Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Modeling with young students—Quantitative and qualitative

  • 47 Accesses

Abstract

THIS ARTICLE COMPLEMENTS the one onMaking Models and Reasoning with Them in this journal in Spring 1997 (Vol 8). The paper is about the Tools for Exploratory Learning Programme, which was part of an ESRC national initiative on Information Technology in Education. The research created both tasks and tools to investigate the quality and nature of pupils’ reasoning when using three kinds of modeling tools (quantitative, qualitative and semiquantitative, this latter kind of modeling was the focus of the 1997 article).

Tasks and tools were used in two innovative modes of learning: expressive, where pupils created their own models, and exploratory, where pupils investigated an expert’s model. Cross curricular tasks covered three topics: health, shops and profits, and traffic congestion. Average pupils, girls and boys between 11 and 14 years, each worked with one tool, on one task, in one mode of learning. After having either created or explored a model, pupils where asked to modify their own or another’s model and/or build new ones.

This article focuses on the qualitative and quantitative modeling. Our findings showed that all pupils could construct a model, even if limited, in expressive tasks, with both tools. With quantitative tasks all pupils could build some model, almost exclusively from binary relations. With qualitative tasks, (where the tool linked situations and actions) generation of actions was easy for all, but situations were more difficult for about half the pupils. However, nearly all pupils generated fairly complex interconnected structures. When exploring quantitative models many pupils saw the model as generating one answer. With qualitative models, the action-situation structure was understood by the majority and about half showed some appreciation of the nature of the model as a whole.

This is a preview of subscription content, log in to check access.

References

Books and Papers

  1. Bliss, J., & Ogborn, J. (1989). Tools for exploratory learning.Journal of Computer Assisted Learning,5,37–50.

  2. Bliss, J., & Ogborn J. (1992a). Reasoning supported by computational tools.Computers in Education,18 (1–3), pp 1–9, (Reprinted in M.R. Kibby & J. R. Hartley Eds., 1992.Computer Assisted Learning.Oxford: Pergamon Press).

  3. Bliss, J., & Ogborn J. (1992b).Tools for exploratory learning end of award report.(1 Executive Report and Summary Report; 2 Technical Report Tools; 3 Technical Reports Semi-Quantitative Reasoning Expressive and Exploratory; 4 Technical Reports Quantitative Reasoning and Qualitative Reasoning) available from the authors.

  4. Bliss, J. (1994). Modelling, a means for expressing thinking: ESRC Tools for exploratory learning research programme. In S. Vosniado, E. De Corte, & H. Mandl (Eds.).Technology-based learning environments psychological and educational foundations. (33–39).Berlin:Springer Verlag. (Published in cooperation with NATO Scientific Affairs Division).

  5. Bliss, J. (1997). Making models and reasoning with them.Journal of Computing in Higher Education,8 (2), 3–28.

  6. Mellar R., Bliss J., Boohan, R., & Ogborn J., & Tompsett, C. (Eds.). (1994).Learning with artificial worlds: Computer based modelling in the curriculum.Lewes, UK:Falmer Press.

  7. Miller, R., Briggs, J., Brough, D., & Ogborn, J. (1992). Tools for exploratory learning end of award report: Technical Report 1 Tools, available from the authors.

  8. Miller, R., Ogborn, J., Briggs, J., Brough, D., Bliss, J., Boohan, R., Brosnan, T., Mellar, H., & Sakondis, B. (1993). Educational tools for computational modelling.Computers and Education,21 (3), 205–261.

  9. Roberts N., Anderson D., Deal R., Garet M., & Shaffer W. (1983).Introduction to computer simulation.New York:Addison Wesley.

  10. Sutherland R (1994). Modeling with spreadsheets. In H. Mellar, J. Bliss, R. Boohan, J. Ogborn, & C. Tompsett (Eds.),Learning with artificial worlds — computer based modelling in the curriculum.Lewes, U.K.:Falmer Press.

Software

  1. Briggs, J. H. (1984).MicroPROLOG rules! [Computer software].London:Logic Programming Associates.

  2. Briggs, J. (1989).Knowledge Pad. [Computer software]Exeter:PEG

  3. Brough, D., Briggs, J., & Watson, L. (1988).Linx88 Authoring Software Manual [Computer software].Exeter:PEG

  4. Expert Builder (1989). [Computer software]. Integrated Modeling Project, Advisory Unit for Microtechnology in Education: Hatfield

  5. Model Builder(1988). [Computer software]. Integrated Modeling Project, Advisory Unit for Microtechnology in Education: Hatfield

  6. Numerator (1990). [Computer software]. Cambridge: Logotron Ltd.

  7. Ogborn, J. (1985).Dynamic Modeling System. [Computer software].London:Longman Micro Software.

  8. Ogborn, J. & Holland, D. (1987).Cellular Modeling System. [Computer software].London:Longman Micro Software

  9. Richmond, B., Peterson, S. & Vescuso, P. (1987).STELLA. [Computer software]New Hampshire, U.S.A.:High Performance Systems.

  10. Schwartz, J. (1987).The Algebraic Proic Inc. [Computer software].

Download references

Author information

Additional information

ABOUT THE AUTHORS

Joan Bliss is Professor at the University of Sussex and Director of the Institute of Education. She is a cognitive, developmental psychologist, who studied and worked with Jean Piaget for 10 years. Her research concerns fundamentals of human thinking and reasoning, particularly how thinking and reasoning develops in children in the areas of science and mathematics. Recently Professor Bliss has explored in greater depth the nature of physical reasoning expanding this to the nature of everyday reasoning in children and adults (with Jon Ogborn). Another aspect of this work is related to how thinking and reasoning can be developed through teaching, this expansion bringing to her work the social context of learning and situated learning. Professor Bliss codirected the programme (with Jon Ogborn) and was responsible for its empirical research, particularly in the fields of semi-quantitative and qualitative modeling. Author’s present address: USIE, EDB, University of Sussex, Falmer, Brighton, England BN 1RG

Jon Ogborn is Professor of Science Education at the Institute of Education, University of Sussex. At present he is Director of a new and innovative project set up by the UK Institute of Physics to change the curriculum in Physics for students between the ages of 16 and 19. Professor Ogborn’s research interests range from computer-based modeling and novel methods of data analysis to curriculum innovation. His interests also include everyday reasoning about physical phenomena. While co-directing the ESRC programme with Joan Bliss, he also managed the interaction between the programme’s development of computational modeling tools and its empirical research.

Harvey Mellar is Senior Lecturer at the University of London Institute of Education where he works within the Institute’s Science and Technology Group. Dr. Mellar’s research covers three specific areas of interest: computer based modeling, computer mediated communication, and Information Technology and teacher education. Dr. Mellar was one of the three managers of the ESRC Tools for Exploratory Learning Programme, contributing substantially both to the programme as a whole, to the construction of modeling tools and to the area of semiquantitative modeling.

Richard Boohan lectures in the School of Education at the University of Reading. His background is chemistry education but he covers the whole range of science education in his work. He has worked with Jon Ogborn in the areas of computer-based modeling, particularly for young children 8 years old onwards, of the development of innovative means of exploring data (EDA), and the creation of novel ways of teaching science particularly, energy. Richard Boohan worked on the quantitative modeling part of the ESRC programme and contributed to the semiquantitative modeling part.

Tim Brosnan lectures in the Science and Technology Group of Institute of Education, University of London. He is a specialist in chemistry education but his work necessarily covers the whole of science education. His main research interests are in the area of children’s reasoning about physical and chemical processes of change. He is also interested in the role of electronic devices in learning, for example, spreadsheets, calculators etc. On the ESRC programme he worked on quantitative modeling, and contributed to the semi-quantitative modeling part of the programme.

Babis Sakonidis is a lecturer at the University of Thrace in Alexandropoulis, Greece. At the time of the ESRC programme he was a research fellow at London University’s Kings College, working on the qualitative modeling part of the programme, contributing also to the semiquantitative modeling. Dr. Sakonidis is a mathematics education specialist and carried out all his postgraduate studies in the UK, his MSc. at the University of Reading and his doctorate at King’s College London where he examined the relationship between language and the learning of algebra with 14–18 year olds.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bliss, J., Ogborn, J., Boohan, R. et al. Modeling with young students—Quantitative and qualitative. J. Comput. High. Educ. 10, 69 (1999). https://doi.org/10.1007/BF02948724

Download citation

Keywords

  • modeling quantitative qualitative
  • modeling-tools
  • models
  • expressive-learning
  • exploratory-learning
  • reasoning
  • children