Dr S Stephens, Director of Science
Why is it that some students seem to breeze through their study and turn up well prepared for tests, whereas others struggle to implement the sort of study strategies that are the most effective for improving their educational outcomes? The results of an investigation of study habits by Hartwig and Dunlosky (2012) show the most reported study strategies are underlining or highlighting while reading (seventy-two per cent); testing oneself with questions or practice problems (seventy-one per cent); rereading chapters, articles, notes (sixty-six per cent); and using flashcards (sixty-two per cent), while a review of the effectiveness of commonly used learning techniques found all but self-testing to be of surprisingly low utility (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013). Relatively few students chose to create diagrams, charts or pictures (fifteen per cent) which have been shown to lead to a deeper understanding of science concepts (Cheng, 2002) and to improve test performance (Davidowitz, Chittleborough, & Murray, 2010; Gobert & Clement, 1999). Why is this so?
Science educators desire more than knowledge acquisition for their students. Science is about understanding; so, in our minds, worthwhile learning is achieved when students understand the underlying principles of scientific concepts and theories in order to be able to explain and solve problems related to real-life phenomena. Given that most of the phenomena studied in science are very complex and unable to be directly observed — either too big, too small, too fast, too slow, too far away, or too inaccessible — scientists use models as a means of simplification or to assist with the visualisation of what is actually happening.
Visualisation can be defined in two ways, both of which are important in the construction of scientific models. External visualisation is the process of making something visible to the eye. Displaying information in a graph or drawing a diagram are forms of external visualisation. Internal visualisation, on the other hand, is the process of making a mental image of a concept. An example of this is trying to imagine the reaction mechanism of a chemical reaction. Students need to develop their visualisation skills and their metavisual capability in order to be able to regulate the multiple modes used to represent and communicate scientific concepts (Gilbert, 2007).
According to Gilbert (2007), models of scientific phenomena can be expressed or represented at three different levels: the macroscopic level, the sub-microscopic level, and the symbolic level. The differences between each type of representation can be illustrated with a precipitation reaction, which is a typical example of a scientific phenomenon that can be observed at the macroscopic level but can only be fully explained using elements that are too small to be observed with the naked eye. When students describe their observations of the reaction taking place in a test tube, they are modelling the reaction at the macroscopic level. Drawing a diagram or writing an appropriate chemical equation represents the reaction at the symbolic level. When students try to make sense of what is going on in the test tube by visualising in their minds’ eyes what is happening to the ions that are involved in the reaction, they are working at the sub-microscopic level. This attempt at internal visualisation leads to the development of mental models.
A deep understanding of a scientific phenomenon involves the ability to construct, and move fluently between, the three levels of representation (Gilbert, 2007). For students who have a deep understanding of precipitation reactions, the balanced chemical equation elegantly represents the behaviour of the reaction species on all three levels.
Yet research has shown that chemistry students who receive extensive tuition and practice in each level of representation may still struggle to achieve expertise in one or more of them, and fail to move between them with the necessary fluency (Wu & Shah, 2004). Young chemistry students flounder at the symbolic level, and struggle to make sense of chemical formulae and equations, while many older students perform well at the macroscopic and symbolic levels, but have a poor understanding of the phenomenon at the sub-microscopic level where real understanding and productive explanation is situated (Hinton & Nakhleh, 1999). This inability to access levels of representation and the lack of fluency moving between them is indicative of poor internal visualisation skills and underdeveloped metavisual capacity and is, clearly, an impediment to learning (Gilbert, 2007).
These weaknesses can make science hard to learn. Cognitive research into the mechanism by which we acquire and apply knowledge, how information is encoded in memory and retrieved from it, and the types of mental representations that result from everyday experiences provides some recommendations for improving a student’s internal visualisation and mental model construction.
Mental models act as internal representations of information gathered by observation from the real world. They are the mechanism by which students gain access to the sub-microscopic level of representation. Rapp (2007) defines mental models as ‘memory structures that can be used to extrapolate beyond a surface understanding of presented information, to build deeper comprehension of a conceptual domain’ (p. 43). While faulty or inaccurate mental models can impede learning, valid and reliable models can facilitate it (Rapp, 2007). Mental models are personal constructs, so students must take responsibility for their own model development. Robust and fruitful mental models are intertwined with a deep understanding. The pursuit of a deep understanding is both effortful and time consuming, so students must be motivated to be actively engaged in the learning process.
Science is neither done, learned nor communicated through verbal language alone because verbal discourse has not evolved sufficiently well to cope with the cognitive demands of the discipline (Lemke, 1998). The everyday experiences of students are not conducive to the formation of useful mental models, so teachers provide visual enhancements to assist in the visualisation process. As a result, communication in science classrooms, as it is in science, is achieved through the integration of verbal text, mathematical expressions, realia, and numerous discipline-specific external visualisations, such as graphs, tables, drawings, abstract diagrams, microscope images, maps, and photographs. The dynamism lost in these types of graphics has been recovered in other more kinetic visualisations such as video and animations, over which the user has some control. Many visualisations are primarily Illustrative and have little or no explanatory power. Illustrative diagrams can facilitate recall and comprehension but diagrams that are more explanatory in nature are needed for higher order conceptual understanding. Modern multimodal technology facilitates the creation of complex visual presentations, so when students are in class learning about a particular domain or at home revising it, they will have a variety of visualisation tools at their fingertips.
Unfortunately, research has shown that students cannot always access the intended learning when presented with diagrams and other visual tools (Canham & Hegarty, 2010). It appears that in many cases they are unable to extricate the most pertinent features of the representation. Human cognition is such that individuals vary in their working memory capacity and therefore attention is capacity-limited. Because objects in a visual display compete for attention, students can suffer cognitive overload, wasting precious working memory processing capacity on irrelevant details. Cognitive overload might also contribute to the failure to make the necessary links between the external visualisation and the macroscopic and sub-microscopic levels of representation (Leutner, Leopold, & Sumfleth, 2009).
Rather than merely presenting students with diagrams, teaching them to generate their own has been shown to improve conceptual understanding and assessment outcomes. Gobert and Clement (1999) investigated the performance of three groups of students studying the domain of plate tectonics. One group just read the text, the second group made a summary, and the third group generated their own diagrams. The researchers hypothesised that the task of generating diagrams while reading would promote richer mental model construction rather than simply reading text or creating summaries, two study strategies very popular with students (Hartwig & Dunlosky, 2012). The posttest (test administered after instruction) assessed knowledge of spatial/static aspects of the domain, for example, ‘Where is the thinnest part of the earth’s crust?’, and causal/dynamic aspects of the domain, for example, ‘Rock from the floor of the Atlantic Ocean tests to be younger than rock from the middle of the North American continent because …’. While the summaries contained more domain-related surface detail, the diagram group outperformed the other two groups in questions related to both static and dynamic aspects of the domain. The authors claim that, as plate tectonics is a domain that is not able to be directly observed by students, they must engage in causal model construction to generate internal representations of complex processes in order to be able to depict these relationships externally in the form of diagrams (Gobert & Clement, 1999).
What is of interest to students studying for upcoming tests is the method used by the researchers (Gobert & Clement, 1999) to scaffold the process of drawing diagrams. Firstly, they asked students to depict a static representation of the domain, in this case the interior layers of the earth. Then they encouraged them to add a depiction of the dynamic processes gleaned from their reading. Finally, students were asked to depict the outcomes of these processes, such as mountain formation and volcanic eruption. This strategy aims to progressively refine students’ understanding of the domain as they progressively construct more complex models of it. It also reflects the evolution from description to explanation commonly employed in science texts.
All processes pertaining to assessment are complex. We assess students’ surface understanding by asking questions that are relatively familiar to them. They succeed on these types of questions by being able to recall what they have heard or read or by being able to apply their knowledge in relatively straightforward situations. We also need to give students with deep understanding the chance to reveal what they know and show what they can do with the expertise they have acquired. For this purpose, we create questions that ask students to reason logically about scientific concepts and theories and to apply them in novel situations, often in unseen domains. Rapp (2007) claims that, as internal representations of a student’s understanding, mental models can act like a mental simulation which can be ‘run’ for the purpose of solving problems. He believes that students can use their mental model of a concept to reason beyond course materials. It gives them the capacity to generate hypotheses, apply their knowledge to an extended range of contexts, and transfer it to new domains. Ultimately, generative mental models contribute to the deep understanding of scientific concepts that science educators desire for their students.
Learning science presents unique challenges when it comes to studying for tests. It is a multimodal discipline and students require multiliteracies to succeed. Clearly, assisting students with the development of their visualisation skills and metavisual capacity to achieve mental model capture should be an imperative of science education.
References
Canham, M., & Hegarty, M. (2010). Effects of knowledge and display design on comprehension of complex graphics. Learning and Instruction, 20, 155–166.
Cheng P. (2002). Electrifying diagrams for learning: Principles for complex representational systems. Cognitive Science, 26, 685–736. Retrieved April 1, 2013, from http://onlinelibrary.wiley.com/doi/10.1207/s15516709cog2606_1/pdf
Davidowitz, B., Chittleborough, G., & Murray, E. (2010). Student-generated submicro diagrams: A useful tool for teaching and learning chemical equations and stoichiometry. Chem. Educ. Res. Pract., 11, 154–164.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. Retrieved April 1, 2013, from http://psi.sagepub.com/content/14/1/4.full.pdf+html?ijkey=Z10jaVH/60XQM&keytype=ref&siteid=sppsi
Gilbert, J. K. (2007). Visualization: A metacognitive skill in science and science education. In John K. Gilbert (Ed.), Visualization in Science Education (pp. 9–27). Dordrecht: Springer.
Gobert, J., & Clement, J. (1999). Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching, 36(1), 39–53. Retrieved April 1, 2013, from http://www.psychology.nottingham.ac.uk/staff/dmr/c8ccde/Readings%20from%20Drawing/gobert.pdf
Hartwig, M. K., & Dunlosky, J. (2012). Study strategies of college students: Are self-testing and scheduling related to achievement? Psychonomic Bulletin and Review, 19, 126–134. Retrieved April 1, 2013, from http://www.gwern.net/docs/2012-hartwig.pdf
Hinton, M. E., & Nakhleh, M. B. (1999). Students’ microscopic, macroscopic, and symbolic representations of chemical reactions, Chem. Educator, 4, 158–167.
Lemke, J. (1998). Multiplying meaning: Visual and verbal semiotics in scientific text. In J. R. Martin & R. Veel (Eds.), Reading Science (pp. 87–113). London: Routledge.
Leutner, D., Leopold, C., & Sumfleth, E. (2009). Cognitive load and science text comprehension: Effects of drawing and mentally imagining text content. Computers in Human Behavior, 25, 284–289. Retrieved April 1, 2013, from http://cmapspublic.ihmc.us/rid%3D1274636304968_448100840_25375/Leutner%2520et%2520al.pdf
Rapp, D. N. (2007). Mental models: Theoretical issues for visualizations in science education. In John K. Gilbert (Ed.), Visualization in Science Education (pp. 43–60). Dordrecht: Springer.
Wu, H., & Shah, P. (2004). Exploring visuospatial thinking in chemistry learning. Science Education, 88(3), 465–492. Retrieved April 1, 2013, from http://onlinelibrary.wiley.com/doi/10.1002/sce.10126/pdf