Experience Discovery: Hybrid Recommendation of Student Activities - - PowerPoint PPT Presentation

Experience Discovery: Hybrid Recommendation

• f Student Activities using Social Network Data

Robin Burke, Yong Zheng, Scott Riley Web Intelligence Laboratory College of Computing and Digital Media DePaul University

Problem

 Service organizations offer many educational programs and activities for youth  Participation (especially by underprivileged youth) is low

 Even though these are the individuals who would benefit the most

 How to get better participation?

 not just a recommendation problem

The Role of Recommendation

 Need for personalization

 Many diverse activities  from basketball to poetry to robots to knitting  Low tolerance for imprecise results

 Need for system initiative

 user research shows that students are unlikely to search and browse  To “pull” opportunities  system should “push” suggestions  we are considering mobile platforms

Partners

 Digital Youth Network

 service organization focused on the creation of digital media  Nichole Pinkard

 YouMedia

 school-based online social network  affiliated with DYN

 Chicago Learning Network

 consortium of museums and non-profits

 Chicago Public Schools  Funders

 MacArthur Foundation  Gates Foundation

Experience Discovery: Research opportunities

 Full cycle observation

 activity enrollments  activity attendance  click-through  post-activity rating, tagging, reviewing

 Social network data

 uploading of digital media  browsing / commenting behavior  friend / follower connections

Research question 1

 There are multiple important knowledge sources

 past enrollment history  content data  social network data  log data

 Mixed vs integrated hybrid recommendation

 should different knowledge sources be integrated in making recommendations?  or should recommendations of different types be presented side-by-side?

Research question 2

 Activities sometimes have a logical planned sequence

 Video editing I -> Video editing II

 Sometimes they are sequenced idiosyncratically

 Digital photography -> Zoo explorer I

 Educational goal

 increase both depth and breadth of student participation

 The role of “curricula”

 how can recommendations be used to increase both breadth and depth of student involvement?  what is the role of top-down vs bottom-up sequences in recommendation?

Research question 3

 Dynamics of interest

 students mature a lot between 11 and 18  old activities may lose their appeal

 Dynamics of offerings

 activities change from year to year and season to season  may not be explicit

 Coping with change

 how can we ensure that recommendations don’t lag student interest?  how to detect and respond to program changes?

Research question 4

 Students aren’t the only ones with questions  Service providers can get value, too

 what activities should I offer and where?  how do my offerings compare to other groups?  what needs are not being met?

 Analytics and recommendations for service providers

 what can we provide that is helpful and comprehensible?

Architecture

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Initial experiments

 Data (2 schools)

 226 students  32 activities  3800 records  (now adding ~2000 enrollments and ~50 activities / month)

 Algorithms

 collaborative / binary  collaborative / pseudo rating  content-collaborative meta-level hybrid  plus behavioral descriptors

Pseudo-ratings

 Some activities are attended multiple times

 evidence of strong interest

 Example

 book discussion group

 Normalize to user’s profile

 weight for activity a = # of times attending a / total attendances

 Can we normalize in other ways?

 take into account how often something was offered

Meta-level hybrid

 Use course topic descriptors

 13 choices  health, music, visual arts, etc.  activities may include several topics

 Build a topic profile by summing over descriptions

• f all activities

 Compare users based on topic profiles

 rather than attendance data

Adding social network data

 Extracted 10 features from the social network

 counts of uploaded media types  overall level of activity

 Used feature combination

 content profile  behavior profile

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Results

 Temporal leave-one-out evaluation

 see Burke, 2010

 Look at a user’s experience over time

 looking at users divided by  # of enrollments (profile size)  profile diversity (# of different enrollments)

 Need to do more research

 Hybrid 2 works best for large, diverse users  Doesn’t matter what you do for non- diverse users

Conclusions

 We are in the early stages here  Eager to get our hands on bigger data  Many research questions  Would like to hear ideas

Thanks

 Questions / Comments / Ideas