MET:Conceptual Change

From UBC Wiki

Conceptual Change refers to a developmental view on how learners gain scientific knowledge. According to conceptual change theory, one cannot cognitively understand new scientific information that conflicts with the person's existing knowledge, and it is this conflict that leads to scientific misconceptions. Instead, a person needs to negotiate with their dissatisfaction and accommodate their new knowledge. This in turn leads to a change in their conceptions (Özdemir & Clark, 2007). Although conceptual change is typically mentioned in terms of a traditional face-to-face classroom, it is also an important part of the digital classroom.



Thomas Kuhn's contribution to conceptual change theory cannot be understated. Kuhn significantly changed how the process of science is viewed by showing how science is not learned through a simple accumulation of knowledge (Moore, 1980). Instead, science is learned and accepted through a process of "revolution" or "paradigm shifts." Kuhn tells us that the scientific method should not refer to a set of rules to be followed, but should explain what scientists really do. For example, when a new topic is studied in science there may be many competing hypothesis with different scientists proposing different approaches to the problem. It is only after a period of time that a particular theory will be accepted by the scientific community.


Like Kuhn, Jean Piaget plays an important role in the development of the conceptual change theory. Piaget's developmental theories say that knowledge is a result of organization and adaptation (Simatwa, 2010). This adaptation consists of assimilation and accommodation. Assimilation is where a new idea is retrieved and fits in with existing knowledge. If the new idea does not fit in with one's existing organizational structure, then accommodation takes place.


Schema Theory, as it relates to education, tells us how knowledge is organized and retrieved from one's memory (Bigenho, 1992). Richard Anderson tells us that schema not only organizes knowledge, but takes new information and constructs "organized knowledge of the world," (Anderson, 1985). Following from Piaget's ideas, assimilation refers to when new knowledge fits in with one's existing schema. If the new knowledge has a conflict with the schema, the schema must change and it is said to undergo accommodation.

Conceptual Change

Conceptual Change therefore refers to how people can gain new scientific knowledge, and how this knowledge undergoes a gradual process of Kuhnian acceptance. It is very difficult for a learner to understand a new idea through assimilation if there is no basis for the new knowledge in the learner's existing schema. Instead, over a period of time or events the learner must go through a conceptual change, where their schema is modified and the new knowledge is accommodated. There is a third option to assimilation and accommodation which is the case that new knowledge is simply rejected.


Misconceptions can arise in science if a learner does not go through a conceptual change. If knowledge is presented in an additive manner that contradicts the learner's existing schema, the individual makes a compromise between the new and old knowledge (Vosniadou, 2007). Misconceptions often arise through direct instruction, where the learner is presented with ideas that do not fit their existing schema. By simply assimilating the ideas, the conflicting frameworks will produce hybrid or synthetic cognitive models, resulting in a misconception (Vosniadou, 2007).

There are many different misconceptions within the scientific community and they prevail even with highly educated students and scientists. Some typical misconceptions include:

  • the mass of trees is mostly made up from nutrients from the ground
  • in an atom, electrons are relatively close to the nucleus
  • there is no gravity in space
  • there are two forces acting on objects that have been thrown in the air (disregarding air friction)
  • in genetics, acquired characteristics can be inherited
  • that the Earth's orbit is the cause for the seasons (see the video below)


Instructional Models

Instructional models have been developed to specifically target misconceptions and enhance conceptual change. The 5E and 7E models are effective at doing this in science education (Guzzetti, Snyder, Glass, & Gamas, 1993).

5E Model

File:5e cycle.jpg
5E Instructional Model

The 5E Instructional model is used to facilitate the progression of inquiry in teaching science. It can be used as a framework for units, modules or lessons, as a way to organize the structure of a constructivist conceptual change environment (Bybee et al., 2006). The model is based on five phases of inquiry:

  • Engagement
  • Exploration
  • Explanation
  • Elaboration
  • Evaluation

7E Model

The 7E Instructional model is based on the 5E model but expands the engagement phase to elicit and engage. As well, the elaborate and evaluate phases are expanded to elaborate, evaluate and extend. The purpose of the 7E model is to emphasize the importance of recognizing prior knowledge and expanding knowledge gained through conceptual change (Eisenkraft, 2003).

7E Change Model


Being able to create a discrepant event in order to trigger accommodation of knowledge is very important for conceptual change. Computer and photo/video technologies can readily assist in creating or demonstrating these discrepant events. Visual technologies are particularly interesting because of human dependence on vision. The importance of visualization in learning science is seen in empirical studies which show that manipulating visual data with technologies such as spatial processing software, improves student understandings and addresses misunderstandings (Friedman & diSessa,1999). Visual representations are used extensively to communicate scientific knowledge, and they can further stimulate meaningful learning and scientific inquiry (Ernst & Clark, 2007). Furthermore, not only do visual technology tools aid in the conceptual change process because of their ability to communicate complex scientific ideas, but these tools offer motivation and engagement (Thomas, Johnson & Stevenson, 1996). Visualization tools can aid in many different educational domains, including physics, chemistry, biology and mathematics. David Jonassen notes how computers, acting as visualization tools, can be considered "Mindtools," and that "computers can make a unique contribution to the clarification and correction of commonly held misconceptions of phenomena by visualizing those ideas" (Jonassen, 2000, p. 195).

Visualization Tools (Jonassen, 2000, p. 196)


Addressing the issue of conceptual change in e-learning is particularly important because there may be less interaction between instructors and students, as well as less peer interaction. This can reduce the number of student misconceptions that get revealsed. Hay et al. (2008) studied how to measure e-learning quality, and noted that "data suggest that students’ prior knowledge is a key determinant of meaningful learning," (Hay et al., p. 1037, 2008). When trying to develop a constructivist pedagogy in e-learning, such as a Community of Inquiry, confronting misconceptions is identified as being one of the three most important instructional steps required (Rourke & Kanuka, 2009).

High-Speed Cameras

High-speed cameras that can capture up to 1000 frames/second are useful for visually showing a variety of physics concepts that normally are not detectable. Using high-speed video with slow-motion review, different phenomena can be clearly demonstrated and explored to create a discrepant event. Instructors can create and distribute videos for e-learning students, or they can be used in a classroom in a face-to-face environment via a traditional classroom or Blended Learning. Some examples where a high-speed camera could be used include:

  • energy transferred in sound waves breaking wine glasses
  • falling objects of different size or mass hitting a surface at the same time, showing the independence of mass from acceleration due to gravity (Heck & Uylings, 2010)
  • objects falling with an acceleration greater than g, the acceleration due to gravite (Heck, Uylings, & Kędzierska, 2010)
  • exploring Newton's First Law and momentum (Kelly, 2006), two concepts which carry common misconceptions (Mazur, 1997)


Computer simulations are used extensively in science and mathematics education. The flexibility of using simulations makes them effective in face-to-face settings as well as e-learning and distance education. When dealing with complex science concepts, simulations allow students to extend and free their cognition processes which helps them construct scientific mental models (Hobson, Trundle, & Sackes, 2010). In terms of mathematics and instructional models, simulations and computer technologies encourage the engagement, exploration, explanation and extension of learning new concepts, as well as visualizing some math concepts better (J. Hohenwarter, M. Hohenwarter, & Lavicza, 2009). Examples of using computer technology and simulations in science and mathematics include:

  • physics java simulations for kinematics, dynamics, energy and electricity topics
  • virtual dissection software for biology
  • using Geogebra to explore shapes and mathematical proofs
  • planetarium software to explore lunar cycles
  • Web-based learning programs and WebQuests (Liao & She, 2009)
  • tracking software for analysis 2-D motion of objects
  • chemistry simulations


Refutational texts are considered to be an effective tool for creating accommodation and conceptual change (Guzzetti et al., 1993) and video clips and movies can accomplish the same. Video clips may provide a further advance in conceptual change if they are more engaging or motivating to the students (Palmer, 2005). Furthermore, video offers an opportunity to include dialogue and it has been shown that dialogue is even more effective for conceptual change compared to refutation (Muller, Bewes, Sharma, & Reimann, 2007). Well crafted videos can offer exposition of a misconception along with refutation and dialogue. The dialogue aspect is therefore an important opportunity for conceptual change in e-learning and distance education, where typical peer interaction and peer instruction are not as readily available. The video below gives an example of how exposition, refutation and dialogue can all play a part in conceptual change.



Not all education researchers agree on the significance of conceptual change. Geraedts & Boersma (2006) claim their research is an example where guided reinvention achieves a conceptual understanding of evolution whereas conceptual change does not. Other research has shown that traditional texts, as part of a non-constructivist instruction strategey, can be as effective as a refutational text (Palmer, 2005).

In more general terms, others argue against the ontological basis of conceptual change and misconceptions. Much of this criticism is based on the absence of a sociocultural context when discussing what a concept actually is (Dillon, 2008). Similarly, it is argued that a misconception cannot be labeled as such, independently from the activity and problem that it is based upon.


Anderson, R. C. (1985). Role of the reader’s schema in comprehension, learning and memory. In H. Singer & R. Ruddell (Eds.), Theoretical models and processing of reading (3rd ed., pp. 372-384). Newark, DE: International Reading Association.

Bigenho, F. W. (1992). Conceptual developments in schema theory. Peabody College of Vanderbilt University. Retrieved from

Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Carlson Powell, J., Westbrook, A., & Landes, N. (2006). The bscs 5e instructional model: origins and effectiveness. Colorado Springs, CO: Office of Science Education National Institutes of Health. Retrieved from$FILE/Appendix%20D.pdf

Dillon, J. (2008). Discussion, debate and dialog: changing minds about conceptual change research in science education. Cultural Studies of Science Education, 3(2), 397-416.

Eisenkraft, A. (2003). Expanding the 5e model. Science Teacher, 70(6), 56-59.

Ernst, J. V., & Clark, A. C. (2007). Scientific and technical visualization in technology education. Technology Teacher, 66(8), 16-20.

Friedman, J. S., & diSessa, A. A. (1999). What students should know about technology: the case of scientific visualization. Journal of Science Education and Technology, 8(3), pp. 175-195.

Guzzetti, B. J., Snyder, T. E., Glass, G. V., & Gamas, W. S. (1993). Promoting conceptual change in science: a comparative meta-analysis of instructional interventions from reading education and science education. Reading Research Quarterly, 28(2), 117-159.

Hay, D. B., Kehoe, C., Miquel, M. E., Hatzipanagos, S., Kinchin, I. M., Keevil, S. F., & Lygo-Baker, S. (2008). Measuring the quality of e-learning. British Journal of Educational Technology, 39(6), 1037-1056. doi:10.1111/j.1467-8535.2007.00777.x

Heck, Andre, & Uylings, P. (2010). In a hurry to work with high-speed video at school?. Physics Teacher, 48(3), 176 - 181.

Heck, André, Uylings, P., & Kędzierska, E. (2010). Understanding the physics of bungee jumping. Physics Education, 45(1), 63-72. doi:10.1088/0031-9120/45/1/007

Hobson, S. M., Trundle, K. C., & Sackes, M. (2010). Using a planetarium software program to promote conceptual change with young children. Journal of Science Education and Technology, 19(2), 165-176. doi:10.1007/s10956-009-9189-8

Hohenwarter, J., Hohenwarter, M., & Lavicza, Z. (2009). Introducing dynamic mathematics software to secondary school teachers: the case of geogebra. Journal of Computers in Mathematics and Science Teaching, 28(2), 135 - 146.

Jonassen, D. H. (2000). Computers as mindtools for schools: engaging critical thinking (2nd ed.). Upper Saddle River, N.J: Merrill.

Liao, Y.-W., & She, H.-C. (2009). Enhancing eight grade students’ scientific conceptual change and scientific reasoning through a web-based learning program. Educational Technology & Society, 12(4), 228-240.

Mazur, E. (1997). Peer instruction - a user’s manual. Upper Saddle River, N.J: Prentice-Hall.

Moore, J. A. (1980). Kuhn’s the structure of scientific revolutions revisited. American Biology Teacher, 42(5), 298-304.

Muller, D. A., Bewes, J., Sharma, M. D., & Reimann, P. (2007). Saying the wrong thing: improving learning with multimedia by including misconceptions. Journal of Computer Assisted Learning, 24(2), 144-155. doi:10.1111/j.1365-2729.2007.00248.x

Özdemir, G., & Clark, D. B. (2007). An overview of conceptual change theories. Eurasia Journal of Mathematics, Science & Technology Education, 3(4), 351-361.

Palmer, D. (2005). A motivational view of constructivist-informed teaching. International Journal of Science Education, 27(15), 1853-1881.

Rourke, L., & Kanuka, H. (2009). Learning in communities of inquiry: a review of the literature. Journal of Distance Education, 23(1), 19-48.

Simatwa, E. M. W. (2010). Piaget’s theory of intellectual development and its implication for instructional management at pre-secondary school level. Educational Research and Reviews, 5(7), 366-371.

Thomas, D. A., Johnson, K., & Stevenson, S. (1996). Integrated mathematics, science, and technology: an introduction to scientific visualization. The Journal of Computers in Mathematics and Science Teaching, 15(3), 267-94.

Vosniadou, S. (2007). Conceptual change and education. Human Development, 50(1), 47-54.

[untitled graphic image of 5E Instructional Model]. Retrieved June 21, 2011, from:

External Links