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The Mind Lab by Unitec | 2016
A Mobile Sensor Activity for Ad-
Hoc Groups
mLearn 2016
David Parsons, Herbert Thomas, Milla Inkila

The Mind Lab by Unitec | 2016The Mind Lab by Unitec | A Mobile Sensor Activity for Ad-Hoc Groups| mLearn 2016
Research Context
➔ 32 week part time Postgraduate Certificate programme
in Digital and Collaborative Learning
➔ Qualified teachers with at least three years of teaching
experience.
➔ Courses cover multiple aspects of pedagogy, leadership
and innovation
➔ Supports applied practice in the use of digital tools for
teaching and learning
➔ BUT limited mobile learning content

★ Survey data gathered from students around their
interests in experiencing mobile learning activities
★ Survey was based on mobile device affordances
Parsons, D., Wishart, J. & Thomas, H. (2016). Exploring Mobile Affordances in
the Digital Classroom. In I. Arnedillo-Sanchez & P. Isaias (Eds.) Proceedings of
12th International Conference on Mobile Learning (Mobile Learning 2016). IADIS.
pp.43-50.
Background to the Study

Mobile Affordances
★ Portability (Naismith et al, 2004)
★ Data gathering (Orr, 2010)
★ Communication (Liang et al. 2005)
★ Interaction (Lai et al., 2007)
★ Contextual learning (So, Kim & Looi, 2008)
★ Outdoor environments (Tan & So, 2015)

Mobile Affordances
★ From the results of the survey, we aimed to develop a
collaborative, outdoor, sensor based activity
★ Design focused on outdoor navigation, gathering and
sharing of data, communication, interaction with
different devices, and learning related to context
★ Learning outcome: being able to use mobile sensors to
measure environmental factors

Sensors
New types of sensor are a significant change to mobile
device capability
3 generic types of sensor:
Motion - some types of measurement, control of software
and other devices
Environment - e.g. weather, noise pollution, light and
shade, geography (orientation, elevation)
Position - guide learners to different locations

Learning Focus
★ Position (GPS) and environmental sensors
★ Enable our students to explore the availability,
reliability and potential uses of such sensors on their
devices
★ Gather a shared pool of environmental data
★ Consider how this data might be analysed

Design Context
● Outdoor, sensor based activity (user choice)
● BYOD (programme practice)
● 30 minute ‘station rotation’ (class context)
● Limited setup time (easy install and use)
● Collaborative group activity (multiple roles)
● No cost (teachers could use in their schools)
● Multiple sites (no setup time, 6 locations)
○ Hence GPS, not QR codes

Navigation Based Apps
A number of tools support mobile learning
experiences in outdoor environments
None provide integrated sensor support
ARIS provides a combination of reliable GPS
performance and powerful tools for
mobile activities
Main drawback is the mobile app only runs
on iOS

ARIS Web-Based Editor

Example Sensor Tool - Sense-it
Sense-it was developed
as part of the nQuire
project
http://www.nuire-
it.org/#/home

ARIS Mobile App

Collaborative, Contextual, Outdoor,
Gathering and Visualising Data

Sensor Readings Taken
★ Light (lx)
★ Sound (dB)
★ Temperature (oC)
★ Pressure (hPa)
★ Humidity (%)
★ Compass (relative orientation)
★ Elevation (meters above sea level)
★ Wind speed (kph)

The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016
Shared Google Sheet for Data
Collaborative
data
gathering
and analysis

Collated Data
Sensor Readings Units of measurement Measurements Range
Light lux (lx) = lumen per square metre 64 589.2 lx to 1977.7 lx
Sound decibel (dB) 75 from 42 dB to 88.4 dB
Temperature degrees Centigrade (oC) 63 15.2 oC to 30 oC.
Atmospheric pressure hectopascal (1 hPa = 100 Pa) 1 millibar 46 982 hPa to 1022 hPa
Humidity percentage (%) 51 from 50% to 80%.
Compass relative orientation 41 -10 to 298 meters above sea level.
Elevation meters above sea level 36 N/A
Wind speed Kilometers per hour (kph) 18 range 0.3 kph to 38.4 kph.

Outcomes
● 42 teams participated across six sites
● Able to compare across three locations and across
sites
● Increased awareness of sensors and potential for
mobile learning activities
● Experience of collaborative BYOD approach for
problem solving and data gathering
● Uses of smartphones for crowdsourcing data by
pooling resources for shared analysis

Dr David Parsons | The Mind Lab by Unitec | April 2016Kathryn MacCallum & David Parsons | mLearn 2016
Dr David Parsons
http://davidparsons.ac.nz/
david@themindlab.com
Contact Details

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Editor's Notes