Increasing High School Students' Chemistry Performance and

Article pubs.acs.org/jchemeduc

Increasing High School Students’ Chemistry Performance and Reducing Cognitive Load through an Instructional Strategy Based on the Interaction of Multiple Levels of Knowledge Representation Dušica D. Milenković,* Mirjana D. Segedinac, and Tamara N. Hrin Department of Chemistry, Biochemistry and Environmental Protection, Faculty of Sciences, University of Novi Sad, Novi Sad 21000, Republic of Serbia S Supporting Information *

ABSTRACT: The central goal of this study was to examine the extent to which a teaching approach focused on the interaction between macroscopic, submicroscopic, and symbolic levels of chemistry representations could affect high school students’ performance in the field of inorganic reactions, as well as to examine how the applied instruction influences students’ assessment of invested mental effort. The total sample of this research included 313 high school students. The survey was conducted during the 2012−2013 school year. As a measuring instrument for student performance, a two-tier multiple-choice test of knowledge was used. Each task in the test was followed by a seven-point Likert-type scale for evaluation of invested mental effort. Our findings indicate that a teaching strategy relying on the interplay between three levels of knowledge representation leads to an increase in students’ performance and also contributes to the reduction of cognitive load. The obtained results for calculated mental efficiency suggest that the applied instructional model represents an effective teaching model. KEYWORDS: High School/Introductory Chemistry, Chemical Education Research, Inorganic Chemistry, Laboratory Instruction, Testing/Assessment, Reactions FEATURE: Chemical Education Research

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INTRODUCTION Once they encounter chemistry in primary school, students are expected to adopt a variety of complex and highly abstract concepts as well as to perceive and comprehend a number of chemical phenomena and processes, which are inaccessible to direct sensory observations. On the other hand, students of this age are characterized by still limited ability for abstract thinking. Therefore, it is not surprising that many of them have difficulties in acquiring chemical concepts.1−5 In addition to abstract chemical concepts, a limited understanding of multiple levels of representation (macroscopic, submicroscopic, and symbolic) is frequently being argued as a possible source of problems that appear in chemistry learning process.6,7 According to Gilbert and Treagust,8 the macroscopic level of representation refers to the tangible and visible properties of chemical phenomena (color change, formation of gas, formation of precipitate, sound effects, physical state) or perceptible properties that can be measured (temperature, mass, density, etc.). The submicroscopic level provides explanations of phenomena experienced with senses at the particulate level (level of atoms, ions, molecules). Finally, the symbolic level refers to the use of chemical symbols, formulas, equations, diagrams, and models to symbolize matter.9 © XXXX American Chemical Society and Division of Chemical Education, Inc.

Research has provided considerable evidence that teachers very often fail to connect levels of representation during teaching10 or, even more often, neglect particular levels. This is especially the case with the submicroscopic level, which is the most difficult for students, as it requires the formation of mental representations of the particulate nature of matter.11 Thereby, this level is crucial for a meaningful understanding of chemical phenomena and processes and its neglecting represents the basis for the formation of various students’ alternative conceptions.12 Numerous studies in the domain of the “chemistry triplet” agree that meaningful understanding of chemical concepts is achievable solely by the development of representational competencies, which implies constant interplay between the macroscopic, submicroscopic, and symbolic levels of thought. According to Gabel,10 this can be achieved through laboratory work, that is, laboratory experiments. Research indicates that experiments play a significant role in improving cognitive, meta-cognitive, and practical skills and change students’ attitude toward chemistry and enhance their interest and motivation for learning chemistry.13 In addition, demonstration experiments have served as a valuable way to assess students’ understanding of the connections among the

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dx.doi.org/10.1021/ed400805p | J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

Article

of cognitive load, which is related to the cognitive capacity allocated to the processing demands imposed by the task, and which, according to Paas et al.,24 is the measure of actual cognitive load. The third concept is performance, which is defined as students’ achievement, measured by the number of correct responses, number of errors, or the time on task. There are several standpoints relating to the measurement of cognitive load, but the basic classification includes objective and subjective approaches. The objective approach is mainly based on physiological and behavioral measures. It involves eyetracking techniques,25 measurements of brain activity,26 cardiovascular indicators27 and the like, using advances in modern technology. On the other hand, subjective measures are based on the assumption that people are able to introspect on their own cognitive processes and assign a numeric value to the perceived mental effort.24 Literature provides evidence that subjective measures (referring primarily to subjective scales for the assessment of invested mental effort) are sufficiently reliable and above all simple and practical for application in a teaching environment.28 Another indicator for cognitive load that has been addressed in recent research is cognitive complexity. The development of instruments for rating cognitive complexity has recently been reported.29,30 Such an instrument is based on an expert-estimate of test item difficulty. Finally, the combined measures of cognitive load should also be mentioned. One well-established method, based on a combination of measurements of invested mental effort and students’ performance, was introduced by Paas and Van Merrienboer.31 Their calculation yields a two-dimensional instructional efficiency measure, and its successful implementation has been widely documented in the literature.24,32−35 The idea that working memory architecture and its limitations should be a major consideration when designing an instructional method24 is central to CLT, and therefore, current research on instructional efficiency, beside students’ performance, should be focused on the investigation of cognitive load, that is, the mental effort that students invest during problem solving.24,31 According to this literature review, the main goal of this research was set to examine the mental efficiency of instructional design based on the intercorrelation of multiple levels of representations.

macroscopic, submicroscopic, and symbolic levels of chemical representation.14 However, according to Gabel,10 lack of learning through chemical experiments is reflected in the fact that teachers and educators often do not realize how little students can learn from them in the ways they are commonly structured. Studies show that chemical reactions are often learned through symbolic representations and that teachers often provide explanations solely at the symbolic level.15 Therefore, Hinton and Nakhleh16 suggest that students are able to identify the macroscopic properties of chemical reactions, and to perform algorithmic operations on chemical equations, but do not understand the essence of the chemical reactions at the submicroscopic level. Therefore, it is of great importance that every change that accompanies a corresponding experiment is studied through all three levels of representation. In fact, it is first necessary to notice sensory changes that are available at the macroscopic level (such as color change, formation of gas, formation of precipitate, etc.), then to explain the observed changes at the level of particles that is to say at the submicroscopic level, and then to present changes symbolically. At the same time, it is important to perform intercorrelation between levels. However, it is important to note that levels of representation should not be employed simultaneously, since it could cause an overload of students’ working memory capacity, as stated by Sirhan in an overview of learning difficulties.4 Thitherto, the basic principles and recent trends in the Cognitive load theory (CLT) will be briefly considered.

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THEORETICAL FRAMEWORK Researchers in the field of science education are commonly concerned with students’ cognitive structure as well as with problems that occur during the learning process due to overload of working memory capacity. Working memory is a central concept of the various information-processing models that have been developed in recent decades.17,18 A common feature for all these models of information processing is that individuals learn in basically the same way. Namely, individuals receive information through the senses from the environment, then select the relevant from the irrelevant pieces of information, process it in working memory and store in longterm memory.19 It is well-known and widely accepted that working memory is short-term and has a limited capacity, which according to Miller20 amounts to 7 ± 2 unrelated elements of information. Working memory limit can be overcome by combining simple elements into more complex ones, that is, constructing schemas.21 These schemas are stored in long-term memory. However, information first must be processed in working memory. Thus, the facility by which information can be processed within working memory is the main concern of Cognitive load theory.22 According to Paas and Van Merrienboer,23 cognitive load is a multidimensional construct that refers to the load imposed on the individual cognitive system while solving a specific problem or task. According to these authors, cognitive load has two dimensions. The first is causal and it is related to the interaction of task and cognitive characteristics of an individual, while the second is the assessment dimension, which reflects the measurable concepts: mental load, mental effort, and performance. Mental load refers to the requirements originating from the structure of the task, whereas mental effort represents an aspect

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METHODOLOGY

Purpose of the Research

The purpose of this study was to examine whether a teaching approach focused on the interaction between macroscopic, submicroscopic, and symbolic levels could improve acquisition of knowledge in the field of inorganic reactions in comparison to the traditional teaching approach, and how the applied instruction influences students’ assessment of invested mental effort. In line with the aim of the research, the following research questions were formulated: 1. Does the teaching approach based on the idea of multiple levels of knowledge representation influence students’ performance in the field of inorganic reactions? 2. Does the teaching approach based on the idea of multiple levels of knowledge representation influence the mental effort that needs to be invested while solving problems in the field of inorganic reactions? B



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Participants

macroscopic changes and properties at the level of particles. At this point the teacher has the role of facilitator and guide, directing students to the correct explanations through the use of molecular models, analogues, and graphics. At the next stage, students are requested to symbolically interpret observed macroscopic properties and submicroscopic reality. Namely, they are asked to write a balanced chemical equation and then directed to recognize the significance of the symbols and the formulas that are used to represent chemical reactions. Therefore, students are encouraged to come up with explanations at all three levels without neglecting certain levels, which is often a characteristic of traditional teaching and learning. Additinally, they are encouraged to integrate that knowledge in a manner that forms one entirety. Afterward, to monitor changes in students’ performances and invested mental effort, during implementation of the intervention strategy, we conducted two tests. At the end of the school year, a fourth, final test was carried out, the results of which are discussed in this paper.

The total sample of this study consisted of 11 classes with 313 students from two general type gymnasia in Novi Sad, Serbia. The respondents were in their second school year and were aged 16−17. All 313 students participated in the initial testing, the aim of which was equalization of groups for subsequent testing that was conducted throughout the school year. On the basis of the performances accomplished at the initial test, 8 classes (four from each school) out of the total number of tested students were chosen for further testing, 4 classes for control, and 4 for the experimental group. The final test involved a total of 189 participants (88 males and 101 females). The respondents represent an urban population with mixed socioeconomic status. Data Collection

This research was conducted in the 2012−2013 school year. As a measuring instrument for evaluation of knowledge, 4 knowledge tests were used (a detailed description of the tests is given in the Research Instrument section). On the basis of the performances accomplished in the initial test, groups of students were equalized and divided into control (C) and experimental (E) classes. The Kolmogorov−Smirnov test was performed on both experimental (KS: p = 0.48; DN = 0.11) and control groups (KS: p = 0.17; DN = 0.11) and has shown that the results from both groups could be considered normally distributed. Afterward, group equivalence was determined for both groups using two one-tailed t-tests according to Schuirmann’s method.36 The results showed that the two groups could be considered statistically equivalent to each other (t1 = 4.70 > t(α=0.1) = 1.29; t2 = 2.40 > t(α=0.1) = 1.29). Training in all eight classes was performed by two chemistry teachers. Each of the teachers taught two experimental and two control classes. It is important to note that the demonstration experiments performed in the E and C groups were identical and both groups invested the same amount of class time working on them, although approaching them in different manners. Namely, in the C group after the teacher demonstrates the experiment, students make their observations related to the perceptible changes. Then, students work on writing and balancing chemical equations, and solving tasks that are in connection with the presented reactions. Regarding submicroscopic level, the teacher presents certain facts, raises questions, but does not provide the connection with the previously presented content at the macroscopic and symbolic levels. This type of teaching has mostly descriptive character. However, while in this group the teachers provided explanations of experiments with an emphasis on conceptual understanding, in the E group, the teachers applied instructional design that relied on a model of multiple levels of knowledge representation. Both teachers were well versed in the theory of multiplelevels of knowledge representations prior to the research, and were instructed to integrate levels of representation as proposed by Jaber and BouJaoude,37 Chandrasegaran et al.,7 and Gabel.10 It is worth noting that one of the authors attended all the experimental and control sessions to provide the validity of applied strategy. During the instruction in E group, the teacher first performs a demonstration of the appropriate experiment. Students are then encouraged to express their observations regarding the experiment (e.g., color change, formation of gas, formation of precipitate, sound effect, etc.) and asked to explain observed

Research Instrument

Each of the four applied tests was in the form of a two-tier multiple-choice test, designed according to the model described in Chandrasegaran et al.38 All the tasks consisted of two tiers, so that the first tier contained the content question, and the second tier contained the reasonable explanation. Not only the correct answer but also each offered distractor in the first tier had a plausible explanation in the second tier. Their locations in the tiers were randomly selected. Furthermore, items in the first tier contained questions at the macroscopic or symbolic level, while the second-tier issues were related to the submicroscopic level. This type of question is very convenient as it help us to find out not only whether students know what happened, but also why it happened, thus providing meaningful reflection. In addition, according to Chandrasegaran et al.,38 these tests are suitable for administration, and the time required for their implementation minimally burdens the available teaching time. The time for test solving was limited to 45 min. The first test (pre-test) consisted of 16 items. Each correctly solved layer in the item was scored one point, so the maximum possible achievement on the test was 32 points. This test included the demonstration experiments, which belong to the following teaching topics: • • • •

Hydrogen Group 1 Elements Group 2 Elements Group 13 Elements

The applied test included the following demonstration experiments: 1.1 Preparation of hydrogen: reaction of zinc with hydrochloric acid. 1.2 Combustion of hydrogen. 1.3 Flame tests for alkali metallic ions. 1.4 Reaction of sodium with water and reaction of potassium with water. 1.5 Combustion of magnesium ribbon. 1.6 Reaction of magnesium with water. 1.7 Testing amphoteric behavior of aluminum, aluminum oxide and aluminum hydroxide: reactions with sodium hydroxide and hydrochloric acid. C



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methods, such as a secondary task. Finally, these scales are very economical and simple to use. On the basis of the aforesaid, Paas, Van Merrienboer, and Adam40 concluded that subjective rating-scale measurement is the most promising technique for research in the context of cognitive load theory. Kalyuga42 states that seven or nine point scales have been mostly used in the previous research. Therefore, in this study we have decided to apply a seven point self-rated scale of mental effort. Namely, for each layer in the task, students were asked to evaluate the mental effort they invested during their solving. Each of the layers in each task was followed by the seven point Likert-type scale with descriptors: extremely easy, very easy, easy, neither easy nor difficult, difficult, very difficult, and extremely difficult. In further analysis, the descriptors were numerically coded from 1 to 7, so that descriptors and codes correlate as follows: extremely easy, code 1; very easy, code 2; easy, code 3; neither easy nor difficult, code 4; difficult, code 5; very difficult, code 6; extremely difficult, code 7. The obtained results were analyzed using SPSS Statistics 19 and Stat Graphics Centurion XVI.I software programs.

The second test contained 12 items. The maximum possible achievement on the test was 24 points. This test included the following teaching topics: • Group 14 Elements • Group 15 Elements This test included the following demonstration experiments: 2.1 Preparation of carbon(IV) oxide: reaction of calcium carbonate with hydrochloric acid. 2.2 Adsorption on activated carbon. 2.3 Combustion of red phosphorus. 2.4 Preparation and properties of ammonia: reaction of ammonium chloride with calcium hydroxide. The third test consisted of 16 items. The maximum possible achievement on the test was 32 points. The test included the following teaching topics: • Group 16 Elements • Group 17 Elements • Transition Metals The third test included the following demonstration experiments: 3.1 Preparation of plastic sulfur. 3.2 Preparation of oxygen: thermal decomposition of potassium permanganate. 3.3 Reaction of hydrochloric acid with sodium acetate. 3.4 Chemical chameleon: reaction of potassium permanganate with sucrose in an alkaline environment. The fourth (final) test covers the topics of the second and third tests that is experiments 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, and 3.4. The test contained 15 items; thus, the maximum possible achievement on the test was 30 points. Examples of tasks from the final test are presented in the Supporting Information. All the applied tests were characterized by pre-test and posttest quality assurance parameters. Pre-test assurance parameters were evaluated by an expert group composed of two university professors and three research assistants in the field of Chemistry teaching methodology, 1 chemistry teacher and 1 university professor of Pedagogy. The experts performed multiple tests validation, assessing diversity of the tasks, used terms, the meaningfulness of requirements, and length of sentences, and agreed that the tests were valid. In addition, they performed content validation, which showed that the used tests were constructed in accordance with Curriculum regulations and the recommended textbook. Post-test assurance parameters−internal consistency (Cronbach α), item difficulty, and test difficultyhave been provided (post-test assurance parameters are discussed in detail in the Data Analysis and Results section). Beside performance, a mental effort rating was collected for each participant in this study. The use of the Likert scale as a reliable measuring instrument for assessment of invested mental effort in experimental educational settings has been widely documented in the literature.32−35,39−41 Those authors emphasize that the aforementioned scales are the most reliable and the most sensitive in detecting relatively small differences in mental effort. Moray, O’Donnell, and Eggemeir (according to Kalyuga)32 have determined that subjective measures of mental effort highly correlate with objective measures (correlations ranging from 0.80 to 0.99). In addition, Kalyuga32 asserts another advantage of self-rating scales and that is the fact that they do not interfere with the task performance as do other

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DATA ANALYSIS AND RESULTS As mentioned earlier, four tests were carried out during this research. The first test aimed at group equalization, while the second and third tests were conducted with the aim of monitoring the progress of students in both the experimental and control group. The fourth, final test was conducted at the end of the school year. Due to the extensiveness of the results and discussion that the analysis of each of the applied tests would require, and the fact that the final test included the entire material of the previous tests (test 2 and test 3), we decided to analyze only the results of the final test for the purposes of this paper. Therefore, the results of the final test will be hereinafter presented, interpreted, and discussed. Beside descriptive statistics, they include determination of significant differences in performance and mental effort by Kolomogorov−Smirnov test as well as the calculation of mental efficiency of applied instructional design, following the procedure described by Paas and Van Merrienboer.31 The applied measuring instrument showed satisfactory metric characteristics. The internal consistency expressed by Cronbach α coefficient for this test is 0.91, indicating excellent reliability. The calculated indices of item difficulty in the E group are in the range from 29.79 to 92.55%, while in the C group, this interval is significantly narrower and is from 23.16 to 51.58%. In addition, the obtained indices of test difficulty for both groups are in line with these results. When these values are compared, it can be noted that the value of 70.74% obtained in the E group is significantly higher than the index of test difficulty for C group (37.72%), which indicates that students in C group perceived the same test as much harder than their peers in E group. The basic statistical test parameters obtained for performance and mental effort parallel for E and C groups are summarized in Table 1. Students in E group accomplished significantly higher average performance on the test (70.73%) in comparison to the C group students (37.73%). In addition, the maximum accomplished performance in the C group is 76.67%, while in the group E, 40% of students accomplished results higher than the aforementioned maximum achievement in the C group. Since the test tasks were designed so that the correct answer and offered distractors in first tier have possible answer in the D



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Table 1. Descriptive Statistic for Performance and Mental Effort Students’ Performance on the Final Testa

Students’ Ratings of Mental Effort on the Final Testb

Parameter

Group E (N = 94)

Group C (N = 95)

Group E (N = 94)

Group C (N = 95)

Average SD Minimum Maximum Range

21.22 5.95 6.00 30.00 24.00

11.32 4.89 3.00 23.00 20.00

4.20 0.83 1.00 7.00 6.00

5.25 1.16 3.27 7.00 3.73

Figure 1. Distribution of students’ performance on final test for E and C groups.

Possible scores on the final test could range from 0 to 30. bPossible ratings for invested mental effort could range from 1 to 7: extremely easy (1) to extremely difficult (7). a

points, while in the C group, this interval ranges from 16 to 23. It is worth mentioning that the applied instruction significantly contributed to the conceptual understanding of chemical concepts in E group (over 75% of the students accomplished an average achievement greater than 50%, while in the C group more than 75% of students achieved an average achievement below 50%). A similar diagram, which refers to the evaluation of invested mental effort, is shown in Figure 2. As can be seen, in the E

second tier, we find suitable to report the results of the student self-consistency when choosing their options, that is, to mention how often students in E and C groups score 0, 1, or 2 correct answers for pair of tasks. The results showed that students in E group scored both tiers correctly in 57.80% of cases, one tier correct in 25.96% and none correct in only 16.24% of cases. It is important to note that in the tasks with two items wrong, students mostly provided self-consistent answers. On the contrary, students in C group scored significantly lower: both tiers correct in 20.42% of cases, one tier in 34.60%, while none of the tiers in 44.98% of cases. However, although the percentage of 0 correct answers for pairs is quite high, it is important to state that the corresponding answers were not self-consistent as it was the case with E group. This indicates that the E group students are most often trying to find a connection between the questions and the logical explanation, even when they do not possess the expected knowledge of some descriptive content. In contrast, the C group students have achieved far less consistent responses, which could indicate their weak engagement during the teaching process which will be discussed later. Looking at the results for students’ assessment of invested mental effort, the difference between the two groups can be easily observed, and the obtained values are consistent with the values obtained for achievements. These values are 4.20 (neither easy nor difficult) and 5.25 (difficult) (for E and C group, respectively). The significance of the obtained differences in achievement as well as in evaluation of invested mental effort between groups was tested by Kolmogorov−Smirnov test for two independent samples. The results of this test showed that there are statistically significant differences between the groups both in achievement (p = 0.00; DN = 0.66) and invested mental effort (p = 0.00; DN = 0.45). Figure 1 illustrates the distribution of students’ scores in both groups in a box plot diagram. This diagram shows that distribution of students in the E group is significantly shifted toward higher results, while in group C, it is shifted toward lower results. In the first quartile of the group E, scores range from 6 and 16, while for C group, this interval is significantly narrower, 3 and 6. It means that the difference in achievements is significant already in the category of least successful students. For the E group, the second quartile scores fall between 17 and 21 and the third quartile scores fall between 22 and 25. For the C group in the second quartile, scores range between 7 and 10, and in the third quartile between 11 and 15. In the fourth quartile, scores in E group extend from 26 to a maximum 30

Figure 2. Distribution of students’ estimations of invested mental effort on final test for E and C groups.

group 50% of the students evaluated the invested mental effort as higher than 4 (neither easy nor difficult), while half of the students evaluated invested mental effort as less than 4. On the other hand, in the C group, 75% of the respondents evaluated invested mental effort as higher than 4, while only 25% of them rated mental effort with the value less than 4. These results are consistent with the results obtained for achievements. Namely, students in E group, who accomplished higher average achievement than students in C group, considered that less mental effort was required to be invested to solve the test tasks than their peers in C group. On the contrary, students in C group, who had lower average achievements, considered that more mental effort is needed for solving identical tasks. To provide information about the applied instructional design, data obtained for performance and invested mental effort had been further processed according to the method suggested by Paas and Van Merrienboer.31 Figure 3 shows a graph of mental efficiency of applied instructional strategy for E and C group. The graph line E = 0 represents a zero efficiency. Shifts to the upper left quadrant indicate an increase in mental efficiency (higher performance and lower mental effort), while shifts to the lower right quadrant indicate a decrease in efficiency (lower performance and higher mental effort). The relative mental efficiency of an instructional design is calculated as the perpendicular distance from a certain point in the coordinate system to the line E = 0.31 E



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the interplay between three levels of knowledge representation can significantly improve the acquisition of chemical concepts. Namely, by highlighting the interconnections among levels of representation, teachers help students to see the linkages between them, thus fostering their performance. In addition, the efficiency of this teaching strategy has been evaluated from the aspect of Cognitive load theory. It is shown that this strategy contributes to the reduction of cognitive load of students, thus increasing the efficiency of teaching, which is particularly important nowadays with the rapid development of chemistry as a science, which additionally burdens chemistry curricula. However, the implementation of such a strategy requires the additional training of teachers, as former investigations suggest that teachers have difficulty in interrelating macroscopic, submicroscopic, and symbolic conceptions.45 Regarding the limitations of this study, it should be noted that the Hawthorne effect could influence the obtained results46 since it is known that students in the experimental group often feel privileged in comparison to their peers in the control group, and therefore work harder during the research.7 Further limitations of this study are reflected in the research sample and investigated contents, since this study included only two high schools and contents of inorganic chemistry for the second grade of gymnasium in the Republic of Serbia. Therefore, future research attention should be focused on investigating the possibilities for implementing this intervention strategy in other areas of chemistry, in other types of schools, and in other levels of education. It would be significant to examine how to apply this method in the primary as well as in college-level chemistry teaching, and investigate how to provide students with opportunities to develop submicroscopic explanations for chemical phenomena,47 as students often tend to attribute macroscopic properties to inappropriate submicroscopic particles, which results in the creation of numerous misconceptions and alternative concepts already in the initial teaching of chemistry.48 As a consequence, students fail to establish correct mental models of the submicroscopic world, which is the basis for a meaningful understanding of chemical concepts as well as for development of abstract chemical thinking. Finally, a similar study should be considered using other theoretical frameworks, such as, for instance, Dual processing theory,43 to determine whether students when solving problems in macroscopic, submicroscopic, and symbolic levels of representation engage primarily in analytical, cognitively demanding processes, or less cognitively demanding, heuristic processes. Apart from these studies that measure the quantum of students’ knowledge, it would be interesting to conduct a similar study that will examine the quality of acquired knowledge and the possibility of applying such knowledge to new situations.

Figure 3. Graph of instructional efficiency for the experimental and control groups.

The obtained value for efficiency in experimental conditions (EE = 0.37), the dot of which is placed in the upper left quadrant, indicates a highly effective instructional strategy. On the other hand, the E value obtained for C group (EC = −0.24), the dot of which is placed in the lower right quadrant, indicates that this strategy is less efficient than the experimental. The quantitative results of this study suggest that the instructional strategy implemented in the E group contributed significantly to the improvement of conceptual understanding of chemical concepts, which is reflected in the significantly higher scores achieved in the E group compared to the scores achieved in the C group. In addition, on the basis of results for evaluation of invested mental effort, it can be concluded that the E group students invested less mental effort solving the same tasks than their peers in the C group. The results obtained for the performance and results obtained for students’ evaluation of invested mental effort provide evidence that the instructional model based on multiple levels of knowledge representation is an effective teaching model. Another way to approach the observed differences between the E and C group students is to consider dual-processing theories. According to these theories, to make decisions, individuals use two modes of reasoning commonly labeled System 1 and System 2.43 System 1 processing is heuristic based and includes quick, automatic and effortless processes, whereas system 2 processing is analytically based and refers to slow, systematic and high effort processes. Therefore, the differences in students’ performance and invested mental effort may imply that students in E group are finding better heuristics to simplify tasks and reduce cognitive load. In addition, as mentioned earlier, students’ engagement during the teaching process could also affect the results obtained. Namely, the author, who attended E and C classes, informed that students from both groups were actively involved in teaching. However, while students in E group established good mutual communication, students in C group, despite their activity, had very little interaction among each other. Moreover, the author reported that in C group, teachers often provided definitive answers and students quite often accepted explanation without further justification. All these factors merit attention and should be further investigated.

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ASSOCIATED CONTENT

S Supporting Information *

Examples of tasks from the final test. This material is available via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

CONCLUSION AND IMPLICATIONS These findings, which are in accordance with previous research,10,12,44 suggest that teaching strategy that relies on

Notes

The authors declare no competing financial interest. F



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ACKNOWLEDGMENTS The results presented are part of the research conducted within the project The Quality of the Education System in Serbia from the European Perspective, Grant Number 179010 of the Ministry of Education, Science and Technological Development of the Republic of Serbia. The authors owe special gratitude to A. L. Chandrasegaran and D. F. Treagust (Science and Mathematics Education Centre, Curtin University of Technology, Australia) who gave insight into the two-tier test and thereby greatly facilitated construction of the authors’ own research instruments. The authors would like to thank the anonymous referees for their constructive comments and suggestions.

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