Personal recommender systems for learners in lifelong learning networks

  • Published on
    17-Jul-2015

  • View
    48

  • Download
    0

Transcript

Personal Recommender Systems for learners in lifelong learning networks Denny Abraham CheriyanDhanyatha ManjunathRoja EnnamShruthi Ramamurthy

ObjectivesLifelong Learning and need for Recommendation Systems in Learning NetworksHow requirements of recommendation systems are different for learning communities Primer on recommendation techniquesKnowledge Maps for Self Directed Learning and RecommendationLifelong Learning Not just limited to childhood/school. Learn at work place, personal growth Control - What,When, Where and HowSelf-DirectedFreedom Learner Centric Demand-pull approach (Minds of Fire - John Seely Brown and Richard P. Adler)

The demand-pull approach is based on providing students with access to rich (sometimes virtual) learning communities built around a practice.It is passion-based learning, motivated by the student either wanting to become a member of a particular community of practice or just wanting to learn about, make, or perform something.Why Recommendation in Learning networksUsing recommendation to provide navigational support in a learning networkNeed advice to decide on most suitable learning activities to meet the learners 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 situationWith the numerous number of options available, it might be overwhelming for the end-user to select learning activities.finding their way through the possibilities offered

ActivityCan 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?Match personal needs, preferences, prior knowledge and current situationDeal with information about the learners (users)and learning activities (items) and would have to combine different levels of complexityfor the different learning situations the learners may be involved in.

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.

distance education. - students rely on the information online - no face to face meetings. be aware of our target group of learners.Learning math is different from learning historyLearning strategy : easier courses before harder ones - preferred media and the characteristics of the learning content when designing instructionvarious stages of learning , educational, psychological, social and cognitive science.

Personal Recommender systems for lifelong learners(Requirements)

Learning goalPrior KnowledgePreferences and Learner characteristics Learner grouping and stereotyping (e.g., study time, study interests and motivation to learn)Ratings of the learning activitiesHistorical information about the successful study behaviourApply the learning strategies

What learners want to learn proficiency level of the learning activity should fit the proficiency level of the learner (prior knowledge).preferences (e.g., preference for distance education or problem-based learning) for learning (learner characteristics).Learners with the same learning goal or similar study time per week could benefit from the ratings received from more advanced learners.historical information about the successful study behaviour of the more advanced learners in the same learning networklearning strategies derived from educational psychology researchgo from simple to more complex tasks or gradually decrease the amount of contact and direct guidance.

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

Resources: Learning articles, Other users with domain expertise, Remedial Exercises. From an embedded and extracted perspective

Data for Recommendation SystemsObtained from Data Analytics (Shum & Ferguson)Social Network AnalysisContent AnalysisDiscourse AnalysisDisposition AnalysisContext Analysis

Learner Specific Analysis (Wise, Zhao, & Hausknecht)Micro and Macro Level AnalysisNetworz,smdsmdasdk AnalysisRecommendation TechniquesCollaborative FilteringContent-based FilteringCollaborative 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 FilteringUser-based techniques correlate users by mining their ratings and then recommend new items that were preferred by similar users.

Figure 1

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

Advantages of Collaborative FilteringDisadvantages of Collaborative FilteringThe information that is being considered for recommendation is domain independent.It does not depend on analysing the content provided by the user.

Cold start problemCan not handle new users and new itemsUnpopular tastes are not strongly supported(Long tail in learning as discussed in Minds of Fire is not possible)Scenario where there is no sufficient information to give the recommendations.This problem is because collaborative filtering depends a lot on the user behaviour from the past.

QuestionHow do you recommend items to a user when he is new to the community?recommend courses

Central learner profiles where users can list courses they have taken from various MOOCS. Like linkedIn profile to solve the cold start problem..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 users 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.

Content based Recommendation is a technique which can be used in such scenario, which maintains user profile containing data to user profile. It matches the preferences in user`s profile with the descriptions of the items, and Summary of Content based Recommendation SystemAdvantagesDisadvantagesUseful 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 LNContent based RecommendationKeeps the learner on track with his/her learning goalsHelps 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 FilteringUseful 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 TechniqueA 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.

The use of a combination ofrecommendation techniques constitutes a recommendation strategy

Bring it back to question 1

Recommendation strategies use domain-specific or historical information about users oritems to decide which specific recommendation technique provides the highest accuracyfor the current user

Related Courses on Coursera

Capturing Learning Goals on Coursera

Capturing Learning goals - I'm assuming that based on your learning goal they'll decide what they do with your data. Drop out rates. I haven't finished a course on coursera yet. I generally watch videos on random topics in these courses

Shruthis RecommendationDennys RecommendationEmail 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 pathThe large number of options available will be overwhelming

The audience of khan academy is targeted towards high school studentsKnowledge Maps and self directed learningFits well with self-directed learningNot 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 previouslyCan choose your own path based on goalsCan discover new topics of interest (Poker and Probability, Game Theory is related to both Economics and Artificial Intelligence)

Have a lot of overlap between courses at ASUKnowledge Maps and RecommendationKnowledge maps can be overwhelming (Need an agent to direct you)Users can be represented as paths in a networkCan recommend paths, based on other users who have mastered similar pathsAbility to suggest paths based on goals (Search)

Obtaining metadata information

Ontology modelling and maintenance is required

Can suggest various paths to goal. Based on time constraints, previous knowledge, etc. Analogous to Google Maps (Best Route, Fewer transfers, Less Walking)Domain knowledge required to construct knowledge maps (prerequisites).Content curation, map construction will have to be done manuallyComplexity in generalizing Knowledge MapsKnowledge Maps - DisadvantagesAdaptive Intelligent Support SystemsAn adaptive support system improves learning and collaborationPersonalized and tailored to student needsProvides more relevant support as the number of support instances increasesErins paper talks about how adaptive systems can assist tutors and foster collaborationRecommending Users to Learner GroupsCharacteristics 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 availableLearner grouping can focus on relevant learning characteristics (eg - similarities in learning behavior, study time, study interests, motivation to learn).Current paper talks about grouping users based on similarityFactors that influence groupsPersonality characteristics (introvert/extrovert)Status (high/low status)GenderRaceLevel of conflict (too less or too much conflict)Lack of co-ordinationNegative socio-emotional processes

Webbs paper talks about other factors that effect groups. Dhanyatha mentioned about Hybrid Collaboration. we could use a mix of recommendation techniques. We should strike the right balance(who defines what the right balance is) between these factors. As Dhanyatha mentioned about Hybrid Collaboration. We could use a mix of recommendation techniquesActivity 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)GenderRaceLevel of conflict (too less or too much conflict)Negative socio-emotional processes

Where can information be captured - At signup, after sign up (user behavior), performance of previously assigned groupsConsider Piazza and the discussion activity we participate in. f the professor was not assigIning groupsReferences 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 NetherlandsAnderson, T. , Toward Theory of Online Learning , Athabasca University, (pp. 3360)Ounnas, A., Davis, H. C., & Millard, D. E. (2009). A Framework for Semantic Group Formation in Education. Educational Technology & Society, 12 (4), 4355. 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 2013Simon Buckingham Shum and Rebecca Ferguson, Social Learning Analytics, Technical Report KMI-11-01, June 2011Alyssa Friend Wise, Yuting Zhao, Simone Nicole Hausknecht, A Pedagogical Model for Intervention with Embedded and Extracted Analytics, Simon Fraser UniversityNoreen M Webb, Information Processing Approaches to Collaborative Learning

Recommended

View more >