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

Human Review

Human Review

TFIDF

What is the problem?

• 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.

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)

Lifecycle Actions Main Information Source

Creation Creatingauthor, components Authoring tool,

Components

Labeling Labelingmetadata 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

Network Analysis

• 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)

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.

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)

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

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.

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.

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

Thank You!

• Questions, Comments, Critics….

Are all welcome!!