Metrics For Learning Object Metadata
-
Upload
xavier-ochoa -
Category
Technology
-
view
1.479 -
download
0
description
Transcript of Metrics For Learning Object Metadata
![Page 1: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/1.jpg)
Xavier Ochoa, ESPOL
Erik Duval, KULeuven
![Page 2: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/2.jpg)
Context of the Research
![Page 3: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/3.jpg)
Learnometrics• Study empirical regularities on data• Develop mathematical models• To understand the influence/impact of LO
• Produce useful metrics
![Page 4: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/4.jpg)
Example of LearnometricsNumber of Downloads does not depends
on number of Object Published
![Page 5: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/5.jpg)
Example of Learnometrics 2The Download of objects follows a
Power Distribution
![Page 6: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/6.jpg)
More than Learning Object Metadata
• All information about Learning Objects– Object Itself– LOM / DC / MPEG7– Contextual Attention Metadata (CAM)– Sequencing Information (SCORM / LAMS)
![Page 7: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/7.jpg)
Uses of Learning Object Metadata Metrics
• To improve Learning Object Tools– Indexing Material
• LOM Quality Metrics
– Searching / Finding• Ranking Metrics • Recommendation Metrics
– Reuse• Adaptation Metrics
![Page 8: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/8.jpg)
Learning Object Metadata Quality
The production, management and consumption of Learning Object
Metadata is vastly surpassing the human capacity to review or process these
metadata.
![Page 9: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/9.jpg)
LOM Quality Metrics
![Page 10: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/10.jpg)
Evaluation LOM Quality MetricsTextual Information Content correlates
highly with human-assigned quality score
![Page 11: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/11.jpg)
LOM Quality Visualization
![Page 12: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/12.jpg)
![Page 13: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/13.jpg)
Ranking Metrics
• Network-Analysis Rank (Popularity)– Most users prefer these objects…
• Similarity Recommendation (Clustering)– If you like this LO, you will also like …
• Personalized Rank (Profiling)– Based on your history, you will like these objects…
• Contextual Recommendation Rank– This object seems right for the lesson you are
creating right now…
![Page 14: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/14.jpg)
Network-Analysis Metrics
• CAM as K-Partite Graph
O 1
O 2
O 3
C 1
C 2
U 1 U 2
A 1
A 2
User Partition
Course Partition Author Partition
Object Partition
![Page 15: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/15.jpg)
Application
![Page 16: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/16.jpg)
Similarity Metric
U1
U2
U3
O1
O2
O3
U4
U5
U6
U1
U2
U3
U4
U6
U5
2-Partite Graph (User and Objects) Folded Normal Graph (Users)
![Page 17: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/17.jpg)
Communities ARIADNE
![Page 18: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/18.jpg)
Application
![Page 19: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/19.jpg)
Personalized Rank
• We can create a profile of the user based on its CAM
• We can use the same LOM record to store this profile
• Instead of having a crisp preference for a value, the user will have a fuzzy set with different degrees of “preference” for all the possible values.
![Page 20: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/20.jpg)
Personalized RankTopic Importance = 0.9
Language Importance = 0.6
U1 = {(0.8/ComputerScience + 0.2/Physics), (0.6/English + 0.2/Spanish + 0.2/French)}
O1 = {(1.0/ComputerScience), (1.0/Spanish)}
O2 = {(1.0/Physics, 1.0/English)}
Rank(O1) = 0.9*0.8 + 0.6*0.2 = 0.84
Rank(O2) = 0.9*0.2 + 0.6*0.6 = 0.54
![Page 21: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/21.jpg)
Contextual Recommending
• If the CAM is considered not only as a source for historic data, but also as a continuous stream of contextualized attention information.
• LMSs could provide much more contextual information.
• Use techniques to exploit contextual information. Most simple: Term Extraction
![Page 22: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/22.jpg)
Evaluation
• Experimentation– Ranking vs. No Ranking– Different Ranking Strategies/Combinations
• User feedback– Machine Learning – Optimization
• Transference– Other reusable components
![Page 23: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/23.jpg)
Research Questions (Summary)
• How information about Learning Objects (Learning Object, LOM, CAM, SCORM) can be used to create a relevance/quality metrics to rank/recommend Learning Objects?
• Are the resulting metrics feasible to calculate, easy to integrate in existing applications and meaningful/useful for the end users?
• Can these metrics be also applied to other reusable components?
![Page 24: Metrics For Learning Object Metadata](https://reader034.fdocuments.us/reader034/viewer/2022052602/559b078a1a28ab87758b471f/html5/thumbnails/24.jpg)
Thank you, GraciasComments, Suggestions, Critics… are
Welcome!
More Information:http://ariadne.cti.espol.edu.ec/M4M