CONCLUSIONS AND FUTURE IMPLICATIONS

The first thing which we must point out is that the limited nature of our sample leads us to be very cautious with the conclusions this experience has led us to. Bearing this in mind, with the data obtained and the critical analysis of the elements which we have used in this study, we believe that it is possible to extract some interesting conclusions:

 The analysis of the didactical scientific simulations, adopting an ontological perspective, has been effective in the learning of the concept “scientific model”, by the teachers in the sample.

 We think that the method used, in which the “know how” and “know what” questions have been alternated, has been particularly important for the achievement of the proposed objective.

 The subjects of the sample, despite having been previously introduced to the concept of “scientific model”, have found some novel questions in our way of approaching the problems of analysing the simulations. This really caught the subjects’ attention, and added a motivational bonus for the work. We feel that these aspects of novelty and motivation may also have influenced in the results obtained.

All the previous considerations lead us to a further conclusion: it is possible to design teacher training courses with the conceptual approach and the methodology used in this work. It has been effective with a small sample, and with subjects who had already been introduced to the concept of “scientific model”.

Now our challenge is the following: Will it work for more representative samples, and with subjects who have not been introduced to the concept? This is the challenge which we propose to take up in the near future.

REFERENCES

Clement, j. J. and Rea-Ramirez, M.A., (2008). Model-based learning and instruction in science, Springer. Dordrecht, the Nethreland:

Cohen, N. J. and Squire, L. R., (1980). Preserved Learning and Retention of Pattern-Analysing Skill in Amnesia: Dissociation of Knowing How and Knowing that. Science, New Series, 210 (4466), 207-210.

Cullin, M., and Crawford, B. A., (2003). Using technology to support prospective science teachers in learning and teaching about scientific models. Contemporary Issues in Technology and Teacher Education, 2 (4), 409-426.

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Danusso, L., Testa. I., and Vicentini, M., (2010). Improving Prospective Teachers’ Knowledge about Scientific Models and Modelling: Design and evaluation of a teacher education intervention. Int. J. of Science Education, 32, (7), 871–905.

Drechsler, M. and Schmidt, H-J., (2005). Textbooks’' and teachers' understanding of acid-base models used in chemistry teaching. Chem. Educ. Res. and Pract., 6 (1), 19-35.

Gutierrez, R. and Pinto, R., (2004). Models and Simulations. Construction of a Theoretically Grounded Analytic Instrument. En: E. Mechlová (ed), Proceedings: Teaching and Learning Physics in New Contexts. Selected Papers. GIREP 2004 International Conference. University of Ostrava. Ostrava, Czech Republic, p. 157-158.

Gutierrez, R. and Pinto, R., (2005). Teachers’ conceptions of scientific model. Results from a preliminary study. In R. Pinto and D. Couso (eds), Proceedings of the Fift International ESERA Conference on Contributions of Research to Enhancing Students' Interest in Learning Science.

Barcelona, Spain, p. 866-868

Gutierrez, R. and Pinto, R., (2008). Teachers' conceptions of scientific models II: Comparison between two groups with different backgrounds. In: E. van der Berg, A. L. Ellermeijer, O. Sloote (eds)

"Modeling in Physics and Physics Education". AMSTEL Institute. Univ. of Amsterdam. Amsterdam.

The Netherland, p 958-963.

Harnad, S., (2007). From Knowing How To Knowing That: Acquiring Categories By Word of Mouth.

Paper presented at: Kazimierz Workshop on Naturalised Epistemology, Kazimierz Dolny, Poland, 1-5 September 2007. http://bacon.umcs.lublin.pl/~ktalmont/KNEW/

Institute for Simulation and Training. University of Central Florida. Orlando Campus.

http://www.ist.ucf.edu/background.htm.

Justi, R. and Gilbert, J. K.,( 2003). Teachers' views of the nature of models. Int. J. of Science Education, 25 (11), 1369-1386.

Justi, R. and Van Driel, J., (2005). The development of science teachers' knowledge on models and modelling: promoting, characterizing, and understanding the process. Int. J. of Science Education, 27 (5), 549-573.

Lemmer, T. N., (2006), The Nature Of Scientific Models In Physics - A Philosophical Perspective. In:

E. van der Berg, A. L. Ellermeijer, O. Sloote (eds), Proceedings of the GIREP 2006 International Conference on "Modeling in Physics and Physics Education", Univ. of Amsterdam. Amsterdam. The Netherland, p 540-545.

Quay, J., (2006), Knowing how and knowing that: a tale of two ontologies. Working Paper.

http://staff.edfac.unimelb.edu.au/~jquay/QuayIOE2004.pdf

Sassi, E., Monroy, G. and Testa, I., (2005), Teacher Training about Real-Time Approaches: Research-based guidelines and Training Materials. Science Education, 89, (1), 28-37.

Van Driel, J.H. y Verloop, N., (1999). Teachers' knowledge of models and modelling in Science. Int. J.

of Science Education, 21 (11), 1141-1153.

Windschitl, M., Thomson, J. and Braaten, M., (2008). Beyond the scientific method: Model-Based Inquiry as a new paradigm of preference for school science investigations. Science Education, 92 (5) 941-967.

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Zhang, B., Liu, X. and Krajcik, J. S., (2006). Expert Models and Modeling Processes Associated with a Computer-Modeling Tool. Science Education, 90 (5), 579– 604.

Dr. Rufina Gutierrez

Emeritus Professor at the Instituto de Estudios Pedagogicos Somosaguas (IEPS), Madrid.

Visitor Professor at Universitat Autònoma de Barcelona (CRECIM).

Universitat Autònoma de Barcelona Campus de Bellaterra, Edifici GL- 304 08193 Bellaterra. Barcelona. Spain.

E-mail: rufina.gutierrez@uab.es Dr. Denise Whitelock

Senior Lecturer

Institute of Educational Technology The Open University

Walton Hall Milton Keynes MK7 6AA

Email: d.m.whitelock@open.ac.uk

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Imagine that a teacher, a work colleague of yours, asked you to tell him how he could define a scientific model, and what its main functions are. What would you tell him? Note down your answer below, using only the words you consider essential, so that your meaning is clear to your colleague.

At most, your explanation can be one page long.

Thank you very much.

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EXPERIMENTA, A SCIENCE TEACHER TRAINING PROGRAM IN CBLIS

Ana Pino Álvarez, Juan de Dios Jiménez Valladares, Ruth Jiménez Liso, Carlos Sampedro Villasán ABSTRACT

Experimenta is a teachers’ training program developed during the last three years by the Parque de las Ciencias de Granada in order to improve the use of CBLIS in secondary schools.

After some experimental sessions to introduce teachers in the use of interface, sensors and CBLIS, the plan includes training activities online as well as experimental tasks developed at school with their students. Teachers are thus allowed to progress in their own training process and practice with their students the use in an actual context of CBLIS methodology.

KEYWORDS

CBLIS, training teachers, network training, methodological change

INTRODUCTION

Experimenta is a teacher’s training program developed since 2009 in order to introduce CBLIS strategies in school. This would be a suitable way to improve success in student science understanding and to update teachers’ knowledge of some features concerning science and science teaching, as well as to improve the scientific literacy of the students and their interest for science in the future.

The program has been planned in three stages, coinciding with the last three school years, with the main goal of exploring the possibility of creating a school network able to share CBLIS equipments, working together to train teachers and to plan joint research activities.