Peyina lin socialecologiesdigitalyouthlis566

Social Ecologies of
Digital Youth
12/30/2011

Peyina Lin
PhD Candidate

UW Graduate School Presidential Fellow 2011-12 @peyinalin
peyina@gmail.com
http://students.washington.edu/pl3

2

Contents
 Research area
 Background

 Research at Microsoft Research:
“Reaching Out: Teens Learning via Extended Networks”
 Take away

@peyinalin--LIS 566 Winter 2012

Research area: 3

Social science studies of youth, social
ecologies, and technology use

geeks family
band
school
friends


4

Contents
 Research area
 Background

 Take away


5

Committee

Michael B. Eisenberg David W. McDonald Katherine Stovel
Professor & Dean Emeritus, iSchool Associate Professor, iSchool Associate Professor, Sociology
Associate Director, CSSS
Research: information literacy, Research: CSCW, HCI Research: dynamics of social
learning in virtual environments organization, social network analysis


6
Research background

Values in technology design
Determining Trustworthiness in SL

Social Groups, extracurricular
Connected Cars participation and technology use

Personal background 7

8

Contents
 Research area
 Background

 Research at Microsoft Research: Peyina Lin Shelly Farnham

 Future directions

 Take away


9

Think back to when you were 15…


10

Motivation: Importance of
self-directed learning (self-learning)

 Life-long learning
 Innovation  more likely outside the classroom
 Motivation:
 intrinsically motivated => better performance
 Technology use:
 More readily engaged (Open University, UK)
 Access to global knowledge base
 Relevance for teens


Exploratory study: 11

Nature of self-learning and technology use
Goal: Design recommendations for social technologies to support self-learning

Shel Pete Livy
Age: 14 Age: 15 Age: 17

“I just don’t like doing “If someone suggested that “I use it [the internet] to
(new) things where I don’t I find interest groups learn different things and get
know anybody.” online, I’d give it a try.” involved in communities.”


12

Inertia… but…
 Time and mobility  inclination to learn from immediate
networks
 Inertia vs. preparation for self-directed learning
 Understanding what distinguishes successful self-learners
 further our understanding of
how to support teens to become successful self-learners


13

Self-directed learning (Self-learning)

 Definition:
Extent to which the learner does not rely on immediate
networks to be successful at the learning activity.
 Soft skills:
 Having self-discipline (can-do attitude)
 Knowing to pursue feedback
 Technical skills
 Technology use
 Information use
 Not alone, but sense of volition
 Not depending primarily on immediate
networks


14

Research questions

 What contributes to self-directed learning
of new topics or activities
outside the bounds of one’s immediate social network?
1. How are (social) technologies used for learning?
2. How are learners who are more self-directed different from
learners who are less self-directed?
3. What are the barriers to use technologies to extend one’s network
for learning?


15

Implicit arguments & recruitment criteria

 Digital divide & strategic use of technologies is
associated with access (van Djik, 2005)
 “Access”  successful learning outcomes

 Proxy: hours of internet use
 15 recruited through outreach
 8 via company internal database
 Criteria:
 50% High access ~ 12+ hours/wk
 50% Low access ~ < 12 hours/wk
 Gender balance
 Oversampled minorities
 Variety of neighborhoods and household income


16

Exploratory mixed-methods study
 90 to 120 mins. semi-structured in-depth interviews
 Short questionnaire: digital skills, technology use

 Analysis:
 Constant comparative approach (informed by self-determination theory)
 Mixed methods analysis

23 Participants 10
17

Frequency
5

 Age range 13 – 17 0
13 14 15 16 17
 12 female, 11 male Age
 16 White, 2 Hispanic, 3 Mixed Race, 1 Asian, 1 African American
 Variety of neighborhoods:
Bellevue (3), Burien (2), Redmond (2), other (non-repeats)

 Household Income:
 $25 – $35 thousand: 4
 $50 - $75 thousand: 8
 $76- $100 thousand: 3
 $101- $150 thousand: 7
 Unknown: 1


18

Participants’ technology use
(average hrs. per week)

Technology Low access High access
(< 12 hrs./week) (> 12 hrs./week)
Internet in general 6.4 20
Facebook 2.3 9.2
Texting 6.7 18.9
Emailing 1 2.8

Internet experience:
Beginners: 9%
Intermediate: 48%
Advanced: 35%
Expert: 9%


19

Top activities

 Physical (15)
 Music (8)
 Making/building (6)
 Video production (5)
 Personal development & service (5)
 Other content production (3)
 Cooking/baking (3)
 Art (2)


20

Research question 2

 What contributes to self-directed learning of new topics or
activities outside the bounds of one’s immediate social
network?
for learning


21

Key concepts of interest

Correlation table of key concepts.
Bold values are significant at p < .05 @peyinalin--LIS 566 Winter 2012

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Relationships between concepts of interest

 Tech Access
 Digital Skills
Successful Self-Learning
 Tech for Extending Networks
 Internalized Motivation

 Social Digital Skills not correlated with Self-Learning
 Social Digital Skills common to all participants


23

Two issues…

1. Potential spurious effect
2. How do we know what participants were gaining from
extending networks for learning?


Potential spurious effect 24

Social Tech for Successful
Digital Tech for Internalized
Digital Extending Self-
Skills Learning M otivation
Skills Network Learning

Tech Access .31 .26 .50 .48 .24 .46

Digital Skills .70 .56 .46 .38 .60

Social Digital Skills .33 .26 .29 .40

Tech for Learning .86 .31 .83

Tech for Extending
.28 .75
Network

Internalized
.51
M otivation Self-learning

Digital Skills spurious
effect

Tech for
Extending
Network

No spurious effect: Tech for Extending 25

Networks explains Self-Learning
Holding constant the effect of
Tech Access and Digital Skills,
Tech for Extending Networks explains
26% to 39% of the variance of
Successful Self-Learning (p <.05)

Low Digital Skills High Digital Skills
Digital Skills

26

Two issues…

1. Potential spurious effect
2. How do we know what participants were gaining from
extending networks for learning?


27
What were participants gaining by using
technologies to extend networks for learning?

 Complement other learning  physical activities
 Learn from role models (YouTube videos)
 Technology was the main source for learning
 baking, making things, content production
 Finding information (Web)
 To avoid bothering sibling (YouTube videos)
 Complement creative effort  art
 Inspiration
(web, Dictionary.com, poetry.com, images)
 Reach wider network  content production
 Self-expression (blog, tagged.com, YouTube)
 Participate in online communities


Technologies used to extend networks: 28

4 tier perspective
20 activities, 17 participants 10 activities, 10 participants 4 activities, 3 participants 7 activities, 6 participants

Tier 1: Tier 2: Tier 3: Tier 4:
Getting info. Getting info. from Reaching (push) 2-way social
(no social people info to people interaction (info.,
interaction) (no social people,
interaction) communities)
World Wide Web (10) YouTube (9) Blogs (2) Twitter (2)
Specific site (4) Community site (1) YouTube (2) Facebook (1)
Wikis (2) Tagged.com (1)
Q&A sites (2) Email (1)
YouTube (3)
YouTube
comments(1)
Total: 22 Total: 10 Total: 4 Total: 5
tech mentions tech mentions tech mentions tech mentions

29
Technology use for learning (RQ1)

 Mostly with people from their immediate
networks via:
 texting
 chat
 Facebook geeks family
band
 If outside of immediate networks, writing
mostly to get information online

Few engaged in social interaction
with people in extended networks
in their learning context
New design space opportunity


30

Research question 3

 What contributes to self-directed learning of new topics or
activities outside the bounds of one’s immediate social
network?
for learning


Sense of relatedness: initial engagement 31

= secondary prompter
= primary prompter for
already colored activity

Engagement prompted by an important other Top activities:
 Swimming (2)
 Dad (7)  Baseball (2)
 Mom (4)  Scouts (2)
 Football (1)
 Best friend / close friends (7)  Basketball (1)
 Upper level, more experienced youth (2)  Volleyball (1)
 Other close adult (3)  Track (1)
 Skiing (1)
 Famous person (1)  Martial arts (1)
 Breakdancing (1)
Team/Crew/Group  Art (2)
 Photography (1)
 Reading/writing (1)
 Guitar (1)
 Video-making (1)
 Minecraft (1)


32
Giving up: Lack of sense of relatedness

“I could relate better to my swim mates… Soccer… it’s a lot
of drama, it’s ridiculous. That’s one of the reasons I chose
swimming.” (Kay)

“I wasn’t like socially included in my team though my coach
liked me… that’s how I decided to leave.” (Livy)

“I didn’t feel good about wrestling… the sport and the
team… people were not my type of people… it was not my
thing.” (Bob)


33

Importance of the prompt
 Sustained passion in activity even though prompted by
another
The prompt
 Purposeful
 Serendipitous

Learner Actor
of behavior to be learned
observing sharing

“If someone suggested that
I find interest groups
online, I’d give it a try.”


34

Barriers to reaching extended networks
 Sense of Community: Inclusion and Exclusion

 Competition: Encouraging and Discouraging Sharing
 “make a music video and we’ll see which one gets more views.”
 “I got 3rd on a race. I really want to put it on Facebook but I don’t
because other girls in the team take it as bragging.”

 Connect-and-learn vs. Negative Feedback and Morality
 “Blogging about what I learn about vegan food helps me relate
better to other vegans.”
 “It’s not ready for sharing, and some people might not be the most
ethical and steal it [the idea].”

 Parents’ attitudes: Don’t use it. You’ll get a Virus!
 “My parents make me work for stuff. I look up to that…it made me
not give up when I can't figure something up.”
 “Mom’s kind of paranoid about viruses… that’s how I avoid
viruses… I tend to be on the computer fairly conservatively.”


35

Recommendations

 Support sense of relatedness in finding people in extended networks
 Design for social inclusion
 Encourage sharing through a mentoring structure
 Support embedded artifacts with diverse content
 Design for the learner’s social ecology
 Encourage sharing through automatic attribution


sense of
competence
psychology
sense of
Contributions
autonomy
sense of
relatedness motivation
i.e., extent of internalization
supported
resources, time,
context
social support

domain know-how
extended immediate learning
person
networks networks learning know-how
social- discipline-
readiness readiness

tech skills

technology use

tech-use to
extend networks


37

Contents
 Research area
 Background

 Take away


38

Social Ecologies of Digital Youth: New Horizons

 Digital Skills + can do attitude
 Sense of relatedness
 Motivation
 Continued engagement

geeks
 Role of
family
adults, educators, librarians, te
band
school
achers…
friends


39

Take away
 Take the relevance of senses of relatedness, autonomy, and competence,
AND of learning from extended networks…
 How can you contribute to your work practices and help youth feel at the
same time:
a) in their comfort zone
b) and willingly try new practices, behavior, information from new connections?

 Are there stories in books or other media that can help harness the sense of
relatedness to distant connections?
 Can you design tasks that help? For example:
 Tasks with appropriate limits and not too difficult yet challenging (to build sense of
autonomy and natural inclination for challenge, and competence)
 Design tasks or ways to make diverse youth, each feel they are “a part of” something
that matters to them
 Utilize multimedia production as a way to help youth find out what matters to them
and how they can share or have an impact on others (e.g., journaling about one self,
one’s community, ways to help, etc.)


40

Acknowledgments
 FUSE research internship
 Shelly Farnham, Researcher, FUSE, Microsoft Research
 Lili Cheng, General Manager, FUSE, Microsoft Research
 Karen Cowan, Business Administrator
 Microsoft UXC Team
 Research participants
 My dissertation and larger research directions
 Mike Eisenberg
 David McDonald
 Kate Stovel
 Lynne Manzo
 Participating High Schools & Research participants
 iPhDs: former dissertation support group (Sunny, Phil, Ok
Nam), Ammy, Natascha, Jeff, Jeff, John, Yuan
 CSSCR- Stephanie Lee
 iSchool faculty, staff in research (Alpha et al.), finance, administration, IT
 Other communication: Loren Terveen, Jonathan Grudin, Jens-Erik Mai, Howard
Rosenbaum, Nathan Freier, Batya Friedman, Molly Andolina, Cliff Zukin
 National Science Foundation
 UW Graduate School Presidential Fellowship
 Frank Lobisser (Microsoft Zunes)
 Edward and Aiden Roberts

41

Thanks!

 pl3@uw.edu
 @peyinalin
 http://students.washington.edu/pl3


Peyina lin socialecologiesdigitalyouthlis566

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