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

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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

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5Neutral Teams

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#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

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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!