Using concept maps as instructional materials to foster the understanding of the atomic model and matter–energy interaction

Abstract

In this paper, we explore the use of concept maps (Cmaps) as instructional materials prepared by teachers, to foster the understanding of chemistry. We choose fireworks as a macroscopic event to teach basic chemical principles related to the Bohr atomic model and matter–energy interaction. During teachers' Cmap navigation, students can experience a sense of disorientation, which is detrimental to the learning process. Two graphical cues were tested as Cmap navigation guidance: (1) colour-coded concepts, to group similar content and (2) numbered propositions to offer a reading sequence. A quasi-experimental pre-test–post-test design combined with mental effort was utilized to measure the efficiency of Cmaps in learning. First-year undergraduate students (n = 85) were randomly assigned to study one of four possible Cmaps. The results showed that all students were able to increase their level of factual knowledge, despite the Cmap being used as an instructional material. The lack of cues impaired conceptual understanding. Signalling similar content using colours was critical to reduce the invested mental effort and foster understanding about chemical concepts.

---

Introduction and background

Concepts regarding the structure of matter and atomic models are fundamental to introductory chemistry. However, the research literature is clear that many students complete high school (and sometimes college) but still lack the conceptual understanding of these concepts (Cros et al., 1986; Harrison and Treagust, 1996; Mulford and Robinson, 2002; Nakiboglu and Taber, 2013). Most of these difficulties may lie in the intrinsic nature of the knowledge as well as in human learning (Johnstone, 2000; Cardellini, 2012). As teachers, we cannot change the nature of chemical knowledge. However, we can change the way in which it is taught. In the past few decades, researchers have been trying to find better ways to improve meaningful learning of chemistry (Novak, 1984; Krajcik, 1991; Johnstone, 1993; Gabel, 1999; Wu et al., 2001; Galloway and Bretz, 2015).

Ausubel (2000) argued that what students might learn in classrooms does not depend only on what teachers know about the content without using the textbooks. In fact meaningful learning can be achieved by combining a well-designed instructional material with learners' engagement in the sense making process. While the former depends on the teachers' expertise, the latter depends on the students' choice.

The conceptual understanding of chemistry is related to the ability to explain chemical phenomena through the use of macroscopic, molecular, and symbolic levels of representation (Johnstone, 1982; Gabel et al., 1987; Gilbert, 1997; Harrison and Treagust, 2002; Treagust et al., 2003; Taber, 2009).

Studies have shown that even after instruction, the learners described Dalton's atomic model as the most important one, the atom as a tiny dumpling or similar to a solar system, the atom as the smallest part of the nucleus or as a concrete particle, and the electrons as particles that “live” between the electrosphere or exist in static and discrete shells (e.g., Griffiths and Preston, 1992; Harrison and Treagust, 2000; Justi and Gilbert, 2000; Taber, 2001). These conceptions hinder the adoption of models that conceived the matter–energy interaction (Bohr, Sommerfield and quantum models) and their correct use to explain macroscopic phenomena, such as the colours in fireworks and phosphorescent paints (Niaz and Cardellini, 2011).

Students live and operate within the macroscopic world of matter. While chemists and chemical educators operate across the macro, micro, and symbolic levels of knowledge quickly and easily, students do not easily shift between them (Johnstone, 1991). During learning, the multi-representational structure needed to understand the abstract, unobservable, and particulate basis of chemistry can be cognitively overwhelming for the students (Ainsworth, 2006). As a consequence, they can face an overload situation, which means that the working memory space surpasses its capacity, impairing learning (Johnstone, 2000). To address this problem, we should change learning events to retain the true nature of chemistry while making it more accessible to young and novice learners.

In this paper, we explore the use of concept maps (Cmaps) as instructional materials prepared by teachers to foster the understanding of chemistry. We choose fireworks as a macroscopic event to teach basic chemical principles related to the Bohr atomic model and matter–energy interaction. Cmaps are graphical organizers used to represent knowledge through a conceptual relationship network (Novak, 2010).

Goal, research questions and hypothesis

In this paper, two graphical cues were tested as Cmap navigation guidance:

(1) a colour-coded concepts for grouping similar content and/or,

(2) numbered propositions to offer a reading sequence.

We employed a quantitative research method to answer the following questions:

Q1. Does the presence of graphical cues on Cmaps foster the conceptual understanding of the Bohr atomic model and matter–energy interaction?

Q2. Which type of Cmap is the most efficient as an instructional material, i.e., lead to conceptual understanding with low invested mental effort?

We expect that adding graphical cues to Cmap will decrease disorientation, improving chemical understanding. If it is valid, the students' who study the Cmaps with colour and/or number will have better learning outcomes with low mental effort.

Cognitive load theory

Sweller's cognitive load theory (de Jong, 2010; Moreno, 2010; Sweller et al., 2011) provides a theoretical framework that embraces knowledge, learning, and instruction components needed to understand the use of concept mapping in classrooms.

Information to be understood and/or learned first must be processed through short-term memory or working memory (WM) that is very limited in both capacity and duration. Information that is successfully processed through WM is held in long-term memory, which is infinitely large with no known limit. Learning achievement can be understood as the construction of schemas, that is, general knowledge structures that encapsulate (by chunking) the new knowledge presented in the material and information already organized in the long-term memory. Initially, learning tasks place heavy demand on WM and require considerable mental effort. After extensive practice, processing will become automatic, which is essential to reduce the load on WM, releasing resources to task-relevant operations. In general ways, the WM load is the critical factor that determines the effectiveness of learning.

This theory argues that designing an instructional material should consider two separate sources of cognitive load: (1) intrinsic load related to content complexity, that is, the more element interactivity the more complex is the content and (2) extraneous load related to instructional methods and materials used during learning tasks, that is, the more ill-structured the material is, the more extraneous the load. Intrinsic and extraneous loads are additives. If they exceeded the limits of WM, then learning would be hindered once there are no resources left to foster generative processes, namely, schema construction and manipulation (Sweller et al., 2011).

For instance, Carlson et al. (2003) demonstrated that learning about how to construct molecular models can impose different levels of cognitive loads (Fig. 1).

Fig. 1 Example of cognitive loads imposed for constructing a molecular model task. Intrinsic load depends on the level of element interactivity, whilst constructing the HCl molecule will impose lower intrinsic load than for the H₂CO₃ molecule. Extraneous load depends on how the instructions are presented, whilst a diagrammatic format for the molecule will impose lower extraneous load than a textual format.

Constructing a molecular model of HCl will impose lower intrinsic load because the students must combine two chemical elements in one possible jointing. On the other hand, constructing the H₂CO₃ molecule will impose a higher intrinsic load once the students must combine six atoms of three different chemical elements in a particular combination of single and double bonds. There is an increasing level of interactivity between the elements that the students must manipulate during a learning task. Similarly, presenting a textual representation of this model has higher extraneous load compared to a diagrammatic representation. The visual format of a molecule enhances information processing in WM, releasing resources to understand the content. The authors proved that learning would be enhanced when we combine a highly intrinsic load molecular model in a diagrammatic format, especially for novice students.

Concept maps as instructional materials

Learning from concept mapping has been widely discussed in the literature for the past three decades. Most studies have assessed conceptual knowledge through student-constructed Cmaps, especially in science education (e.g., Pendley et al., 1994; Kinchin, 2000; Nicoll et al., 2001; Johnstone and Otis, 2006; Lopez et al., 2011; Burrows and Mooring, 2015; Yaman and Ayas, 2015). However, less attention has been given to teacher-provided Cmaps used as instructional materials. Some studies have confirmed that learning outcomes are enhanced when students read Cmaps prepared by the teacher compared to when they are invited to draw their own Cmaps (Hwang et al., 2004; Stull and Mayer, 2007; Salmerón et al., 2009; Hagemans et al., 2013).

Developed by Novak and colleagues in the 1970s (Novak, 2010), Cmaps are graphical organizers that make explicit conceptual relationships through propositions (Fig. 2) formed by initial concept – linking phrase → final concept (e.g. chemical elements – are characterized by their → atomic number). Concepts are embedded into a propositional network that allows processing information using semantic content (text) in a visuospatial organization.

Fig. 2 Concept map with colours to group similar content and numbers at propositions as reading guidance used as an instructional material in the WCWN group.

During teachers' Cmap navigation, students had to process difficult contents expressed in an unfamiliar organizational way. As a consequence, they could experience a sense of disorientation, which increased detrimental extraneous load during study (Amadieu et al., 2009). Adding graphical cues can offer a guidance, thereby facilitating navigation and reducing the cognitive load associated with it (e.g., Jeung et al., 1997; Ozcelik et al., 2009). In this paper, we explored two types of graphical cues used in Cmap navigation guidance: colour to visually group similar concepts and numbers at propositions to offer a reading sequence.

The chemistry of fireworks

Some types of content seem to be intrinsically more difficult compared to others. Certain concepts have emergent ontological characteristics, making them even more difficult for students to understand (Chi, 2005). Even in simple topics of scientific knowledge, the concepts used to describe the domain are not revealed in an obvious way; actually, they are constructs that have been conceived in an attempt to interpret and explain the observed nature. Consequently, the symbolic world of science is settled by entities, such as atoms, electrons, ions, and energy. Individuals are unlikely to discover these ontological entities, organizing concepts, and practices of science through their own observations of nature (Driver et al., 1994).

Within the science education perspective, such knowledge must be constructed through learners' mental activity rather than transmission from the teacher. Moreover, students must engage in this process to achieve the best learning outcomes (Ausubel, 2000; Taber, 2014).

Teachers and higher education books have been using fireworks as a context to show the macroscopic application of the Bohr atomic model postulates and to help students with the sense making process (e.g., Kotz et al., 2006; Steinhauser and Klapötke, 2010). Nevertheless, the chemistry of fireworks is quite complex, depending on cause–consequence relationships. Each firework contains an oxidizing agent (usually nitrates, chlorates and perchlorates), a fuel (gunpowder), a metallic salt (Table 1), and a binder (usually an organic compound such as dextrin) that holds all components together. In the presence of a flame or a spark, the oxidizing agent and the fuel are involved in complex chemical reactions that create intense heat and gas. As a consequence luminescence, and/or incandescence phenomena can take place. Luminescence occurs when the metallic salt absorbs energy by burning; the electrons of each chemical element that constitute this substance absorb a quantized energy, leading to a high-level state of energy (excited level). When the electron returns to its ground-energy state, this energy is released as an electromagnetic wave. Each wavelength corresponds to a different colour in the visible spectrum (Table 1).

Table 1 Metallic salts usually used in fireworks and their corresponding colours

Metallic salts	Colour	Wavelength (nm)
a >700 nm: infrared; <400 nm: ultraviolet.
Strontium and lithium salts (SrCl2; Sr(NO3)2; Li2CO3; LiCl2)	Red	620–700^a
Calcium salts (CaCl2; CaSO4)	Orange	590–620
Sodium salts (NaCl, Na2CO3)	Yellow	570–590
Barium salts (BaCl2; BaClO2)	Green	495–570
Copper(I) salts (CuCl, Cu2O)	Blue	450–495
Mixture of Sr and Cu salts	Violet	400–450^a

---

Incandescence occurs when the metallic salt is heated so much that it starts to glow (similar to a tungsten filament lamp), initially emitting infrared radiation followed by red, orange, yellow and white. When the temperature of a firework is controlled, the glow of its metallic substances can be manipulated to be a desired colour at the appropriate time (Russell, 2008).

Method

Participants and design

Undergraduate students (n = 85) participated in this study. They were assigned into one of four possible experimental conditions: Cmap without graphical cues (no colour, no number: NCNN, n = 21), Cmap with numbers at propositions (no colour, with number: NCWN, n = 20), Cmap with colours to group similar content (with colour, no number: WCNN, n = 22) and Cmap with both graphical cues, colours, and numbers (with colour, with number: WCWN, n = 22). The WCWN Cmap is presented in Fig. 2. All participants were treated in accord with APA ethical standards. They had signed the Informed Consent for participating in the research.

Materials

Cmap elaboration. The Cmap, developed as an instructional material, had 30 concepts and 35 propositions to answer the focus question “Why do fireworks present different colours?” The propositional network has a cyclic pathway (Safayeni et al., 2005), showing the systemic thinking that explains the chemical process of fireworks. Different areas of chemical knowledge must be integrated to understand this process: (a) firework components and the burning process, (b) chemical elements and the Rutherford–Bohr atomic structure of matter, atoms and sub-particles, (c) electronic organization into levels and sublevels of energy and (d) atomic absorption and emission of quantized energy (luminescence) and the incandescence phenomenon, both leading to different colours represented in the electromagnetic spectrum. In the colour-coded groups (WCWN, WCNN), the Cmaps' concepts were coloured in relation to the four content areas described above (a–d). In the number-coded Cmap groups (NCWN, WCWN), the propositions were numbered using a simple-to-complex strategy.

Pre-test and post-test. Prior knowledge was assessed using 30 affirmations about the chemical content presented in the Cmap (Cronbach's alpha = 0.817). The post-test was prepared using some pre-test affirmations plus new ones (Cronbach's alpha = 0.764). The students had to judge each affirmation, agreeing or disagreeing with them (examples in Table 2). After each test, the students had to answer the written question: Why orange and green fireworks present different colours?

Table 2 Sample of affirmations used in pre- and post-tests. The students received 1 pt for correct judgements and no point for wrong judgements

Sample of affirmations	Agree	Disagree	Score
Fireworks present colours due to a pigment in their composition		×	1
The atoms' electrosphere is divided into levels and sublevels of energy	×		1
Luminescence occurs when the electron releases energy as visible light	×		1
Substances are always made by a single chemical element	×		0
The number of protons characterizes the chemical element	×		1
Chemical elements present in the fireworks are responsible for their colours		×	0

---

Mental effort rating. Participants rated their mental effort to understand the Cmap content using a 7-point Likert-type subjective-rating-scale (Paas, 1992). The scale was designed to translate the expended effort into a numerical value based on students' answer for the question: “What was your mental effort to understand the Cmap content?” (1 = extremely low mental effort, 2 = very low, 3 = low, 4 = neither low nor high, 5 = high, 6 = very high and 7 = extremely high mental effort).

Procedure

First, participants had 10 minutes to answer the pre-test. Second, each group received one type of instruction sheet with the Cmap printed on back. They had 5 minutes to read the specific instructions, and all students were encouraged to ask questions if they did not understand any aspect of the instruction. Third, students studied their Cmap for no longer than 20 minutes. Upon the completion of the study time, they completed the post-test in 7–10 minutes. At this time, they also rated their mental effort to understand the Cmap content. Under no circumstances the students were allowed to use aside materials neither to keep the instructional Cmap.

Data analysis

Pre-test and post-test scores were transformed into a 0–10 point scale after considering 1 point for each correct answer and 0 point for a wrong answer (examples in Table 2). The students' responses to the written question were classified in categories and subcategories (Table 3), which revealed a progressive level of understanding of the atomic model and matter–energy interaction to explain the firework phenomenon. We proposed these categories based on patterns found in the students' responses that was aligned to affirmations and Cmap contents. Each subcategory received a relative score from 0 (in blank) to 10 (electromagnetic issue). The same point scale (0–10) was adopted to both tests (affirmation and written question) in order to ensure same comparing conditions. Misconceptions and confused statements were scored as −2 because they did not reveal an explanation scientifically accepted. The sum of affirmation and written question scores indicated the learners' overall performance in a maximum of 20 points.

Table 3 Categories used to classify the students' answers into written questions in the pre-test and the post-test. We attributed a relative score depending on the level of explanation given by the students, from lower level – misconceptions (−2) to upper level – electromagnetic spectrum (10)

Category	Subcategory	Acronym	The student declares that the difference between colours on fireworks is due to…	Example of real answers	Relative score
Conceptual error and/or misconception	—	MIS	Some kind of phenomena or explanation that is not acceptable according to scientific knowledge and/or a confusing statement.	“Fireworks can be understood as combustion of chemical atoms.”	−2
Absent	—	ABS	Answer in blank.	—	0
			Do not know the answer.	“I don't have any idea.”
Chemical composition	Composition	CHC	The chemical composition and/or the substances into fireworks.	“The chemical substance that composes the fireworks.”	2
	Chemical element	CHE	The chemical element that composes the fireworks.	“The difference is the type of chemical elements.”	4
Electronic transition	Level/sublevel of energy	LSE	Level/sublevels of energy and/or the difference between them.	“The level of energy of each electron before and after burning.”	6
	Absorption of energy/emission of light	AEL	The absorption of energy and/or emission of light, energy or heat. May or may not have mention about electrons or quantized energy.	“It depends on the quantized energy released.”	8
	Electromagnetic spectrum	EMS	Some phenomena that involve electronic excitation, light emitted and its correspondence to the electromagnetic spectrum. May or may not have mention about the visible region, luminescence and incandescence phenomena.	“The energy emitted as visible light is different from each other. Then, the frequency and wavenumber on the electromagnetic spectrum is related to one colour only.”	10

---

Instructional efficiency. Paas and van Merriënboer (1993) defined an approach to estimate the relative efficiency of an instructional method (E) comprising performance (P) and mental effort (M), both transformed into Z-scores (standardized), as one single measure:

In this study, P represented the overall performance in the post-test and M represented the mental effort to understand the Cmap content. One single value for each group was obtained by averaging participants' P and M.

One-way ANOVA was conducted followed by independent and pairwise t-tests to compare the experimental conditions or variables. All analyses were conducted using SPSS 22.0 (IBM, USA) with 95% confidence levels.

Results and discussion

Three main discussions were considered in this study: (1) the learners' prior knowledge regarding both affirmations and written question performances in the pre-test (Table 4); (2) the learners' outcomes regarding performances in the post-test (Table 4); and (3) the efficiency of the Cmap as an instructional material given by a combined measure between performance and mental effort ratings.

Table 4 Means (standard deviation) for pre-test and post-test performances considering affirmations, written question and overall performances

Groups	Pre-test scores			Post-test scores
	Affirmations^a	Written question^a	Overall^b	Affirmations	Written question	Overall
a 0–10 point scale. b Sum of affirmations and written question performances, maximum of 20 points.
NCNN (n = 21)	7.13 (1.24)	1.80 (2.50)	8.46 (3.21)	8.64 (0.95)	2.76 (3.06)	11.41 (3.09)
NCWN (n = 20)	6.45 (1.30)	1.33 (2.99)	8.25 (3.09)	8.59 (1.02)	3.90 (3.14)	12.49 (3.21)
WCNN (n = 22)	6.75 (1.28)	3.36 (3.51)	10.11 (4.03)	8.87 (0.74)	5.54 (3.85)	14.42 (3.98)
WCWN (n = 22)	7.02 (1.48)	2.45 (3.08)	9.47 (3.41)	8.64 (1.08)	4.63 (3.29)	13.27 (3.30)

---

Students' prior knowledge

One-way ANOVA showed no significant differences between groups in affirmations F(3,81) = 1.06, written questions F(3,81) = 1.78, and overall performances, F(3,81) = 0.61, all p_s > 0.05. Students presented equivalent knowledge before the instruction.

The pairwise t-test confirmed that the students in the same group performed better on affirmations compared to written questions, t(84) = 12.99, p < 0.001, r = 0.096. Apparently, it is easier to judge statements than to write an answer. Bloom's revised taxonomy allowed us to classify both assessment objectives according to their cognitive process and type of knowledge dimension (Krathwohl, 2002). In the affirmations, the students must remember factual knowledge, which means they must recognize relevant knowledge in the material and retries specific elements from the memory that must be known to be acquainted with the content. On the other hand, answering the written question requires the explanation of conceptual knowledge, which means understanding the interrelationship among basic elements presented in a Cmap format within theories, structures and models. Mayer (2002) argued that remembering, as a cognitive process, is closely related to retention whilst understanding/explaining is more related to transfer. The fact that the former dimension is hierarchically less complex compared to the latter could explain higher performance on affirmations instead of the written question.

The students' answers to the written question revealed a low percentage of misconceptions (n = 13, 15%). A closer exploration of their answers showed that:

• 38% of students used the reaction aspects to explain the different colours in fireworks incorrectly. For instance, student S67 stated the difference is due to “The chemical element used as fuel”; S21 stated that “The different compositions of fireworks leads to different combustion reactions”; S57 affirmed that the difference is related to “Different chemical elements that explode leading to colours”.

• 23% of students attributed the presence of dyes to the colours in fireworks. For example, S28 explicitly declared, “The dyes in the composition of each one” or S75 implicitly stated that “I assume that the powder used in fireworks contains different colours.”

• 23% erroneously related the fireworks' colour to the level/energy sublevel. This is the case of S47 who affirmed, “The difference lies in the speed in which the energy is spread into the sublevels of energy”; or the case of S61 who stated, “There is a difference between their electrospheres”.

• 8% incorrectly related it to the emission of light. For example, S60 said that the difference between colours in fireworks is due to the “Amount of infrared light emitted”.

• 8% incorrectly adopted the model to explain the phenomenon. This is the case of S18, who justified, “The chemical element that composes each firework defines its colour (for example, copper is blue).” She used the solvation model of metallic cations (copper solution is blue) instead of the atomic model of Bohr (electronic excitation of copper would generate a green colour in fireworks).

Learning outcomes

One-way ANOVA revealed an effect of graphical cues on the overall post-test performance, F(3,81) = 2.99, p = 0.04 and the written question performance, F(3,81) = 2.63, p = 0.05. On the other hand, no difference emerged between performances in affirmations F(3,81) = 1.78, p > 0.05. Independent t-tests confirmed that students who studied the colour-coded Cmap (WCNN) performed better compared to participants who studied the Cmap without graphical cue (NCNN), considering both written question t(41) = −2.61, p = 0.012 and overall performances, t(41) = −2.76, p = 0.009. Evidence suggests that adding colour as navigation guidance fosters chemical conceptual understanding.

Regardless of the experimental conditions, the pairwise t-tests confirmed that students perform better in the post-test than in the pre-test. Students who studied the colour-coded Cmap (WCNN) showed the highest difference between the pre-test and post-test affirmation tests, t(21) = 9.28, p < 0.001. Moreover, the subjects who studied the Cmap without cues (NCNN) did not improve their learning outcomes in the written question, t(20) = −1.67, p = 0.11. Evidence suggests that adding colour to group concepts of the same content would improve learning outcomes. On the other hand, the lack of guidance impaired transfer performance.

Looking closely at the learners' answers in the post-test, we can infer that the frequency of students with missing answers decreased from 21% to 7%, as those categorized as MIS dropped from 15% to 6% after studying the Cmap (with or without graphical cue). It was expected that the complexity of students' explanations in the written question would increase after studying the Cmap. Their answers should migrate from common sense conceptions (CHC) through surface chemical knowledge (CHE) to more elaborate scientific conceptions (LSE) involving light and matter interaction (AEL) and its relation to the electromagnetic spectrum (EMS). In other words, it was expected that students would increase their level of explanation about the difference of colours in fireworks. This pattern could be seen in all groups, even though it was more pronounced after studying the Cmaps with graphical cues (either one or both).

After studying the WCWN Cmap, 77% of the students increase their level of explanation and no students downgraded. This is the case of S37 who initially affirmed, “The difference lies in the emission of light” (AEL) and continued that “The colours of fireworks are differentiated by the chemical element that generates phenomena of luminescence and glowing” (EMS).

After studying the NCWN Cmap, 60% of the students increased their level of explanation at least to one higher category (according to Table 3). For instance, the learner S43 expressed his knowledge more thoroughly. In the pre-test, he affirmed, “The difference is the amount of energy provided to the electron excitation, which changes it level” (LSE). In the post-test, he completed his answer, saying, “The difference is the energy absorbed by the electrons causing changes in the level of energy. When the electron returns to its fundamental state, energy is released in the light form, which can be seen as colour” (AEL). In the WCNN condition, 36% of students increased their level of explanation and the majority (59%) maintained the same level. For instance, the aforementioned student S75 corrected his misconception to the highest category (electromagnetic concepts). In the post-test, S75 stated that “Orange fireworks generate different electromagnetic visible spectra of those in green” (EMS).

Finally, most students who studied the NCNN Cmap retained the same level of explanation (57%) or even downgraded (29%). This can be highlighted in the speech of S04 who initially stated, “The difference can be explained by the release of energy and light” (AEL) and finished saying that “The difference is due to chemical elements present in each firework” (CHE).

Although colour and number are both graphical cues, they lead to different performance results. We raise two possible explanations for this result. First explanation is based on Paivio's dual-coding theory (Paivio, 1990), which argues that numbers must be processed in the auditory/verbal channel (auditory input and verbal representations), as concepts and propositions embedded in the Cmap network. In the NCWN group, the learners must keep the content elements in their limited WM while searching for numbers sequentially. Consequently, few resources remain to enhance learning or even retrieval practice. On the other hand, this theory says that colours were processed in a separate visual/pictorial channel (visual input and pictorial representations), which does not compete for conceptual information processing. Mayer and Moreno (2003) discussed that adding bimodal information during navigation (visual plus verbal) should enhance learning by reducing extraneous load. Studies on map-type graphical organizers presented similar findings. For example, Hall and Sidio-Hall (1994a, 1994b) identified that subjects who studied colour-coded knowledge map (the colour was used to indicate related concepts) recalled more information compared to those who studied the black-and-white map. Wallace et al. (1998) also demonstrated that incorporating some Gestalt principles (such as proximity and colour) during knowledge map elaboration helped students retrieve more information after the learning phase. Colour coding effect results from guiding of attention by information that is relevant for the task. In this case, attention is guided by low level visual features.

The second explanation is that colour-coded Cmaps divided the content into areas of knowledge, offering a way to reduce the intrinsic load by presenting the content in a partitioned way (i.e. part-to-whole and simple-to-complex strategies). For example, Pollock et al. (2002) artificially reduced the complexity of instructional materials, presenting the content as isolated elements of information. They proved that information is better learned when is processed serially rather than simultaneously.

Efficiency of Cmaps as instructional materials

The overall performance in the post-test and mental effort were transformed into Z-scores (Table 5) and combined into a single measure (efficiency, E).

Table 5 Cmap instructional efficiency calculated by a combined measure between the overall performance in the post-test and mental effort to understand the chemical content. All the values are expressed in Z-scores

Groups	Instructional efficiency (Z-scores)
	Performance	Mental effort^a	Efficiency
a Participants who did not declare their mental efforts were not considered for the efficiency calculus.
NCNN	−0.43	−0.03	−0.28
NCWN	−0.12	+0.13	−0.17
WCNN	+0.42	−0.20	+0.44
WCWN	+0.10	+0.12	−0.01

---

According to Paas and van Merriënboer (1993), there is a hypothetical baseline condition in which each unit of mental effort invested equals one unit of performance. The values of E can be visually spread around a square diagonal . The E values will assume positive values if P > M, i.e. the instruction has high efficiency when the students have high performance with a low invested mental effort. On the other hand, E will assume negative values if P < M, i.e. the instruction has low efficiency when students had poor performance with a high perceived mental effort. Fig. 3a and b compare the efficiency of each Cmap used as an instructional material.

Fig. 3 Mean overall instructional efficiency of each Cmap used as a study material. Diagram (a) indicates the mental effort and overall performance which coordinate and reveal low or high efficiency. Z-Score values in Table 5. Diagram (b) highlights the interaction between color and number that affects efficiency.

The one-way ANOVA highlighted a significant difference in instructional efficiency, F(3,77) = 2.60, p = 0.05. Among the conditions, the black-and-white and no number Cmap had the lowest efficiency (NCNN, E = −0.28). Although the students perceived their invested mental efforts relatively low (M = −0.03), they were not capable of improving their learning outcomes, resulting in poorer performance (P = −0.43). During Cmap navigation without guidance, the learners were likely to face disorientation, leading to problems in constructing the pathways across the propositions simultaneously with their conceptual mental representation (Cress and Knabel, 2003). In this case, the available cognitive resources of WM to deal with the content's complexity are overtaken by non-relevant processes, thereby impairing the learning process. Even though adding numbers at propositions (NCWN) relatively increased the performance (P = −0.12) and the mental effort (M = +0.13) compared to the Cmap without cues, the independent t-test showed no significant difference between these two conditions, t(39) = −0.33, p > 0.05.

The Cmap with colour and no numbers was the only one characterized by a favourable effort–performance ratio (P > M), which means a highly efficient instruction (E = +0.44). The t-test showed that the WCNN Cmap was more efficient compared to both Cmaps without cues, t(40) = 2.45, p = 0.02 and with number, t(39) = 2.12, p = 0.04. As discussed, the colour, as navigation guidance, offers a way to manage the intrinsic load while reducing the extraneous load. Consequently, the mental effort was the lowest among conditions (M = −0.20), releasing WM space to deal with the content complexity leading to the best learning outcomes (P = +0.42).

Finally, using both graphical cues on the Cmap simultaneously (WCWN) had a combined effect. The presence of colour-coded concepts to group the same content helped students manage the intrinsic load, leading to a relatively high performance (P = +0.10), similar to WCNN situation. However, the presence of the number increased the mental effort (M = +0.12), probably due to disorientation or extraneous load, similar to NCWN situation. Consequently, the efficiency was nearly zero. Independent t-tests showed that the WCWN Cmap is as efficient as both NCNN, t(38) = 0.91 and NCWN, t(37) = 0.56. However, using both graphical cues was statistically less efficient compared to the WCNN condition, t(38) = 1.93, p = 0.05.

Conclusions

The applied methodology allowed us to answer our two research questions.

Q1. Is the presence of graphical cues on Cmaps capable of fostering the conceptual understanding of the Bohr atomic model and matter–energy interaction?

All students were capable of increasing their level of factual knowledge, despite the Cmap being used as a study material. The lack of graphical cues hampered the conceptual understanding of the chemistry behind fireworks. Contrary to our hypothesis, adding number at propositions as reading guidance increased the mental effort, leading to poorer performance. Lastly, signalling similar concepts using colour is critical to reduce the demand on WM, enhancing learning outcomes and understanding of the basis of chemical knowledge.

Q2. Which type of Cmap is the most efficient as an instructional material, i.e., leads to conceptual understanding with low invested mental effort?

The Cmap with colour and no number was the most efficient as an instructional material. Adding colour to the concepts (to group the similar content) offered a way to reduce the extraneous load with a cue that does not compete with relevant information processing and to manage the intrinsic load by a part-to-whole strategy. As a consequence, the Cmap was able to foster generative processes, leading to factual and conceptual understanding of low invested mental effort. All these results highlight the importance of instructional design when using concept mapping in classroom settings.

Educational implications, research limitations and future studies

The presence of colours in fireworks is a phenomenon that can only be explained by the adoption of atomic models, which consider the electronic excitation and correspond to the electromagnetic spectrum (border between Bohr, Sommerfield and quantum models). Learning obstacles and misconceptions are likely to arise in this context due to the required level of abstraction and the transition between the representational levels of chemical knowledge. A general overview of the results allows us to conclude that concept mapping is a potential tool to enhance learning of chemical concepts, even when students do not produce it. Only a few studies have explored the teacher-prepared Cmap as a teaching material, especially in real classroom settings. It is important, in a research like ours, to develop teaching and learning implications.

First, to achieve the best learning outcomes, we recommend that teachers follow high-quality Cmap standards, which means providing an amenable structure that would contain well-defined and hierarchically organized key concepts as well as clear and correct propositions that answer a specific focus question (Aguiar et al., 2014; Derbentseva and Kwantes, 2014; Cañas et al., 2015). Second, we strongly encourage the use of colours in the group with similar content. In this paper, this type of cue proved to be highly effective because it minimized the disorientation imposed by a non-linear propositional network while it moderated the shift between macroscopic to submicroscopic levels of chemical knowledge. We were able to foster factual and conceptual understanding. Lastly, Cmaps should be used in combination with other educational tools, varying in application and settings. For instance, it can be used to (i) open a discussion, (ii) end and review a topic, (iii) raise or assess prior knowledge, and (iv) study and retrieve practice, among others.

The presented results are consistent with instructional theories and similar empirical studies; however, we did not want to exhaust our ideas in this paper. Based on this research, future studies should consider a set of: (a) learning environments (e.g. high school, elementary, children, classroom settings, computer-mediated, collaborative works); (b) participants' characteristics (high and low levels of prior knowledge); (c) chemical/science contents equally complex and abstract (e.g. chemical bonds, chemical equilibrium, redox reactions, acid–base concepts, general relativity, enzymatic catalysis, virus reproduction, etc.); (d) possible Cmap optimizations (e.g. the amount of concepts and propositions, the type of structure and hierarchy, the presence of clickable hyperlinks with explanations or media files, etc.).

Acknowledgements

The authors thank FAPESP (grant #2012/22693-5, São Paulo Research Foundation) for funding our research group. J. G. A. thanks CAPES (Coordination for the Improvement of Higher Education Personnel) for her scholarship.

References

1. Aguiar J. G., Cicuto C. A. T. and Correia P. R. M., (2014), How can we prepare effective concept maps? Training procedures and assessment tools to evaluate mappers proficiency, J. Sci. Educ., 15(1), 12–19.
2. Ainsworth S., (2006), A conceptual framework for considering learning with multiple representations, Learn. Instr., 16(3), 183–198.
3. Amadieu F., van Gog T., Paas F., Tricot A. and Mariné C., (2009), Effects of prior knowledge and concept-map structure on disorientation, cognitive load, and learning, Learn. Instr., 19(5), 376–386.
4. Ausubel D. P., (2000), The acquisition and retention of knowledge: a cognitive view, Kluwer Academic Publishers.
5. Burrows N. L. and Mooring S. R., (2015), Using concept mapping to uncover students' knowledge structures of chemical bonding concepts, Chem. Educ. Res. Pract., 16(1), 53.
6. Cañas A. J., Novak J. D. and Reiska P., (2015), How good is my concept map? Am I a good Cmapper? Knowl. Manage. E-Learn., 7(1), 6–19.
7. Cardellini L., (2012), Chemistry: why the subject is difficult? Educ. Quím., 23(2), 305–310.
8. Carlson R., Chandler P. and Sweller J., (2003), Learning and understanding science instructional material, J. Educ. Psychol., 95(3), 629–640.
9. Chi M. T. H., (2005), Common sense conceptions of emergent processes: Why some misconceptions are robust, J. Learn. Sci., 14(2), 161–199.
10. Cress U. and Knabel O. B., (2003), Previews in hypertexts: effects on navigation and knowledge acquisition, J. Comput. Assisted Learn., 19(4), 517–527.
11. Cros D., Maurin M., Amouroux R., Chastrette M., Leber J. and Fayol M., (1986), Conceptions of first-year university students of the constituents of matter and the notions of acids and bases, Eur. J. Sci. Educ., 8(3), 305–313.
12. De Jong T., (2010), Cognitive load theory, educational research, and instructional design: some food for thought, Instr. Sci., 38(2), 105–134.
13. Derbentseva N. and Kwantes P., (2014), Cmap readability: propositional parsimony, map layout and semantic clarity and flow. Concept Mapping for Learning and Innovation, in Correia P. R. M., Infante-Malachias M. E., Cañas A. J. and Novak J. D. (ed.) Proceedings of the Sixth International Conference on Concept Mapping, Santos, Brazil.
14. Driver R., Asoko H., Leach J., Mortimer E. and Scott P., (1994), Constructing scientific knowledge in the classroom, Educ. Res., 23(7), 5–12.
15. Gabel D., (1999), Improving Teaching and Learning through Chemistry Education Research: A Look to the Future, J. Chem. Educ., 76(4), 548–554, 1999.
16. Gabel D. L., Samuel K. V. and Hunn D., (1987), Understanding the particulate nature of matter, J. Chem. Educ., 64, 695–697.
17. Galloway K. R. and Bretz S. L., (2015), Measuring meaningful learning in the undergraduate general chemistry and organic chemistry laboratories: a longitudinal study, J. Chem. Educ., 92(12), 2019–2030.
18. Gilbert, J. K., (1997), Exploring models and modeling in science and technology education, The University of Reading, the New Bulmershe Papers.
19. Griffiths A. K. and Preston K. R., (1992), Grade 12 students' misconceptions relating to fundamental characteristics of atoms and molecules, J. Res. Sci. Teach., 29(6), 611–628.
20. Hagemans M. G., van der Meij H. and de Jong T., (2013), The effects of a concept map-based support tool on simulation-based inquiry learning, J. Educ. Psychol., 105(1), 1–24.
21. Hall R. H. and Sidio-Hall M. A., (1994a), The effect of color enhancement on knowledge map processing, J. Exp. Educ., 62(3), 209–217.
22. Hall R. H. and Sidio-Hall M. A., (1994b), The effect of student color coding of knowledge maps and test anxiety on student learning, J. Exp. Educ., 62(4), 291–302.
23. Harrison A. G. and Treagust D. F., (1996), Secondary students mental models of atoms and molecules: implications for teaching science, Sci. Educ., 80(5), 509–534.
24. Harrison A. G. and Treagust D. F., (2000), Learning about atoms, molecules, and chemical bonds: a case study of multiple-model use in grade 11 chemistry, Sci. Educ., 84(3), 352–381.
25. Harrison A. G. and Treagust D. F., (2002), The particulate nature of matter: challenges in understanding the submicroscopic world, in Gilbert J. K., De Jong O., Justi R., Treagust D. F. and Van Driel J. (ed.) Chemical Education: Towards Research-based Practice, Kluwer, pp. 189–212.
26. Hwang G., Kuo F., Chen N. and Ho H., (2004), Effects of an integrated concept mapping and web-based problem solving approach on students' learning achievements, perceptions and cognitive loads, Comput. Educ., 71, 77–86.
27. Jeung H. J., Chandler P. and Sweller J., (1997), The role of visual indicators in dual sensory mode instruction, J. Educ. Psychol., 17(3), 329–343.
28. Johnstone A. H., (1982), Macro-and micro chemistry, Sch. Sci. Rev., 64(227), 377–379.
29. Johnstone A. H., (1991), Why is science difficult to learn? Things are seldom what they seem, J. Comput. Assisted Learn., 7(2), 75–83.
30. Johnstone A. H., (1993), The development of chemistry teaching, J. Chem. Educ., 70(9), 701–705.
31. Johnstone A. H., (2000), Teaching of chemistry – logical or psychological?, Chem. Educ.: Res. Pract. Eur., 1(1), 9–15.
32. Johnstone A. H. and Otis K. H., (2006), Concept mapping in problem based learning: a cautionary tale, Chem. Educ. Res. Pract., 7(2), 84–95.
33. Justi R. and Gilbert J., (2000), History and philosophy of science through models: some challenges in the case of ‘the atom’, Int. J. Sci. Educ., 22(9), 993–1009.
34. Kinchin I. M., (2000), Concept mapping in biology, J. Biol. Educ., 34(2), 61–68.
35. Kotz J. C., Treichel P. M. and Weaver G. C., (2006), Chemistry and Chemical Reactivity, Thomson Learning.
36. Krajcik J. S., (1991), Developing students' understanding of chemical concepts, in Glynn S. M., Yeany R. H. and Britton B. K. (ed.) The psychology of learning science, Lawrence Erlbaum, pp. 117–147.
37. Krathwohl D. R., (2002), A revision of Bloom's taxonomy: an overview, Theory Pract., 41(4), 213–218.
38. Lopez E., Kim J., Nandagopal K., Cardin N., Shavelson R. J. and Penn J. H., (2011), Validating the use of concept-mapping as a diagnostic assessment tool in organic chemistry: implications for teaching, Chem. Educ. Res. Pract., 12(2), 133–141.
39. Mayer R. E., (2002), Rote versus meaningful learning, Theory Pract., 41(4), 226–232.
40. Mayer R. E. and Moreno R., (2003), Nine ways to reduce cognitive load in multimedia learning, J. Educ. Psychol., 38(1), 43–52.
41. Moreno R., (2010), Cognitive load theory: more food for thought, Instr. Sci., 38, 135–141.
42. Mulford D. R. and Robinson W. R., (2002), An inventory for alternate conceptions among first-semester general chemistry students, J. Chem. Educ., 79(6), 739–44.
43. Nakiboglu C. and Taber K. S., (2013), The atom as a tiny solar system: Turkish high school students' understanding of the atom in relation to a common teaching analogy, in Tsaparlis G. and Sevian H. (ed.) Concepts of Matter in Science Education, Springer.
44. Niaz M. and Cardellini L., (2011), What can the Bohr–Sommerfeld model show students of chemistry in the 21st century, J. Chem. Educ., 88(2), 240–243.
45. Nicoll G., Francisco J. S. and Nakhleh M., (2001), An investigation of the value of using concept maps in general chemistry, J. Chem. Educ., 78(8), 1111.
46. Novak J. D., (1984), Application of advances in learning theory and the philosophy of science to the improvement of chemistry teaching, J. Chem. Educ., 61(7), 607–612.
47. Novak J. D., (2010), Learning, Creating, and Using Knowledge: Concept Maps as Facilitative Tools in Schools and Corporations, Routledge.
48. Ozcelik E., Karakus T., Kursun E. and Cagiltay K., (2009), An eye-tracking study of how color coding affects multimedia learning, Comput. Educ., 53(2), 445–453.
49. Paas F. G. W. C., (1992), Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach, J. Educ. Psychol., 84(4), 429–434.
50. Paas F. G. W. C. and van Merriënboer J. J. G., (1993), The efficiency of instructional conditions: an approach to combine mental effort and performance measures, Hum. Factors, 35(4), 737–743.
51. Paivio A., (1990), Mental representations: a dual coding approach, Oxford University Press.
52. Pendley B. D., Bretz R. L. and Novak J. D., (1994), Concept maps as a tool to assess learning in chemistry, J. Chem. Educ., 71(1), 9.
53. Pollock E., Chandler P. and Sweller J., (2002), Assimilating complex information, Learn. Instr., 12(1), 61–86.
54. Russell M. S., (2008), The Chemistry of Fireworks, Royal Society of Chemistry.
55. Safayeni F., Derbentseva N. and Cañas A. J., (2005), A theoretical note on concept maps and the need for cyclic concept maps, J. Res. Sci. Teach., 42(7), 741–766.
56. Salmerón L., Baccino T., Cañas J. J., Madrid R. I. and Fajardo I., (2009), Do graphical overviews facilitate or hinder comprehension in hypertext? Comput. Educ., 53(4), 1308–1319.
57. Steinhauser G. and Klapötke T. M., (2010), Using the chemistry of fireworks to engage students in learning basic chemical principles: a lesson in eco-friendly pyrotechnics, J. Chem. Educ., 87(2), 150–156.
58. Stull A. T. and Mayer R. E., (2007), Learning by doing versus learning by viewing: three experimental comparisons of learner-generated versus author-provided graphic organizers, J. Educ. Psychol., 99(4), 808–820.
59. Sweller J., Ayres P. and Kalyuga S., (2011), Cognitive Load Theory, Springer.
60. Taber K. S., (2001), When the analogy breaks down: modelling the atom on the solar system, Phys. Educ., 36(3), 222–226.
61. Taber K. S., (2009), Learning at the symbolic level, in Gilbert J. K. and Treagust D. (ed.) Multiple representations in chemical education, Springer, pp. 75–105.
62. Taber K. S., (2014), Student thinking and learning in science: perspectives on the nature and development of learners' ideas, Routledge.
63. Treagust D. F., Chittleborough G. and Mamiala T. L., (2003), The role of submicroscopic and symbolic representations in chemical explanations, Int. J. Sci. Educ., 25(11), 1353–1368.
64. Wallace D. S., West S. W. C., Ware A. and Dansereau D. F., (1998), The effect of knowledge maps that incorporate Gestalt principles of learning, J. Exp. Educ., 67(1), 5–16.
65. Wu H. K., Krajcik J. S. and Soloway E., (2001), Promoting conceptual understanding of chemical representations: students' use of a visualization tool in the classroom, J. Res. Sci. Teach., 38(7), 821–842.
66. Yaman F. and Ayas A., (2015), Assessing changes in high school students' conceptual understanding through concept maps before and after the computer-based predict–observe–explain (CB-POE) tasks on acid–base chemistry at the secondary level, Chem. Educ. Res. Pract., 16(4), 843–855.

---