although I’m not sure I see the link that demonstrates that they definitely understood the material. Unsurprisingly, given the writing code results, Novices were weaker in loops and similar programming constructs. More than 53% of Novices though the Scratch framing was useful.

In terms of Advanced learner engagement, there were more Advanced projects generated. Unsurprisingly, Advanced projects were far more complicated. (I missed something about Most-Loved projects here. Clarification in the comments please!) I don’t really see how this measures engagement – it may just be measuring the greater experience.

Summarising, Scratch seemed to help Novices but not with actual coding or working with C++, but it was useful for basic concepts. The author claims that the larger complexity of Advanced user projects shows increased engagement but I don’t believe that they’ve presented enough here to show that. The sting in the tail is that the Scratch intervention did not help the Novices catch up to the Advanced users for the type of programming questions that they would see in the exam – hence, you really have to question its utility.

The next paper is “Enhancing Syntax Error Messages Appears Ineffectual” presented by Paul Denny, from The University of Auckland. Apparently we could only have one of Paul or Andrew Luxton-Reilly, so it would be churlish to say anything other than hooray for Paul! (Those in the room will understand this. Sorry we missed you, Andrew! Catch up soon.) Paul described this as the least impressive title in the conference but that’s just what science is sometimes.

Java is the teaching language at Auckland, about to switch to Python, which means no fancy IDEs like Scratch or Greenfoot. Paul started by discussing a Java statement with a syntax error in it, which gave two different (but equally unhelpful) error messages for the same error.

if (a < 0) || (a > 100)
  error=true;

// The error is in the top line because there should be surrounding parentheses around conditions
// One compiler will report that a ';' is required at the ||, which doesn't solve the right problem.
// The other compiler says that another if statement is required at the ||
// Both of these are unhelpful - as well as being wrong. It wasn't what we intended.

The conclusion (given early) is simple: enhancing the error messages with a controlled empirical study found no significant effect. This work came from thinking about an early programming exercise that was quite straightforward but seemed to came students a lot of grief. For those who don’t know, programs won’t run until we fix the structural problems in how we put the program elements together: syntax errors have to be fixed before the program will run. Until the program runs, we get no useful feedback, just (often cryptic) error messages from the compiler. Students will give up if they don’t make progress in a reasonable interval and a lack of feedback is very disheartening.

The hypothesis was that providing more useful error messages for syntax errors would “help” users, help being hard to quantify. These messages should be:

  • useful: simple language, informal language and targeting errors that are common in practice. Also providing example code to guide students.
  • helpful: reduce the number of non-compiling submissions in total, reduce number of consecutive non-compiling submissions AND reduce the number of attempts to resolve a specific error.

In related work, Kummerfeld and Kay (ACE 2003), “The neglected battle fields of Syntax Errors”, provided a web-based reference guide to search for the error text and then get some examples. (These days, we’d probably call this Stack Overflow. 🙂 ) Flowers, Carver and Jackson, 2004, developed Gauntlet to provide more informal error messages with user-friendly feedback and humour. The paper was published in Frontiers in Education, 2004, “Empowering Students and Building Confidence in Novice Programmers Through Gauntlet.” The next aspect of related work was from Tom Schorsch, SIGCSE 1995, with CAP, making specific corrections in an environment. Warren Toomey modified BlueJ to change the error subsystem but there’s no apparent published work on this. The final two were Dy and Rodrigo, Koli Calling 2010, with a detector for non-literal Java errors and Debugging Tutor: Preliminary evaluation, by Carter and Blank, KCSC, January 2014.

The work done by the authors was in CodeWrite (written up in SIGCSE 2011 and ITiCSE 2011, both under Denny et al). All students submit non-compiling code frequently. Maybe better feedback will help and influence existing systems such as Nifty reflections (cloud bat) and CloudCoder. In the study, student had 10 problems they could choose from, with a method, description and return result. The students were split in an A/B test, where half saw raw feedback and half saw the enhanced message. The team built an error recogniser that analysed over 12,000 submissions with syntax errors from a 2012 course and the raw compiler message identified errors 78% of the time. (“All Syntax Errors are Not Equal”, ITiCSE 2012). In other cases, static analysis was used to work out what the error was. Eventually, 92% of the errors were classifiable from the 2012 dataset. Anything not in that group was shown as raw error message to the student.

In the randomised controlled experiment, 83 students had to complete the 10 exercises (worth 1% each), using the measures of:

  • number of consecutive non-compiing submissions for each exercise
  • Total number of non-compiling submissions
  • … and others.

Do students even read the error messages? This would explain the lack of impact. However, examining student code change there appears to be a response to the error messages received, although this can be a slow and piecemeal approach. There was a difference between the groups, but it wasn’t significant, because there was a 17% reduction in non-compiling submissions.

I find this very interesting because the lack of significance is slightly unexpected, given that increased expressiveness and ease of reading should make it easier for people to find errors, especially with the provision of examples. I’m not sure that this is the last word on this (and I’m certainly not saying the authors are wrong because this work is very rigorous) but I wonder what we could be measuring to nail this one down into the coffin.

The final talk was “A Qualitative Think-Aloud Study of Novice Programmers’ Code Writing Strategies”, which was presented by Tony Clear, on behalf of the authors. The aim of the work was to move beyond the notion of levels of development and attempt to explore the process of learning, building on the notion of schemas and plans. Assimilation (using existing schemas to understand new information) and accommodation  (new information won’t fit so we change our schema) are common themes in psychology of learning.

We’re really not sure how novice programmers construct new knowledge and we don’t fully understand the cognitive process. We do know that learning to program is often perceived as hard. (Shh, don’t tell anyone.) At early stages, movie programmers have very few schemas to draw on, their knowledge is fragile and the cognitive load is very high.

Woohoo, Vygotsky reference to the Zone of Proximal Development – there are things students know, things that can learn with help, and then the stuff beyond that. Perkins talked about attitudinal factors – movers, tinkerers and stoppers. Stoppers stop and give up in the face of difficulty, tinkers fiddle until it works and movers actually make good progress and know what’s going on. The final aspect of methodology was inductive theory construction, while I’ll let you look up.

Think-aloud protocol requires the student to clearly vocalise what they were thinking about as they completed computation tasks on a computer, using retrospective interviews to address those points in the videos where silence, incomprehensibility or confused articulation made interpreting the result impossible. The scaffolding involve tutoring, task performance and follow-up. The programming tasks were in a virtual world-based pogromming environment to solve tasks of increasing difficulty.

How did they progress? Jacquie uses the term redirection to mean that the student has been directed to re-examine their work, but is not given any additional information. They’re just asked to reconsider what they’ve done. Some students may need a spur and then they’re fine. We saw some examples of students showing their different progression through the course.

Jacquie has added a new category, PLANNERS, which indicates that we can go beyond the Movers to explain the kind of behaviour we see in advanced students in the top quartile. Movers who stretch themselves can become planners if they can make it into the Zone of Proximal Development and, with assistance, develop their knowledge beyond what they’d be capable of by themselves. The More Competent Other plays a significant role in helping people to move up to the next level.

Full marks to Tony. Presenting someone else’s work is very challenging and you’d have to be a seasoned traveller to even reasonably consider it! (It was very nice to see the lead author recognising that in the final slide!)

 


ITiCSE 2014, Day 3, Session 7B, Peer Instruction, #ITiCSE2014 #ITiCSE

The first talk was “Peer Instruction: a Link to the Exam” presented by Daniel Zingaro from University of Toronto. Peer Instruction (PI) is an active learning pedagogy developed for physics and now heavily used in computing. Students complete a reading quiz prior to class and teachers use multiple-choice quizzes to assess knowledge. (You can look this one up in a number of places but I’ve discussed it here before a bit.) There’s a lot of research that shows gains between individual and group vote, with enduring improvements in student learning. (We can use isomorphic questions to reduce the likelihood of copying.) Both students and instructors value the learning.

PI appears to demonstrate improved learning outcomes on the final exam grades, as well as perceived depth of learning. (Couple of studies here from Beth Simon et al, and Daniel himself, checking Beth’s results.) But what leads to this improved outcome? The peer discussion. The class wide discussion? Both? If one part isn’t useful then we can adapt it to make it more useful to Computer Scientists. Daniel is going to use isomorphic questions to investigate relationships between PI components and final exam grades.

The isomorphic questions test the same concept with different questions, where if they get the first one right, we hope that they get the second one right – and if people learn how to do one, then that knowledge flows on to the other. (The example given was of loop complexity in nested loops depending on different variables.)

Daniel has two question modes in this experiment, which are slightly different. Both modes include the PI components, but the location of the isomorphic questions vary between the two approaches in the second question. In the Peer ℗ mode, the isomorphic question comes directly after the group vote and the second mode (Combined – C), the Q2 isomorphic questions occur direct after the instructor has had a chance to influence the class.

Are the questions really isomorphic and of the same difficulty? An external ranker was used to verity this and then the question pairs and mode were randomised. The difficulty of the questions was found to be statistically equivalent, based on the percentage of Q1 that were found to be correct.

Daniel had two hypotheses. Firstly, that peer scores will correlate to final exam scores. Secondly, that combined scores will also correlate with final exam scores, but the correlation should be stronger than for Peer, with the Combined questions representing learning from the full PI cycle. In terms of the final exam, there were three measures of the final exam grades: total exam score, score on the tracing question (similar to PI questions) and score on a code-writing question (very different to PI questions).

The implementation was a CS1 course with 3 lectures/week, with reading quizzes worthy 4% submitted prior to each lecture, clicker responses worth 5%, where the lectures on average contained three PI cycles- one cycle per lecture contained the follow-up isomorphic question. Multiple regression was used to test relationships between PI and final exam scores.

All of the results were statistically significant. For code-tracing, hat students know before exam explains 13% of their scores in the final exam. With the peer questions, it goes up to 16%. With combined as well, it goes up to 19%. Is this practically significant? Daniel raised this question because it doesn’t rise very much.

In terms of code writing, Baseline is 16%, + Peers is 22% and +Combined is 25%, so we’re starting to see more contribution from peers than instructor in this case. Are we measuring the different difficulty of a problem that peers couldn’t correct, which is why the instructor does less?

Overall? Baseline 21%, Peer 30% and then Combined is 34%. (Any questions about the stats, please read the paper. 🙂 )

Maybe adding combined questions to peer questions increases our predictive accuracy, just because we’re adding more data and this being able to produce a better model?

In discussion, PI performance related to final exam scores (as expected). Peer learning alone is important and the instructor-led discussion is important, over and above peer learning. This validates the role of the instructor in a “student-centred” classroom. Given that PI uses MCQs, we might expect it to only correlate with code-tracing but it does appear to correlate with code-writing problems as well – there may be deep conceptual similarities between PI questions and programming skills. But would the students that learned from PI also the students that would have learned from any other form of instruction? Still an open question and there’s a lot of ongoing work still to do.

The next paper was “Comparing Outcomes in Inverted and Traditional CS1” presented by Diane Horton from U Toronto. I’ve been discussing early intervention and student attendance issues in inverted/hybrid courses with Jennifer Campbell and Michelle Craig, also from U Toronto and also on this paper, as part of an attempt to get some good answers so I’d just come straight out of a lunch, discussing inverted classrooms and their outcomes. (Again, this is why we come to conferences – much of the value is in the meetings and discussion that are just so hard to have when you’re doing your day job or fitting a Skype meeting into the wee small hours to bridge the continental time gap.)

As a reminder, in inverted teaching, some or all of the material is delivered outside the classroom. Work typically done as homework is done in the lecture with the help of instructor or TAs. There were three research questions. Would the inverted offerings have better outcomes? Would inverted teaching affect students’ behaviour or experience? Would particular subgroups respond differently, especially English-language learners and beginner programmers?

The CS1 course at Toronto is a 12 week course in Python, objects-early, classes-late, with most students in 1st year and less than half looking to major in CS. The lectures are roughly 200 students, with 5 of these lecture sections.

Before the lecture, students prepared by watching videos, from the instructors, mostly screencasts of live programming with voice over, credit for attempting quizzes embedded in videos is 0.5% per week. (It’s scary how small that fraction has to be and really rather sad, from a behavioural perspective.)

During the lecture, the instructors used a worked example and students worked on worksheet-based exercises for most of the lectures, with assistance, solo or in pairs. This was a responsive teaching approach because the instructor could draw the class together as required. There was no mark reward or penalty that depended on attendance. If you were solid on the material, it was okay to miss the lecture. The mark scheme reflected some marks for lecture preparation with an increased number of online exercises and decreased weighting on labs.

The inverted CS1 course had gone well in the pilot in January 2013, which was published in a peer at SIGCSE ’14, but it was hard to compare this with the previous class as the make-up of the cohort varies from September to January courses. The study was run again in a more similar cohort in September 2014. The data presented here is for a similar cohort with a high overlap of instructors, compared the traditional offering.

For the present study, there were pre- and post-course surveys about attitude and behaviour, competed on-paper in the lecture. Weekly lecture attendance counts were made and standard university course valuations collected. In terms of attendance, the inverted pilot was the lowest, but the inverted class had lower attendance most of the time – an effect that we have also seen under some circumstances and are still thinking about. Interestingly, students thought that the inverted lectures weren’t seen as being as useful as face-to-face lectures but the online support materials were seen to be very helpful. As a package, this seems to be an overall positive experience.

The hypothesis was that students in the inverted offering would self-report a higher quality of learning experience and greater enjoyment but this wasn’t supported in the data, nor was it for beginners in particular. However, when asked if they wanted more inverted courses, there was a very strong positive response to this question.

The authors expected that beginners would benefit more because they need more helps and the gap between beginners and experiences students would reduce – this wasn’t supported by the data. Also, there was no reduction of gaps for English language learners, either.

Would the inverted course help people stay on and pass the course? Well, however success was defined, the pass rate was remarkably consistent, even when the beginners were isolated. However, it does appear that the overall level of knowledge, as measured by the final exam grades, actually improved in the inverted offerings, across two exams of similar difficult, with a jump from an average grade of 66% to 74% between the terms. Is this just due to the inverted teaching?

Maybe students learned more in the inverted offering because they spent more time on task? Based on self-reported student time, this doesn’t appear to be true. Maybe the beginners got killed off early to reduce their numbers and raise the mark? No, the drop rates among beginners were the same. It appears that the 8 percentage point increase may be related to the inverted mode, although, obviously, more work is required.

Is it worth it? They used no additional TA resources but the development time was enormous. You may not be ready for this investment. There are other options, you don’t need to use videos and you can use pre-existing materials to reduce costs.

Future work involves looking at dropping patterns – who drops when – and student who stumble and recover. They’re also looking at a full online CS1 course for course credit.

The final talk was on “Making Group Processes Explicit to Students: A Case for Justice” presented by Ville Isomöttönen. Project courses have a very long history in Computer Science, as capstones, using authentic customer projects, and the intention is to provide a realistic experience. (Editor’s note: It’s worth noting that some of this may be coming from the “We got punished like this so you can be too.”) What do students actually learn from this? Are they learning what we want them to learn or they are learning something very different and, potentially, much darker?

(This sounds like the kind of philosophical paper I’d give, let’s see where it goes! 🙂 )

If we have tertiary students, why can’t we just place them into a workplace for work experience? They’re adults – maybe we can separate this aspect and the pedagogue. The author’s study wants to look at how to promote conceptual learning in the response of realistic course work. Parker (1999) proposes that students are spending their effort of building wiring products, rather than actually learning about and reflecting upon the professional issues we consider important. The conjecture is that just because the situation is realistic doesn’t mean that the conceptual learning is happening as we intended.

The study is based around a fairly straight forward project-based learning structure, but had a Pass/Fail grade, with no distinction grading as far as I could tell. The teaching was baed on weekly group discussions, with self/peer evaluations, also housed in a group situation, and technical supervision offered by teaching assistant. Throughout the course, students are prompted to think about their operation at a conceptual level. Hmm. I’m not sure what the speaker means by this as, without a very detailed description of what is going on, this could have many different implementations.

We then cut to a diagram of justice conceptualised – I may have missed something as I’m not quite sure how this sits with the group work. I can’t find the diagram online but it involves participation, involving and negotiating with others – fused together as the skill of justice. This sits above statuses, norms and roles. Some of the related work deals with fairness (Richards 2009) as a key attribute of successful group work, Clear 2002 uses it in diagnostic technique, and Pieterse and Thompson 2010 mentioned ‘social loafers’ and ‘diligent isolates’.

I’m dreadfully sorry, dear reader, but I’m not following this properly so this may be a bit sketchy. Go and read the paper and I’ll try to get this together. Everyone else in the room appears to be getting this so I may just be tired or struggling with my (not very good) hearing and someone who is speaking rather quietly.

The underlying pedagogy comes from the social realist mindset (Moore,2000, Maton and Moore, 2010) and “avoids the dilemma between constructivist relativism and positivist absolutism”. We should also look at the Integrative Pedagogy (Tynjana (sp)), where the speaker feels that what they are describing is a realist version of this.

The course was surveyed with a preliminary small study (N=21/26, which is curious. Which one is it? Ah, 21 out of 26 enrolled, there we go.). The survey questions were… rather loose and very open to influence, unfortunately, from my quick glance at them but I will have to read the original paper.

Justice is a difficult topic to address, especially where it’s reified as a professional skill that can be developed, and discussing the notion of justice in terms of the ways that a group can work together fairly is very important. I suppose I’m not 100% convinced how much is added in this context through the use of a new term that is an apparent parent to communication and negotiation, with the desired outcome of fairness, because the amalgamation seems to obscure the individual components that would be improved upon to return to a fair state. The very small study, and a small survey, is a valid approach for a case study or phenomenographic approach, but I get the feeling that I was seeing a grounded theory argument. We do have to expose our desired processes to students if we’re going to achieve cognitive apprenticeship and there is a great deal of tension between industrial practice and key concepts, so this is a very interesting area to work in. I completely agree with the speaker that our heavy technical focus often precludes discussions of the empathic, philosophical and intangible, but I’m yet to see how this approach contributes.

The discussions mentioned as important are very important but group reports and discussion are a built-in part of many SE process models so I wonder how the justice theme amplifies this aspects. Again, getting students to engage in a dialogue that they do not expect to have in CS can be very challenging but we could be discussing issues such as critical thinking and ethics, which are often equally alien and orthogonal to the technical, without forming a compound concept that potentially obscures the underlying component mechanisms.

Simon asked a very good question: you didn’t present anything that showed a problem where the students would have needed the concept of justice. Apparently, this is in the writings that are yet to be analysed. The answer to the question ended up as an unlabelled graph on the blackboard which was focused on a skill difference with more experienced peers. I still can’t see how justice ties into this. I have to go and get my hearing checked.


ITiCSE 2014: Monday, Keynote 1, “New Technology, New Learning?” #ITiCSE2014 #ITiCSE

This keynote was presented by Professor Yvonne Rogers, from University College of London. The talk was discussing how we could make learning more accessible and exciting for everyone and encourage students to think, to create and share our view. Professor Rogers started by sharing a tweet by Conor Gearty on a guerrilla lecture, with tickets to be issued at 6:45pm, for LSE students. (You can read about what happened here.) They went to the crypt of Westminster Cathedral and the group, split into three smaller groups, ended up discussing the nature of Hell and what it entailed. This was a discussion on religion but, because of the way that it was put together, it was more successful than a standard approach – context shift, suspense driving excitement and engagement. (I wonder how much suspense I could get with a guerrilla lecture on polymorphism… )

Professor Rogers says that suspense matters, as the students will be wondering what is coming next, and this will hopefully make them more inquisitive and thus drive them along the path to scientific enquiry. The Ambient Wood was a woodland full of various technologies for student pairs, with technology and probes, an explorative activity. You can read about the Ambient Wood here. The periscope idea ties videos into the direction that you are looking – a bit like Google Glass without a surveillance society aspect (a Woodopticon?). (We worked on similar ideas at Adelaide for an early project in the Arts Precinct to allow student exploration to drive the experience in arts, culture and botanical science areas.) All of the probes were recorded in the virtual spatial environment matching the wood so that, after the activity, the students could then look at what they did. Thus, a group of 10-12 year olds had an amazing day exploring and discovering, but in a way that was strongly personalised, with an ability to see it from the bird’s eye view above them.

And, unsurprisingly, we moved on to MOOCs, with an excellent slide on MOOC HYSTERIA. Can we make these as engaging as the guerrilla lecture or the ambient wood?

hysertia

MOOCs, as we know, are supposed to increase our reach and access to education but, as Professor Rogers noted, it is also a technology that can make the lecturer a “bit of a star”. This is one of the most honest assessments of some of the cachet that I’ve heard – bravo, Professor Rogers. What’s involved in a MOOC? Well, watching things, doing quizzes, and there’s probability a lot of passive, rather than active, learning. Over 60% of the people who sign up to do a MOOC, from the Stanford experience, have a degree – doing Stanford for free is a draw for the already-degreed. How can we make MOOCs fulfil their promise, give us good learning, give us active learning and so on? Learning analytics give us some ideas and we can data mine to try and personalise the course to the student. But this has shifted what our learning experience is and do we have any research to show the learning value of MOOCs?

In 2014, 400 students taking a Harvard course:

  1. Learned in a passive way
  2. Just want to complete
  3. Take the easy option
  4. Were unable to apply what they learned
  5. Don’t reflect on or talk to their colleagues about it.

Which is not what we want? What about the Flipped Classroom? Professor Rogers attributed this to Khan but I’m not sure I agree with this as there were people, Mazur for example, who were doing this in Peer Instruction well before Khan – or at least I thought so. Corrections in the questions please! The idea of the flip is that we don’t have content delivery in lectures with the odd question – we have content beforehand and questions in class. What is the reality?

  1. Still based on chalk and talk.
  2. Is it simply a better version of a bad thing?
  3. Are students more motivated and more active?
  4. Very labour-intensive for the teacher.

So where’s the evidence? Well, it does increase interaction in class between instructors and students. It does allow for earlier identification of misconceptions. Pierce and Fox, 2012, found that it increased exam results for pharmacology students. It also fostered critical thinking in case scenarios. Maybe this will work for 10s-100s – what about classes of thousands? Can we flip to this? (Should we even have classes of this size is another good question)

Then there’s PeerWise, Paul Denny (NZ), where there is active learning in which students create questions, answer them and get feedback. Students create the questions and then they get to try other student’s questions and can then rate the question and rate the answer. (We see approaches like this, although not as advanced, in other technologies such as Piazza.)

How effective is this? Performance in PeerWise correlated with exam marks (Anyadi, Green and Tang, 2013), with active student engagement. It’s used for revision before the exams, and you get hihg-quality questions and answers, while supporting peer interaction. Professor Rogers then showed the Learning Pyramid, from the National Training Laboratories, Bethel, Maine. The PeerWise system plays into the very high retention area.

pyramid

Professor Rogers then moved on to her own work, showing us a picture of the serried rank nightmare of a computer-based classroom: students in rows, isolated and focused on their screens. Instead of ‘designing for one’, why don’t we design to orchestrate shared activities, with devices that link to public displays and can actively foster collaboration. One of Professor Rogers’ students is looking at ways to share simulations across tablets and screens. This included “4Decades“, a a simulation of climate management, with groups representing the different stakeholders to loo at global climate economics. We then saw a video that I won’t transcribe. The idea is that group work encourages discussion, however we facilitate it, and this tends to leading to teaching others in the sharing of ideas. Another technology that Professor Rogers’ group have developed in this space is UniPad: orchestrating collaborate activities across multiple types of devices, with one device per 6-7 students, and used in classes without many researchers present. Applications of this technology include budgeting for students (MyBank), with groups interacting and seeing the results on a public display. Given how many students operate in share houses collaboratively, this is quite an interesting approach to the problem. From studies on this, all group members participated and used the tablet as a token for discussion, taking ownership of a part of the problem. This also extended to reflection on other’s activities, including identifying selfish behaviour on the part of other people. (Everyone who has had flatmates is probably groaning at the moment. Curse you, Love Tarot Pay-By-The-Minute Telephone Number, which cost me and my flatmates a lot of dollars after a flatmate skipped out on us.)

The next aspect Professor Rogers discussed was physical creation toolkits, such as MaKey MaKey, where you can build alternative input for a computer, based on a simple printed circuit board with alligator clips and USB cables. The idea is simple: you can turn anything you like into a keyboard key. Demonstrations included a banana space bar, a play dough MarioKart gamepad, and many other things (a water bowl in front of the machine became a cat-triggered photo booth). This highlights one of the most important aspects of thinking about learning: learning for life. How can we keep people interested in learning in the face of busy, often overfull, lives when many people still think about learning as something that had to be endured on their pathway into the workforce? (Paging my climbing friends with their own climbing wall: you could make the wall play music if you wanted to. Just saying.)

One of the computers stopped working during a trial of the MaKey MaKey system with adult learners and the collaboration that ensued changed the direction of the work and more people were assigned to a single kit. Professor Rogers showed a small video of a four-person fruit orchestra of older people playing Twinkle Twinkle Little Star. (MORE KIWI!) This elicited a lot of ideas, including for their grandchildren and own parent, transforming exercise to be more fun, to help people learn fundamental knowledge skills and give good feedback. We often heavily intervene in the learning experience and the reflection of the Fruit Orchestra was that intervening less in self-driven activities such as MaKey MaKey might be a better way to go, to increase autonomy and thus drive engagement.

Next was the important question: How can we gets to create and code, where coding is just part of the creating? Can we learn to code differently beyond just choosing a particular language? We have many fascinating technologies but what is the suite of tools over the top that will drive creativity and engagement in this area, to produce effective learning? The short video shown demonstrated a pop-out prefabricated system, where physical interfaces and gestures across those represented coding instructions: coding without any typing at all. (Previous readers will remember my fascination with pre-literate programming.) This early work, electronics on a sheet, is designed to be given away because the production cost is less than 3 Euros. The project is called “code me” from University College London and is designed to teach logic without people realising it: the fundamental building block of computational thinking. Future work includes larger blocks with Bluetooth input and sensors. (I can’t find a web page for this.)

What role should technology play in learning? Professor Rogers mentioned thinking about this in two ways. The inside learning using technology to think about the levels students to reach to foster attainment: personalise, monitor, motivate, flexible, adaptive. The outside learning approach is to work with other people away from the screen: collaborate, create, connect, reflect and play. Professor Rogers believes that the choice is ours but that technology should transform learning to make it active, creative, collaborative, exciting (some other things I didn’t catch) and to recognise the role of suspense in making people think.

An interesting and thought-provoking keynote.

 


Education and Paying Back (#AdelEd #CSER #DigitalTechnologies #acara #SAEdu)

On Monday, the Computer Science Education Research Group and Google (oh, like you need a link) will release their open on-line course to support F-6 Primary school teachers in teaching the new Digital Technologies curriculum. We are still taking registrations so please go the course website if you want to sign up – or just have a look! (I’ve blogged about this recently as part of Science meets Parliament but you can catch it again here.) The course is open, on-line and free, released under Creative Commons so that the only thing people can’t do is to try and charge for it. We’re very excited and it’s so close to happening, I can taste it!

Here’s that link again – please, sign up!

I’m posting today for a few reasons. If you are a primary school teacher who wants help teaching digital technologies, we’d love to see you sign up and join our community of hundreds of other people who are thinking the same thing. If you know a primary school teacher, or are a principal for a primary school, and think that this would interest people – please pass it on! We are most definitely not trying to teach teachers how to teach (apart from anything else, what presumption!) but we’re hoping that what we provide will make it easier for teachers to feel comfortable, confident and happy with the new DT curriculum requirements which will lead to better experiences all ’round.

My other reason is one that came to me as I was recording my introduction section for the on-line course. In that brief “Oh, what a surprise there’s a camera” segment, I note that I consider the role of my teachers to have been essential in getting me to where I am today. This is what I’d like to do today: explicitly name and thank a few of my teachers and hope that some of what we release on Monday goes towards paying back into the general educational community.

You know who this is for.

You know who this is for.

My first thanks go to Mrs Shand from my Infant School in England. I was an early reader and, in an open plan classroom, she managed to keep me up with the other material while dealing with the fact that I was a voracious reader who would disappear to read at the drop of a hat. She helped to amplify my passion for reading, instead of trying to control it. Thank you!

In Australia, I ran into three people who were crucial to my development. Adam West was interested in everything so Grade 5 was full of computers (my first computing experience) because he arranged to borrow one and put it into the classroom in 1978, German (I can still speak the German I learnt in that class) and he also allowed us to write with nib and ink pens if we wanted – which was the sneakiest way to get someone’s handwriting and tidiness to improve that I have ever seen. Thank you, Adam!  Mrs Lothian, the school librarian, also supported my reading habit and, after a while, all of the interesting books in the library often came through me very early on because I always returned them quickly and in good condition but this is where I was exposed to a whole world of interesting works: Nicholas Fisk, Ursula Le Guin and Susan Cooper not being the least of these. Thank you! Gloria Patullo (I hope I’ve spelt that correctly) was my Grade 7 teacher and she quickly worked out that I was a sneaky bugger on occasion and, without ever getting angry or raising a hand, managed to get me to realise that being clever didn’t mean that you could get away with everything and that being considerate and honest were the most important elements to alloy with smart. Thank you! (I was a pain for many years, dear reader, so this was a long process with much intervention.)

Moving to secondary school, I had a series of good teachers, all of whom tried to take the raw stuff of me and turn it into something that was happier, more useful and able to take that undirected energy in a more positive direction. I have to mention Ken Watson,  Glenn Mulvihill, Mrs Batten, Dr Murray Thompson, Peter Thomas, Dr Riceman, Dr Bob Holloway, Milton Haseloff (I still have fossa, -ae, [f], ditch, burned into my brain) and, of course, Geoffrey Bean, headmaster, strong advocate of the thinking approaches of Edward de Bono and firm believer in the importance of the strength one needs to defend those who are less strong. Thank you all for what you have done, because it’s far too much to list here without killing the reader: the support, the encouragement, the guidance, the freedom to try things while still keeping a close eye, the exposure to thinking and, on occasion, the simple act of sitting me down to get me to think about what the heck I was doing and where I was going. The fact that I now work with some of them, in their continuing work in secondary education, is a wonderful thing and a reminder that I cannot have been that terrible. (Let’s just assume that, shall we? Moving on – rapidly…)

Of course, it’s not just the primary and secondary school teachers who helped me but they are the ones I want to concentrate on today, because I believe that the freedom and opportunities we offer at University are wonderful but I realise that they are not yet available to everyone and it is only by valuing, supporting and developing primary and secondary school education and the teachers who work so hard to provide it that we can go further in the University sector. We are lucky enough to be a juncture where dedicated work towards the national curriculum (and ACARA must be mentioned for all the hard work that they have done) has married up with an Industry partner who wants us all to “get” computing (Thank you, Google, and thank you so much, Sally and Alan) at a time when our research group was able to be involved. I’m a small part of a very big group of people who care about what happens in our schools and, if you have children of that age, you’ve picked a great time to send them to school. 🙂

I am delighted to have even a small opportunity to offer something back into a community which has given me so much. I hope that what we have done is useful and I can’t wait for it to start.


ASWEC 2014 – Now with Education Track

The Australasian Software Engineering Conference has been around for 23 years and, while there have been previous efforts to add more focus on education, this year we’re very pleased to have a full day on Education on Wednesday, the 9th of April. (Full disclosure: I’m the Chair of the program committee for the Education track. This is self-advertising of a sort.) The speakers include a number of exciting software engineering education researchers and practitioners, including Dr Claudia Szabo, who recently won the SIGCSE Best Paper Award for a paper in software engineering and student projects.

Here’s the invitation from the conference chair, Professor Alan Fekete – please pass this on as far as you can!:

We invite all members of the Australasian computing community to ASWEC2014, the Australasian Software Engineering Conference, which will be held at VIBE hotel in Milson’s Point, Sydney, on April 7-10, 2014.
 
The conference program is at http://www.aswec2014.org/programme/schedule/ Highlights include
  • Keynote by a leader of SE research, Prof Gail Murphy (UBC, Canada) on Getting to Flow in Software Development.
  • Keynote by Alan Noble (Google) on Innovation at Google.
  • Sessions on Testing, Software Ecosystems, Requirements, Architecture, Tools, etc, with speakers from around Australia and overseas, from universities and industry, that bring a wide range of perspectives on software development.
  • An entire day  (Wed April 9) focused on SE Education, including keynote by Jean-Michel Lemieux (Atlassian) on Teaching Gap: Where’s the Product Gene?
Register at http://www.aswec2014.org/registration/ Single-day registration is available. The conference is colocated with WICSA http://www.wicsa.net/  (International Conference on Software Architecture) and immediately preceded by an SEI course on software architecture http://www.australia.cmu.edu/events/software-architecture-principles-and-practices
We look forward to seeing many of you at ASWEC2014!

 


The Bad Experience That Stays With You and the Legendary Bruce Springsteen.

I was talking with a friend of mine and we were discussing perceptions of maths and computing (yeah, I’m like this off duty, too) and she felt that she was bad at Maths. I commented that this was often because  of some previous experience in school and she nodded and told me this story, which she’s given me permission to share with you now. (My paraphrasing but in her voice)

“When I was five, we got to this point in Math where I didn’t follow what was going on. We got to this section and it just didn’t make any sense to me. The teacher gave us some homework to do and I looked at it and I couldn’t do it but I didn’t want to hand in nothing. So I scrunched it up and put it in the bin. When the teacher asked for it back, I told her that I didn’t have it.

It turns out that the teacher had seen me put it in the bin and so she punished me. And I’ve never thought of myself as good at math since.”

Wow. I’m hard-pressed to think of a better way to give someone a complex about a subject. Ok, yes, my friend did lie to the teacher about not the work and, yes, it would  have been better if she’d approached the teacher to ask for help – but given what played out, I’m not really sure how much it would have changed what happened. And, before we get too carried away, she was five.

Now this is all some (but not that many) years ago and a lot of things have changed in teaching, but all of us who stand up and call ourselves educations could do worse than remember Bruce Springsteen’s approach to concerts. Bruce plays a lot of concerts but, at each one, he tries to give his best because a lot of the people in the audience are going to their first and only Springsteen concert. It can be really hard to deal with activities that are disruptive, disobedient and possible deliberately so, but they may be masking fear, uncertainty and a genuine desire for the problem to go away because someone is overwhelmed. Whatever we get paid, that’s really one of the things we get paid to do.

We’re human. We screw up. We get tired. But unless we’re thing about and trying to give that Springsteen moment to every student, then we’re setting ourselves up to be giving a negative example. Somewhere down the line, someone’s going to find their life harder because of that – it may be us in the next week, it may be another teacher next year, but it will always be the student.

Bad experiences hang around for years. It would be great if there were fewer of them. Be awesome. Be Springsteen.

EMBRACE YOUR AWESOMENESS! Don't make me come over and sing "Blinded by the Light!"

EMBRACE YOUR AWESOMENESS! Don’t make me come over and sing “Blinded by the Light!”


Enemies, Friends and Frenemies: Distance, Categorisation and Fun.

As Mario Puzo and Francis Ford Coppola wrote in “The Godfather Part II”:

… keep your friends close but your enemies closer.

(I bet you thought that was Sun Tzu, the author of “The Art of War”. So did I but this movie is the first use.)

I was thinking about this the other day and it occurred to me that this is actually a simple modelling problem. Can I build a model which will show the space around me and where I would expect to find friends and enemies? Of course, you might be wondering “why would you do this?” Well, mostly because it’s a little bit silly and it’s a way of thinking that has some fun attached to it. When I ask students to build models of the real world, where they think about how they would represent all of the important aspects of the problem and how they would simulate the important behaviours and actions seen with it, I often give them mathematical or engineering applications. So why not something a little more whimsical?

From looking at the quote, we would assume that there is some distance around us (let’s call it a circle) where we find everyone when they come up to talk to us, friend or foe, and let’s also assume that the elements “close” and “closer” refer to how close we let them get in conversation. (Other interpretations would have us living in a neighbourhood of people who hate us, while we have to drive to a different street to sit down for dinner with people who like us.) So all of our friends and enemies are in this circle, but enemies will be closer. That looks like this:

FREN1

I have more friends than enemies because I’m popular!

So now we have a visual model of what is going on and, if we wanted to, we could build a simple program that says something like “if you’re in this zone, then you’re an enemy, but if you’re in that zone then you’re a friend” where we define the zones in terms of nested circular regions. But, as we know, friend always has your back and enemies stab you in the back, so now we need to add something to that “ME” in the middle – a notion of which way I’m facing – and make sure that I can always see my enemies. Let’s make the direction I’m looking an arrow. (If I could draw better, I’d put glasses on the front. If you’re doing this in the classroom, an actual 3D dummy head shows position really well.) That looks like this:

Same numbers but now I can keep an eye on those enemies!

Same numbers but now I can keep an eye on those enemies!

Now our program has to keep track of which way we’re facing and then it checks the zones, on the understanding that either we’re going to arrange things to turn around if an enemy is behind us, or we can somehow get our enemies to move (possibly by asking nicely). This kind of exercise can easily be carried out by students and it raises all sorts of questions. Do I need all of my enemies to be closer than my friends or is it ok if the closest person to me is an enemy? What happens if my enemies are spread out in a triangle around me? Is they won’t move, do I need to keep rotating to keep an eye on them or is it ok if I stand so that they get as much of my back as they can? What is an acceptable solution to this problem? You might be surprised how much variation students will suggest in possible solutions, as they tell you what makes perfect sense to them for this problem.

When we do this kind of thing with real problems, we are trying to specify the problem to a degree that we remove all of the unasked questions that would otherwise make the problem ambiguous. Of course, even the best specification can stumble if you introduce new information. Some of you will have heard of the term ‘frenemy’, which apparently:

can refer to either an enemy pretending to be a friend or someone who really is a friend but is also a rival (from Wikipedia and around since 1953, amazingly!)

What happens if frenemies come into the mix? Well, in either case, we probably want to treat them like an enemy. If they’re an enemy pretending to be a friend, and we know this, then we don’t turn our back on them and, even in academia, it’s never all that wise to turn your back on a rival, either. (Duelling citations at dawn can be messy.) In terms of our simple model, we can deal with extending the model because we clearly understand what the important aspects are of this very simple situation. It would get trickier if frenemies weren’t clearly enemies and we would have to add more rules to our model to deal with this new group.

This can be played out with students of a variety of ages, across a variety of curricula, with materials as simple as a board, a marker and some checkers. Yet this is a powerful way to explain modelsspecification and improvement, without having to write a single line of actual computer code or talk about mathematics or bridges! I hope you found it useful.


Matt Damon: Computer Science Superstar?

There was a recent article in Salon regarding the possible use of celebrity presenters, professional actors and the more photogenic to present course material in on-line courses. While Coursera believes that, in the words of Daphne Koller, “education is not a performance”, Udacity, as voiced by Sebastian Thrun, believes that we can model on-line education more in the style of a newscast. In the Udacity model, there is a knowledgeable team and the content producer (primary instructor) is not necessarily going to be the presenter. Daphne Koller’s belief is that the connection between student and teacher would diminish if actors were reading scripts that had content they didn’t deeply understand.

My take on this is fairly simple. I never want to give students the idea that the appearance of knowledge is an achievement in the same league as actually developing and being able to apply that knowledge. I regularly give talks about some of the learning and teaching techniques we use and  I have to be very careful to explain that everything good we do is based on solid learning design and knowledge of the subject, which can be enhanced by good graphic design and presentation but cannot be replaced by these. While I have no doubt that Matt Damon could become a good lecturer in Computer Science, should he wish to, having him stand around and pretend to be one sends the wrong message.

Matt Damon demonstrating an extrinsic motivational technique called "fear of noisy death".

Matt Damon demonstrating an extrinsic motivational technique called “fear of noisy death”.

(And, from the collaborative perspective, if we start to value pleasant appearance over knowledge, do we start to sort our students into groups by appearance and voice timbre? This is probably not the path we want to go down. For now, anyway.)

 


Skill Games versus Money Games: Disguising One Game As Another

I recently ran across a very interesting article on Gamasutra on the top tips for turning a Free To Play (F2P) game into a Paying game by taking advantage of the way that humans think and act. F2P games are quite common but, obviously, it costs money to make a game so there has to be some sort of associated revenue stream. In some cases, the F2P is a Lite version of the pay version, so after being hooked you go and buy the real thing. Sometimes there is an associated advertising stream, where you viewing the ads earns the producer enough money to cover costs. However, these simple approaches pale into insignificance when compared with the top tips in the link.

Ramin identifies two games for this discussion: games of skill, where it is your ability to make sound decisions that determines the outcome, and money games, where your success is determined by the amount of money you can spend. Games of chance aren’t covered here but, given that we’re talking about motivation and agency, we’re depending upon one specific blindspot (the inability of humans to deal sensibly with probability) rather than the range of issues identified in the article.

I dont want to rehash the entire article but the key points that I want to discuss are the notion of manipulating difficulty and fun pain. A game of skill is effectively fun until it becomes too hard. If you want people to keep playing then you have to juggle the difficulty enough to make it challenging but not so hard that you stop playing. Even where you pay for a game up front, a single payment to play, you still want to get enough value out of it – too easy and you finish too quickly and feel that you’ve wasted your money; too hard and you give up in disgust, again convinced that you’ve wasted your money. Ultimately, in a pure game of skill, difficulty manipulation must be carefully considered. As the difficulty ramps up, the player is made uncomfortable, the delightful term fun pain is applied here, and resolving the difficulty removes this.

Or, you can just pay to make the problem go away. Suddenly your game of skill has two possible modes of resolution: play through increasing difficulty, at some level of discomfort or personal inconvenience, or, when things get hard enough, pump in a deceptively small amount of money to remove the obstacle. The secret of the P2P game that becomes successfully monetised is that it was always about the money in the first place and the initial rounds of the game were just enough to get you engaged to a point where you now have to pay in order to go further.

You can probably see where I’m going with this. While it would be trite to describe education as a game of skill, it is most definitely the most apt of the different games on offer. Progress in your studies should be a reflection of invested time in study, application and the time spent in developing ideas: not based on being ‘lucky’, so the random game isn’t a choice. The entire notion of public education is founded on the principle that educational opportunities are open to all. So why do some parts of this ‘game’ feel like we’ve snuck in some covert monetisation?

I’m not talking about fees, here, because that’s holding the place of the fee you pay to buy a game in the first place. You all pay the same fee and you then get the same opportunities – in theory, what comes out is based on what the student then puts in as the only variable.

But what about textbooks? Unless the fee we charge automatically, and unavoidably, includes the cost of the textbook, we have now broken the game into two pieces: the entry fee and an ‘upgrade’. What about photocopying costs? Field trips? A laptop computer? An iPad? Home internet? Bus fare?

It would be disingenuous to place all of this at the feet of public education – it’s not actually the fault of Universities that financial disparity exists in the world. It is, however, food for thought about those things that we could put into our courses that are useful to our students and provide a paid alternative to allow improvement and progress in our courses. If someone with the textbook is better off than someone without the textbook, because we don’t provide a valid free alternative, then we have provided two-tiered difficulty. This is not the fun pain of playing a game, we are now talking about genuine student stress, a two-speed system and a very high risk that stressed students will disengage and leave.

From my earlier discussions on plagiarism, we can easily tie in Ramin’s notion of the driver of reward removal, where players have made so much progress that, on facing defeat, they will pay a fee to reduce the impact of failure; or, in some cases, to remove it completely. As Ramin notes:

“This technique alone is effective enough to make consumers of any developmental level spend.”

It’s not just lost time people are trying to get back, it’s the things that have been achieved in that time. Combine that with, in our case, the future employability and perception of that piece of paper, and we have a very strong behavioural driver. A number of the tricks Ramin describes don’t work as well on mature and aware thinkers but this one is pretty reliable. If it’s enough to make people pay money, regardless of their development level, then there are lots of good design decisions we can make from this – lower risk assessment, more checkpointing, steady progress towards achievement. We know lots of good ways to avoid this, if we consider it to be a problem and want to take the time to design around it.

This is one of the greatest lessons I’ve learned about studying behaviour, even as a rank amateur. Observing what people do and trying to build systems that will work despite that makes a lot more sense than building a system that works to some ideal and trying to jam people into it. The linked article shows us how people are making really big piles of money by knowing how people work. It’s worth looking at to make sure that we aren’t, accidentally, manipulating students in the same way.


Let’s not turn “Chalk and Talk” into “Watch and Scratch”

We are now starting to get some real data on what happens when people “take” a MOOC (via Mark’s blog). You’ll note the scare quotes around the word “take”, because I’m not sure that we have really managed to work out what it means to get involved in a course that is offered through the MOOC mechanism. Or, to be more precise, some people think they have but not everyone necessarily agrees with them. I’m going to list some of my major concerns, even in the face of the new clickstream data, and explain why we don’t have a clear view of the true value/approaches for MOOCs yet.

  1. On-line resources are not on-line courses and people aren’t clear on the importance of an overall educational design and facilitation mechanism. Many people have mused on this in the past. If all the average human needed was a set of resources and no framing or assistive pedagogy then our educational resources would be libraries and there would be no teachers. While there are a number of offerings that are actually courses, applying the results of the MIT 6.002x to what are, for the most part, unstructured on-line libraries of lecture recordings is not appropriate. (I’m not even going to get into the cMOOC/xMOOC distinction at this point.) I suspect that this is just part of the general undervaluing of good educational design that rears its head periodically.
  2. Replacing lectures with on-line lectures doesn’t magically improve things. The problem with “chalk and talk”, where it is purely one-way with no class interaction, is that we know that it is not an effective way to transfer knowledge. Reading the textbook at someone and forcing them to slowly transcribe it turns your classroom into an inefficient, flesh-based photocopier. Recording yourself standing in front a class doesn’t automatically change things. Yes, your students can time shift you, both to a more convenient time and at a more convenient speed, but what are you adding to the content? How are you involving the student? How can the student benefit from having you there? When we just record lectures and put them up there, then unless they are part of a greater learning design, the student is now sitting in an isolated space, away from other people, watching you talk, and potentially scratching their head while being unable to ask you or anyone else a question. Turning “chalk and talk” into “watch and scratch” is not an improvement. Yes, it scales so that millions of people can now scratch their heads in unison but scaling isn’t everything and, in particular, if we waste time on an activity under the illusion that it will improve things, we’ve gone backwards in terms of quality for effort.
  3. We have yet to establish the baselines for our measurement. This is really important. An on-line system us capable of being very heavily tracked and it’s not just links. The clickstream measurements in the original report record what people clicked on as they worked with the material. But we can only measure that which is set up for measurement – so it’s quite hard to compare the activity in this course to other activities that don’t use technology. But there are two subordinate problems to this (and I apologise to physicists for the looseness of the following) :
    1. Heisenberg’s MOOC: At the quantum scale, you can either tell where something is or what it is doing – the act of observation has limits of precision. Borrowing that for the macro scale: measure someone enough and you’ll see how they behave under measurement but the measurements we pick tend to fall into the stage they’ve reached or the actions they’ve taken. It’s very complex to combine quantitative and qualitative measures to be able to map someone’s stage and their comprehension/intentions/trajectory. You don’t have to accept arguments based on the Hawthorne Effect to understand why this does not necessarily tell you much about unobserved people. There are a large number of people taking these courses out of curiosity, some of whom already have appropriate qualifications, with only 27% the type of student that you would expect to see at this level of University. Combine that with a large number of researchers and curious academics who are inspecting each other’s courses, I know of at least 12 people in my own University taking MOOCs of various kinds to see what they’re like, and we have the problem that we are measuring people who are merely coming in to have a look around and are probably not as interested in the actual course. Until we can actually shift MOOC demography to match that of our real students, we are always going to have our measurements affected by these observers. The observers might not mind being heavily monitored and observed, but real students might. Either way, numbers are not the real answer here – they show us what but there is still too much uncertainty in the why and the how.
    2. Schrödinger’s MOOC: Oh, that poor reductio ad absurdum cat. Does the nature of the observer change the behaviour of the MOOC and force it to resolve one way or another (successful/unsuccessful)? If so, how and when? Does the fact of observation change the course even more than just in enrolments and uncertainty of validity of figures? The clickstream data tells us that the forums are overwhelmingly important to students, with 90% of people who viewed threads without commenting, and only 3% of total students enrolled every actually posted anything in a thread. What was the make-up of that 3% and was it actual students or the over-qualified observers who then provided an environment that 90% of their peers found useful?
    3. Numbers need context and unasked questions give us no data: As one example, the authors of the study were puzzled that so few people had logged in from China, which surprised them. Anyone who has anything to do with network measurement is going to be aware that China is almost always an outlier in network terms. My blog, for example, has readers from around the world – but not China. It’s also important to remember that any number of Chinese network users will VPN/SSH to hosts outside China to enjoy unrestricted search and network access. There may have been many Chinese people (who didn’t self-identify for obvious reasons) who were using proxies from outside China. The numbers on this particular part of the study do not make sense unless they are correctly contextualised. We also see a lack of context in the reporting on why people were doing the course – the numbers for why people were doing it had to be augmented from comments in the forum that people ‘wanted to see if they could make it through an MIT course’. Why wasn’t that available from the initial questions?
  4. We don’t know what pass/fail is going to look like in this environment. I can’t base any MOOC plans of my own on how people respond to a MIT-branded course but it is important to note that MIT’s approach was far more than “watch and scratch”, as is reflected by their educational design in providing various forms of materials, discussions forums, homework and labs. But still, 155,000 people signed up for this and only 7,000 received certificates. 2/3 of people who registered then went on to do nothing. I don’t think that we can treat a success rate of less than 5% as a success rate. Even where we say that 2/3 dropped out, this still equates to a pass rate under 14%. Is that good? Is that bad? Taking everything into account from above, my answer is “We don’t know.” If we get 17% next time, is that good or bad? How do we make this better?
  5. The drivers are often wrong. Several US universities have gone on the record to complain about undermining their colleagues and have refused to take part in MOOC-related activities. The reasons for this vary but the greatest fear is that MOOCs will be used to reduce costs by replacing existing lecturing staff with a far smaller group and using MOOCs to handle the delivery. From a financial argument, MOOCs are astounding – 155,000 people contacted for the cost of a few lecturers. Contrast that with me teaching a course to 100 students. If we look at it from a quality perspective, and dealing with all of the points so far, we have no argument to say that MOOCs are as good as our good teaching – but we do know that they are easily as good as our bad teaching. But from a financial perspective? MOOC is king. That is, however, not how we guarantee educational quality. Of course, when we scale, we can maintain quality by increasing resources but this runs counter to a cost-saving argument so we’re almost automatically being prevented from doing what is required to make the large scale course work by the same cost driver that led to its production in the first place!
  6. There are a lot of statements but perhaps not enough discussion. These are trying times for higher education and everyone wants an edge, more students, higher rankings, to keep their colleagues and friends in work and, overall, to do the right thing for their students. Senior management, large companies, people worried about money – they’re all talking about MOOCs as if they are an accepted substitute for traditional approaches – at the same time as we are in deep discussion about which of the actual traditional approaches are worthwhile and which new approaches are going to work better. It’s a confusing time as we try to handle large-scale adoption of blended learning techniques at the same time people are trying to push this to the large scale.

I’m worried that I seem to be spending most of my time explaining what MOOCs are to people who are asking me why I’m not using a MOOC. I’m even more worried when I am still yet to see any strong evidence that MOOCs are going to provide anything approaching the educational design and integrity that has been building for the past 30 years. I’m positively terrified when I see corporate providers taking over University delivery before we have established actual measurable quality and performance guidelines for this incredibly important activity. I’m also bothered by statements found at the end of the study, which was given prominence as a pull quote:

[The students] do not follow the norms and rules that have governed university courses for centuries nor do they need to.

I really worry about this because I haven’t yet seen any solid evidence that this is true, yet this is exactly the kind of catchy quote that is going to be used on any number of documents that will come across my desk asking me when I’m going to MOOCify my course, rather than discussing if and why and how we will make a transition to on-line blended learning on the massive scale. The measure of MOOC success is not the number of enrolees, nor is it the number of certificates awarded, nor is it the breadth of people who sign up. MOOCs will be successful once we have worked out how to use this incredibly high potential approach to teaching to deliver education at a suitably high level of quality to as many people as possible, at a reduced or even near-zero cost. The potential is enormous but, right now, so is the risk!