Daniel in his post of a criticism of computer science models or modeles says:
The problem is made worse by the fact that researchers working on modèles more easily get the upper hand. They are never wrong. They can endlessly refine their modèles and re-evaluate them. As long as there is no actual problem to be solved, the modèles will tend to displace the models. Cargo cult science wins.
Of course, the reverse phenomenon may exist within industry. People working with modèles are at a disadvantage. They can’t make useful predictions. They can only explain, in retrospect, what is observed. All their sophistication fails to help them when real-world results are what matters.
I agreed with Daniel’s views. How would this scientific model be applicable to Higher Education? Or can we really explain the MOOCs phenomena using the scientific modelling?
May I share some ideas below, which I think is relevant to the building of models in education?
What I noted in recent years is that ideas and concepts seem to be more convincing than the empirical data and experimental proof, especially in “social science”. Why?
As Clayton Christensen mentions here, most academics are looking for data for analysis before they would make recommendations for further action in the introduction of innovation. The first cMOOCs were run based exactly on Theory (Connectivism as a new and emerging learning theory, as proposed by George Siemens and Stephen Downes). The xMOOCs were again run based on the Theory of Instructivism where Mastery Learning and Video based learning (coupled with flipped classroom) would work.
The current MOOCs proved that is the case, based on the assumptions that Mastery Learning and Instructivism are what drive learning to be achieved, though “peer learning” was added when researchers later found it had happened. The video lectures were again “augmented” with the flipped classroom model, in order to explain why xMOOCs are so successful as a special pedagogy, where the whole phenomena was explained with a post-mortem basis.
There have been some researches done in explaining the cMOOCs movement from the basis of Complexity Theory and Chaos Theory, Self-organizing Theory and Theory of Emergence. Not many people seem to have applied that in the case of xMOOCs.
Indeed, when we examine the xMOOCs pattern of education and learning, the whole notion of learning could be explained when individual learners interacted with the content and made use of the LMS as a platform for some of the information sources. The participation and completion did fall under a similar pattern to the cMOOCs though xMOOCs are normally far “richer” in terms of the information provision and “instruction” via the video lectures. Indeed the quizzes and examination are merely “transferred” from the typical face-to-face courses, only that they are all based on auto-grading, and thus address some of the challenges that once weren’t fully covered in cMOOCs.
So, my conclusion is that people often tried to explain a phenomena by pre-conceived and well-designed instructions and wonderful pedagogy in order to fulfill the self-fulfilling prophecy, which may unfortunately not always be representing the actual pattern of education and learning that has taken place. The current xMOOCs can likely be explained much better through the interaction learning theory, with Complexity Theory of Education and Theory of Emergence, and Connectivism as a model of education. There are obvious conflicts to the mission of education under an institution framework, as the low completion rate of MOOCs don’t align with original goals set off in Higher Education. There are many major conflicts with institution mission as mentioned by Clayton Christensen in the discussion of MOOCs.
Here is how a cMOOC work, and that could explain partially why xMOOC work too.