Use of Contextualized Attention Metadata for Ranking and Recommending Learning Objects Xavier Ochoa, ESPOL, Ecuador Erik Duval, KULeuven, Belgium

Agenda What is the problem? Use of CAM for Ranking and Recommending Demo: ShareLOk Conclusions

What is the problem?

Use of CAM for Ranking and Recommending

Demo: ShareLOk

Conclusions

Human Review

Human Review

TFIDF

What is the problem? A Ranking mechanism must be: MEANINGFUL and SCALABLE

A Ranking mechanism must be:

MEANINGFUL and SCALABLE

Ranking Strategies for LO There is a lot of ideas that could help to implement a Ranking Mechanism for Learning Objects BUT THE SEMANTICS ARE DIFERENT Web pages  Papers  LO Eg. A link from one page to other means support. A link from one LO to other does not carry the same meaning.

There is a lot of ideas that could help to implement a Ranking Mechanism for Learning Objects

BUT THE SEMANTICS ARE DIFERENT

Web pages  Papers  LO

Eg. A link from one page to other means support. A link from one LO to other does not carry the same meaning.

Network Analysis There is not a linking structure between LO like in Web pages or Scientific papers But we have plenty of relations between different entities and LO Authors, Instructors, Readers, Courses, LMS The main source of this information can be extracted from Contextualized Attention Metadata (CAM)

There is not a linking structure between LO like in Web pages or Scientific papers

But we have plenty of relations between different entities and LO

Authors, Instructors, Readers, Courses, LMS

The main source of this information can be extracted from Contextualized Attention Metadata (CAM)

Lifecycle Actions Main Information Source Creation Creating author, components Authoring tool, Components Labeling Labeling metadata format, origin, confidence Authoring tool or Metadata generator Offering Inserting inserter LOR or Sharing app. Selecting Searching query, results LOR’s search tool Recommending objects recommended Recommender Browsing Time LOR or Recommender Selecting object identifier LOR or Recommender Using Publicating LMS context LMS Sequencing list of sequenced objects ID tool or Packager Viewing Time, tool used Browser or app. Annotating rate or review LMS Retaining Retaining decision to keep or delete LMS

Network Analysis 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

CAM as K-Partite Graph

Network Analysis CAM as 2-Partite Graph (User-Object)

CAM as 2-Partite Graph (User-Object)

Network Analysis Metrics From this Graph we can create serveral metrics. For example: Popularity Rank (PR) Author-Corrected Popularity Rank (ACPR) Weighted Popularity Rank (WPR) “ Best-Sellers” Rank (BSR)

From this Graph we can create serveral metrics. For example:

Popularity Rank (PR)

Author-Corrected Popularity Rank (ACPR)

Weighted Popularity Rank (WPR)

“ Best-Sellers” Rank (BSR)

Similarity Metric The K-Partites Graphs generated from CAM can be folded into Normal Graphs These graphs have a link between two nodes in the same partition, if they have been linked to the same object in the folded partition.

The K-Partites Graphs generated from CAM can be folded into Normal Graphs

These graphs have a link between two nodes in the same partition, if they have been linked to the same object in the folded partition.

Similarity Metric

Similarity Metric Object Similarity based on Downloads Object Similarity based on Re-Use Users similarity based on Downloads Author similarity based on Re-Use of Components

Object Similarity based on Downloads

Object Similarity based on Re-Use

Users similarity based on Downloads

Author similarity based on Re-Use of Components

Object Similarity

Communities ARIADNE

Personalized Rank We can create a profile of the user based on its CAM We will 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.

We can create a profile of the user based on its CAM

We will 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.

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, we can use very recent CAM to generate recommendations based on what the user is focusing his/her attention at the moment.

If the CAM is considered not only as a source for historic data, but also as a continuous stream of contextualized attention information, we can use very recent CAM to generate recommendations based on what the user is focusing his/her attention at the moment.

Demo

Conclusions Meaningful and Scalable Ranking Metrics can be created for Learning Objects as has been done for other fields But first we need to measure, analyze and understand the data about Learning Objects… We need Learnometrics Development in Learnometrics could be easily translated in better tools

Meaningful and Scalable Ranking Metrics can be created for Learning Objects as has been done for other fields

But first we need to measure, analyze and understand the data about Learning Objects… We need Learnometrics

Development in Learnometrics could be easily translated in better tools

Thank You! Questions, Comments, Critics…. Are all welcome!!

Questions, Comments, Critics….

Are all welcome!!