Personal recommender systems for learners in lifelong learning networks

Personal Recommender

Systems for learners in

lifelong learning networks

Denny Abraham Cheriyan

Dhanyatha Manjunath

Roja Ennam

Shruthi Ramamurthy

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

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)

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

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?

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.

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

Activity 1

Choose an online learning community. What type of

recommendations would you prefer to help achieve your

learning goals?

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

Recommendation Techniques

● Collaborative Filtering

● Content-based Filtering

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.

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

Item-based Collaborative Filtering

Item-based techniques correlate the items by mining item

ratings and then recommend new,similar items.

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)

Question

How do you recommend items to a user when he is new to the

community?

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

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.

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.

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.

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.

Related Courses on Coursera

Capturing Learning Goals on Coursera

Shruthi’s Recommendation Denny’s

Recommendation

Email Recommendations at Coursera

How recommendations adapts and changes over time

How recommendations adapts and changes over time

How recommendations adapts and changes over time

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

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)

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)

● 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

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

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).

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

Activity 2From 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

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’