Understanding Physics in Relation to Working Memory

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Research in Science & Technological Education Vol. 27, No. 2, July 2009, 151–160

Understanding physics in relation to working memory Wen-Chao Chena and Rex Whiteheadb* a

Hsinchu County Hukou High School, Taiwan; bUniversity of Glasgow, UK

Research 10.1080/02635140902853624 CRST_A_385534.sgm 0263-5143 Original Taylor 202009 27 rexwhitehead@mac.com RexWhitehead 00000July and & Article in Francis (print)/1470-1138 Francis 2009 Science & Technological (online) Education

The aim of physics education is to generate understanding of physics and there is considerable anecdotal evidence that passing examinations in physics is not the same as understanding the subject. This paper describes how the areas of difficulties in understanding of physics were determined for pupils in Taiwan aged 13–15. Test material which placed little load on working memory was then developed for several of these areas and the pupil performance was related to measured working memory capacity. Those with higher working memory capacities were found consistently to understand the ideas of physics better. The implications are discussed. Keywords: working memory; school physics learning; understanding physics

Introduction There is considerable anecdotal evidence that passing examinations in science is not the same as understanding the subject. At the university level, for example, Haghanikar (2003) tested the understanding of students who had recently passed an examination in positional astronomy by presenting them with a series of simple tasks necessary for finding one’s location on Earth (as when shipwrecked on a desert island). Their performance was poor, showing that, while they were comfortable with the use of the formulae and coordinate systems of spherical astronomy, they had little understanding of how these were related to observations of the Sun and stars. On the other hand, some areas of physics are notoriously fragile. Special relativity is one such. It is often observed that one year’s class cannot do the problems set by last year’s lecturer despite the complete identity of the material taught. Evidently, the students’ understanding is poorly developed and even small differences in problem style or presentation can cause trouble. Understanding is not easy to define. It seems to have rather different meanings in different contexts. In science, at least, though, to understand something normally means that you can see how to answer any question you may be asked (which is not the same as actually answering it), regardless of the direction from which it comes. When confronted with a question that you cannot see how to tackle, your understanding has been challenged. However, this is not merely a didactic issue. Science itself progresses by uncovering gaps in understanding through the asking of challenging questions.

*Corresponding author. Email: rexwhitehead@mac.com. ISSN 0263-5143 print/ISSN 1470-1138 online © 2009 Taylor & Francis DOI: 10.1080/02635140902853624 http://www.informaworld.com


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Background This study started with the observation by one of us that, in middle school physics in Taiwan, pupils had difficulties with physics that seemed puzzling. For example, if asked to calculate a voltage, given a resistance and a current, using Ohm’s law, they had no difficulty. Asked to calculate the current given the voltage and the resistance was another matter. On the other hand, if V = IR was presented as a mathematical problem and the pupils were asked to solve for ‘I’ they could do it easily. This paper describes an investigation into this phenomenon. It seeks explicitly to test the understanding of the pupils and relate it to the way in which the teaching and testing is done. A fairly obvious first step was to examine the connection, if any, with working memory capacity. It is now well established that working memory is that part of the brain where thinking, understanding and problem-solving take place (Miller 1956; Baddeley and Hitch 1974; Baddeley 1994; Johnstone 1997; Baddeley 2002; Kirschner et al. 2006). Its capacity is limited although it can be used more efficiently by means of chunking where information is grouped in ways meaningful to the individual, thus reducing pressure on the limited space. Taking the Ohm’s law example, a beginner will see V = IR, as three or four separate things. This is already getting close to the working memory capacity of a typical middle school pupil (age 13–15) and there cannot be much space left for further processing. An expert (someone accustomed to such things) will have learned to automatically chunk efficiently and will see V = IR as a single thing along, possibly, with its various other forms, I = V/R, W = I2R = V2/R, and so on. The corresponding mathematical problem, on the other hand, will normally be presented without meaning attached to the symbols and thus be less likely to overload the working memory mechanism. The building of understanding takes place by the strengthening and weakening of connections in the long-term memory (Reid and Yang 2002), but the working memory provides the pointers, or keys, to the appropriate parts of the long-term memory. Habitual confusion as a result of working memory overload can be regarded as a learned behaviour which teachers wisely seek to avoid. This work makes a preliminary attempt to relate understanding of physics to the working memory capacities of middle school (junior high school) pupils in Taiwan. Areas of difficulty, which the students themselves believed they had failed to understand, were identified. The pupils were tested in a way that was designed to show understanding, or lack of it, in several of these areas. Finally, the results of the test were related to the measured working memory capacities of the pupils.

Areas of physics difficulty for middle school pupils in Taiwan A sample of 320 typical middle school pupils was surveyed by asking them to assign easy, moderate, difficult or not studied to a list of 51 topics covering the syllabus. Table 1 gives the percentage of students who indicated that they found each topic difficult. Topics registering less than 10% have been omitted. Because of the timing of the survey, some important areas of physics, like mechanics, do not appear because they had not yet been encountered. These results are in broad agreement with Johnstone and Mughol’s (1976) survey of Scottish secondary school pupils, and Zapiti’s (1999) review of a large number of studies from various countries.


Research in Science & Technological Education Table 1.

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Survey of difficult topics as reported by 320 Taiwanese middle school pupils.

Topics

% difficulty (N = 320)

Reflection and plane mirrors Refraction and lenses Calorific capacity Specific heat Buoyancy Friction strength Pressure Electrostatic induction Electric circuits Voltage Electric current Resistance and Ohm’s law Magnetic effect of electric currents Direct current and alternating current Thermal results of electric currents Power transportation and consumption

17 26 17 23 29 10 16 12 12 18 18 31 10 13 14 11

Note: Only topics which 10% or more regarded as difficult have been included here.

In addition to simply rating each topic, the pupils were invited to make verbal comments on topics they found difficult. Typical comments were: do not understand, hard to understand, too many concepts, complicated diagrams, applying knowledge is difficult. These were taken as evidence that the pupils knew that difficulty and lack of understanding were connected. The topics chosen for further investigation were: electricity, electrical circuits, heat and temperature and how light travels which select from or combine some of the more difficult areas in the above table. The next stage involved developing test material and relating the performance of the pupils in this with their measured working memory capacity. Testing understanding Structural communication grids (Egan 1972; Duncan 1974; Johnstone and Mughol 1978, 1979; Bahar et al. 2000; Hassan et al. 2004) were used to test the pupils’ understanding of the topics selected. One advantage of this method is that the test itself puts very little load on the working memory capacity. Almost all of any working memoryrelated effect discovered must come in at the learning stage and not in the testing. When tackling structural communication grid questions, the pupil has to look at each box in turn. Any working memory overload must, therefore, arise from one box. The use of this assessment technique has been explored thoroughly by Johnstone and Ambusaidi (2001). Typically, in a structural communication grid, a question is posed and an array of possible answers from which to choose is provided. The structural communication grid differs from a simple multiple-choice question in the richness of the information it can yield. For example, there may be more than one right answer provided and the pupils may well get one of them but miss others, or they might select some right and


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some wrong answers. It is thus possible to see a gradation in understanding within a single question. It is also possible to detect systematic misunderstandings across a whole group of students in this way (Reid 2003). Figure 1 shows one of the structural communication grids used here. The others are in the Appendix. Figure 1. The structural communication grid used to test understanding of heat

Figure 1.

The structural communication grid used to test understanding of heat.


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Here, Questions 1, 2, 3, and 4 have two, seven, two, and nine correct answers respectively. In no case is any extensive calculation required. Of the 262 pupils tested 45%, 16%, 32% and 29% selected all of the correct answers and no wrong ones for the four questions respectively while the others made every possible combination of right and wrong choices. Structural communication grids can be used to provide useful information without being scored numerically, but here a numerical score is needed since it is intended to try to relate the performance to working memory capacity. One commonly-used marking system (Reid 2003) is: Number of correct answers selected Total number of correct answers Number of wrong answers selected − Total number of wrong answers Mark for each question =

Thus, +1 is awarded if all the answers selected are correct and -1 if they are all wrong. In this study, the marks for each question were scaled to lie in the range 0 to 1. Measuring working memory capacity The working memory capacities of 151 of the pupils who took the above test (the remainder being inaccessible) were measured by Pascual-Leone’s Figural Intersection Test (Pascual-Leone 1970; Pascual-Leone and Burtis 1974). The pupil is asked to find, within a fixed time, the common area enclosed by a number of overlapping shapes which are presented both overlapped and separately. Success with up to N shapes indicates a working memory capacity of X = N. This is a highly visual test and may perhaps put less-visual thinkers at a disadvantage, but it gives results which agree well with other measures of working memory (Johnstone and El-Banna 1986, 1989). For pupils in the 13- to 15-year age range, as these were, one expects to find working memory capacities mainly between five and seven (Miller, 1956). The results are shown in Table 2. Comparison with the test of understanding For those pupils whose working memory capacity and test score were both available (N = 151) it was possible to analyse performance in all the structural communication grids questions as a function of working memory capacity. The results are shown in Table 3.

Table 2.

Working memory measurements.

Working memory capacity 5 6 7 Total

Number of pupils 64 54 33 151


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It can be seen that performance rises markedly for those whose working memory capacity is larger and this is confirmed by the Pearson correlation coefficient for this data: r = 0.30 (p < 0.001), (see Table 3). It is possible to analyse each question in turn, with similar patterns being evident (Tables 4 to 7). Apart from one anomalous, and unexplained, result (working memory six, in Table 7), there is a clear relationship between working memory capacity and performance on the four parts of the test of understanding. Thus, those who have higher working memory capacities do better, the work of Johnstone and El-Banna (1986, 1989) in chemistry showing that this is almost certainly cause and effect. Table 3.

Means scores and working memory capacity.

Working memory capacity

Mean score in total physics test (%)

5 6 7 Table 4.

47.1 49.7 59.5 Mean scores and electricity questions.

Working memory capacity 5 6 7

Table 5.

57.8 72.2 72.2

Means scores and electrical circuits questions.

Working memory capacity 5 6 7 Table 6.

Means scores and heat and temperature questions.

5 6 7

Mean score (%) 47.6 50.5 61.1

Mean scores and light questions.

Working memory capacity 5 6 7

Mean score (%) 41.8 44.4 57.3

Working memory capacity

Table 7.

Mean score (%)

Mean score (%) 41.1 31.5 47.1


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Discussion and conclusions There are numerous studies which have explored the relationship between working memory capacity and various subject areas and many of these have been discussed by Reid (2007, 2008). There have been few studies in physics and this paper seeks to fill this gap. However, there is an important point which, perhaps, needs to be made explicit. In the seminal work of Johnstone and El-Banna (1986, 1989) in chemistry, it was the test which imposed the critical working memory load to the extent of masking the students’ real understanding. Here, we are trying to test the impact of working memory capacity on the pupils’ understanding of material they have already been taught (quite independently of this work). The test material, by its design and the way the pupils have to look separately at each possible box on the grid on its own, aimed to impose no excessive load on working memory. This is, as far as we are aware, the first attempt to relate the development of understanding of physics in young learners to working memory capacity. That we find a clear relationship has important implications for teachers. It is also worth noting that structural communication grids (whose use in physics is also not widespread) can be valuable tools for assessing pupils’ progress. There is limited opportunity for guessing, while the patterns of right and wrong answers selected reveal much about the extent to which the pupils have understanding as well as the areas where they are confused. Teachers need to be aware that their pupils’ working memory capacities vary and that they impose limitations on the rate of development of understanding. They also need to be able to easily and readily monitor that development. It is important to note that working memory capacity has little to do with ‘intelligence’, but that negligent overload of capacity can prevent ability from showing itself. There are have been many suggestions relating to ways by which physics can be made more accessible (see Woolnough 1994 for some interesting thoughts). However, the review of Kirschner et al. (2006, 76) makes the point very clearly when they note that, ‘Any instructional procedure that ignores the structures that constitute human cognitive architecture is not likely to be effective’. It is possible to achieve some measure of success in physics tests and examinations by recalling memorised knowledge or procedures. Thus, while pupils may be quite happy with questions that amount to the straightforward application of a learned formula, this is very different when compared to the deeper kind of understanding (where you just know that, if the voltage stays the same, the current is inversely related to the resistance). The aim of physics education is to generate a level of understanding which enables the learners to know how to answer any question they may be asked regardless of the direction from which it comes. This is a demanding goal. In order for the goal to be attainable, the teaching and learning process must be developed so that the limited working memory capacities of the learners are not overwhelmed.

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Bahar, M., M.H. Hansell, and A.H. Johnstone. 2000. Structural communication grids: A valuable assessment and diagnostic tool for science teachers. Journal of Biological Education 34, no. 2: 87–9. Duncan, I.M. 1974. The use of programmed learning materials to investigate learning processes in difficult areas in school chemistry. MSc thesis, University of Glasgow. Egan, K. 1972. Structural communication – A new contribution to pedagogy. Programmed Learning and Educational Technology, 1: 63–78. Haghanikar, M.M. 2003. Understanding and accessibility in physics. MSc thesis, University of Glasgow. Hassan, A.K., R.A. Hill, and N. Reid. 2004. Ideas underpinning success in an introductory course in organic chemistry. University Chemistry Education 8: 40–51. Johnstone, A.H. 1997. Chemistry teaching – science or alchemy? Journal of Chemical Education 74, no. 3: 262–68. Johnstone, A.H., and A.R. Mughol. 1976. Concepts of physics at secondary school level. Physics Education 11: 466–69. Johnstone, A.H., and A.R. Mughol. 1978. The concept of electrical resistance. Physics Education 13: 46. Johnstone, A.H., and A.R. Mughol. 1979. Testing for understanding. School Science Review 61: 174–80. Johnstone, A.H., and H. El-Banna. 1986. Capacities, demands and processes: A predictive model for science education. Education in Chemistry 233: 80–84. Johnstone, A. H., and El-Banna, H. 1989. Understanding learning difficulties – a predictive research model. Studies in Higher Education 14, no. 2: 9–68. Johnstone, A.H. and A. Ambusaidi. 2001. Fixed response questions with a difference. Chemistry Education Research and Practice 2, no. 3: 313–27. Kirschner, P.A., J. Sweller, and R.E. Clark. 2006. Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based learning. Educational Psychologist 41, no. 2: 75–86. Miller, G. 1956. The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review 63, no. 2: 81–97. Pascual-Leone, J. 1970. A mathematical model for the transition rule in Piaget’s developmental stages. Acta Psychologica 32: 301–45. Pascual-Leone, J. and P.J. Burtis. 1974. Unpublished manuscript. Ontario, York University. Reid, N. 2003. Getting started in pedagogical research in higher education. Hull: Higher Education Academy. Reid, N, 2007. A scientific approach to the teaching of the sciences. In Proceedings of the Third Annual Conference for Middle East Teachers of Science, Mathematics and Computing, ed. S.M. Stewart, J.E. Olearski, P. Rodgers, D. Thompson, and E.A. Hayes. Abu Dhabi: METSMaC. Reid, N. 2008. A scientific approach to the teaching of chemistry (The Royal Society of Chemistry Nyholm Lecture, 2006–2007). Chemistry Education Research and Practice 9, no. 1: 51–9. Reid, N. and M.-J. Yang. 2002. Open-ended problem solving in school chemistry: A preliminary investigation. International Journal Science Education 24, no. 12: 1313–32. Woolnough, B.E. 1994. Why students choose physics, or reject it. Physics Education 29: 368–74. Zapiti, S. 1999. Difficulties in physics. MSc thesis, University of Glasgow.


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