Can mobile devices facilitate more integrated learning?
Can mobile devices facilitate more integrated learning?
Can mobile devices facilitate more integrated learning?
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<strong>Can</strong> <strong>mobile</strong> <strong>devices</strong> <strong>facilitate</strong> <strong>more</strong><br />
<strong>integrated</strong> <strong>learning</strong>?<br />
Yvonne Rogers<br />
Pervasive Interaction Lab<br />
Open University
Popular claims about <strong>mobile</strong> <strong>learning</strong><br />
• Convenience<br />
– any place any time, e.g., walking, eating,<br />
waiting, cycling<br />
• Context<br />
– have ready access to web and other<br />
information resources, e.g., podcasting<br />
when at museums, historical sites, etc.<br />
• Motivation<br />
– increases engagement and improves <strong>learning</strong>,<br />
e.g., writing, note-taking, organizing
<strong>Can</strong> <strong>mobile</strong> <strong>learning</strong> be <strong>more</strong>?<br />
• Facilitate moving between different <strong>learning</strong> spaces,<br />
e.g., private and public<br />
• Facilitate conversations across context<br />
– “coming to know through continuous conversations across multiple<br />
contexts amongst people and interactive technologies” (Sharples<br />
et al, 2007)<br />
• Facilitate <strong>integrated</strong> <strong>learning</strong><br />
– reflecting on ongoing activities<br />
– switching between observation and analysis<br />
– taking the initiative what to do next<br />
• Our focus: scientific inquiry processes
Typical model of scientific inquiry<br />
• Abstract knowledge taught in the classroom poorly<br />
<strong>integrated</strong> with concrete experiences in the field<br />
– Follows a linear model<br />
– Inquiry activities performed separately<br />
– Collation, analysis and reasoning about data collected<br />
in the field delayed until back in classroom<br />
– PC software is largely procedure-based<br />
– Students rarely take responsibility for their inquiries<br />
• Curtails opportunities for students to analyze<br />
and reflect on their feet when make discoveries<br />
Experiment Goals<br />
in Classroom<br />
Data Collection<br />
in field<br />
Data Analysis<br />
in lab<br />
Conclusion<br />
in classroom
An alternative approach<br />
• Encourage students to:<br />
– Observe and think<br />
– Connect, reflect and explain<br />
the real with the represented<br />
– Formulate own researchable<br />
questions<br />
– Interpret and compare own and<br />
other’s data<br />
– Communicate results to others
Sense-making<br />
• Social construction of knowledge (Schoenfield, 1992)<br />
– switching between abstractions<br />
– <strong>learning</strong> to think like a scientist<br />
• Conversations and dialogic struggles students engage in<br />
– making connections between digital representations (e.g. graph, tables)<br />
and scientific phenomena (e.g. global warming)<br />
• Mobile technologies can support sense-making<br />
– enable conversations across context<br />
– provide contextually-relevant information and pertinent data
First steps: Ambient Wood<br />
• Part of a large EPSRC IRC collaboration - Equator<br />
(2000-2006)<br />
– involving partners and schools from Nottingham, Bristol, Sussex,<br />
RCA and Southampton<br />
• Designing a fieldtrip with a difference<br />
– encourage children to explore and hypothesize about a woodland<br />
while collecting own data<br />
– reflect upon data collected in the physical environment<br />
• Diversity of <strong>mobile</strong> <strong>devices</strong> and visualization tools<br />
were designed<br />
– provided access to contextually-relevant digital information
Interactive <strong>devices</strong> and tools<br />
Probing <strong>devices</strong><br />
Listening <strong>devices</strong><br />
Viewing <strong>devices</strong><br />
Visualizing tools<br />
Analyzing tools<br />
Communication<br />
tools
Role of <strong>devices</strong> and digital<br />
– focus and provoke<br />
• make the invisible<br />
visible<br />
• make the far near<br />
• make the inaudible<br />
audible<br />
augmentation<br />
• make the past present
An example: Probing tool with<br />
dynamic visualization<br />
• Combined hand-crafted<br />
probing tool and PDA<br />
display<br />
Immediate feedback showing<br />
relative levels of moisture<br />
and light
• Predicting moisture<br />
levels for different<br />
parts of the clearing<br />
• Self-initiated, creative<br />
and collaborative<br />
<strong>learning</strong> activity<br />
Probing in action
Collaborative reasoning<br />
Girl 2 probes ground<br />
Girl 1 looks at reading on PDA: Yeah, it<br />
is much higher[<br />
Girl 2: Oh yeah, right, so it is much<br />
wetter<br />
Girl 2 walks off to tree: shall we try a<br />
leaf?<br />
Girl 1 stays put: Do it, do, do, do, do it<br />
here again so we can see<br />
Girl 2 returns and probes where asked<br />
Girl 1: It is exactly the same, so<br />
Girl 2: That’s right, because they’re the<br />
same…<br />
Girl 1: yeah, they’re the same<br />
Girl 2 leads girl 1 to tree<br />
Girl 2: How about we try a dry leaf?
Reflecting,<br />
comparing and<br />
hypothesizing<br />
• Pairs of children came<br />
together and shared<br />
experiences in ’den’<br />
• Unaware that their probe<br />
readings had been tracked<br />
• Fascinated by visualizations<br />
of the different sets of data<br />
• Much predicting of each of<br />
their readings before<br />
interacting with the data
Next steps: Lilly Arbor project<br />
• Ongoing environmental restoration project at<br />
Indiana University where scientists and students<br />
collaborate<br />
– Measuring changes to environment (e.g., trees, water quality)<br />
– Conduct experiments to assess best way of restoring riverbanks
Comparing different tree planting<br />
methods for floodplains<br />
– Containerized trees in a row<br />
– Bare root seedlings in a row<br />
– Bare root seedlings random
Paper-based approach to measuring<br />
• Teams measure tree growth twice a year<br />
– 3-4 students, a corporate volunteer and scientist<br />
• Each team provided with measuring<br />
<strong>devices</strong>, a map and paper check-list<br />
– Need to first locate a tree, identify it and take<br />
measurements<br />
– Then record and add comments
LillyPad<br />
• Paper-based method not optimal for <strong>learning</strong><br />
– OK for recording measurements but poor for supporting<br />
analysis<br />
• Proposed a <strong>mobile</strong> <strong>learning</strong> device<br />
– Enable students to readily switch between observation, data<br />
collection and analysis<br />
– Enter new measurements electronically and be able to<br />
compare with previous measurements<br />
– Have access to information to help identification and<br />
explanation
Goals<br />
• Learning goals<br />
– Use digital information to understand <strong>more</strong> about<br />
observations<br />
– Share and discuss observations with each other<br />
– Reflect on discoveries when measuring<br />
• Usability goals<br />
– Enter measurements and look up data quickly<br />
– Rapid <strong>learning</strong> of functionality<br />
– Use continuously for 6-8 hours
Screenshots of Lillypad<br />
Data entry pages<br />
Info and stats pages
The first in-situ study<br />
• 6 teams of 3-4 students and 1-2 volunteers<br />
• Each team given a PDA<br />
• Training took place in situ
Evaluating LillyPad device<br />
• Collected a mix of data:<br />
– Logs of page clicks on PDAs<br />
– Focus group at end with team leaders<br />
– Student running commentary<br />
– Video data for each group
What happened?<br />
• Did not meet our <strong>learning</strong> or usability goals<br />
– Data entry was successful but cumbersome<br />
– Entering comments was extensive but tedious<br />
– Information and stats pages used only occasionally<br />
• Very little reflection about the state of the<br />
restoration site
Visualization of task-related and sense-making activities<br />
Q1<br />
S3: “So we want to figure out when it died?”<br />
PDA: “Once dead, now alive?”<br />
T: “Go ahead and accept that and then look at the stats page.<br />
Dead dead dead dead dead dead dead dead our every measurement”<br />
S2 “Wow it has been dead?”<br />
S3: We got a comeback. It is…”
What were the problems?<br />
• Task switching or task interruption?<br />
• Task overload for person holding the device<br />
causing bottleneck?<br />
• Tension between completing the measuring<br />
task and reasoning about anomalies?<br />
• Device too fiddly?<br />
• Environment not conducive?
Are we asking too much?<br />
• <strong>Can</strong> students switch between task and sensemaking<br />
activities?<br />
– different perspectives and representations?<br />
• Does this lead to <strong>more</strong> <strong>integrated</strong> <strong>learning</strong>?
Led to major redesign of LillyPad<br />
• Revisit our <strong>learning</strong> and usability goals<br />
– <strong>more</strong> proactive use of device<br />
– integrate data collection and analysis activities<br />
• Reduce cognitive load<br />
– Enhance interface elements<br />
– Provide two PDAs per team<br />
• Provide <strong>more</strong> task-relevant information<br />
• Include graphical representations to support sensmaking<br />
• Increase awareness across groups<br />
– Enable comparisons of measurements via text messaging between remote<br />
groups
Old and new info page
Example of task relevant pages
Graph function
Message function
But had we overdone it?<br />
• Too many functions now?<br />
• Was it harder to learn?<br />
• Was the interface too complex to use in situ?<br />
• Would the multiple changes <strong>facilitate</strong> <strong>more</strong><br />
analysis and reflection?
Main findings from 2nd in-situ study<br />
• More engagement<br />
– Info and stats pages frequently used to help in process of identifying trees<br />
– Team leaders tailored questions that could be answered by looking up<br />
relevant information on the device that led to discussion<br />
• More analysis<br />
– The graphing function used to reason and make hypotheses about<br />
anomalies (e.g. why a tree seemed to have shrunk)<br />
– More instances (15-25 per group) of working out cause of growth rate<br />
(local, species, method)<br />
• More sharing<br />
– ‘Over the shoulder’ showing and looking at images, data, and graphs<br />
– Lots of reading out aloud history details and information
Mean number of page clicks for<br />
version I and II of LillyPad
Vignette of <strong>integrated</strong> sense-making<br />
• An anomalous observation triggered an extensive<br />
and distributed chain of reasoning<br />
– Why a tree appears to have shrunk<br />
• Members fluidly switch between their observations<br />
of the physical environment and the different<br />
representations on the PDA<br />
• Large number of hypothesis and interpretations<br />
made<br />
• Contest each other’s suggestions using the graphs to<br />
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…<br />
• Messaging facility<br />
used only<br />
occasionally<br />
• Local ongoing activities<br />
too fast paced<br />
• Too distracting<br />
10:57:52 | Area6 | bindweeds are dead<br />
11:36:14 | Area7 | hi<br />
11:41:12 | Area8 | we r seeing catalpzs in the trees<br />
11:41:40 | Area8 | our bindweeds r dead as well<br />
11:46:29 | Area6 | bindweed dead<br />
11:48:13 | Area6 | catepillers<br />
11:49:15 | Area6 | did you mean catepillers<br />
12:00:13 | Area8 | we have a seedling cottonwood, Lenore is very excited!<br />
12:02:55 | Area8 | on what are the catterpillers? and what kind?<br />
12:13:41 | Area7 | is lunch ready<br />
12:21:34 | Area7 | lunch is ready come get it<br />
13:51:49 | Area6 | W hat tree are you on?<br />
13:57:34 | Area8 | 8096<br />
“You know I don’t want to mess with messages. I want to be out<br />
here. I don’t want to miss anything.”
Is this <strong>integrated</strong> <strong>learning</strong>?<br />
• Much evidence of using data, graphs and<br />
info in combination with locating, measuring<br />
and recording<br />
– identify trees and reason about their growth patterns<br />
• LillyPad II supported switching between<br />
observations and sense-making<br />
• Sometimes the students initiated questioning<br />
• Collaborative sense-making<br />
– through sharing and showing of data and representations
Conclusions<br />
• Many opportunities for augmenting <strong>learning</strong><br />
using <strong>mobile</strong> technologies<br />
– <strong>Can</strong> result in task overload<br />
– <strong>Can</strong> <strong>facilitate</strong> switching between physical observations,<br />
abstractions used in science and contextual information<br />
– Enable students to generate questions based on what they<br />
discover in the physical world with ‘at hand’ data<br />
– Encourages team leaders to ask different kinds of<br />
questions leading to discussions
Acknowledgements<br />
• Collaborators on the Lilly Arbor project:<br />
– Kay Connelly, Lenore Tedesco, Polly Baker, Bob Hall, Kara Salazar,<br />
Richie Hazlewood, Andy Kurtz, Tammy, Toscos, Josh Hursley<br />
– Team leaders, service <strong>learning</strong> students and Lilly volunteers<br />
• Collaborators on the Ambient Wood project:<br />
– Mike Scaife (in memoriam), Sara Price, Eric Harris, Hilary Smith,<br />
Cliff Randell, Henk Muller, Claire O’ Malley, Danae Fraser Stanton,<br />
Mark Thompson, Mark Weal, Ted Phelps, Danielle Wilde, Mia<br />
Underwood, Paul Marshall, Rowanne Fleck<br />
• Funding:<br />
– Lilly Foundation, Pervasive Technology Labs, Indiana University<br />
– EPSRC Equator IRC
Spring<br />
Trees easy to find but hard to identify<br />
Autumn<br />
Trees hard to find but easy to identify