Gene Glass’s suggestion is unpopular among academics because development of theories trumps development of applications for those on the tenure track. It’s the difference between science and technology. (A gross simplification for sure, but you get the idea.)

Regarding the flashcard retention stuff. Its connection to connectivism comes from David’s original post where he quotes Anderson and Schooler’s article. They found “reflections” of the environment in human memory. And to quote from the original thread that David was responding to: “Connectivism addresses the principles of learning at numerous levels – biological/neural, conceptual, and social/external.” Biological/neural patterns for encoding information appear to be tuned to patterns of information in the environment.

Paired-associate learning is one of the simplest forms of learning we know of. A physical representation is a flashcard–look at the front, try recall the back. Repeat as necessary.

I developed a software application that optimized this type of learning (under the assumption that “optimal” means the most efficient way to acquire and retain paired-associates if long term retention and fluency are your objectives).

You can download and play with the application here:

http://andrewvs.blogs.com/factor

It runs in Flash on Macs and PCs. And yes, it’s Open Source. That’s what happens when Wiley is your dissertation chair. ; )

This other business of flash-card memory retention. Does it have legs? What does it have to do with connectivism?

Good catch: the ideas sound similar to smart.fm’s “science” because I was one of the founders and the Chief Scientist of the company (formerly known as Cerego). You can read my 1999 patent here:

http://www.google.com/patents/about?id=ACMNAAAAEBAJ&dq=6652283

Incidentally, ideas similar to your student’s seem to inform the “science” behind Tokyo-based start-up smartFM

http://smart.fm/tour

1. “The value of a psychological theory is judged not only by its explanatory and predictive power, but also by its operational power to improve human functioning.” (Bandura, 1986)

2. And here is a question and answer from an interview with Educational Researcher. (Note: Gene Glass developed the research technique of meta-analysis.)

Educational Researcher: If you could offer educational research one suggestion to improve its standing, what would that be? And/or what specifically could we educational researchers begin doing to improve our field?

Gene Glass: It’s not a popular suggestion, I predict, and it surely goes against the grain to some extent, but educational research would do well to regard itself not as a science seeking theory to explain such phenomena as classroom learning, teaching, aptitude and the like, but as a technology designing and evaluating lessons, programs, and systems. Some will regard this as a comedown from the search for grand theory. I regard it as a productive advance to a level of relevance and contribution not yet experienced by educational researchers.

Finally…

I offer you a relatively quick read (one of those unpublished grad school throw-away papers) that *applies* the findings of Anderson and Schooler’s research toward a practical educational purpose:

AN ADAPTIVE, AUTOMATED INSTRUCTIONAL SYSTEM TO SUPPORT THE LEARNING OF FACTUAL INFORMATION

http://andrewvs.blogs.com/usu/files/avs_bentley_prosem_paper.pdf

In a process of learning the key is the apprentice. It is he who decides what imputs interest you and which do not.

Therefore, not all stimuli gets an answers, but, instead, there are always arising consequences, whether by act or omission.

I wonder if Connection can be defined as S-R learning?

i.e Connection (C) = Stimulus-Response (S-R)

where stimulus is thought of as a proximal association (coming into contact with) and response is thought of as a change of state (a causal relationship or instantiation) in the connecting entities (call them nodes if you like)

Perhaps there is a research project in this, if one could focus in on a proper question(s). If learning = connecting, how wide-spread is S-R learning in connectivist learning?

Given that the same sceptre covers the micro (connectionist neural network) and the macro (connectivist social network) one could probably extrapolate principles from the micro (Boltzmann engines?) to the macro as Stephen has suggested, and research/analyze the S-R learning at the social network level. Any suggestions on how this might be done?

There are natural connections (neurals, environmental, …) and others that only are possible through technology (internet, …) and that are research object.