Geography Reference
In-Depth Information
world is important. But there exist debates surrounding the type of teaching and
learning undertaken on fieldwork. Arguments exist about the relative merits of
instructor led versus student centered learning and top-down versus bottom up
knowledge discovery and acquisition.
The benefits of eLearning environments are also generally well known and
widely discussed (Jones and Newman
2006
; Linsey et al.
2010a
). Students expect
to be able to acquire materials digitally and interact with lecturers via email and
discussion boards within virtual learning environments (VLE). Students also make
widespread use of social networks (e.g. Facebook) in their personal lives, not to
meet learning outcomes. Some research has been undertaken on approaches to
merge social networks (and other Web 2.0 technology) with eLearning approaches
to create an ''eLearning 2.0''. mLearning, a mobile adaptation of eLearning 2.0,
(using mobile technology in the field) has been suggested as a means to enhance
the student experience. Students continue to have this expectation of being able to
interact with staff and operate within a familiar learning environment even when
away from the University on fieldwork (Linsey et al.
2010b
).
What we present here is an implementation that links current eLearning prac-
tices with mLearning and social networks within a weeklong fieldwork class for
GIS Bachelors and Masters students. We build on current trends of spatially
enabled, interacting, data sharing user communities and develop a geocollabora-
tory (a map based, online collaborative web map environment) where we
''mashup'' spatial data (to provide context), social network posts from the students
(taking place as part of discussions) and spatially reference those discussions.
Our ultimate goal of improving collaborative data gathering arose out of a
desire to minimize the time spent by students homogenizing data at the end of each
data collection day. Historically this would take several hours as different data
categorizations were resolved. Instead we wanted students to conduct these dis-
cussions in situ. These discussions would enable a common data collection stan-
dard to be used enabling students to focus their end of day efforts on data analysis.
To accomplish this we identified a need to:
• Create mechanisms for student-student group interaction when small groups are
dispersed across the study area;
• Provide a means for students to collaborate remotely with staff, to develop
methodologies as part of the exercise itself, provide technical support for stu-
dents, collate interim results, and assess interim work to provide formative
feedback;
• Manage the data collection process to cater for the in situ modification of the
collection methodology by students and/or staff; and
• Develop content that can be delivered remotely in the field to enhance student
experiential learning (e.g. to provide challenges during an exercise or to intro-
duce changes to data requirements to assess adaptability and ingenuity).
In achieving this objective we also intended to meet the objectives of a larger
project centered on Mobilising Remote Student Engagement (Linsey et al.
2010a
)
including:
