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A preliminary study where a multiplayer location-based game’s logfiles were used for the assessment of the overall practice of teams. We explore the use of activity metrics previously introduced and applied to CSCL settings. We argue that these metrics, if adapted in a meaningful way, will provide insight of the progress of a location-based gaming activity and its quality regarding the score. Moreover, we assert that this can be achieved in an automated way. A small set of activity metrics, related to game characteristics and player activity, is applied to a set of gaming activities. The results are analyzed regarding team performance and score. The paper proposes a way to analyze group activity in the context of location-based games while taking into account the characteristics of successful collaborative activities. Future work is proposed towards the development of automated metrics for the analysis of location-based gaming activities with emphasis on collaboration and group dynamics.
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an Analysis of Team-Gaming Activity
Irene-Angelica Chounta, Christos Sintoris, Melpomeni Masoura, Nikoleta Yiannoutsou, Nikolaos Avouris
HCI Group, University of Patrashttp://hci.ece.upatras.gr
{houren, sintoris, masoura, nyiannoutsou, avouris}@upatras.gr
Objective
• Use of activity metrics within a mobile learning context
• Can the logfiles tell the good practice from the bad and the neutral?
• Preliminary study on the adaptation of automated metrics for the analysis and evaluation of mobile collaborative activities
Mobile-Learning: a special case of collaborative learning….
• Learners always on the move• Learning across space and within the context
Why automated metrics in a mobile-learning scenario:– Learners on the move action immediate and
continuous. – No round table meetings for argumentation or
planning. Successful collaboration efficiently portrayed by activity metrics
MuseumScrabble: playing and learninghttp://hci.ece.upatras.gr/museumscrabble/
• location-based multiplayer game that facilitates children visiting a museum
• teams collaborate to gather points using a PDA device
• Game objective: Linking topics to museum exhibits using RFID tags
Work together, for the win!
MuseumScrabble: playing and learning
• 17 students 7 teams (3-4players)• 25 minutes approximately• 1 handheld device per team• Activity recorded by the MuseumScrabble application
Analysis of Activity• Split teams into 3 categories with respect to final
game score:– The Good (2 teams, score>17points)– The Bad (3 teams, score =0 points)– The… Neutral (2 teams, score: 4 to 8 points)
• Study each team’s activity based on its basic activity metrics (sum of actions, linking activity, avg time gap between actions)
An action can be a) a successful scan, b) an unsuccessful scan, c) a link action, d) an unlink action, e) enter a topic, f) exit a topic
activity metrics per team category
gt00 gt01 nt00 nt01 bt00 bt01 bt020
50100150200250300350400
#events
gt00 gt01 nt00 nt01 bt00 bt01 bt0200:00
00:08
00:17
00:25
00:34
00:43
00:51#avg_time_gap in seconds
gt: good teams, nt: neutral teams, bt: bad teams
the Good: • intense activity• temporally dense
the Bad: • minimum activity• extremely large time gaps
between consequent events
the Neutral: • somewhere in the middle
activity in time
0120
240360
480600
720840
9601080
12001320
0
1
2
3
4
5Good Teams
0120
240360
480600
720840
9601080
12001320
0
1
2
3
4
5Neutral Teams
0120
240360
480600
720840
9601080
12001320
0
1
2
3
4
5Bad Teams
#Link eventsThe Good:• links are distributed
throughout the whole duration of the activity.
• No long periods of inactivity (approx. 1min)
• A late start might indicate strategy planning
The Neutral/Bad :• Links take place mostly during
the first minutes of the activity,
• gradually fade out • coming to a halt almost after
the first half of the activity duration.
Discussion• The chosen metrics successfully captured the efficient team
practices• Towards an automated analysis framework for mobile
collaboration– extensive, large-scale studies within various settings– Further analysis of the interaction among users of the
same team
Our contribution:– Point out the need of an automated analysis framework
for mobile-collaborative activities regardless the context – Propose an automated analysis/evaluation schema for
mobile learning collaborative scenarios deriving from CSCL methods
activity in time0 60 120
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60Good Teams
0 60 120
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60Bad Teams
0120
240360
480600
720840
9601080
12001320
0
10
20
30
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60Neutral Teams
Total Events
The Good: • intense, continuous activity
throughout the game.
• Periods of zero activity are extremely rare
The Bad: • low activity (1-2 actions per minute) • Periods of zero activity are more
frequent and last longer• Rare outbursts of high activity
The Neutral: • somewhere in the middle
activity metrics per team category
gt00 gt01 nt00 nt01 bt00 bt01 bt020
50100150200250300350400
#events
gt00 gt01 nt00 nt01 bt00 bt01 bt0202468
101214 #links - #unlinks (#dlu)
gt00 gt01 nt00 nt01 bt00 bt01 bt0200:00
00:08
00:17
00:25
00:34
00:43
00:51#avg_time_gap in seconds
gt: good teams, nt: neutral teams, bt: bad teams
the Good: • intense activity• temporally dense
the Bad: • minimum activity• extremely large time gaps
between consequent events
the Neutral: • somewhere in the middle
Work together, for the win!
