Personal recommender systems for learners in lifelong learning networks

1. Personal Recommender
Systems for learners in
lifelong learning networks
Denny Abraham Cheriyan
Dhanyatha Manjunath
Roja Ennam
Shruthi Ramamurthy

2. Objectives
● Lifelong Learning and need for Recommendation
Systems in Learning Networks
● How requirements of recommendation systems are
different for learning communities
● Primer on recommendation techniques
● Knowledge Maps for Self Directed Learning and
Recommendation

3. Lifelong Learning
● Not just limited to childhood/school. Learn at work place,
personal growth
● Control - What,When, Where and How
● Self-Directed
● Freedom
● Learner Centric
● Demand-pull approach (Minds of Fire - John Seely Brown and
Richard P. Adler)

4. Why Recommendation in Learning networks
● Using recommendation to provide navigational support in a
learning network
● Need advice to decide on most suitable learning activities to
meet the learner’s individual learning goal (or Learner
group)
● Self-directed learners need an overview of the available
learning activities and must be able to determine which of
these would match their personal needs, preferences, prior
knowledge and current situation

7. Activity
Can we use commercial recommendation technique to a
learning management system ? Are there any specific
requirements of a LMS, you think that a recommender system
must consider?

8. The specific demands for learning
• the importance of the context of learning
• the inherent novelty of most learning activities
• the need for a learning strategy
• the need to take changes and learning processes into
account.

9. Personal Recommender systems for lifelong
learners(Requirements)
● Learning goal
● Prior Knowledge
● Preferences and Learner characteristics
● Learner grouping and stereotyping (e.g., study time, study
interests and motivation to learn)
● Ratings of the learning activities
● Historical information about the successful study behaviour
● Apply the learning strategies

10. Activity 1
Choose an online learning community. What type of
recommendations would you prefer to help achieve your
learning goals?

11. Data for Recommendation Systems
● Obtained from Data Analytics (Shum & Ferguson)
○ Social Network Analysis
○ Content Analysis
○ Discourse Analysis
○ Disposition Analysis
○ Context Analysis
● Learner Specific Analysis (Wise, Zhao, & Hausknecht)
○ Micro and Macro Level Analysis

12. Recommendation Techniques
● Collaborative Filtering
● Content-based Filtering

13. Collaborative Filtering Techniques:
Collaborative Filtering Techniques use the collective behaviors
of all the learners in the Learning Network.
The following are some collaborative filtering techniques:
● User -based Collaborative Filtering.
● Item - based Collaborative Filtering.

14. User- based Collaborative Filtering
User-based techniques correlate users by mining their ratings
and then recommend new items that were preferred by similar
users.
Figure 1

15. Item-based Collaborative Filtering
Item-based techniques correlate the items by mining item
ratings and then recommend new,similar items.

16. Advantages of Collaborative
Filtering
Disadvantages of Collaborative
Filtering
● The information that is being
considered for
recommendation is domain
independent.
● It does not depend on
analysing the content
provided by the user.
● Cold start problem
● Can not handle new users
and new items
● Unpopular tastes are not
strongly supported(Long tail
in learning as discussed in
Minds of Fire is not possible)

17. Question
How do you recommend items to a user when he is new to the
community?

18. Prompt the user to rate certain movies before being able to provide personalized recommendation.

19. Content Based Recommendation Technique
● Maintains a user profile- a structured representation of user interests.
● Matches the descriptions of items with the attributes of the user profile to recommend
similar items.
● Results of analysis represents the user’s level of interest in that item.
● If user preferences are accurately reflected by a user profile, the recommendation is
advantageous.
● Useful in handling the ‘cold start’ problem as no behaviour data is needed.

20. Summary of Content based
Recommendation System
Advantages Disadvantages
Useful in solving cold start problem-
When a user is new to the community,
Overspecialization- items that are highly
correlated with user profile or interest are
only recommended.
Can map user needs to the items-
Attributes of the user profile are matched
with the attributes of the item.
Can work with information that can be
described as a set of attributes-
Learning content/Items must be classified
into categories.Needs Category modelling
and maintenance.Sensitive to changes to learner profile-
Change in the value of the attributes of
learner profile affect recommendation
results.

21. Usefulness of Recommendation Techniques in LN
Content based Recommendation
● Keeps the learner on track with his/her learning goals
● Helps the user to specialize in a domain.
● Useful in recommending items to a user, when he/she is new to the
community.
● Can map the characteristics of lifelong learners to that of learning
activities. For example: learning goal, prior knowledge, available study
time.
Collaborative Filtering
● Useful for Learner Networks which offer courses in different domains.
● Learning activities which are frequently positively rated and their sequence
can be identified as popular learning tracks.
● Could use learner information to group similar learners together in a
learning group.

22. Hybrid Recommendation Technique
● A combination of recommendation techniques constitutes a
hybrid recommendation or a recommendation strategy.
● Hybrid recommendation technique could provide the most
accurate recommendations by compensating for the
disadvantages of single techniques.
Example:
If no efficient information is available to carry out CF techniques, it
would switch to a CB technique.
● Uses historical information about users or items to decide
which specific recommendation technique provides the highest
accuracy.

23. Related Courses on Coursera

24. Capturing Learning Goals on Coursera

25. Shruthi’s Recommendation Denny’s
Recommendation
Email Recommendations at Coursera

26. How recommendations adapts and changes over time

27. How recommendations adapts and changes over time

28. How recommendations adapts and changes over time

29. Knowledge Maps and Khan Academy
● Each node in the graph represents a topic, with exercises
related to the topic. If the user masters the topic it turns
blue.
● Have to manually look at the entire concept map and
manually choose path
● The large number of options available will be overwhelming

30. Knowledge Maps and self directed learning
● Fits well with self-directed learning
● Not constrained by a course start date and end date
● Breakdown course into independent topics. Can change
path mid-way and still use whatever knowledge has been
accrued previously
● Can choose your own path based on goals
● Can discover new topics of interest (Poker and Probability,
Game Theory is related to both Economics and Artificial
Intelligence)

31. Knowledge Maps and Recommendation
● Knowledge maps can be overwhelming (Need an agent to
direct you)
● Users can be represented as paths in a network
● Can recommend paths, based on other users who have
mastered similar paths
● Ability to suggest paths based on goals (Search)

32. ● Domain knowledge required to construct knowledge maps
(prerequisites).
● Content curation, map construction will have to be done
manually
● Complexity in generalizing Knowledge Maps
Knowledge Maps - Disadvantages

33. Adaptive Intelligent Support Systems
● An adaptive support system improves learning and
collaboration
● Personalized and tailored to student needs
● Provides more relevant support as the number of support
instances increases

34. Recommending Users to Learner Groups
● Characteristics of the current learner could be taken into
account to allocate learners into groups.
● Stereotype recommendation technique can be used to
allocate learners into groups if no behavior data is available
● Learner grouping can focus on relevant learning
characteristics (eg - similarities in learning behavior, study
time, study interests, motivation to learn).

35. Factors that influence groups
● Personality characteristics (introvert/extrovert)
● Status (high/low status)
● Gender
● Race
● Level of conflict (too less or too much conflict)
● Lack of co-ordination
● Negative socio-emotional processes

36. Activity 2
From Piazza list out the information that can be captured to form learner groups.
How will you use this information to recommend who to collaborate with?
In Webbs paper, some of the factors that influence groups -
● Personality characteristics (introvert/extrovert)
● Status (high/low status)
● Gender
● Race
● Level of conflict (too less or too much conflict)
● Negative socio-emotional processes

37. References
● H. Drachsler, H.G.K. Hummel and R. Koper , ‘Personal recommender
systems for learners in lifelong learning networks: the requirements,
techniques and model’, Open University of the Netherlands
● Anderson, T. , ‘Toward Theory of Online Learning’ , Athabasca University,
(pp. 33–60)
● Ounnas, A., Davis, H. C., & Millard, D. E. (2009). A Framework for Semantic
Group Formation in Education. Educational Technology & Society, 12 (4),
43–55.
● John Seely Brown and Richard P. Adler, Minds of Fire
● Erin Walker & Nikol Rummel & Kenneth R. Koedinger, Adaptive Intelligent
Support to Improve Peer Tutoring in Algebra, International Artificial
Intelligence in Education Society 2013
● Simon Buckingham Shum and Rebecca Ferguson, Social Learning Analytics,
Technical Report KMI-11-01, June 2011
● Alyssa Friend Wise, Yuting Zhao, Simone Nicole Hausknecht, ‘A Pedagogical
Model for Intervention with Embedded and Extracted Analytics’, Simon Fraser
University
● Noreen M Webb, ‘Information Processing Approaches to Collaborative
Learning’