an Analysis of Team-Gaming Activity
Irene-Angelica Chounta, Christos Sintoris, Melpomeni
Masoura, Nikoleta Yiannoutsou, Ni...
Objective
• Use of activity metrics within a mobile
learning context
• Can the logfiles tell the good practice from the
ba...
Mobile-Learning: a special case of
collaborative learning….
• Learners always on the move
• Learning across space and with...
MuseumScrabble: playing and learning
http://hci.ece.upatras.gr/museumscrabble/
• location-based multiplayer game that faci...
Work together, for the win!
MuseumScrabble: playing and learning
• 17 students  7 teams (3-4players)
• 25 minutes approximately
• 1 handheld device p...
Analysis of Activity
• Split teams into 3 categories with respect to final
game score:
– The Good (2 teams, score>17points...
activity metrics per team category
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#events
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activity in time
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Discussion
• The chosen metrics successfully captured the efficient team
practices
• Towards an automated analysis framewo...
activity in time
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#events
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Work together, for the win!
Chountaetal - team-gaming activity analysis - @ectel meets ecscw 2013
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Chountaetal - team-gaming activity analysis - @ectel meets ecscw 2013

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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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Chountaetal - team-gaming activity analysis - @ectel meets ecscw 2013

  1. 1. an Analysis of Team-Gaming Activity Irene-Angelica Chounta, Christos Sintoris, Melpomeni Masoura, Nikoleta Yiannoutsou, Nikolaos Avouris HCI Group, University of Patras http://hci.ece.upatras.gr {houren, sintoris, masoura, nyiannoutsou, avouris} @upatras.gr
  2. 2. 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
  3. 3. 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
  4. 4. MuseumScrabble: playing and learning http://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
  5. 5. Work together, for the win!
  6. 6. MuseumScrabble: playing and learning • 17 students  7 teams (3-4players) • 25 minutes approximately • 1 handheld device per team • Activity recorded by the MuseumScrabble application
  7. 7. 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
  8. 8. activity metrics per team category 0 100 200 300 400 gt00 gt01 nt00 nt01 bt00 bt01 bt02 #events 00:00 00:09 00:17 00:26 00:35 00:43 00:52 gt00 gt01 nt00 nt01 bt00 bt01 bt02 #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
  9. 9. activity in time 0 1 2 3 4 5 0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 Good Teams 0 1 2 3 4 5 0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 Neutral Teams 0 1 2 3 4 5 0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 Bad Teams #Link events The 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.
  10. 10. 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
  11. 11. activity in time 0 10 20 30 40 50 60 0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 Good Teams 0 10 20 30 40 50 60 0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 1320 Bad Teams 0 10 20 30 40 50 60 0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 10… 10… 11… 12… 12… Neutral 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
  12. 12. activity metrics per team category 0 100 200 300 400 gt00 gt01 nt00 nt01 bt00 bt01 bt02 #events 0 5 10 15 gt00 gt01 nt00 nt01 bt00 bt01 bt02 #links - #unlinks (#dlu) 00:00 00:09 00:17 00:26 00:35 00:43 00:52 gt00 gt01 nt00 nt01 bt00 bt01 bt02 #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
  13. 13. Work together, for the win!

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