Mecagenius, a serious game for mechanical engineering in higher education : A trace driven analysis of knowledge and learning
Author(s):
Michel Galaup (presenting / submitting) Chantal Amade-Escot (presenting) Thierry Montaut Fabienne Viallet
Conference:
ECER 2012
Network:
16. ICT in Education and Training
Format:
Paper

Session Information

16 SES 03 A, Games in Education

Parallel Paper Session

Time:
2012-09-18
17:15-18:45
Room:
FCT - Aula 12
Chair:
Ed Smeets

Contribution

This study is about Mecagenius*: a serious game for teaching mechanical engineering at university; its objective is to present how students play the game and what they learn. The computer traces collected allow the identification of different indices on the students path in the game against the background of the expected knowledge. Through a qualitative analysis of twelve students’ game reports we show the different strategies used in this educational environment.

The aim of Serious Games is to teach and learn while having fun through computer applications (Zyda, 2005). Within a didactical theoretical framework, we consider the serious games as an educational medium (Brousseau, 1997). We agree with Sanchez (2011), that Serious Games can be modeled as an “adidactical situation”. According to Brousseau an adidactical situation is « a situation which provide her [the student] with the most independent and the most fruitful interaction possible » to the knowledge at stake (Brousseau 1997, page 30). It is a theoretical construct that « tells under which constraints a situation is likely to allow the intended learning, if the learning is guaranteed if the learner plays the proposed game » (Balacheff, 2012).

When using a serious game in class, the student is placed in an environment for knowledge acquisition in tacit agreement with the teacher. In this research, we study reports between students and knowledge crystallized in the Mecagenius Serious Game which is dedicated to mechanical engineering learning. The objective of the study is to model "what happens" when a teacher used this serious game as a instructional tool in higher education. The research questions are: (i) what is the impact of this learning environment on learners? (ii) What learning strategies they used? (iii) What is the learners’ knowledge in terms of the intended knowledge?

For the purpose of this presentation, we describe what players did in one of the twelve learning games of Mecagenius. This learning game, named "The King of the Pref", targets the knowledge and the skills related to the use of a numerical command machine tool. The intended knowledge and know how in this specific game concern: display the tool using the manual input of the human man interface, feel the datum with a gauge to set up the numerical command machine tool; settle down a numerical command machine tool; calculate the vector between the measurement reference and the datum.

Method

Our aim is to account for the dynamic in which students act and react to the learning games proposed by Mecagenius. We implemented in Mecagenius a tool to trace the different actions and the scores of the gamers students. Two types of data were analyzed: • macro types data: level of play, number of games played, score, types of errors • micro type data: chronological report of each player’s actions when playing The study involved two mechanical engineering teachers of Toulouse University and six groups of students engaged in a professional undergraduate degree in Mechanical Engineering. In each setting, we focused on two kind of contrasted students: one (A1) is considered as a high skilled student by the teacher (pre interview) “having no difficulties in the specific subject matter”, the other (A2) is considered as a low skill student, “having difficulties in mechanical engineering". Following Leutenegger (2003), we consider that these two contrasted students (in terms of their position of excellence in the classroom) are relevant subjects to understand the range of learners strategies when using serious game in a training system.

Expected Outcomes

From the collection of Mecagenius traces, the results show different learning strategies used by the two groups of students (6 students A1 and 6 students A2). High skilled students (A1) who have no learning difficulties, score generally higher. They also show that they test all three levels of the game (beginners, intermediate, experts), scoring points in each of them. They spent more time at the most difficult game level. Sometimes they accumulate points a lot of time remaining on the same game to increase their score. Students with learning difficulties (A2), used a great number of shots. They spent a lot of time in settings the numirical command machine tool. They need more time to get the first critical adjustment. Finally, with respect to errors and problems, the results point out that the number of failures (broken probe, sign error, lack of precision) is higher. The strategy of them may be summurized as a succession of “trials and errors”. The results also suggest that Mecagenius offers an adidactical situation for learning the knowledge at stake. The computer traces showed that students take initiatives, decisions and engage in serious games. These elements support the idea that Mecagenius provides pertinent adidactical situations.

References

*Web site: http://mecagenius.univ-jfc.fr/ Balacheff, N. http://www-didactique.imag.fr/Brousseau/BrousseauFAQUK.html accessed le 30 janvier 2012. Brousseau, G. (1990). Le contrat didactique : le milieu. Recherches en Didactique des Mathématiques, 9(3), 309-336. Brousseau, G. (1997). Theory of didactical situations in mathematics. Dordrecht, Netherlands: Kluwer Academic Publishers. Leutenegger, F. (2003). Etude des interactions didactiques en classe de mathématiques : un prototype méthodologique. Numéro spécial Bulletin de psychologie, tome 56 (4), 466, 559 -571. Sanchez, E. (2011). Usage d’un jeu sérieux dans l’enseignement secondaire. Modélisation comportementale et épistémique de l’apprenant. RSTI – RIA – 25/2011. Zyda, M. (2005). "From Visual Simulation to Virtual Reality to Games", IEEE Computer Society.

Author Information

Michel Galaup (presenting / submitting)
Université Toulouse II - Le Mirail
IUFM Midi-Pyrénées - Ecole Interne
Toulouse
Chantal Amade-Escot (presenting)
Université Toulouse II - Le Mirail, France
Thierry Montaut
Centre Universitaire de Formation et de Recherche Jean-François Champollion - SGRL - Albi - France
Fabienne Viallet
Université Toulouse III - Paul Sabatier, France

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