This document summarizes research conducted on utilizing context in mobile learning environments. Students used mobile applications that delivered content based on time, location, or activity. Usage data was collected over 10 weeks. Preliminary results showed that interactions peaked on Wednesdays and Fridays and declined over the term. Further research will profile student usage patterns and test delivering content based on different contextual dimensions to improve mobile learning.

1. IADIS M-Learning Conference, Lisbon
2013
Laura Crane Dr.Phil Benachour
School of Computing & Communication School of Computing & Communication
Systems Systems
l.crane@lancaster.ac.uk p.benachour@lancaster.ac.uk
@laura_crane @phil_benachour

2.  Motivations of Research
 Previous Research
 Dimensions of Context
 Precedence of Context
 Rationale of Study
 Architecture of Applications
 Deployment
 Results
 Conclusions

3.  Investigate student interactions with mobile
devices
 Profile student usage of mobile applications
which support their organisation of learning
 Can we utilize context in pervasive learning
environments
 Precedence of contextual dimensions

5.  Investigated RSS as an information channel
for mobile learning
 Compared RSS to Twitter
 Delivered RSS based upon a time or location
mechanism
 Can we deliver RSS based alerts using more
than time and location?

6. Identity
Relationship
Location
to n.
Activity Time

9. Mean Values from Student Responses
Relationships to n.
Your Identity
Activity
Location
Time
0 0.5 1 1.5 2 2.5 3 3.5
Your Relationship
Time Location Activity
Identity s to n.
Mean Response 2.996727267 2.850189667 2.926665333 2.39890303 1.979968443

11. • Automatically detect the places
(including the name and
category) that the user visits.
• Minimize battery power
consumption while gathering
data from the mobile device.
• Get notifications when a user
arrives at or departs from a place.
• Automatically get the number of
times a user visits a place, and
how much time is spent there per
visit.
• Automatically understand a
user’s mobile motion state (e.g.
stationary, walking or driving).
https://www.alohar.com/developer/learnmore.html

12. Time Application Location Application Activity Application

14.  3 Groups of participants
 5 participants in each group for
Time, Location & Activity
 Deployed onto 15 devices
 Full term of 10 weeks (70 days)

16. Percentage of Interactions over Term by Day of the Week
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
2%
8% 17%
29% 17%
9%
18%

17. Number of Interactions per Week of the Term
50
45
40
35
Number of Interactions
30
Time
25
Location
20 Activity
15
10
5
0
1 2 3 4 5 6 7 8 9 10

19.  Build useful user profiles and patterns of
usage
 Ensure wireless access is available in
situations where interactions take place
 Currently in the second term of academic
year long study
 The groups have been deployed with a
different context application
 Awaiting results April to May 2013