advancements in large scale assessments heiko rolke
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Advancements in Large-scale
Assessment
Heiko Rlke & Krisztina TothDIPF - Deutsches Institut fr Internationale
Pdagogische Forschung
Frankfurt am Main
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Overview
Trends in LSA
Closer Look at Trends
Example: Making Use of Data
Challenges in LSA
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Trends
Main trend: CBA
Sub-trends/effects of CBA:
CAT
towards formative assessment
Complex items
Simulation, interconnection
Interweaving different tests, questionnaires, etc.
Big data
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Trend: CBA
Computer-based Assessment has to serve a purpose
Costs?
Time!
Validity!
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CAT
Also not practiced for its own sake
Not as important in pure summative assessment
But:
Strict time limits (e.g. PIAAC)
Growing demand for formative aspects in summative
assessment
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Complex Items
Closer at reality
Simulation
Most important:
Ability to assess new domains
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Examples of Complex Items
Simulation of an Email / Web Scenario
The test person receives an email and should book
cinema tickets online.
Dynamic Model (MicroDYN)
The test person should explore and master a dynamic
system with input (exogenous) variables influencing
output (endogenous) variables.
Automaton with Finite State Machine (MicroFIN)
The test person should interact with a mobile phone and set time to
summertime.
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Interweaving of Tests and
Questionnaires
Framing
test only if preconditions are fulfilled
Double-check
findings of questionnaire
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Big Data
Log data, not only results
Find out what is going on
Make use of data
E.g. partial scoring
-> Examples from
PIAAC PS-TRE
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Simulation-based assessment
Modern assessments:
real life situations: e.g. web environments
complexity of instrumentvarious ways
Monitoring studentsactions - log files Analysis of test-taking paths is a relatively new field in education
(reason: printed vs. computer-based data collection)
Process data requires to consider and evaluate methods
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Aim
to investigate how log data can be integrated intothe process of
educational assessment and evaluation to support researchers
and practitioners in making use of the data assembled in
computer-based test delivery
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Aim
to investigate how log data can be integrated intothe process of
educational assessment and evaluation to support researchers
and practitioners in making use of the data assembled in
computer-based test delivery
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Hypertext Item
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Example
log
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Promising methods
Statistics and visualisation
Clustering
Classification
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Process Measures
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1. Number of page visits
2. Number of different page visits
3. Visit of relevant page
4. Time spent on the relevant page
5. Ratio of time spent on the relevant page
6. Ratio of time spent on the opening screen
7. Completion time
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Statistics
Process measures MeanStandard
Deviation
Number of page visits 5.18 3.13
Number of different page visits 2.38 1.38
Time spent on the relevant page 10.87 9.77
Ratio of time spent on the relevant page .14 .11
Ratio of time spent on the opening screen .64 .18
Completion time
67.74
27.49
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Distribution of different page
visits
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Distribution of different page
visits
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Visualisation
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Visualisation
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Cluster Analysis
Features Cluster 1 Cluster 2 Cluster 3 Cluster 4
Nr. of page visits
Nr. of different page visitsCompletion time
Relevant page visited (Y/N)
Ratio of time on relevant page
Ratio of time on starting page
Distribution of sequences (%) 23% 16% 34% 27%
Ratio of correct responses
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Cluster Analysis
Features Cluster 1 Cluster 2 Cluster 3 Cluster 4
Nr. of page visits
Nr. of different page visitsCompletion time
Relevant page visited (Y/N)
Ratio of time on relevant page
Ratio of time on starting page
Distribution of sequences (%) 23% 16% 34% 27%
Ratio of correct responses 25.0% 1.7% 92.6% 84.4%
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Cluster Analysis
Features Cluster 1 Cluster 2 Cluster 3 Cluster 4
Nr. of page visits .72 5.41
Nr. of different page visits .36 2.54Completion time 28.38 62.08
Relevant page visited (Y/N) No No
Ratio of time on relevant page 0 0
Ratio of time on starting page .93 .60
Distribution of sequences (%) 23% 16%
Ratio of correct responses 25.0% 1.7%
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Cluster Analysis
Features Cluster 1 Cluster 2 Cluster 3 Cluster 4
Nr. of page visits 4.73 9.57
Nr. of different page visits 2.29 4.53Completion time 59.60 91.60
Relevant page visited (Y/N) Yes Yes
Ratio of time on relevant page .20 .17
Ratio of time on starting page .62 .45
Distribution of sequences (%) 34% 27%
Ratio of correct responses 92.6% 84.4%
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Cluster Analysis
Features Cluster 1 Cluster 2 Cluster 3 Cluster 4
Nr. of page visits 5.41 4.73
Nr. of different page visits 2.54 2.29Completion time 62.08 59.60
Relevant page visited (Y/N) No Yes
Ratio of time on relevant page 0 .20
Ratio of time on starting page .60 .62
Distribution of sequences (%) 16% 34%
Ratio of correct responses 1.7% 92.6%
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Cluster Analysis
Features Cluster 1 Cluster 2 Cluster 3 Cluster 4
Nr. of page visits .72 5.41 4.73 9.57
Nr. of different page visits .36 2.54 2.29 4.53Completion time 28.38 62.08 59.60 91.60
Relevant page visited (Y/N) No No Yes Yes
Ratio of time on relevant page 0 0 .20 .17
Ratio of time on starting page .93 .60 .62 .45
Distribution of sequences (%) 23% 16% 34% 27%
Ratio of correct responses 25.0% 1.7% 92.6% 84.4%
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Summary and Conclusions
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Summary and Conclusions
Features Cluster 1 Cluster 2 Cluster 3 Cluster 4
Nr. of page visits .72 5.41 4.73 9.57
Nr. of different page visits .36 2.54 2.29 4.53Completion time 28.38 62.08 59.60 91.60
Relevant page visited (Y/N) No No Yes Yes
Ratio of time on relevant page 0 0 .20 .17
Ratio of time on starting page .93 .60 .62 .45
Distribution of sequences (%) 23% 16% 34% 27%
Ratio of correct responses 25.0% 1.7% 92.6% 84.4%
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Future work
Pilot studysample size
Validation
Other types of items require new process measures
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Future work
Pilot studysample size
Validation
Other types of items require new process measures
We have a lot of work to do:
Software developer
Test developer
Psychometricans
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(Selected) Challenges
Authoring and Management
Delivery
Re-use and exchange
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(Selected) Challenges
Authoring and Management
Delivery
Re-use and exchange
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(Selected) Challenges
Authoring and Management
Delivery
Re-use and exchange
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(Selected) Challenges
Authoring and Management
Delivery
Re-use and exchange