designing statistical learning environments with

of 30 /30
CAL'09 - Brighton - Patrick Wessa Designing Statistical Learning Environments with Educational Compendium Technology

Author: others

Post on 25-Feb-2022

22 views

Category:

Documents


0 download

Embed Size (px)

TRANSCRIPT

Compendium Technology
Design of SLE Empirical Findings Building Guidelines Educational Research Educational Quality
Control
Claerbout's principle*
An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and that complete set of instructions that generated the figures.
*Source: Jan de Leeuw
CAL'09 - Brighton - Patrick Wessa
My question
If academic statisticians find it hard (if not impossible) to verify or review the results in empirical papers, how could we possibly expect students to learn from statistical results without the proper tools to easily review, verify, or challenge them?
CAL'09 - Brighton - Patrick Wessa
Wessa P., “Reproducible Computing: a new Technology for Statistics Education and Educational Research”, IAENG Transactions on Engineering Technologies, Volume II, American Institute of Physics, 2009, forthcoming
CAL'09 - Brighton - Patrick Wessa
Learning Statistics based on the Compendium and Reproducible Computing, Proceedings of the World Congress on Engineering and Computer Science 2008, ISBN: 978-988-98671-0-2,
UC Berkeley, San Francisco, USA
CAL'09 - Brighton - Patrick Wessa
Snapshot of “Blogged” Computation
CAL'09 - Brighton - Patrick Wessa
Social Interaction, Collaboration, Networking, ...
CAL'09 - Brighton - Patrick Wessa
Feedback (Peer Review)
Submitting Peer Review (feedback) is a good learning activity – not a good grading procedure
CAL'09 - Brighton - Patrick Wessa
Lectures 13 weeks (semester)
Week 2-12: Workshops + Peer Assessments
Week 13: Final Exam (multiple choice)
Grades received from Peers do NOT count => there is no penalty for making mistakes!!
The quality of feedback messages is graded by the educator
Week 1 Week 2 Week 3 Week 4 ...
Exam L1 L2 L3 L4 L5
WS1 WS2 WS3 WS4 WS5
Rev 1 Rev 2 Rev 3 Rev 4 ...
...
Week 1 Week 2 Week 3 Week 4 ...
Exam L1 L2 L3 L4 L5
WS1 WS2 WS3 WS4 WS5
Rev 1 Rev 2 Rev 3 Rev 4 ...
...
Female 58 53
Male 53 76
Female 41 45
Male 42 74
Statistical Computation = Core Object of Study Statistical Computation = Core IT Object
=> Communication (peer review) should be an function
of the Computation Hierarchical Parent-Child relationships between
computations are maintained & can be browsed
CAL'09 - Brighton - Patrick Wessa
Y0 (U) Y0 (C) Y1 (U) Y1 (C) Y1*(C)
Correctly Classified (RT) 75.0 % 82.9 % 75.7 % 88.6 % 90.1%
Correctly Classified (CV) 44.6 % 72.9 % 36.6 % 75.2 % 80.2%
Kappa Statistic (RT) 0.6015 0.5914 0.6259 0.7183 0.7345
Kappa Statistic (CV) 0.1382 0.386 0.0201 0.3863 0.4757
Number of leaves 29 13 36 11 7
Size of tree 57 25 71 21 13
Peer Review Moodle Compendium Platform
CAL'09 - Brighton - Patrick Wessa
Overfitting problems
=== Confusion Matrix ===
  a  b  c  d   <­­ classified as  13  1  4  0 |  a = Excellent   1 73  8  0 |  b = Fail   2 18 57  0 |  c = Guess   4  7  4 10 |  d = Pass
Correctly Classified Instances          153               75.7426 % Incorrectly Classified Instances         49               24.2574 % Kappa statistic                           0.6259
=== Confusion Matrix ===
  a  b  c  d   <­­ classified as   1 14  2  1 |  a = Excellent   9 41 26  6 |  b = Fail   4 38 30  5 |  c = Guess   2 14  7  2 |  d = Pass
Correctly Classified Instances           74               36.6337 % Incorrectly Classified Instances        128               63.3663 % Kappa statistic                           0.0201
In-sample Out-of-sample