Vossen 2013.05.10 authentic assessment poster
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Transcript of Vossen 2013.05.10 authentic assessment poster
x = , ⋯ ,
x
-score
x
rating profile
δ-score
σ
δ-score
δ
λ-score
= ,⋯ ,
w = , ⋯ ,
p
-score
p
p = , ⋯ ,
holistic approach
σ
δ-score
δ
probability profile
σ
δ-score
δ
frequency profile
f
-score
f
f = , ⋯ ,
analytic approach
standard polarity reverse polarity
weight vector
score vector
reference model
© SQUIRE Research Institute Paul Hubert Vossen 2013-05-10
rating version
frequency version
probability version
score qualification : -curves (δ=0,70) score qualification : σ-curves (δ=0,70)
quality criteria: finite granularity
reverse polaritystandard polarity
score aggregation : random weights score aggregation : equal weights
reference model
© SQUIRE Research Institute Paul Hubert Vossen 2013-05-10
→→
× ≝ 1 − 1 −
1 − 1 −× σ
quality criterion(infinite granularity)
score
δ
impact
δ-score
score qualification
rating version
rating profile
, ⋯⋯⋯ ,x
tolerance
↔ x ≝ x + 1 − xx x
x 1 −1
∑ ∑ , − 1
1,∑
probability version
tolerance
p 1 −1− 1
1,∑
p1− 1
1,∑ p ↔ p ≝ p + 1 − p
probability profile
, ⋯⋯⋯ ,p
≝ 1 − ⊕= 1
• 1 −↔ ≝ + 1 −
lenience
1 − 1 − ≝ ⊕= 1
•, ⋯ ,
scores
s , ⋯ ,
weights
w
λ-score
score aggregation
standard polarity
reverse polarity
− −
+ = 1
−−
+ = 1
score
-score
frequency version
tolerance
f ↔ f ≝ f + 1 − f
frequency profile
, ⋯⋯⋯ ,f f 1 −1
∗ ,∑ − 1∗ ,
f1
∗ ,∑ − 1∗ ,
quality criteria(finite granularity)
tight grading
loose grading tight grading
loose grading
lowest grading
highest grading
highest grading
lowest grading
unbiased grading
¼ ¾0 1½
unbiased grading
¼1 0½¾
highest grademiddle grade
+ < 1 > 1
lowest gradecurvature
calibrating
≤
reference model
© SQUIRE Research Institute Paul Hubert Vossen 2013-05-10