Abstract
One well-known learning obstacle is that students rarely use the concepts in the way that scientists use them. Rather, students mix up closely related concepts and are inclined towards matter-based conceptualisations. Furthermore, some researchers have argued that certain difficulties are rooted in the student’s limited repertoire of causal schemes. These two aspects are conveniently represented in the recent proposal of the systemic view of concept learning. We applied this framework in our analyses of university students’ explanations of DC circuits and their use of concepts such as voltage, current and resistance. Our data consist of transcribed group interviews, which we analysed with content analysis. The results of our analysis are represented with directed graphs. Our results show that students had a rather refined ontological knowledge of the concepts. However, students relied on rather simple explanation models, but few students were able to modify their explanations during the interview. Based on the analysis, we identified three processes of change: model switch, model refinement and model elaboration. This emphasises the importance of relevant relational knowledge at a later stage of learning. This demonstrates how concept individuation and learning of relational structures occurs (and in which order) and sets forth interesting research questions for future research.
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Notes
Chi et al. (1994) posit that “entities in the world may be viewed as belonging to different ontological categories” such as matter, mental states or processes (p. 28), Consequently, students’ ontologies can be described as knowledge of “what kind of entities there are in the world” (Amin et al., 2014, p. 59).
Here we avoid discussing differences in covariation in models and laws (for a discussion adapted to science education, see Koponen (2007)).
As an example, consider Ohm’s law U = RI, relating current, voltage and resistance or Q = CΔT, relating heat and change in temperature through heat capacity.
Note that the relations are not always causal. It is possible to view the relational structure as constraining laws or covariation of concepts in the form of constrained determination.
Related descriptions include Clement’s (2002) intermediate models and the learning paths proposed by Duschl et al. (2011).
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The authors thank Anu Saari and Johanna Jauhiainen for their contributions in transcribing and analysing the data.
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Appendix 1
Appendix 1
Excerpt From the Analysis of Students 21 and 22
Transcribed text | Condensed | Relations | Attributes (constraints) |
---|---|---|---|
Interviewer: “So, they are equally bright. But, why does the same current that goes here [bulb A] also go here [D and E]…?” | |||
Student 21: “Is it due to the voltage difference, since the voltage difference here [over D and E] is the same, and they have the same resistance, and that’s why same current goes in there?” | A, D, and E have the same voltage and the same resistances, so the same current goes through both of them. The same voltage comes from the battery. | R2: U ➔ I, when R | i4 u1(c2) u1(c4) |
Interviewer: “So, how would you explain the next one [circuit BC]?” | |||
Student 21: “Well, the same voltage that’s here [over bulbs B and C]…is also between here. So, the voltage decreases… or potential.” | The voltage is divided in the series. The battery provides the same voltage. | u2(c1) u1(c4) | |
Student 22: “So, the resistance is double because they are in a series” | Resistance is greater in a series. | r7(c1) | |
Student 21: “So, the current halves.” | The greater the resistance, the lower the current. | R1: 1 / R ➔ I |
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Kokkonen, T., Mäntylä, T. Changes in University Students’ Explanation Models of DC Circuits. Res Sci Educ 48, 753–775 (2018). https://doi.org/10.1007/s11165-016-9586-y
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DOI: https://doi.org/10.1007/s11165-016-9586-y