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Rich Content, Not Content-Rich Alan J. Cann Department of Biology University of Leicester. Evaluation Synthesis Analysis Application Understanding Knowledge Bloom's Taxonomy of Educational Objectives Benjamin Bloom, 1956: e-Learning 5 E-mod - supporting, responding

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Rich Content,

Not Content-Rich

Alan J. Cann

Department of Biology

University of Leicester.


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Evaluation

Synthesis

Analysis

Application

Understanding

Knowledge

Bloom's Taxonomy of Educational ObjectivesBenjamin Bloom, 1956:


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e-Learning

5

E-mod - supporting, responding

Tech - providing links outside closed conferences

Development

Knowledge construction

4

E-mod - facilitating process

Tech - conferencing

Information exchange

3

E-mod - facilitate tasks; support use of learning materials

Tech - searching, personalizing

Online socialization

2

E-mod - familiarization; building bridges (cultural, social & learning environment)

Tech - sending & receiving messages

Access & motivation

1

E-moderating - welcome & encourage

Technical - Setting up & accessing

Salmon, E-Moderating, Routledge, (2004)


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Rich Content

  • Push technology

  • Keyword-specific RSS

  • Dynamically generated content:

    • PHP

    • Javascript

    • HTML


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Why Discuss ?

  • Discussion promotes reflection.

  • Deep versus superficial learning.

  • Reflection encourages higher level learning competencies and inclusivity (ownership of learning).


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The Model

  • A model of online assessment for (bioscience) students.

  • Optimum group size8-15:

    • Smaller groups than this do not have the critical mass to sustain discussion

    • Larger groups are difficult to assess / allow anonymity.

  • Contributions to the discussion boards were explicitly linked to assessment.


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What's needed?

  • Prior to the commencement of any discussions, the entire class engaged in an online E-tivity, an icebreaker to promote group cohesion, in this case, construction of a homepage on the VLE to introduce themselves to other module participants (Salmon, G. (2002) E-tivities: The key to active online learning. Kogan Page).

  • To accommodate the new form of assessment, the previous in course essay was dropped in favour of three one hour essays written under exam conditions and submitted electronically via the VLE.


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Module Hits

Outcome 1

  • 87,000 hits in 10 week period, an average of 256 per student per week.

  • 66% of total module hits.


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Hits per post

Outcome 2

  • Average 22 hits to each post - they *are* reading.

  • Ratio highest for general module forum, lowest for week 10.


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Outcome 3

  • Evidence of fatigue.

  • Need to inject new enthusiasm with e-tivities midway through module - eLearning demands novelty for engagement!

Total Hits Per Week


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Random Statistics

  • Very popular with students (feedback).

  • No significant difference in end of module marks.

  • Gender difference: females contribute significantly more hits, but no significant differences in posts or marks.


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Wikis

  • Online collaborative writing: Motivation?

  • Discussion group becomes the tool to moderate and firefight contributions.

  • Assessment scheme records minimal acceptable contribution - online community of practice assesses quality.


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Questions

  • How do you motivate learners to engage the full potential of eLearning?

  • What's in it for me?


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www-micro.msb.le.ac.uk/AJC/talks.html

alan.cann@leicester.ac.uk

Education costs money

- ignorance costs more.


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