Virtual
Field Trips (VFTs) can be used to go to places that are impossible to visit (mid Atlantic Ridge), or act as a replacement for students unable to physically attend a field trip. An example of one produced by colleagues at the Open University is previewed in the video below (source):
VFTs have been produced using 3D platforms such as Google Earth but it is only recently that developments in software and hardware have meant that the technology is robust enough to use in everyday teaching.
Tracking Students: One idea we had in our Google research project was to see if tracking students flying around VFTs can be used to inform tutors and students about student's learning. This topic isn't well covered in the literature so worth investigating. A paper Muki, Paolo Viterbo and I have just submitted
to a journal describes our work in this area. We collected 4D data (3D with time) using the Google Earth API of students navigating around to complete an educational search task. In some VFTs students are limited to walking but in ours they had access to zoom and pan.
Two
Visualisations: In the paper we describes two visualisations
which help users’ (either tutors or students) make sense of the complex 4D tracking data. One is a static
graphic (not covered today), the other
is an animation:
The animation links an altitude vs distance graph with a 3D view of the
track in space using Google Earth’s cross section functionality. We think that these visualisations are quick and effective ways to evaluate student's search activities.
Experiment
Summary: In
the experiment students:
1.
Viewed a Google Earth tour which
explained how to identify paleo-geographical features (lake banks surrounding a
lake long since dried up).
2.
They were then set a task
searching for their own example in a defined study area. An important feature of the task was that students
could not complete their search without zooming in to check characteristics in
more detail. Their route through 3D
space was tracked and saved to a server.
3.
They marked their answer on the map.
Visualised
data: The simple 3D path in space looks like
spaghetti thrown into the air (top section above diagram), it’s difficult to
interpret. However, by plotting altitude
against distance along path in a linked graph (bottom part) the actions of the
student zooming in and out on targets can be clearly seen. In the
main view (top of image) the red arrow shows camera location and the hair line
on the graph (bottom) shows the relevant point on the graph. You can control the hair line to explore the
path, this page links to a sample KML file and the youtube clip explains how to
set it up and what it shows in more detail.
What
Does it Show? From interacting with this visualization
several aspects of the students’ performance can be easily gauged:
·
Did the student zoom in on sensible targets (i.e. the
‘answer’ area and other areas that needed checking out)?
·
Did the student get disorientated (stray outside the yellow
study area box or spend an overly long time in one area)?
·
Were they thorough in their search or just do the bare minimum (did they zoom
in on a number of sensible locations, just a few or did they fail to zoom in at
all)?
Possible
Uses: This
technique could be applied to a number of virtual field trip situations. The case study we’ve already looked at
represents a physical geography/earth science application. It also could be used for:
human
geography: e.g. if students are taught that poorer
neighborhoods are likely to be further from the centre of a city you could then ask
students to identify poor neighborhoods in a sample city. Tracking a successful search would show
students navigating to sample sites around the edge of the city and then zooming
into streetview to check their if they were right or not.
Student
created maps: Students are first tasked with identifying volcanoes in a
country. They mark three answers on a class
shared map in the first stage. In the
second stage, they assess their peers' work and are tracked zooming in on each other's placemarks. You could see how good
their performance was in the second stage from the tracking animation e.g. did
they check out suggestions in enough detail. IMHO This last example has the advantage of
representing deeper learning, it challenges students to think critically about
each other’s work.
Ethics: Learning Analytics is a powerful new tool for teaching, used carefully it has huge
potential to assist students and tutors.
However, it also raises real teaching issues such as will students react
well to the extra kind of feedback they can now receive? Will institutions use it to measure tutors
performance in a confrontational manner? IMHO we need to approach this new tool with an open, student focused, frame of mind.
