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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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slide1
Rich Content,

Not Content-Rich

Alan J. Cann

Department of Biology

University of Leicester.

bloom s taxonomy of educational objectives benjamin bloom 1956
Evaluation

Synthesis

Analysis

Application

Understanding

Knowledge

Bloom's Taxonomy of Educational ObjectivesBenjamin Bloom, 1956:
e learning
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)

rich content
Rich Content
  • Push technology
  • Keyword-specific RSS
  • Dynamically generated content:
    • PHP
    • Javascript
    • HTML
why discuss
Why Discuss ?
  • Discussion promotes reflection.
  • Deep versus superficial learning.
  • Reflection encourages higher level learning competencies and inclusivity (ownership of learning).
the model
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.
what s needed
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.
outcome 1
Module HitsOutcome 1
  • 87,000 hits in 10 week period, an average of 256 per student per week.
  • 66% of total module hits.
outcome 2
Hits per postOutcome 2
  • Average 22 hits to each post - they *are* reading.
  • Ratio highest for general module forum, lowest for week 10.
outcome 3
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

random statistics
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.
wikis
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.
questions
Questions
  • How do you motivate learners to engage the full potential of eLearning?
  • What's in it for me?
slide15
www-micro.msb.le.ac.uk/AJC/talks.html

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