Can mobile devices facilitate more integrated learning?

Can mobile devices facilitate more
integrated learning?
Yvonne Rogers
Pervasive Interaction Lab
Open University

Popular claims about mobile learning
• Convenience
– any place any time, e.g., walking, eating,
waiting, cycling
• Context
– have ready access to web and other
information resources, e.g., podcasting
when at museums, historical sites, etc.
• Motivation
– increases engagement and improves learning,
e.g., writing, note-taking, organizing

Can mobile learning be more?
• Facilitate moving between different learning spaces,
e.g., private and public
• Facilitate conversations across context
– “coming to know through continuous conversations across multiple
contexts amongst people and interactive technologies” (Sharples
et al, 2007)
• Facilitate integrated learning
– reflecting on ongoing activities
– switching between observation and analysis
– taking the initiative what to do next
• Our focus: scientific inquiry processes

Typical model of scientific inquiry
• Abstract knowledge taught in the classroom poorly
integrated with concrete experiences in the field
– Follows a linear model
– Inquiry activities performed separately
– Collation, analysis and reasoning about data collected
in the field delayed until back in classroom
– PC software is largely procedure-based
– Students rarely take responsibility for their inquiries
• Curtails opportunities for students to analyze
and reflect on their feet when make discoveries
Experiment Goals
in Classroom
Data Collection
in field
Data Analysis
in lab
Conclusion
in classroom

An alternative approach
• Encourage students to:
– Observe and think
– Connect, reflect and explain
the real with the represented
– Formulate own researchable
questions
– Interpret and compare own and
other’s data
– Communicate results to others

Sense-making
• Social construction of knowledge (Schoenfield, 1992)
– switching between abstractions
– learning to think like a scientist
• Conversations and dialogic struggles students engage in
– making connections between digital representations (e.g. graph, tables)
and scientific phenomena (e.g. global warming)
• Mobile technologies can support sense-making
– enable conversations across context
– provide contextually-relevant information and pertinent data

First steps: Ambient Wood
• Part of a large EPSRC IRC collaboration - Equator
(2000-2006)
– involving partners and schools from Nottingham, Bristol, Sussex,
RCA and Southampton
• Designing a fieldtrip with a difference
– encourage children to explore and hypothesize about a woodland
while collecting own data
– reflect upon data collected in the physical environment
• Diversity of mobile devices and visualization tools
were designed
– provided access to contextually-relevant digital information

Interactive devices and tools
Probing devices
Listening devices
Viewing devices
Visualizing tools
Analyzing tools
Communication
tools

Role of devices and digital
– focus and provoke
• make the invisible
visible
• make the far near
• make the inaudible
audible
augmentation
• make the past present

An example: Probing tool with
dynamic visualization
• Combined hand-crafted
probing tool and PDA
display
Immediate feedback showing
relative levels of moisture
and light

• Predicting moisture
levels for different
parts of the clearing
• Self-initiated, creative
and collaborative
learning activity
Probing in action

Collaborative reasoning
Girl 2 probes ground
Girl 1 looks at reading on PDA: Yeah, it
is much higher[
Girl 2: Oh yeah, right, so it is much
wetter
Girl 2 walks off to tree: shall we try a
leaf?
Girl 1 stays put: Do it, do, do, do, do it
here again so we can see
Girl 2 returns and probes where asked
Girl 1: It is exactly the same, so
Girl 2: That’s right, because they’re the
same…
Girl 1: yeah, they’re the same
Girl 2 leads girl 1 to tree
Girl 2: How about we try a dry leaf?

Reflecting,
comparing and
hypothesizing
• Pairs of children came
together and shared
experiences in ’den’
• Unaware that their probe
readings had been tracked
• Fascinated by visualizations
of the different sets of data
• Much predicting of each of
their readings before
interacting with the data

Next steps: Lilly Arbor project
• Ongoing environmental restoration project at
Indiana University where scientists and students
collaborate
– Measuring changes to environment (e.g., trees, water quality)
– Conduct experiments to assess best way of restoring riverbanks

Comparing different tree planting
methods for floodplains
– Containerized trees in a row
– Bare root seedlings in a row
– Bare root seedlings random

Paper-based approach to measuring
• Teams measure tree growth twice a year
– 3-4 students, a corporate volunteer and scientist
• Each team provided with measuring
devices, a map and paper check-list
– Need to first locate a tree, identify it and take
measurements
– Then record and add comments

LillyPad
• Paper-based method not optimal for learning
– OK for recording measurements but poor for supporting
analysis
• Proposed a mobile learning device
– Enable students to readily switch between observation, data
collection and analysis
– Enter new measurements electronically and be able to
compare with previous measurements
– Have access to information to help identification and
explanation

Goals
• Learning goals
– Use digital information to understand more about
observations
– Share and discuss observations with each other
– Reflect on discoveries when measuring
• Usability goals
– Enter measurements and look up data quickly
– Rapid learning of functionality
– Use continuously for 6-8 hours

Screenshots of Lillypad
Data entry pages
Info and stats pages

The first in-situ study
• 6 teams of 3-4 students and 1-2 volunteers
• Each team given a PDA
• Training took place in situ

Evaluating LillyPad device
• Collected a mix of data:
– Logs of page clicks on PDAs
– Focus group at end with team leaders
– Student running commentary
– Video data for each group

What happened?
• Did not meet our learning or usability goals
– Data entry was successful but cumbersome
– Entering comments was extensive but tedious
– Information and stats pages used only occasionally
• Very little reflection about the state of the
restoration site

Visualization of task-related and sense-making activities
Q1
S3: “So we want to figure out when it died?”
PDA: “Once dead, now alive?”
T: “Go ahead and accept that and then look at the stats page.
Dead dead dead dead dead dead dead dead our every measurement”
S2 “Wow it has been dead?”
S3: We got a comeback. It is…”

What were the problems?
• Task switching or task interruption?
• Task overload for person holding the device
causing bottleneck?
• Tension between completing the measuring
task and reasoning about anomalies?
• Device too fiddly?
• Environment not conducive?

Are we asking too much?
• Can students switch between task and sensemaking
activities?
– different perspectives and representations?
• Does this lead to more integrated learning?

Led to major redesign of LillyPad
• Revisit our learning and usability goals
– more proactive use of device
– integrate data collection and analysis activities
• Reduce cognitive load
– Enhance interface elements
– Provide two PDAs per team
• Provide more task-relevant information
• Include graphical representations to support sensmaking
• Increase awareness across groups
– Enable comparisons of measurements via text messaging between remote
groups

Old and new info page

Example of task relevant pages

Graph function

Message function

But had we overdone it?
• Too many functions now?
• Was it harder to learn?
• Was the interface too complex to use in situ?
• Would the multiple changes facilitate more
analysis and reflection?

Main findings from 2nd in-situ study
• More engagement
– Info and stats pages frequently used to help in process of identifying trees
– Team leaders tailored questions that could be answered by looking up
relevant information on the device that led to discussion
• More analysis
– The graphing function used to reason and make hypotheses about
anomalies (e.g. why a tree seemed to have shrunk)
– More instances (15-25 per group) of working out cause of growth rate
(local, species, method)
• More sharing
– ‘Over the shoulder’ showing and looking at images, data, and graphs
– Lots of reading out aloud history details and information

Mean number of page clicks for
version I and II of LillyPad

Vignette of integrated sense-making
• An anomalous observation triggered an extensive
and distributed chain of reasoning
– Why a tree appears to have shrunk
• Members fluidly switch between their observations
of the physical environment and the different
representations on the PDA
• Large number of hypothesis and interpretations
made
• Contest each other’s suggestions using the graphs to
support their claims

Visualizations of sense-making and task-related activities

Visualizations of sense-making and task related activities

But the students did not want to text…
• Messaging facility
used only
occasionally
• Local ongoing activities
too fast paced
• Too distracting
10:57:52 | Area6 | bindweeds are dead
11:36:14 | Area7 | hi
11:41:12 | Area8 | we r seeing catalpzs in the trees
11:41:40 | Area8 | our bindweeds r dead as well
11:46:29 | Area6 | bindweed dead
11:48:13 | Area6 | catepillers
11:49:15 | Area6 | did you mean catepillers
12:00:13 | Area8 | we have a seedling cottonwood, Lenore is very excited!
12:02:55 | Area8 | on what are the catterpillers? and what kind?
12:13:41 | Area7 | is lunch ready
12:21:34 | Area7 | lunch is ready come get it
13:51:49 | Area6 | W hat tree are you on?
13:57:34 | Area8 | 8096
“You know I don’t want to mess with messages. I want to be out
here. I don’t want to miss anything.”

Is this integrated learning?
• Much evidence of using data, graphs and
info in combination with locating, measuring
and recording
– identify trees and reason about their growth patterns
• LillyPad II supported switching between
observations and sense-making
• Sometimes the students initiated questioning
• Collaborative sense-making
– through sharing and showing of data and representations

Conclusions
• Many opportunities for augmenting learning
using mobile technologies
– Can result in task overload
– Can facilitate switching between physical observations,
abstractions used in science and contextual information
– Enable students to generate questions based on what they
discover in the physical world with ‘at hand’ data
– Encourages team leaders to ask different kinds of
questions leading to discussions

Acknowledgements
• Collaborators on the Lilly Arbor project:
– Kay Connelly, Lenore Tedesco, Polly Baker, Bob Hall, Kara Salazar,
Richie Hazlewood, Andy Kurtz, Tammy, Toscos, Josh Hursley
– Team leaders, service learning students and Lilly volunteers
• Collaborators on the Ambient Wood project:
– Mike Scaife (in memoriam), Sara Price, Eric Harris, Hilary Smith,
Cliff Randell, Henk Muller, Claire O’ Malley, Danae Fraser Stanton,
Mark Thompson, Mark Weal, Ted Phelps, Danielle Wilde, Mia
Underwood, Paul Marshall, Rowanne Fleck
• Funding:
– Lilly Foundation, Pervasive Technology Labs, Indiana University
– EPSRC Equator IRC

Spring
Trees easy to find but hard to identify
Autumn
Trees hard to find but easy to identify