Understanding Possibilities and Limitations of Abstract Chemical

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International Journal of Science Education

ISSN: 0950-0693 (Print) 1464-5289 (Online) Journal homepage: http://www.tandfonline.com/loi/tsed20

Understanding Possibilities and Limitations of Abstract Chemical Representations for Achieving Conceptual Understanding David M. J. Corradi, Jan Elen, Beno Schraepen & Geraldine Clarebout To cite this article: David M. J. Corradi, Jan Elen, Beno Schraepen & Geraldine Clarebout (2014) Understanding Possibilities and Limitations of Abstract Chemical Representations for Achieving Conceptual Understanding, International Journal of Science Education, 36:5, 715-734, DOI: 10.1080/09500693.2013.824630 To link to this article: http://dx.doi.org/10.1080/09500693.2013.824630

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Date: 21 March 2017, At: 00:47

International Journal of Science Education, 2014 Vol. 36, No. 5, 715 – 734, http://dx.doi.org/10.1080/09500693.2013.824630

Understanding Possibilities and Limitations of Abstract Chemical Representations for Achieving Conceptual Understanding David M. J. Corradia∗ , Jan Elena, Beno Schraepenb and Geraldine Clarebouta a

Department of Psychology and Educational Science, Centre for Instructional Psychology and Technology, University of Leuven (KU Leuven), Leuven, Belgium; bDepartment of Special Education, Plantijn University College, Antwerpen, Belgium

When learning with abstract and scientific multiple external representations (MERs), low prior knowledge learners are said to have difficulties in using these MERs to achieve conceptual understanding. Yet little is known about what these limitations precisely entail. In order to understand this, we presented 101 learners with low prior knowledge of abstract scientific MERs to see (a) how many, and what kind of ideas (propositions) learners remembered from these MERs and (b) what the impact of these ideas is on conceptual understanding of the content. Propositional analysis indicates that learners created flawed internal representations. The discussion analyses the potentials that the learners have in using abstract representations to increase their understanding of scientific information and possible effects of instruction.

Keywords: Multiple representations; Chemistry education; Low prior knowledge learners; Conceptual understanding

Introduction In many learning and teaching situations, rather than communicating all information through a single representation (e.g. a text), multiple external representations (MERs; e.g. a text and a picture) are offered to learners (de Jong et al., 1998). Several studies, ∗

Corresponding author. Department of Psychology and Educational Science, Centre for Instructional Psychology and Technology, University of Leuven (KU Leuven), Dekenstraat 2, bus 3773, Leuven 3000, Belgium. Email: [email protected] # 2013 Taylor & Francis

716 D. M. J. Corradi et al. both empirical and theoretical, indicate that MERs have an advantage over single representations as MERs have the potential to create deep conceptual understanding of the information presented (Ainsworth, 1999, 2006; Kozma, 2003; Seufert, 2003). This advantage is on condition that learners use all the information actively by attempting to understand what each representation independently communicates (Foltz, Kintsch, & Landauer, 1998; Van der Meij, 2007) and by linking and translating between the MERs (Berthold & Renkl, 2009). This linking and relating of information is an important learning behaviour, because in order to achieve conceptual understanding of a certain subject, learners need to form a coherent mental model of the information, preferably from different representational types (Schnotz, 2005; Seufert, 2003). One series of studies found a positive effect of multiple representations on achieving learning goals, such as comprehension, when explanatory texts were combined with depictive (e.g. pictures) and/or descriptive (e.g. symbols) representations (Butcher, 2006; Carney & Levin, 2002; Glenberg & Langston, 1992; Kolloffel, Eysink, de Jong, & Wilhelm, 2008; Levie & Lentz, 1982; Mayer, 1997). Multiple representations can help learners in supporting memory, guiding attention, improving comprehension and helping organize information (Butcher, 2006; Carney & Levin, 2002; Glenberg & Langston, 1992; Homer & Plass, 2010; Levie & Lentz, 1982). Building further on the multimedia model by Mayer (1997), the construction–integration theory by Kintsch (2004) and the dual coding theory by Sadoski and Paivio (2007), the ‘integrated text and picture comprehension’ model by Schnotz (2005, 2008) and Schnotz and Bannert (2003) details how and why the combination of multiple representations from different formats can be beneficial for learners. Schnotz argues that descriptive representations (texts and symbolic representations) are processed differently in working memory and stored differently in the long-term memory (LTM) from depictive representations (images and schemas). Texts or symbols are first analysed for their superficial features, after which information is encoded into a propositional representation. Information from this internal propositional representation is reprocessed into a mental model through coherence forming. In contrast to descriptive representations, a depictive representation, such as an image, is, after it is perceived, immediately processed into a mental model through analogue structure mapping (i.e. using gestalt psychological coding principles), where it also interacts with the internal propositional representation through model inspection. This difference in processing explains most empirical results and is termed as the ‘illustration effect’; presenting learners with only a text makes achieving comprehension of the information in the text harder than when that text is accompanied with a (compatible) representation (Carney & Levin, 2002; Gyselinck, Jamet, & Dubois, 2008; Schnotz & Bannert, 2003). Yet the model of Schnotz and previous studies fail to thoroughly explain why learners with low prior knowledge are sometimes found to fail using MERs to achieve learning goals and benefit more, or as much, from single representations than from multiple ones (Prangsma, van Boxtel, Kanselaar, & Kirschner, 2009). Many studies focussing on scientific representations (in contrast to other text and picture

The Nature of Low Prior Knowledge Learners 717 comprehension research) find that, when presenting MERs to learners, they are often unable to use these representations correctly unless an instructional aid is provided (Berthold & Renkl, 2009; Corradi, Elen, & Clarebout, 2012; Van der Meij, 2007). Explanations concerning why learners fail focus on the fact that learners often lack prior knowledge of the represented domain and/or the representational system of that domain, as well as lack the schemata to which the information in the representation can connect (Cook, 2006; Cook, Wiebe & Carter, 2008; de Vries, Demetriadis, & Ainsworth, 2009). Second, learners give most of their attention to a single representation, mostly texts, while other types of representations are almost ignored (Barnea & Dori, 1999; Corradi et al., 2012). When other representations are actually given attention, learners with low prior knowledge pay attention to superficial features of these representations (Rappoport & Ashkenazi, 2008; Taber, 2009), rather than accessing the underlying conceptual information (Kozma, 2003; Kozma & Russell, 1997). Researchers seem to agree that some form of topdown processing is necessary to use MERs for learning (Anderson & Leinhardt, 2002; Kozma, 2003). So when learners mainly apply bottom-up learning with presented MERs, conceptual understanding remains hard to achieve, even for those who remember more correct propositions (i.e. ideas) from those MERs. It is known that low prior knowledge learners tend to memorize facts separately rather than look for concepts and links between concepts in (M)ER (Machiels-Bongaerts, Schmidt, & Boshuizen, 1995; Shuell, 1990). In this study, the learning goal of conceptual understanding is defined as a deep comprehension of the ‘principles that govern a domain and the interrelations between pieces of knowledge in that domain’ (Rittle-Johnson & Wagner Alibali, 1999, p. 175), and it is said that only through the use of MERs can this learning goal be fully achieved (Kozma, 2003). Yet, having low prior knowledge with limited schemata on the subject or giving little attention towards a representation does not necessarily mean that the information is not processed. These explanations, in other words, remain at the superficial level and do not detail, for example, in terms of the model of Schnotz, why precisely learners lack the ability to use MERs to increase their understanding. Second, based on these explanations, we also have no view on the possibilities of learners using MERs to increase their understanding. This lack of understanding results in research attempting to find the best way to increase learners’ understanding or enhance the use of MERs using instructional aids or guidance without providing a clear and detailed (cognitive) rationale or theoretical framework as to why any of the tested aids would be optimal (Ainsworth, 2011; Kozma & Russell, 2005). This study aims to understand in a more detailed way both the limits and possibilities of learners with low prior knowledge when scientific multiple representations are offered—and this is compared to single representations, to achieve conceptual understanding of the represented information.

718 D. M. J. Corradi et al. The Case of Abstract Representations in Chemistry Though many domains have abstract representations as part of their curriculum (e.g. maths, astronomy and philosophy), the nature of chemical representations is such that one representation can entail both very concrete information and highly abstract or symbolic information (Taber, 2009; Taskin, Bernholt, & Parchmann, 2011). Our focus lies on three different representations, namely symbolic representations, submicroscopic representations and texts. Texts have a central role in our study, clarifying the basic concepts (as can be found, for example, in textbooks on the subject). Symbolic and submicroscopic representations are added to the texts. Submicroscopic Representations In many teaching and learning situations, submicroscopic representations are often used to communicate the visuo-spatial relationship between atoms and molecules (Chandrasegaran, Treagust, & Mocerino, 2008; Johnstone, 2006). When teaching low prior knowledge students, submicroscopic representations can be found communicating the atomic model of Bohr or the model of Schro¨dinger (Barnea & Dori, 1999) to explain or show the interrelationship between neutrons, protons and electrons. They are also found communicating molecular models (e.g. to present methane gas or salt). This type of representation also holds symbolic information which is often difficult for low prior knowledge learners to grasp (Harrison & Treagust, 2002). Studies found, for example, that learners can misinterpret the lattice connecting two atoms in a molecular model, which actually represents electronegative force (and is not really present) (Gabel, Samuel, & Hunn, 1987). In many cases, learners are found to give attention to the superficial aspects of submicroscopic representations (Kozma & Russell, 1997; Rappoport & Ashkenazi, 2008) such as colour or shape, while ignoring the concepts entailed in the depictive representation (Devetak, Vogrinc, & Glazˇar, 2009; Taber, 2009). Symbolic Representations Symbols are crucial for communicating chemical information. In contrast to (depictive) submicroscopic representations, symbolic representations are descriptions and are more concise and more abstract than complete texts (Schnotz, 2005, 2008; Schnotz & Bannert, 2003). Symbols in chemistry are used, for example, to describe chemical reactions (Marais & Jordaan, 2000). Low prior knowledge level learners often receive symbolic representations in the form of the periodic table, which is rather dense in amount of information (Mishra & Nguyen-Jahiel, 1997). Studies on symbolic representations found that learners often give attention to superficial features of the symbols such as the type or the print (Taber, 2009). Once decoded, the information in symbols can be quite concrete; however, some very similar symbolic formulas can be challenging (Kozma & Russell, 1997). For example, Cokelez, Dumon, and Taber (2008) mention that learners have a hard time understanding

The Nature of Low Prior Knowledge Learners 719 the differences between the hydrogen chloride gas described as HCl and the crystal of sodium chloride, NaCl, simply because both descriptions are nearly identical. The level of abstractness of chemical representations can, in other words, provide quite a challenge for learners who have low prior knowledge of and little experience with the representational system of chemistry.

Textual Representations Texts offer an advantage for learners since most information can simply be read off, as it is a sequence of propositions placed into a narrative structure (Foltz et al., 1998; Kintsch & van Dijk, 1978). Compared to submicroscopic and symbolic representations, texts are more concrete than abstract. Our definition of an abstract representation follows Egan (2002) as being a representation with a high amount of novel information. Text might have novel information but in contrast to symbolic or submicroscopic representations does not need to be decoded as much and can be directly read off. This does not mean texts are completely without abstract information. Studies (Ben-Zvi, Eylon, & Silberstein, 1986) found that the jargon of chemistry can be abstract and confusing for learners who have little prior knowledge of chemistry. A chemical ‘bond’, for example, does not imply an actual physical connection but symbolizes electronegative force bringing together different atoms into a molecule. A ‘shell’ does not mean that an atom has an actual physical shell around the atom, yet in both cases low prior knowledge learners are found to make mistakes in the interpretation (Ben-Zvi et al., 1986; Taber, 2009). Texts in our study are used to communicate basic information, as is usually done in a learning environment with static representations (e.g. textbooks, websites, etc.).

Learning Using MERs The way learners deal with the abstract information in MERs lies at the heart of whether or not learners will achieve conceptual understanding of the presented subject. There are indications from the previous research with abstract representations that a filtering process takes place, which is related to the process of understanding and the process of relating information within MERs and with internal schemata (Hill, 1988; Treagust, Chittleborough, & Mamiala, 2002). This is further supported by the recent literature on mental model construction (Rapp & Kurby, 2008), that specific propositions can make more or less contribution to comprehension processes, influenced by (amongst others) their prior knowledge (Duit & Treagust, 2012). But as far as we know, there is no research that clearly answers the question of what the precise effect is of the filtering process of abstract representations on the construction of an internal representation, and consequently, the deep conceptual understanding of the represented information with low prior knowledge learners.

720 D. M. J. Corradi et al. Research Questions and Hypotheses Research Question 1: What is the nature of the internal representation when low prior knowledge learners are asked to study abstract scientific representations? To understand which information is filtered and stored, we use a procedure often employed in text comprehension research (Recht & Leslie, 1988), and in chemical MER research (Kozma & Russell, 1997), to get an indication of how the internal representation is constructed. Representational comprehension can be measured by asking learners to reproduce all remembered information in separate ideas. These ideas can be compared to lists of pre-stated ideas, termed propositions, by experts (Recht & Leslie, 1988) and categorized as correct or incorrect, as overlapping (the idea can be found in two or three representations) or unique (the idea can be found in only one representation) (Kozma & Russell, 1997; Recht & Leslie, 1988). These dependent variables will help us understand the relationship between the study process of the external representations and the ideas remembered from these external representations, but also how these ideas are linked into coherent internal representations measured as conceptual understanding. As such, propositions can be seen as a process measure in the achievement of deep comprehension of the represented information (Recht & Leslie, 1988). As indicated in the ‘Introduction’ section, the majority of previous research indicates a positive effect of adding other representations to text. Hypothesis 1: The increase in conceptual understanding will be significantly higher in the group where texts are accompanied by symbols and submicroscopic representations compared to other conditions (Table 1). As the illustration effect (Schnotz, 2005) indicates, the external representation influences the internal representations. Therefore, Hypothesis 2 predicts that most ideas (propositions) will be remembered from the condition where text is accompanied by symbols and submicroscopic representations compared to other conditions. The question remains whether low prior knowledge learners are able to filter the information in a correct fashion (Harrison & Treagust, 1996). As learners will have more ideas, there will be more chance that these ideas are correct. Hypothesis 3a predicts that most correct propositions will be found in the group where text is accompanied by symbols and submicroscopic representations. Hypothesis 3b predicts that the percentage of correct proposition will also be significantly higher for the group of text with symbols and submicroscopic representations. Table 1. Overview of all conditions with type of intervention n Group 1 Group 2 Group 3 Group 4

Intervention

23 Receives textual representation (control group) (TEXT) 25 Receives textual and symbolic representations (TEXT+SYMB) 26 Receives textual and submicroscopic representations (TEXT+SUBM) 27 Receives textual, submicroscopic and symbolic representations (TEXT+SUBM+SYMB)

The Nature of Low Prior Knowledge Learners 721 However, little information can be found on the precise relation between the number of propositions remembered and the number of correct propositions. Is the internal representation filled with a lot of incorrect information because of miscomprehension (Taber, 2009) or will there be little information but all correct because learners pay little attention to representations (Corradi et al., 2012)? Therefore, Hypothesis 4 has two versions: (a) propositions remembered . correct propositions, favouring the first finding that the amount of ideas remembered is significantly higher than the amount of correct ideas and (b) propositions ¼ correct propositions, favouring the second finding, indicating that if ideas are filtered, they are immediately correct. As previous research confirms that learners often focus on only one representation, ignoring or giving little attention to other representations (Corradi et al., 2012), Hypothesis 5 states: Unique propositions . overlapping propositions (unique propositions remembered originate from one representation, and overlapping propositions are present in two or three representations). Based on previous research, we know that learners spend more time on textual representations and text is more easily readable; therefore, Hypothesis 6a: Learners will remember significantly more unique propositions from text than from other representations. In line with Corradi et al. (2012), learners find it harder to link text with submicroscopic representations, compared to linking text with symbolic representations; therefore, Hypothesis 6b: Learners will remember significantly more overlapping propositions from text and symbolic representations than from text and submicroscopic representations. Research Question 2: How does the internal representation influence conceptual understanding of the represented information? Conceptual understanding is measured as learning gains (difference between the starting position of the learner—i.e. pre-test—and final position of the learner— post-test). Based on previous research on the subject (Recht & Leslie, 1988), which found that propositions that low prior knowledge learners remember from learning situations have little influence on understanding, our prediction for the relationship between the internal representation and conceptual understanding is rather pessimistic. Hypothesis 7: The amount of correctly remembered propositions influences the increase in conceptual understanding. This last hypothesis is explained by Schnotz’s model. The internal representation helps to create understanding through mental model creation and model inspection. This is, of course, on the precondition that the information in the internal representation is correct. Figure 1 gives an overview of Hypotheses 1–5 and Research Question 2.

Methods Participants Participants were 101 college students who were in their first or second year of a bachelor’s degree in socio-educational care work. Participants were chosen based

722 D. M. J. Corradi et al.

Figure 1. Overview of Hypotheses 1–5 and Research Question 2

on their curriculum: they did not have any exact science, and only a minority (7%) had chosen exact science as a major in high school. Sixty per cent had followed technical or vocational options in high school. Average age was 19.2 (SD ¼ 1.2). Only 18% were male. Scores on the pre-test (M ¼ 20%; SD ¼ 14%) confirmed that participants had low prior knowledge of the represented content. Participants received a small financial reward (E5) for their participation.

Design The study was a pre –post randomized design. Each student received a text on introductory concepts and principles in chemistry spread over two separate pages (720 words). Depending on the condition, as described in Table 1, learners received a set of symbolic representations and/or a set of submicroscopic representations. Both sets of representations were spread over two separate pages.

The Nature of Low Prior Knowledge Learners 723 Pages were not bound together, so learners could place the representations next to each other. Material Texts, submicroscopic and symbolic representations were constructed by the authors, together with two lecturers in chemistry education, about several interrelated concepts and principles, i.e. structure of the atom (e.g. electron, neutron and proton), chemical bonds (ionic and covalent) and molecules. The content of the text was the model of Bohr, differences in bonds (ionic and covalent) and the structure of molecules. The content of the submicroscopic representations was three models of Bohr of helium, potassium and oxygen; three images of bonding atoms (H and H; Na and Cl; H and H and O); and one depiction of a molecule (CH4). The content of the symbolic representations was the table of Mendeleev with hydrogen, oxygen and krypton highlighted and explained in detail (i.e. the atomic number and mass), and the formula of electronegative force in the case of ionic (for fluor and potassium) and covalent (for chloride and chloride) bonds. We constructed a list of propositions for each representational format. Two experts in chemistry checked until consensus was reached over each set of propositions. Between 25% and 30% of propositions of the text representation format were overlapping with one other representation. About 10% of the propositions of the text were overlapping with the three representations. Text had 80 propositions, symbols 52 and submicroscopic representations 54. Instrument To measure the gain in understanding the represented concepts, we constructed an instrument, together with several lecturers in chemistry education. Questions focussed on understanding the concepts and principles that were mentioned in the texts, symbols and submicroscopic representations. To be as accurate as possible concerning the learning gains, the pre- and post-test were identical. Validation of the Conceptual Understanding Test Instrument To validate the instrument, 26 college students who followed teacher education with majors in fashion and home economics participated in a small study. Students who had fashion as a major did not have any science courses in their curriculum (38%); students who had home economics as their major did follow introductory science courses (62%). Learners were requested to fill in three questionnaires. Two questionnaires were constructed by Shwartz, Ben-Zvi, and Hofstein (2006). These questionnaires were constructed to measure chemical literacy at the novice level, and were previously used to measure the conceptual understanding of low prior knowledge-level learners (Corradi et al., 2012). These instruments were previously validated and were found to

724 D. M. J. Corradi et al.

Figure 2. Example of a question used in the conceptual understanding test

have good construct and concept validity (Shwartz et al., 2006). The two questionnaires by Shwartz (termed ‘functional’ and ‘conceptual’ literacy) consist of a test using open questions on chemical terminology and a multiple-choice test. Functional chemistry literacy is seen as having the vocabulary to describe basic concepts in chemistry. Conceptual literacy is seen as the understanding of concepts and the relationship between basic concepts of chemistry (Shwartz et al., 2006). Our test consisted of questions similar to the conceptual literacy multiple-choice test, but adapted to the concepts mentioned in the learning material used in this study. Just like the multiple-choice test of Shwartz, each test question in the conceptual understanding test started with a contextualization, followed by statements whereby the participant had to fill in: ‘correct’, ‘incorrect’, ‘not determinable’ or ‘do not know’. Figure 2 gives a translation of a test question with contextualization and statements. The conceptual understanding test contained 26 such statements and participants got a point per correct statement (to a maximum of 26 out of 26). This adaptation of Shwartz’s test was necessary to see in detail how learners used the material in this study to increase their conceptual understanding of the represented information. Results show that all tests seem to measure a similar type of knowledge. We found a correlation of 0.93 (p , .01) between the multiple-choice conceptual literacy test by Shwartz and our test, and a 0.54 (p , .05) correlation between the functional literacy open questions by Shwartz and our test. We conclude that the test seems valid for measuring conceptual understanding. Reliability measures were good to very good (Cronbach’s alpha between .80 and .84). Procedure Participants were randomly divided into the four conditions. After receiving explanations of the study, learners received the pre-test. When they had finished, learners

The Nature of Low Prior Knowledge Learners 725 could start with the intervention, which was studying the information in the representations. Participants were asked to study the information so they could understand everything. They were told that, after studying the information, they would be questioned about it. Learners could not take notes, write down or indicate anything on the representations. After the intervention, learners received an intermediate task (a small puzzle) to remove information from their working memory. Finally, learners received the posttest. This included the assignment to write down in short sentences (no drawings or symbols) what they remembered from the intervention, measuring the propositions remembered, and the same questions as the pre-test, to measure gains in conceptual understanding. There was no time limit for any of the steps in the procedure, except for the intermediate task, for which they received about 5 minutes to solve the puzzle. Results Analysis of the pre-test confirms that there is no significant difference between the four groups F(3, 97) ¼ 0.269, p ¼ .848, h2p ¼ .008. This confirms that randomization was done successfully. Learners scored on average 20.8% (SD ¼ 14%) on the pre-test, confirming their low prior knowledge of the concepts. Mean score on the post-test was 36.8% (SD ¼ 12.7%). The difference between scores on the pre- and post-test is significant t(100) ¼ 12.6, p , .001, so learners’ understanding increased significantly. Analysis of variance (ANOVA) of the learning gains of conceptual understanding rejects Hypothesis 1: there is no significant difference between the four groups F(3, 97) ¼ 1.552, p ¼ .206, h2p ¼ .046. This indicates that learners did not use the information in symbols and submicroscopic representations to deepen their understanding. To answer the first research question: ANOVA does not completely reject Hypothesis 2. There is a significant difference between the four groups concerning the number of propositions remembered, F(3, 97) ¼ 3.662, p ¼ .016, h2p ¼ .101. Tukey’s HSD post hoc test finds that group 3, where text was accompanied by submicroscopic representations, scored significantly higher, 6.39, p ¼ .019, than group 4 with three representations. There is also a non-significant difference between group 3 (TEXT+SUBM) and group 1, the text-only group, 5.58, p ¼ .07, that is worth mentioning. Figure 3 and Table 2 both give an overview of the number of propositions reproduced (remembered). How they were categorized as correct, unique or overlapping can be read off from Table 2. Hypothesis 3a was that learners in the condition with three representations would remember more correct ideas than when just a text was presented. Just like Hypothesis 1, we found a marginally significant difference, F(3, 97) ¼ 2.663, p ¼ .054, h2p ¼ .075, but contrary to our expectation, this was caused by the correct propositions in group 3 (TEXT+SUBM), which was significantly higher than group 4 (TEXT+ SUBM+SYMB), 25.54, p ¼ .039 according to the Tukey HSD post hoc test. We could not find support for Hypothesis 3b. When comparing the proportion of correct propositions versus all propositions, we did not find a significant difference

726 D. M. J. Corradi et al.

Figure 3. Mean number of propositions counted for each group

Table 2.

Mean (SD) of the internal propositional representation recorded (prop ¼ propositions) TEXT+SUBM+SYMB TEXT+SUBM TEXT+SYMB

Propositions Correct propositions Unique propTEXT Unique propSUBM Unique propSYMB Overlapping propTEXT+SUBM Overlapping propTEXT+SYMB Overlapping propTEXT+SYMB+SUBM

TEXT

11.15 (6.72) 6.93 (6.47) 1.15 (1.43) 0.22 (0.51) 0.52 (0.98) 1.3 (1.71)

17.54 (9.53) 12.46 (9.45) 7.31 (5.64) 0.31 (0.84) N/A 4.8 (4.6)

14.36 (7.31) 9.8 (6.9) 3.12 (3.07) N/A 1.24 (1.8) N/A

11.96 (6.66) 8.39 (6.37) 8.39 (6.37) N/A N/A N/A

2.70 (2.66)

N/A

5.44 (4.29)

N/A

1.04 (1.32)

N/A

N/A

N/A

between the four groups, F(3, 97) ¼ 1.618, p ¼ .19, h2p ¼ .048. This means that, as the amount of ideas remembered increased, the amount of incorrect ideas also increased.

The Nature of Low Prior Knowledge Learners 727 Hypothesis 4a is also confirmed. We found that: propositions . correct propositions, t(100) ¼ 15.2, p , .001. The internal representation seems to contain a lot of incorrect ideas. Hypothesis 5 that unique propositions . overlapping propositions (excluding the control condition as it has no overlapping propositions) is not supported by our findings, t(77) ¼ 21.01, p ¼ .317. Learners remember as many unique propositions as redundant (overlapping) propositions, even though there were many more unique propositions in each representation than redundant ones. We found confirmation for Hypothesis 6a. Learners remembered more unique propositions from text than from symbols t(51) ¼ 3.316, p ¼ .002 (group 3 and group 4 excluded), and also from submicroscopic representations, t(52) ¼ 5.699, p , .001 (group 2 and group 4 excluded). Learners remembered more unique propositions from symbols than from submicroscopic representations, t(52) ¼ 2.2602, p ¼ .012 (results of group 1 and 2 for symbols and group 1 and 3 for submicroscopic representations; group 4 excluded). Hypothesis 6b, concerning the overlapping representations, finds different results. Learners remembered as many overlapping propositions from text and symbols as they did from text and submicroscopic representations t(52) ¼ 1.429, p ¼ .159. Concerning the second research question, we found some indication of a relationship between the construction of the internal representation measured through propositions and deep comprehension (conceptual understanding) of the subject. The more learners increased their proportion of correct propositions versus all propositions remembered, the more learning gains increased in the TEXT+SUBM condition (only in this condition) r ¼ .39, p ¼ .04. We also found a significant correlation between overlapping propositions in the TEXT+SYMB+SUBM condition and the learning gains 0.547, p ¼ .003. Based on these correlations, we used multiple regression analysis (forward selection) using three predictors, namely percentage correct propositions, unique propositions and overlapping propositions. When excluding the control condition (as it has no overlapping propositions), only the variable ‘percentage of correct propositions’ seems to be a significant contributor to the learning gains (Beta ¼ .30), with R2 ¼ .09, F(1, 76) ¼ 7.447, p ¼ .008 (other variables were excluded from the model). This means that Hypothesis 7 is not falsified. The construction of the internal representation has an influence on comprehension, but incorrect ideas seem to be the major stumbling block for learners with low prior knowledge of the domain. Discussion The main focus of this study was, first of all, to understand how an internal representation is formed using MERs, compared to a single representation. Second, how the construction of the internal representation influences conceptual understanding of the represented subjects. As found in previous studies, learners increased their understanding to a similar level whether MERs were provided or not (Corradi et al., 2012). This means the

728 D. M. J. Corradi et al. information presented in the symbolic and the submicroscopic representation did not have any added value for learners to increase their understanding—a result that contradicts some research that finds positive effects from combinations of text and pictures and symbols (Carney & Levin, 2002; Schnotz & Bannert, 2003). An important side note is that we did not find a harmful effect from MERs on understanding as some previous studies indicate (Schnotz & Bannert, 2003; Seufert, 2003). Contrary to the claims of text–picture research, adding chemical depictions or descriptions to a text cannot be seen as an instructional tool for low prior knowledge learners. Indeed, when presenting learners with chemical MER, instructional designers should consider to add instructional tools to the material. What these instructional tools could be is detailed in the following sections. Nature of the Internal Representation Even though adding representations to a text did not help to increase understanding of the content, the information from these submicroscopic and symbolic representations is not necessarily ignored. Our results shed light on a number of important cognitive aspects when learning with MERs. As indicated in the ‘Introduction’ section, previous studies on text and picture comprehension have indicated that there is a definite advantage to adding pictures to texts (Schnotz, 2005), as it increases understanding significantly more (Carney & Levin, 2002) than when only text is presented. Our results give an indication why this multimedia effect has rarely been replicated with scientific representations (Kolloffel et al., 2008). Based on the number of propositions learners remembered, there was an initial advantage of text combined with submicroscopic representations. Learners seemed to remember more than in the condition where three representations were presented. There was also a slight indication of an advantage of MERs compared to the text-only condition. The multimedia effect and the model by Schnotz, as detailed in the ‘Introduction’ section, would have predicted similar results: three representations are seen as too much information presented at the same time, increasing the chance that learners understand less than when only two representations are presented (Schnotz & Bannert, 2003). Yet, in this case two representations only led to more information remembered, but not more understanding than when one representation was offered. When we analysed the correctness of the information, and put this in proportion with all the information filtered from the representations (Hypothesis 2b), learners were all at the same level, regardless of whether texts were presented alone or accompanied by symbols, submicroscopic representations or both. This means that learning with multiple scientific representations is hampered, not necessarily by the fact that learners receive a lot of information (as some research on cognitive load would imply, e.g. Moreno, 2010), but mainly by learners’ inability to make a distinction between correct and incorrect information in their construction of the internal representation as the (relatively) low proportion of correct propositions, the correlation and the regression analysis suggest. These results lie at the heart of this study. Scientific representations are given less attention, causing the internal representation, which is constructed while learning

The Nature of Low Prior Knowledge Learners 729 the represented information, to interact too little with other (aspects of the) external representations. When instruction is conceived to help learners deal with MERs, it is exactly this process that needs the support. We will offer suggestions for types of instructions in the ‘Instructional and Theoretical Implications’ section. Previous research found that learners sometimes express cognitive economy (Introduction) when learning with MERs: they focus on the easiest accessible representation and give minimal attention to other representations (Schnotz, 2005). Our analysis of propositions indicates a potential for using MERs. Based on the overlapping propositions and the unique propositions that learners remembered, we found that learners actually paid attention to representations other than only texts. The key here seems to be the redundant (overlapping) ideas in the texts, which helped to guide learners’ attention towards other representations. As learners read the texts, the information that was also in the other representations caught their attention. The redundant information could be helpful for low prior knowledge learners to help them deal with the more difficult information coded in symbols and submicroscopic representations. Support for this was found, for example, in the correlations (Hypothesis 7), which showed that the more learners remembered overlapping representations—especially overlapping with all three representations—the more they seemed to increase their understanding of the concepts. Instructional and Theoretical Implications In the domain of learning with representations, a mishmash of instructional aids has been proposed that have tried to help learners increase understanding (Ainsworth, 2011). In line with the adage of Kirschner, Chandler, and Sweller (2006), any instructional recommendation that does not or cannot specify the changes in the LTM, or that does not increase the efficiency with which relevant information is stored in or retrieved from the LTM, is likely to be ineffective. Indeed, without proper understanding of what happens with learners with low prior knowledge, gains in learning might simply reveal the novelty effect that instructional aids sometimes cause (Clark, 1983). This also means that a well-formulated theoretical framework is necessary to counteract the hindsight bias, a human tendency to exaggerate how much one could have predicted after the event has occurred (Werth, Strack, & Fo¨rster, 2002). Detailing the precise impact of representations (as we did in this study) and possibly additional instructional aids is therefore necessary to overcome such limitations found in previous research (Introduction; Kozma & Russell, 2005). Our study has the potential to help understand more advanced instructional research using MERs as its core focus, to increase deep comprehension (Ainsworth, 2008; diSessa, 2004). Understanding how internal representations are formed and constructed, and how these representations, in turn, impact deep conceptual understanding is fundamental to our comprehension of how instruction can provide the most efficient way to increase learning understanding. As the construction of internal representations measured through propositions forms a sort of cogwheel in processing the information into coherent schemata of LTM, it is a good indicator of learners’

730 D. M. J. Corradi et al. possibilities and an interesting addition to the model of Schnotz as described in the ‘Introduction’ section. One part of the Schnotz model this study adds to is the principle of cognitive economy. We know that learners give little attention to any representations, other than the most easily accessible (Rappoport & Ashkenazi, 2008), in our case the text learners received (i.e. cognitive economy). This is also confirmed in our study: learners remember far fewer unique ideas from symbols and submicroscopic representations. This is partly understandable, since propositions can be almost directly read off from texts, but have to be interpreted from images and decoded from symbols. But our results indicate a number of potentials. Learners use texts to help them understand the information confined in the representations; they remember many more overlapping propositions than unique propositions from pictures and symbols. This means that texts help them explore other representations; it does not necessarily hold them back as the cognitive economy principle might imply (Schnotz, 2005). The fact that learners, when texts were combined with pictures, remembered much more information than when three representations were presented indicates the potential for learners that the right combination of representations helps to explore the information more. Learners, even when they have low prior knowledge, have the potential to use MERs for learning, whether they are abstract and filled with symbolic information or not. Hence, the actual limitations of prior knowledge lie not in learners’ attention processes. Indeed, the cognitive economy principle would imply that learners would wilfully ignore MER. This wilfulness was not found in our study. It was found that first of all a large part of the information remembered was stored and interpreted incorrectly in the internal representation. Second, results of the regression indicate that this is an important reason for the limited increase in understanding. As such, it can be concluded that the actual limitation of low prior knowledge learners are situated in the filtering process of MER. They are simply unable to read chemical MER in a correct manner. In other words, this limitation lies not in the attention process but in the construction of the internal representation. This also means that this limitation immediately indicates a potential to enhance learning of abstract information for learners with low prior knowledge in a given domain. Instructional designers should be aware of the fact that even the best constructed representations can be misinterpreted. As one reviewer indicated, this conclusion might be limited by the fact that we did our analysis over a whole range of representations (such as ‘submicroscopic representations’), rather than seeing how each individual representation affects learning. Future research might try to see how each specific representation influences comprehension processes (Corradi, Clarebout, & Elen, 2013). When designing aids to help learners avoid constructing a flawed internal representation, the focus could lie on the way the internal representation interacts with the external representation. If, for example, a learner takes notes or makes a summary of the information during the learning and instruction process, he actually expresses the internal representation into an external representation (a descriptive representation), and based on our study, our hypothesis for further studies would be that

The Nature of Low Prior Knowledge Learners 731 such a condition would help learners more than when there is no externalization of the internally constructed representation. Other types of aids would include drawing (diSessa, 2004), creating a schema of the material (descriptive and depictive representation) (Ainsworth & Loizou, 2003), reflecting out loud on the material (auditive representation) and making and giving a presentation of the material (auditive, descriptive and depictive representation). In conclusion, using propositional analysis, our research contributes to a long line of mental model research in science education (Duit & Treagust, 2012; Prain & Tytler, 2012; Rapp & Kurby, 2008) that emphasizes that externalizations of internal representations of all forms are highly important for learners to achieve higher order learning goals such as conceptual understanding. All these aids can do three things: first, help overcome the abstractness of certain representations; second, guide learners’ attention towards representations other than texts, and thus overcome cognitive economy when learning with MERs (Schnotz, 2005); and finally, improve the correctness of the internal representation. This in turn should lead to what Schnotz (2005) calls ‘coherence forming’: the interaction of internal representations from different codalities that creates a deep form of understanding (Seufert, 2003).

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