From Mobile Learning to Pervasive Learning Environments

1
From Mobile Learning to Pervasive Learning
Environments

• Antti Syvänen (antti.syvanen_at_uta.fi)
• Petri Nokelainen (petri.nokelainen_at_uta.fi)

Paper presented at the ED-MEDIA 2005 conference,
29.6.2005, Montreal, Canada Salon Jarry
2
Structure

• Introduction
• Study on Mobile Learning Future Views
• Examples of Pervasive Learning Environments
• Towards evaluation of Pervasive Learning
  Environments
• Conclusions

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Introduction

• Mobile (Technology Supported) Learning as an
  established concept is as problematic as, e.g.
  e-learning.
• Learning defined through the media being used
  does not tell much about the activity and to what
  kind of principles the pedagogical solutions
  should be founded on.
• Thus, instead just coupling mobile and
  learning, we suggest that one should pay more
  attention to the contexts where the terms are
  applied.
• In this paper a concept of pervasive learning
  environment is introduced to address this need.

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Introduction

• Syvänen (2005) has proposed that one clear
  characteristic of mobile learning is seeking
  information more freely from different domains
  (both from physical and virtual) and constructing
  knowledge based on information from different
  contexts.
• Pervasive learning Activities supported with
  mobile technology

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Introduction

• Pervasive computing takes part in an experience
  of immersion as a mediator between the learners
  mental (e.g. needs, preferences, prior
  knowledge), physical (e.g. objects, other
  learners close by) and virtual (e.g. content
  accessible with mobile devices, artifacts)
  contexts.
• Where these contexts overlap and form a single
  entity is addressed here as pervasive learning
  environment.

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Mobile Learning Future Views

• Syvänen, Nokelainen, Pehkonen Turunen (2004)
  studied the future views of Finnish (n 4,
  interviews) mobile learning experts.
• International MOBIlearn project experts (n 14)
  evaluated a scenario built upon the Finnish
  experts strengths, weaknesses, opportunities and
  threats (SWOT) analysis with an online survey
  that was active in MOBIlearn homepage from
  November 2003 to March 2004.

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Mobile Learning Future Views

• The MOBIlearn experts were asked to evaluate the
  SWOT of mobile learning presented in a narrative
  scenario.
• In the online questionnaire three out of six
  Mobile Learning Components (MLC, Syvänen,
  Nokelainen, Ahonen Turunen, 2003) were
  presented to respondent by random selection.

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Mobile Learning Components
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Mobile Learning Future Views

• The experts were asked to evaluate the components
  in the context of the story, and more widely, in
  the overall context of mobile learning.
• Half of the components were not presented in
  order to let the experts themselves define
  possible other missing aspects.

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(Syvänen, Nokelainen, Pehkonen Turunen, 2004)
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Paradoxes contradictions in future views

• Future views were compared and further
  categorized with SWOT-analysis
• Strength and Weakness (present)
• Opportunity and Threat (future)

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M
M
M
M
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• Some of the future views were contradictory as
  they were present in both Opportunities and
  Threaths

Interaction-interactivity Strength New types of
interactivities (MMS, video-clips, etc.) add new
possibilities for interaction. Weakness Thus,
selecting the most appropriate interaction
methods becomes a less trivial task
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• Some of the future views were contradictory as
  they were present in both Opportunities and
  Threaths

Learning management-continuity Strength
possibility to flexibly coordinate activities
regardless of time and place and make notes of
things just as they occur. Weakness Although
less effort is taken in planning activities,
coordinating many people's schedules becomes more
complex and integration of different memos and
notes afterwards is difficult (W).
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Paradoxes contradictions in future views

• Above mentioned issues are addressed here as
  paradoxes, illustrating the pervasive nature of
  future mobile technology supported learning.
• As such, it is important to notice that these
  paradoxes also reflect the concrete and still
  possible pros and cons of future pervasive
  learning environments already available and under
  further development.
• Next we present two examples in more detailed
  way.

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Examples MOBIlearn

• European Union 5th Framework IST research and
  development project MOBIlearn developed generic,
  adaptive user interface that supports three
  different kinds of learner groups (MOBIlearn
  2005).

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Examples MOBIlearn

• In the MOBIlearn system adaptivity was designed
  in relation with the context-aware subsystem
  emphasizing the pervasive learning environment
  approach.
• Following recommendations were formulated

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Examples MOBIlearn

• Organizing the information provided to the user
  according to the availability for cooperation
  (students), advice (experts, instructors) and
  groups available at a given moment
• Supporting the communication between users by
  providing tools, such as the news groups and
  chats, that are presented to the user by their
  current popularity in the learning community
  (placing first the most popular, or the most
  relevant to the learner according to the profile,
  at any given moment).
• Encouraging users to cooperate and affiliate by
  pushing the information when relevant
  opportunities occur.
• Offering information according to the patterns,
  preferences, interests or goals perceived by the
  system but not necessarily perceived or stated
  (in settings) by the learner.
• Providing multimodal information (pictures,
  sound, text, notion maps, etc.) according to a
  learning style of the learner.
• Adjusting automatically contrast/sounds according
  to the physical qualities of the environment
  (louder system sounds in noisy environment,
  etc.,) (Ahonen et al., in press).

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Examples ActiveCampus

• University of California, San Diego (UCSD)
  wireless campus network, ActiveCampus.
• ActiveCampus Explorer location aware
  applications, including location-aware instant
  messaging and maps of the users location
  annotated with the dynamic hyperlinks of nearby
  buddies, digital graffiti, etc.
• ActiveClass classroom activities e.g. anonymous
  asking of questions, polling, and student
  feedback (Griswold, Shanahan, Brown, Boyer,
  Ratto, Shapiro Truong, 2004)

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Examples ActiveCampus

• Designing campus-wide pervasive learning
  environment seems feasible as the students were
  willing to share location information with
  buddies and even non-buddies suggests promise to
  location-aware social computing.
• Findings of the use of ActiveClass stress that in
  order to have wider impact, significant changes
  are required not only to hardware, software and
  physical infrastructure but also to teaching and
  learning practices.

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Towards evaluation of pervasive learning
environments

• Mobile Learning Components (MLC) model (Syvänen
  et al., 2003) was developed for qualitative
  evaluation and to be used as a heuristic design
  tool for mobile learning materials.
• MLC was utilized in designing technical and
  pedagogical mobile usability evaluation criteria
  (Syvänen Nokelainen, 2004), a structure for
  quantitative evaluation of mobile learning
  materials and environments.
• MLC was tested with mobile learning experts and
  in a comprehensive school pilot (n 143).

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Towards evaluation of pervasive learning
environments
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Conclusion and Discussion

• The evaluation framework presented in this paper
  has a link to ubiquitous computing evaluation
  frameworks.
• In the context of ubiquitous computing, the
  user-system interactions are seen as physically
  embedded (Scholtz Consolvo, 2004, 86).
• Therefore, the framework for ubiquitous computing
  evaluation serves as a useful tool for this work.

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Conclusion and Discussion

• The specific items of the evaluation framework,
  such as Continuity between learning contexts and
  adaptability, are large entities, not features,
  of the learning activities and need to be
  scrutinized more deeply.
• This is the most probable reason why the mobile
  learning experts did not see it as a relevant
  component.

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Conclusion and Discussion

• It is important to further elaborate the most
  important characteristics of pervasive learning
  environments that should be taken into account in
  the earliest design process stages.
• Empirical evaluation of mobile and pervasive
  learning environments will help us finding such
  characteristics.
• As mobility introduces high variability to
  contexts of use, we must keep in mind that
  evaluations relying only on surveys do not give
  full understanding of the quality of the
  pervasive learning environments.
• Complementary data need to be gathered with
  qualitative research methods such as interviews
  to shed light on the contextual features.

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For more information

• Contact
• Antti Syvänen antti.syvanen_at_uta.fi
• Petri Nokelainen petri.nokelainen_at_uta.fi
• See Mobile Research Group website
• http//www.uta.fi/hyper/projektit/mobile