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Personalization Challenges in E-Learning

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In the talk presented at last RecSys conference we discussed some of the common challenges in the e-learning industry that we are facing at Cloud Academy such as: heterogeneity of content to recommend and specific recommendation goals targeting the user training objectives.

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Personalization Challenges in E-Learning

  1. 1. Personaliza+on Challenges in E-Learning Roberto Turrin 29th Aug 2017
  2. 2. About us Roberto Turrin Head of Technology, PhD @robytur cloudacademy.com/
  3. 3. Personalized Thema4c path Search/explore CTR CONSUM. DRIVER CCR
  4. 4. Intent-based User task - standard Roberto Turrin Personaliza0on Challenges in E-Learning Watching a movie Listening to a song Planning a travel I know what I want to achieve I don’t know what to do Explora0on level Discovery Goal-driven Search Watching a movie with my partner Planning a travel with my family I know how I want to get sth Watching the last movie of TaranPno Finding the Pmetable of flights to Madrid Standard Enjoyment
  5. 5. Intent-based User task - educaPon Roberto Turrin Personaliza0on Challenges in E-Learning Studying something I know what I want to achieve I don’t know what to do Explora0on level Discovery Goal-driven Search Learning Python Preparing for a cerPficaPon TesPng the level of knowledge Mastering BBQ cooking Becoming a data scienPst I know how I want to get sth Doing an advanced course about
 deep learning Educa4on Learning
  6. 6. User profile - interests Standard Roberto Turrin Personaliza0on Challenges in E-Learning Interests/tastes Educa4on Interests/tastes Comedy vs drama movies Rock vs pop songs
 Statues vs painPngs Sea vs mountain vacaPon Astrology Machine Learning What I am interested in
 What I prefer What I am interested in
 What I prefer Enjoyment Learning
  7. 7. User profile - interests Roberto Turrin Personaliza0on Challenges in E-Learning Educa4on Interests/tastes Astrology Machine LearningWhat I am interested in
 What I prefer Learning ?
  8. 8. User profile - educaPon-specific Standard Roberto Turrin Personaliza0on Challenges in E-Learning Interests/tastes Educa4on Interests/tastes Comedy vs drama movies Rock vs pop songs
 Statues vs painPngs Sea vs mountain vacaPon Astrology Machine Learning Skills/knowledge Java Excel Novice in ML Expert of astrology NLP What I am interested in
 What I prefer What I am interested in
 What I prefer What I know Enjoyment Learning
  9. 9. User profile - signals Roberto Turrin Personaliza0on Challenges in E-Learning What I know What I am interested in Consuming a resource What I am interested in Educa4onStandard The activities done by the user affect his skills. 
 In fact, as I study a change my knowledge, I learn more about a topic, I increase my understanding, I enable myself to learn something more complex on the same topic. Since skills are part of my profile, I practically change my profile. We can so say that use profile in education really changes over time Watching/discovering a new kind of movie
 might modify my interests
  10. 10. User profile & User task Roberto Turrin Personaliza0on Challenges in E-Learning Educa4on User profile Java Excel Novice in ML Expert of astrology NLP Learning Python Preparing for a cerPficaPon TesPng the level of knowledge Mastering BBQ cooking Becoming a data scienPst User task “Changing what I know” What I want to achieve/know What I know
  11. 11. Heterogeneity - resources Roberto Turrin Personaliza0on Challenges in E-Learning Learning Tes0ng Video lectures Hands-on Quizzes
  12. 12. Time evoluPon Roberto Turrin Personaliza0on Challenges in E-Learning S3 BigQuery 0me Learning Tes0ngLearning Learning Recommender goal: • “providing learning resources to make the user profile close to the user goal” • “providing training resources to improve the confidence of user profile representa+on”
  13. 13. Heterogeneity - connecPons Roberto Turrin Personaliza0on Challenges in E-Learning Video lectures Hands-on Quizzes
  14. 14. Heterogeneity - bundles and paths Roberto Turrin Personaliza0on Challenges in E-Learning Video lectures Hands-on Quizzes Learning paths Exams
  15. 15. User raPngs Roberto Turrin Personaliza0on Challenges in E-Learning User ra+ngs not par+cularly useful for the recommender: • They are rare. Most of user signals are implicit. • They are more related to the quality of the resource than to the interest of the user or to their uPlity for the user goal. • They are more useful for the content producer than for the user as they represent a feedback for the content. 
 In fact, there is a high correlaPon between the raPng mean and the number of negaPve and posiPve comments.
  16. 16. Algorithms User profile transparency is o^en a requirement: • the user profile represents the current user skills • the user is curious about “himself” Roberto Turrin Personaliza0on Challenges in E-Learning Experiments with pure collabora0ve did not succeed • not aligned with the user learning task • a lot of new content ? • Currently, a hybrid is being used • Working on embedding learning tasks through an ontology.
  17. 17. Other peculiariPes of on-line training: open points Lack of a physical class: • social features • forum • pair-tasks Roberto Turrin Personaliza0on Challenges in E-Learning User recommendaPons Time constraints: • user learning pace • user deadlines • resource Pming • resource Pme availability Planning
  18. 18. Conclusions Roberto Turrin Personaliza0on Challenges in E-Learning • Learning goals drive most of user consumpPons. • User profile also represents skills. • The main user goal can be translated into “changing my skills”, i.e., changing my profile. • Consequently, • profile conPnuously changes over Pme. • profile is something the user is interested into. • Use carefully raPngs and collaboraPve filtering
  19. 19. Thank you! Roberto Turrin roberto.turrin@cloudacademy.com 29th Aug 2017 cloudacademy.com/

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