OLI: Marsha Lovett

March 10, 2008

marsha lovett at OLI

Marsha was hit by technical gremlins – no powerpoint slides  for most of the talk but she managed very well! Her talk moves us on to Learning Science. The methodologies that she highlights are to develop comparative studies with random allocation and lab based instrumented studies. However these are difficult to make authentic. So she also talked about what she called “design experiments” where attempts are made within classrooms to improve learning experience. (Perhaps we would call this Action Research?) Difficult though to then impose controls. So the suggestion is to combine methods and OERs offer an opportunity to apply this approach as it is possible to gather fine grained data about experience by automated instrumentation. OERs also give a base of real content for use in experiments. (I very much agree with this as in my previous life I had great difficulty getting data for work on mobile learning and OERs could be just the examples that we need.)

Aspects to measure are

  • Pre and post to measure learning gain
  • Retention measures – how long to be able to retain i
  • Transfer to daily life: e.g. probabilty checks by asking questions about sports
  • Speed of learning on second exposure to the same learning materials
  • Preparation for future learning

People often collect satisfaction or enjoyment. This is indirectly related and needs to be looked at as a factor in time to apply to the learning material.

 

Marsha then moved on to principles to apply illustrated by statutor. Three principles were picked out:

1. The chance to practice. She used latency in selecting the right answer – but the graph is very bumpy unless more care is taken to extract the separate skills that are being asked. The feedback is then that the experts who combine graphing as a single topic should perhaps be changed. This was cited as an example of how learning science analysis can feedback into the science of learning.

Open questions include:

  • How to represent the skills
  • How do skills change qualitativele
  • How to spot when skills are not being practiced

What incentives are there to complete practice (without “gaming”)

 

2. New knowledge acquired through lens of prior knowledge.

Experts may well have a “blind spot” in seeing things as a student. This was illustrated with various ways in which experts see problems – e.g for physics novices will match blocks on inclined planes, experts will look for involved equilibrium of energy. She pointed to Richard Mayer’s books on what is need for simulations to work for learners.

 

  1. Learners refine their knowledge through timely feedback.

Online gives good opportunites for this feedback and link to particular points. Open questions though about how to do this – and in fact if delayed feedback can actually be more effective.

 

Overall summary – that online student data is a goldmine and be used to inform design, science, and student performance. 

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2 Responses to “OLI: Marsha Lovett”


  1. […] entries across at my OCHRE OpenLearn blog. I was taking pictures to illustrate by blog about Marsha Lovett’s presentation and it was not working. Strange error messages resulted asking me to “checktickets”. […]

  2. Stian Haklev Says:

    I heard abt this conference when I met Candace Thille at the Open Ed conference in Utah, and I thought it sounded like a wonderful idea. I would have given my right arm to participate, but I am very grateful that they have posted the videos online. Have been watching several sessions today. It’s a bit frustrating though, because half of it I know (from my participation in the OER movement) and half of it is quite new – I am so used to skimming through large collections of text, and extracting what is new, but with video, there is not much you can do.


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