Impact of fill-in-the-nodes concept maps on low prior-knowledge students learning chemistry: a study on the learning achievements and attitude toward concept maps

Abstract

Numerous studies have proven the learning benefits of concept maps in science subjects, particularly for students with low prior knowledge. There is a scarcity of research dedicated to the examination of chemistry courses at the university level, and the findings pertaining to academic performance in that subject exhibit a lack of consistency. This study examined the impact of concept maps on students of a General Chemistry course who had low prior knowledge. The study applied a quasi-experimental design to collect data on two topics: uncertainties of measurements (Topic 1) and acid–base (Topic 2). Fill-in-the-nodes concept maps were developed and served as learning materials. ANCOVA and Johnson–Neyman techniques were used to analyze the scores of concept tests of Topic 1 and Topic 2, respectively. In both Topics 1 and 2, the results showed that the treatment group outperformed the control group. However, the aforementioned finding was limited to the subset of students whose pre-test scores were below 30.7 out of a total of 47. From the analysis of the attitude questionnaire, the authors concluded that the students appreciated the usefulness of concept maps. However, they might hesitate to engage in using this new learning tool. The study's findings strengthen the evidence of the learning benefits of concept mapping. Moreover, using concept maps in teaching is feasible because of their low cost and minimally invasive modification to instructional design. The practices for implication of concept mapping are also discussed.

---

Introduction

Although chemistry is a challenging subject, chemistry teaching at the university level still predominately relies on the lecturing method (Francisco et al., 1998). This teaching method favours the rote learning process but fails to help students acquire permanent understanding (Turan-Oluk and Ekmekci, 2018). This teaching approach is not recommended for subjects with numerous abstract concepts like chemistry. Pickering (1990) showed that lecturing could guide students to solve mathematical problems in chemistry, but did not guide them to understand chemical concepts. Some studies also indicated the ineffectiveness of the lecturing method in teaching senior secondary school chemistry (Mitee and Obaitan, 2015), undergraduate STEM subjects (Freeman et al., 2014), and undergraduate physics (Hake, 1998). The lecturing method is a teacher-centered approach in which lecturers present knowledge, and the central role of students is to passively receive the information without any prior learning engagement or learning experience. The problem with this approach is that learning is not merely listening, memorizing, and repeating. Students may lack sufficient prior knowledge to understand and interpret new information (Byers and Eilks, 2009, pp. 5–22).

Students with low prior knowledge seem to face challenges in learning chemistry. According to Ausubel's assimilation theory of learning (Seel, 2012), students build up their knowledge through prior knowledge (background knowledge), or what they have learned previously. Students always bring to the classroom their existing concepts about the nature of the world. If these concepts are incorrect or inconsistent with true science principles, they will interfere with and create barriers to learning new concepts. The levels of prior knowledge correlate with the comprehension of scientific texts (Best et al., 2004). Students with low prior knowledge have a diminished capacity to comprehend and employ chemical understanding due to their limited repertoire of foundational concepts, hence impeding their ability to build novel knowledge. According to the interactive compensation model of learning, three predictors of students’ learning in chemistry are cognitive ability, prior knowledge, and motivational beliefs. Among these predictors, prior knowledge is the most important factor influencing learning outcomes (Crippen et al., 2005; Crippen and Brooks, 2009; Seery, 2009).

Concept maps have been recognized as powerful learning tools that have received substantial attention in science education research for enhancing the learning outcomes of students with low prior knowledge. Concept maps can serve as scaffolds for cognitive processing. Students with low prior knowledge may benefit most when learning with concept maps because they could retrieve a more central understanding than learning with texts (O’donnell et al., 2002). Chu and colleagues (2019) indicated that with the assistance of concept maps, low-achieving students could learn English with less pressure. The integration of concept maps and cooperative learning was recommended as a useful model to help low prior knowledge students catch up with their superior peers (Zubaidah et al., 2019). A recent study also showed the effectiveness of concept maps on the conceptual understanding of chemistry knowledge (Turan-Oluk and Ekmekci, 2018). However, the results of studies concerning the effectiveness of concept maps are still inconsistent. While some studies indicated that using concept maps would help students understand subject contents significantly better (Martínez et al., 2013; Aguiar and Correia, 2016; Turan-Oluk and Ekmekci, 2018), several studies showed no significant difference when concept map teaching was compared with traditional teaching (Markow and Lonning, 1998; Talbert et al., 2020).

The purpose of this research is to investigate the effectiveness of using concept maps in teaching chemistry, thereby filling in the evidence gaps as mentioned. This study is driven by two research questions:

(1) What are the impacts of using concept maps as a learning tool on students’ performance?

In this study, the authors compared students' learning performance in traditional teaching (without concept maps) with their performance when using concept maps to investigate the impacts of concept mapping.

(2) What are students’ attitudes toward using concept maps as a learning tool?

The learning performance was examined through the topics of uncertainties of measurements (Topic 1) and acid–base chemistry (Topic 2). The first primary reason they were chosen was their importance to the participants of the study. The study was conducted in the General Chemistry course of the Department of Natural Resources and Environmental Studies (National Dong Hwa University, Taiwan). The undergraduate program in this department offers courses in both science and humanities strands. The topic of uncertainties of measurement in General Chemistry is the only topic that guides students in this department on how to calculate and report results of measurements. While the subject matter has importance for students studying environmental sciences and their future professional endeavors involving measures, it appears that the department's curriculum does not sufficiently reinforce this issue at advanced levels of education. This study prioritized the topic of acid–base because it is considered a core and fundamental topic to understand and predict diverse chemical phenomena. Multiple chemistry concepts relate to the acid–base topic, including chemical equilibrium, types of chemical reactions, and solutions. The second reason for choosing Topics 1 and 2 was that few studies on teaching these topics have been documented. For example, recent chemistry education research on the topic of uncertainties of measurements and acid–base has primarily focused on students’ misconceptions rather than teaching interventions.

Over the past 25 years, only a few studies on concept mapping in higher education chemistry courses have been conducted, and the findings concerning learning outcomes have been inconsistent (Markow and Lonning, 1998; Su, 2013; Aguiar and Correia, 2016; Turan-Oluk and Ekmekci, 2018; Talbert et al., 2020; Wang et al., 2020; Wong et al., 2020; Ye et al., 2020). The present study is significant because it aims to fill the existing evidence gap regarding the effects of concept maps on learning chemistry, particularly focusing on the fill-in-the-nodes task. Additionally, the validated concept maps developed in this study could be used as a contribution to the contemporary theoretical frameworks developed by other science education researchers to develop multi-tier tests to diagnose students’ misconceptions. Moreover, in higher education, it is inevitable that foundation courses offered by interdisciplinary departments, which include both strands of science and strands of humanities, will have learners with low prior knowledge in chemistry. Along with contributions to extending knowledge of concept maps, the study aimed to use concept maps to help students with low prior knowledge in chemistry to learn the General Chemistry course more effectively. In the context of this study, the General Chemistry course is the only chemistry course that students have in their program.

Theoretical framework

Ausubel's view about assimilation in learning is the pillar of concept maps' development (Novak and Cañas, 2006a). Assimilation theory elaborates on meaningful learning, how to foster meaningful learning and mechanisms of learning. In the theory, Ausubel made a clear distinction between meaningful learning and rote learning. Meaningful learning occurs when new concepts or propositions are incorporated into pre-existing knowledge structures of learners. In contrast, rote learning refers to arbitrary and non-substantive new knowledge storage without considering previously learned knowledge. While meaningful learning helps students construct more solid knowledge structures, and transfer knowledge to new contexts and support skill developments, rote learning only benefits assessments with verbatim recall questions (Novak, 1998, p. 61). Knowledge acquired by rote learning is not rooted deeply in cognitive structure, so it tends to be forgotten soon. Ausubel indicated three conditions conducive to meaningful learning (Novak, 1993). First, learning materials must have potential meanings associated with learners’ cognitive structure. Second, learners must have prior concepts and propositions that could anchor new learning materials. Thirdly, learners must choose to engage in meaningful learning, which involves deliberately and substantively connecting new knowledge to their existing cognitive structures. In short, Ausubel believed new concept meanings are constructed based on prior knowledge and meaningful learning leads to the construction of more explicit, more precise relationships between concepts.

Additionally, Ausubel assumed an individual's cognitive structure has a hierarchical organization. More general and inclusive concepts are in higher organization levels in the cognitive structure, and less inclusive concepts are subsumed under the general ones. With such an assumption, he proposed three mechanisms of learning. The first is derivative and correlative subsumption (when new concepts are at lower levels). The second one is superordinate learning (when new concepts are at higher levels). And the third one is combinatorial learning (when new concepts are at the same levels). Table 1 shows the definitions of three mechanisms of learning and their examples.

Table 1 Definitions of learning according to Ausubel's Assimilation Theory

	Definitions	Examples (new concept is in the grey shade)
Derivative subsumption	A new concept is a specific example, supportive, or illustrative of learned concepts or general propositions. The existing ideas are unchanged (Ausubel, 2000, p. 90).
Correlative subsumption	A new concept is an elaboration, extension, modification, or qualification of learned concepts or general propositions—the existing ideas modified through the new concept (Ausubel, 2000, p. 90).
Superordinate learning	The new inclusive concept emerges from several less inclusive prior concepts or propositions (Ausubel, 2000, p. 91).
Combinatorial learning	“New concept is related to prior concepts but is neither more inclusive nor more specific than prior concepts. In this case, a new concept is seen to have some criteria attributes in common with preexisting concepts.” (Ausubel, 2000, p. 106)	The man is stepping upward while the escalator is moving down. The two speeds are the same, so the system keeps still. This system is like a chemical equilibrium state.

---

In practice, Ausubel proposed a teaching strategy called advance organizers. The function of these organizers is to bridge the gap between new learning knowledge and pre-existing knowledge. Learning materials are considered as advanced organisers if they are more abstract, general, and inclusive than the specific materials presented later and are relatable to students’ prior knowledge (Ausubel, 2000, p. 149). Since advance organizers consider what students have known, they can quickly reframe or build up their cognitive structure when interacting with the organizers. In this way, advance organizers serve as cognitive scaffoldings in learning.

Based on the Assimilation Theory of Ausubel, Novak developed concept maps as powerful advance organizers because concept maps mimic learners' cognitive structures which are assumed to have hierarchical structures. When students construct concept maps from scratch or fill in prepared concept maps, they will engage in meaningful learning (Novak, 1998, p. 71).

Literature review

What is a concept map?

In his book entitled “Learning How to Learn”, Novak (1930–2023) defined a concept map as “a schematic device for representing a set of concept meanings embedded in a framework of propositions” (Novak and Gowin, 1984, 15). Basically, a concept map encompasses three components, including nodes, linking phrases, and connecting lines. Nodes (circles or boxes) contain concepts (usually nouns). Linking phrases (usually verbs) connect concepts to form propositions representing meaningful statements articulating concepts’ relationships. Different linking phrases establish different relationships of concepts, for example “lead to” (causal), “consists of” (part-whole), “follow” (temporal), and “is different from” (comparison) (Schwendimann, 2019). Connecting lines direct the connections of concepts. In a concept map, broader and more inclusive concepts are placed at the top, while narrower and more specific concepts are organized below. When concepts are arranged from top to bottom, the hierarchical structure of the concept map can be displayed clearly. Some concept maps may have cross-links that connect concepts of different domains in the maps. Cross-links are usually displayed as lines with arrows to guide reading flows. Moreover, Novak and Cañas (2006a) recommended that a concept map should have a focus question that specifies the ideas or problems that the concept map will convey or solve respectively. Fig. 1 shows an example of a concept map with the focus question: what is a concept map?

Fig. 1 Concept map with the focus question: What is a concept map?

Concept maps can be used as an assessment tool and a learning tool. Novak and colleagues originated the concept map from the need for a better assessment tool. The goal was to understand and probe the changes in students’ scientific knowledge, and the authors found it difficult to identify students’ understanding by examining interview transcripts (Novak, 1990). According to the authors, one page of the concept map could represent 15–20 pages of the transcript without missing concept meanings expressed by interviewees (Novak and Cañas, 2006b). Novak used concept maps to ascertain what students know and changes in their knowledge over time. This particular use of concept maps was viewed as an assessment tool being applied to uncover students’ knowledge structure in chemistry education research (Burrows and Mooring, 2015; Anzovino and Bretz, 2016).

Ruiz-Primo and co-workers (2001) categorized concept mapping tasks based on the degree of directedness, which depends on the number of components of concept maps provided. Fill-in-the-map is the task with the highest degree of directedness. In this task, the structure of a concept map, concepts, and linking phrases are provided, and map builders need to fill in missing concepts (fill-in-the-nodes) or linking phrases (fill-in-the-lines). If no components are provided, map builders will engage in a construct-a-map from scratch task with the lowest directedness degree.

Schau and colleagues (2001) reported three disadvantages of this kind of task, including time-consuming, lack of a universal scoring method, and effects of individual's communication skills. The degree of directedness of a concept map task can be increased by supplying more components. Concept maps with a high degree of directedness, like fill-in-the-lines or fill-in-the-nodes, can overcome the disadvantages. However, for the fill-in concept maps, the learning benefits of concept maps may not be optimized since students do not create structures of representations to generate their own understandings. In teaching practices, teachers assign types of concept map tasks to students according to their educational purposes and backgrounds of students.

Reasons for using concept maps as learning tools

Science educators have given different reasons to explain why constructing concept maps brings learning benefits to students. Promoting meaningful learning and reducing cognitive load are the main justifications for concept maps’ benefits (Novak and Cañas, 2006b; Nesbit and Adesope, 2013).

Novak and Cañas (2006b) argued that concept maps could function as templates or scaffolds to help students organize and sequence new knowledge with prior knowledge. When students construct a concept map, they choose what concepts should be added and what relationships among concepts should be established to maintain the hierarchical structure and ensure the concept map is clean-looking. Accomplishing this task requires prior knowledge to engage with the meanings of concepts and their relationships. In consequence, the students promote meaningful learning.

Nesbit and Adesope wrote a comprehensive chapter about the theory and practice of concept maps (2013). They summarized the main reasons posed by theorists. According to the chapter, concept maps can reduce learners’ cognitive load. First, reading concept maps enables learners to share loads of verbal processing with visual processing. The reason is the connections of concepts create meaningful syntactic units that contain both verbal and visual information. Second, transferring texts into concept maps can integrate a concept with multiple occurrences into a node. In this way, concept maps can show the macrostructure and relationships among concepts. Consequently, learners can reduce the visual search process and relate ideas of the texts more efficiently.

Given that concept maps seem to facilitate learning successfully, they do have disadvantages. First, it is time-consuming to create a good quality concept map and train students to master concept mapping skills. Jang (2010) examined how students (n = 114) thought about concept maps. The students said concept mapping was time-consuming and challenging to write. An overview report by Lawless and colleagues (1998) claimed that it took time to train students to master concept mapping skills. Hence, concept maps may not be an ideal learning tool for a course with a tight schedule. Second, compared with reading text from a page, reading concept maps may need extraneous cognitive load because readers have to decide where to start (Nesbit and Adesope, 2013). Blankenship and Dansereau (2000) referred to the hesitation of reading maps as map shock.

Studies on using concept maps in chemistry classrooms

Over the past three decades, many studies have investigated the effectiveness of using concept maps as learning tools in science classrooms. Schroeder et al. (2018) conducted a comprehensive analysis of 142 studies for 42 years and found that students using concept maps can achieve a moderately significant difference. The effect of concept map learning was better than other teaching conditions. Concept maps were helpful for knowledge learning in science-related and non-science-related fields.

Schwendimann (2019) conducted a meta-analysis on the concept map learning of science courses, including science teachers and schools at all levels, and found that a concept map was a universal tool for teaching, learning, and assessment purposes. A concept map was also a powerful tool that can support the integration of scientific knowledge and understanding of the context of complex concepts. However, the concept map has not been widely used as other learning and evaluation tools. It suggested that more science teachers devote themselves to concept map design, implement concept map teaching, and assist students in learning concepts.

Additionally, in the systematic review of Machado and Carvalho (2020) on 60 university courses (medicine, education, business, engineering, and science) from 1988 to 2018, they found that the concept map can help college students develop critical thinking, problem-solving and in-depth understanding of concepts. Implementing concept map teaching is challenging because students struggle with choosing concepts and connectives, resist the mentality of using concept maps, and find it difficult to use concept mapping software. However, through timely and appropriate guidance by teachers, students can finally acknowledge that concept maps are beneficial to meaningful learning.

Doing a meta-analysis goes beyond this study's scope. Alternatively, the authors reviewed studies that targeted chemistry classrooms in higher education. Each of the following research was discussed in turn. The results of the studies were discrepant from the effects of concept maps on students’ learning performance.

On the one hand, concept maps could show no effects on students’ learning (Markow and Lonning, 1998; Talbert et al., 2020; Ye et al., 2020). Markow and co-workers (1998) conducted quasi-experiment research on 32 non-science majors enrolled in Principles of Inorganic and Organic Chemistry. The treatment group needed to construct pre-lab and post-lab concept maps, whereas the control group wrote essays to explain experiments. In the research, a 25-item test was used as a pre-test and post-test to measure students’ understanding. The result showed no significant difference between the treatment group and control group regarding students’ conceptual understanding. However, since the authors analyzed pre-test and post-test separately, the result inflated type 1 error and did not consider the effects of the pre-test on the post-test. In 2020, Talbert and colleagues reproduced the same research design on 238 students in the General Chemistry course (Talbert et al., 2020). Students in the treatment group constructed concept maps of chapters and connected important concepts of different chapters. Students in the control group wrote a weekly journal to summarize what they learned. Concept inventory was adopted to serve as a pre-test and post-test. The authors used multiple regression to hold constant the effects of the pre-test on the post-test, and the result indicated that both groups had no significant difference in the post-test. Compared with the study of Markow and Lonning, Talbert concluded with a more robust statistical technique. However, in the latter study, the treatment and control groups were taught by two different instructors, which led the instructors to become independent variables. Consistent with the findings of Markow and Talbert, the research conducted on 111 students in General Chemistry by Ye et al. (2020) also indicated learning with concept maps brought no significant difference when compared with the control group. The study of Ye also had different teaching assistants for the control group and the treatment group.

On the other hand, several studies showed positive impacts of concept maps on students’ conceptual understanding of chemistry. A quasi-experiment study by Su (2013) compared the learning achievements of students (n = 75) with and without concept map guidance on the topic of molecular chemistry. The analysis of covariance concluded that students in the treatment group significantly outperformed. Rather than comparing control and treatment groups, some research focused on the effectiveness of different concept mapping techniques. Table 2 summarizes the results of these studies.

Table 2 Research focusing on the effectiveness of various concept mapping techniques

Authors	Research design and subjects	Compared concept mapping techniques	Results
Aguiar and Correia (2016)	Quasi-experimental pre-test–post-test design	With colors and numbers at propositions	Concept maps with colors only were the most efficient.
	University students (n = 85)	With colors only
		With numbers at propositions only
		No colors and numbers at propositions
Turan-Oluk and Ekmekci (2018)	Case study	Select and fill in the nodes	Four concept mapping techniques could increase students’ conceptual understanding.
	University students (n = 19)	Select and fill in the lines
		Create and fill in the lines
		Select and fill in the nodes & lines
Wong et al. (2020)	Single-factor between-subjects experimental design	Static	Students learning with static concept maps containing full concepts and linking phrases performed better than those learning with the other techniques.
	University students (n = 44)	Fill-in-nodes
		Fill-in-lines
Wang et al. (2020)	Randomized between-group experimental design	Translating a complete map to a paragraph	The map-translation group showed a better understanding than the other groups.
	University students (n = 212)	Fill-in-nodes
		Fill-in-lines

---

From the literature review, scarce evidence reported concept maps’ benefits in chemistry classrooms in higher education. With restrictions to peer-reviewed journals, the authors found only five studies. Among the studies, only Su's research indicated positive effects of concept maps based on a quasi-experimental control group design (Su, 2013).

Students’ attitudes toward concept mapping

Unfamiliar teaching methods would degrade students’ participation in class because they might feel puzzled and discouraged. It was recommended that lecturers should investigate students’ attitudes or opinions toward a new teaching method (Kober, 2015, 162, 165). Using concept maps is an active teaching approach, and many studies examined students’ attitudes toward concept mapping (Briscoe and LaMaster, 1991; Markow and Lonning, 1998; Kinchin, 2000; Laight, 2004; Turan-Oluk and Ekmekci, 2018). The summary guidelines of Briscoe and LaMaster (1991) and Kinchin (2000) stated that students initially regarded concept maps as unfavorable learning tools. The students were familiar with rote learning strategies, so they were unwilling to adopt concept mapping which requires more effort, time, and thinking. However, after practicing several times, the students believed constructing concept maps could enhance their understanding and upgrade their test scores. Research also showed that students with low prior knowledge had less intention to use concept maps because they were frustrated when building the maps (Briscoe and LaMaster, 1991). In the study of Markow and Lonning, students felt that concept maps could serve as summary tools that helped them understand and remember concepts more easily. This result was consistent with the survey data of Turan-Oluk in that most of the students agreed on the positive effects of concept maps on their learning.

Student's prior knowledge and concept mapping techniques

Low prior knowledge students have difficulties learning because they experience a high cognitive load when exposed to new learning materials. Since concept maps can reduce cognitive load demand by scaffolding, learning with concept maps will significantly benefit students with low prior knowledge. Chu et al. (2019) conducted a study on an English course to examine the learning achievements of students (n = 130) who had low and high prior knowledge. The author concluded that concept maps could help low prior knowledge students learn grammar with less pressure. In another quasi-experimental study (n = 125), Zubaidah et al. (2019) found that low prior knowledge students who learned with concept maps had equal achievement as high prior knowledge students who did not learn with concept maps.

Concept map tasks vary from low directedness to high directness, and research showed that the latter type is more likely to guide students with low prior knowledge to favorable learning outcomes (Chang et al., 2001; Wang and Dwyer, 2004). The study of Chang had 48 participants (Chang et al., 2001). The authors used three types of concept map tasks in biology class: construct-by-self (concepts provided), construct-on-scaffold (concepts and structure provided), and paper-and-pencil. Although students in the construct-on-scaffold group had the lowest prior knowledge, their performance after the intervention was the highest. Wang and Dwyer (2004) compared the effects of low and high directedness concept maps on students’ achievements. The study had 290 participants, and the result indicated low prior-knowledge students learned better with high directedness concept map tasks. With such evidence of the benefits of high directedness concept maps for low prior knowledge students, the authors of this study decided to use fill-in-the-nodes concept mapping tasks for the General Chemistry course.

Research method

Research design

The authors used a quasi-experimental pre and post-test design to collect quantitative data. The duration was three weeks for Topic 1 and four weeks for Topic 2. Fig. 2 shows the diagram of the research design for this study. Details of the learning activities of the treatment group are described as follows:

Fig. 2 Diagram of research design.

Students in the treatment group received training on concept mapping. The training took 30 minutes in the second week of the course. They learned the definition of a concept map and how to construct a concept map appropriately. In training, the students practiced creating a concept map with provided concepts. The focus question was “What are the functions of a tree?”. The lecturer and the teaching assistant (TA) of the course went around the classroom and gave students feedback about their work. At the end of the training, the lecturer explained the fill-in-the-nodes task to the students.

After concept map training, the students received the pre-test and then learned Topic 1 and Topic 2 from lectures in class. After that, they were assigned concept maps with missing nodes as exercises. The students needed to complete the exercises and submit their answers to the TA. The TA gave them feedback about their concept maps. When students had questions or misunderstandings about the exercises, the lecturer of the course and the TA guided them without providing answers. Before the post-test one week, the lecturer summarized the topics based on validated concept maps. The lecturer then gave answers and explained more about connections in the maps.

After learning Topic 1 and Topic 2, the students in the treatment group filled in a questionnaire to express their attitudes toward the tools.

Both treatment and control groups received instructions from the same lecturer and the same teaching time for Topic 1 (3 hours) and Topic 2 (12 hours). While the control groups did exercises in the textbook, the treatment group engaged in concept map exercises. At the end of the topics, both groups had exercise discussions based on the keys of the exercises with the lecturer. In the case of the treatment group, the exercises in the textbook were introduced as optional practices to maximize the utility of the textbook.

Research participants

This research involved a convenience sample taken from the General Chemistry course of the Department of Natural Resources and Environmental Studies (National Dong Hwa University, Taiwan, ROC) in the academic year of 2019–2020 (treatment group) and 2020–2021 (control group). Enrollment in this department does not require a standardized chemistry test. Most of the participants had low prior knowledge in chemistry since they did not take or barely passed chemistry courses in high school. Moreover, apart from the General Chemistry course (selective course) there is no other chemistry courses in their undergraduate program. The number of participants (Table 3) in Topic 1 and Topic 2 changed because some students dropped the course or were absent from testing. At the beginning of the study, the researchers showed students an ethics statement and explained it to them. This ethics statement received ethical review approval later from the Research Ethics Committee at National Taiwan University (Taiwan, ROC). All participants of both the control group and the treatment group engaged in their respective exercises.

Table 3 Number of participants in the treatment group and control group

	Topic 1	Topic 2
Treatment group	38	30
Control group	37	35

---

Concept maps (learning tools) and concept tests

Developing a concept map requires identifying propositional knowledge statements at the level of sophistication which is appropriate to target students. Then, the concept maps are constructed based on these propositions. In this research, propositions related to uncertainties of measurements and acid–base were extracted from Chapter 1 and Chapter 14 of the textbook–Chemistry 10th edition (Zumdahl et al., 2017). The authors used Cmap software to draw concept maps. To assure instruments’ validity, three reviewers holistically evaluated the correctness, adequacy, and structure of both propositions and concept maps. University chemistry lecturers were the reviewers in this research. After considering the judgments of the reviewers, the authors modify and improve the propositions and concept maps. Eventually, twenty-six and thirty-eight propositions were finalized for Topic 1 and Topic 2 respectively. These propositions were divided into sub-topics named by focused questions, as shown in Table 4.

Table 4 Number of propositions and focused questions of two topics

Topics	Number of propositions	Focused questions
Uncertainties of measurements	26	What does a measurement involve?
		What affects the reliability of a measurement?
		How to determine significant figures?
		What calculation rules are associated with significant figures?
		What are the functions of scientific notation?
Acid–base	38	How to explain the acid–base properties of chemicals?
		What acid–base properties does water have? What is the pH value?
		What is the acid–base equilibrium constant? How do acid/base strengths affect dissociation?
		What are the properties of polyprotic acids?
		How to determine the acid–base properties of salts?
		How to determine the acid–base properties of HXO?

---

Table 5 and Fig. 3 show a sample of the propositions and the concept map of the focused question: What calculation rules are associated with significant figures?

Table 5 Proposition of focused question: What calculation rules are associated with significant figures?

Propositions

For addition or subtraction, the result has the same number of decimal places as the measurement with the fewest decimal places.

For multiplication and division, the result has the same number of significant figures as the measurement with the fewest significant figures.

If the digit removed is equal or more than 5, the preceding number is increased by 1

If the digit removed is less than 5, the preceding number is unchanged

---

Fig. 3 Concept map of focused question: What calculation rules are associated with significant figures? (Black linking lines can connect with any other linking lines. Colored linking lines can only connect with the same colored lines.)

The concept tests in this research are achievement tests, which attempt to measure “an individual's knowledge or skill in a given area or subject” (Fraenkel et al., 2011, 127). The propositional knowledge statements of the two topics serve as content domains for developing the tests. The items of the tests were written by the authors and then later served as the pre-test and the post-test in this study. The alignment between the propositions and the items of the test was judged by the reviewers to assure the face validity. Most of the chemical names, numbers, and examples in the post-test have been changed in the pre-tests to reduce the memorization of items. Fig. 4 illustrates the process of developing concept maps and concept tests. The concept tests consist of twenty-six items for Topic 1 (total score = 26) and fourteen items for Topic 2 (total score = 47). The concept tests had true-false items and multiple-choice items with a single answer or multiple answers (see the Appendix, ESI†). The reliabilities of the concept tests were evaluated by the values of Cronbach alpha – an internal consistency coefficient. The concept test of Topic 1 obtained a Cronbach alpha of 0.72, indicating a moderate level of reliability. The concept test of Topic 2 had low reliability since the Cronbach alpha was 0.63. One possible reason was the limited number of items. Moreover, concept tests tend to measure students’ understanding of several concepts of a topic, so it Is understandable for concept tests to have a low Conbrach alpha (Adams and Wieman, 2011; Berger and Hänze, 2015). Pearson correlation analyses between concept tests and exam scores of the topics were performed to check the predictive validity of the concept tests. The results showed that the predictive validity was confirmed for both Topic 1 (r = 0.36, p < 0.05) and Topic 2 (r = 0.517, p < 0.05).

Fig. 4 Process of developing concept maps and concept tests.

Attitudinal questionnaire

The questionnaire was developed in 2016 and then used by the originating author in another study in 2018 (Turan-Oluk and Ekmekci, 2018). The authors of the present study chose this questionnaire because its target students were undergraduates at the same level as this research. The original version of the questionnaire consists of 23 closed items with a 5-point Likert scale format (1-highly disagree, 2-disagree; 3-neutral, 4-agree, 5-highly agree). Based on the wordings of the items, the researcher categorized the items into three components of attitude, including: cognition-acknowledging the usefulness of concept maps, affection-feeling about using concept maps, and behaviour-intention to use concept maps as a learning tool. The reliabilities of cognition, affection, behavior components, and the overall questionnaire were examined (n = 31) and found to have good internal consistency values (0.88, 0.84, 0.89, and 0.95, respectively).

Data analysis

Shapiro-Wilk test for the concept tests showed that all p values were higher than 0.05. The authors concluded that the data of pre-test and post-test were normally distributed. Since Topic 1 met the assumptions of ANCOVA, the authors ran ANCOVA for Topic 1. The data of Topic 2 violated the homogeneity of regression slope, so the authors used the Johnson–Neyman procedure instead of ANCOVA. According to Johnson (2016), the Johnson–Neyman technique has been used as a straightforward alternative to ANCOVA when the assumption of the homogeneity of the regression slope is violated. In this study, the authors took advantage of the Excel workbook created by Carden and colleagues (2017) to run the Johnson–Neyman procedure. ANCOVA and the Johnson–Neyman technique were used to answer the research question 1.

To answer the research question 2, descriptive statistics (means and standard deviations) were calculated for the three components of the questionnaire. Besides, the authors grouped and summed the number of responses on “highly agree” and “agree” as “agree” for each item. Similarly, “highly disagree” and “disagree” were grouped and summed as “disagree”. The percentages of responses were also reported to examine students’ attitudes toward concept maps.

Results

Impacts of concept maps on students’ learning

For Topic 1, one-way ANCOVA was conducted to determine the difference between the treatment group and control group on the post-test controlling the pre-test. As shown in Table 6, the treatment effect was significant, F(1.72) = 6.07, p = 0.016 < 0.05, and η² = 0.078. Using pre-test as covariance, the adjusted mean of the treatment group (M = 15.89, SE = 0.54) was significantly higher than that of the control group (M = 13.98, SE = 0.55). It showed that using concept maps can help students understand Topic 1 better than using traditional teaching. The effect size of the ANCOVA result was medium (η² = 0.078 > 0.06, but <0.14) according to Cohen (1992). This study concluded that students who engaged in concept map tasks performed better than those who did not when learning Topic 1.

Table 6 Result of ANCOVA (Topic 1 – uncertainties of measurements)

Group	n	M (SD)	Adj M (SE)	F	p	Effect size η^2
Treatment	38	15.95 (3.40)	15.89 (0.54)	6.08	0.016 < 0.05	0.078
Control	37	13.92 (3.69)	13.98 (0.55)

---

For Topic 2, the mean (SD) of the pre-test and post-test of the treatment group were 30.20 (6.50) and 35.33 (5.47) respectively, and those of the control group were 28.17 (4.31) and 30.54 (5.66) respectively. Since the data of Topic 2 did not meet the assumption of homogeneity of the regression slope, the authors used the Johnson–Neyman procedure to examine the difference in the post-test of two groups with restriction to a certain range of pre-test. Fig. 5 is the Johnson–Neyman plot retrieved from the Excel workbook. In the plot, the vertical axis shows the simple slope of the group predicting the post-test, and the horizontal axis shows the pre-test values. The shaded region indicates 95% confidence intervals, and any values of the pre-test for which the shaded regions contain zero have an insignificant effect of the group on the post-test. It was clear that the significant range is on the left-hand side of 30.7. By examining the post-test values of the corresponding pre-test values, which are less than 30.7 in Fig. 6 (the scatterplot of the pre-test and the post-test), it could be concluded that the post-test of the treatment group was significantly higher than that of the control group. The d effect size of the treatment group was large (d = 0.86) and was also higher than that of the control group (d = 0.48).

Fig. 5 The Johnson–Neyman plot for Topic 2 – acid and base.

Fig. 6 Pre and post-test scatterplot by groups.

Attitude toward concept mapping

Mean scores of the cognition, affection, and behavior components were 3.72 (SD = 0.29), 3.42 (SD = 0.10), and 3.42 (SD = 0.26) respectively. The results showed that the students had moderate positive attitude toward concept mapping.

Fig. 7 shows means and percentages of responses to items of cognition-acknowledging the usefulness of concept maps. Inspection of the items revealed that the treatment group agreed that concept maps were useful. The percentage of the agreement ranged from 58% to 77%. However, more students had neutral opinions about item 6 – “Concept maps improve my thought system”, item 8 – “Concept map lets me learn a topic permanently”, and item 13 – “Concept Map is useful for sharing my knowledge of the topic with others”.

Fig. 7 Means and percentages of responses to items of cognition component-acknowledging the usefulness of concept maps.

Regarding the items related to the feeling about using concept maps, only item 18 – “When I create Concept maps, I participate more actively in lessons” received the agreement of most of the students, 52%. Most of the students chose neutral options for the other items, ranging from 52% to 61%, as shown in Fig. 8.

Fig. 8 Means and percentages of responses to items of cognition affection-feeling about using concept maps. Note. * = negative item.

Concerning the items related to the intention to use concept maps as a learning tool, most students disagreed with negative items, which means they intended to use this tool in learning. The percentage of disagreement ranged from 42% to 65% as shown in Fig. 9. The items that received more neutral opinions were item 3 – “Preparing Concept maps is wasting my time.”, item 7 – “It's hard to work with Concept maps”, item 15 – “I like to learn about the Concept map”, item 19 – “I prefer studying for the topic using other ways than preparing Concept maps”. It was clear from these four items that the students still had a certain degree of hesitation to learn and use concept maps.

Fig. 9 Means and percentages of responses to items of cognition component behaviour-intention to use concept maps as a learning tool. Note. * = negative item.

Discussion and conclusion

Regarding the impacts of fill-in-the-nodes concept maps, when compared with the control group, the students using concept maps as learning tools outperformed with a medium effect size for Topic 1 and a large effect size for Topic 2. However, for Topic 2, this conclusion only works for the students whose pre-test scores are less than 30.7. The following reasons can explain the learning benefits of concept mapping:

1. Concept maps could function as templates or scaffolds to help students organize and sequence new knowledge with prior knowledge (Eppler, 2006).

2. Reading concept maps could reduce cognitive load (Nesbit and Adesope, 2013).

3. Concept maps could provide a holistic view of contents (Turan-Oluk and Ekmekci, 2018).

Through the quasi-experimental control group design, this study has given a new insight into the positive effect of fill-in-the-nodes concept maps on the learning chemistry of low prior knowledge students. If only restricting to the quasi-experimental control group design, the results match the study of Su (2013) that reported using concept maps in teaching chemistry had some advantages over traditional teaching. However, the study results are inconsistent with those of Markow and Lonning, Talbert, Ye (Markow and Lonning, 1998; Talbert et al., 2020; Ye et al., 2020). These studies showed no significant difference between treatment and control groups. The nature of teaching contents and research design could be the reasons for the inconsistency in the findings. While this present study focused on the lecturing course-General Chemistry, Markow and Lonning researched chemistry laboratory courses. Talbert and Ye employed different teaching staff which could be a confounding variable in the research. However, only one lecturer taught in both the control and treatment groups in the present study.

Generally, the treatment group students acknowledge the usefulness of concept maps and have the intention to use concept maps as a learning tool, but interestingly they neither have a positive nor negative feeling about concept maps. The findings of this study show that the students recognize the usefulness of concept maps, which are similar to the findings of Markow and Lonning, and Turan-Oluk. Despite this, the students still have hesitation in using concept maps in their learning. Possible reasons for this result could be:

1. The unfamiliarity with the concept mapping made students frustrated (Chiou, 2008).

2. Doing concept maps was time-consuming and took extra work (Brondfield et al., 2019).

3. Students already had preferred learning methods (Brondfield et al., 2019).

Further research like focus group interviews needs to be conducted to have more in-depth insight into why students are unwilling to use concept maps.

In conclusion, fill-in-the-nodes concept mapping indeed is a helpful learning tool for students with low prior knowledge. Concept maps promote students’ understanding of Topic 1 (Uncertainties of measurements) and Topic 2 (Acid–base). Compared with the control group, students learning with concept maps have significantly higher achievements. Moreover, students have moderate positive attitudes toward this tool. They believe concept maps are a useful learning tool that can help them understand key concepts.

Practical implications, limitations, and future studies

The results of this study may benefit chemistry instructors, STEM-subject lecturers, and students. It does not only inform how to use concept maps in teaching, which requires just a little extra cost and minimally invasive modification to instructional designs, but also help students with low prior knowledge have a deeper understanding of fundamental chemical concepts. In addition, the study offers a better insight into students’ attitudes toward this tool. Chemistry instructors may adopt or consult this study from instructional design and validated concept maps for their teachings. The study provides validated concept maps as learning tools or reviewing tools to students (see the Appendix, ESI†). Moreover, science educators may consider the validated concept maps as research instruments for further studies.

For practical implications in the classroom, the authors suggest chemistry instructors consider consulting the following sequence:

1. Decide what type of concept mapping task is appropriate for students. If the students are new to concept maps, fill-in-the-nodes task is a good start, especially for low prior knowledge students.

2. Identify key propositions of topics from reading materials, such as textbooks, websites, and syllabi.

3. Develop standard concept maps and concept tests based on key propositions. The standard concept maps should have hierarchical structures and focus questions to ensure their intelligibility.

4. Give concept maps training to students. The training should allocate sufficient time (at least 30 minutes) to cover the definition of concept maps, features, functions of concept maps, examples, and practices. As indicated in this study, students have hesitation in using concept maps, so instructors may consider sharing the benefits of concept maps through empirical studies to engage students in this learning tool.

5. Implement lectures and give concept map exercises to students.

6. Summarize the topic based on standard concept maps. The students get correct answers to concept maps exercises from this step.

Given that the results of the present study are promising for the effectiveness of concept mapping, it has several important limitations which need to be considered. Firstly, this study is a classroom-based study which involved convenience sampling and small number of participants. Therefore, the generalizability of the findings is limited. Future research should focus on developing concept maps for other topics and investigating the learning impacts of these concept maps on classes with larger sizes. This kind of study will bring tremendous practical benefits to the chemistry education community. Secondly, the intervention in this study seems to lack elements of learning engagement, leading to the students’ hesitation in concept mapping. For this reason, future research could consider studying what makes students reluctant to use concept maps for learning, as well as exploring strategies to enhance students’ engagement with concept mapping. For the first question, it is necessary to have in-depth interviews with students throughout concept mapping interventions. For the second question, utilizing interactive online platforms which enable teachers to provide immediate feedback on students' concept maps could be a promising approach. The free Web-based inquiry science environment developed by UC Berkeley might be used for this purpose because it offers concept mapping exercises in its authoring tools.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

The first author acknowledges the financial support from the scholarship of the Ministry of Education, Republic of China (Taiwan). The corresponding author acknowledges the financial support from the Ministry of Science and Technology, Republic of China (Taiwan): [Grant Number MOST-110-2511-H-259-003-]. Any findings, opinions, conclusions, or recommendations expressed in this study are those of the authors and do not necessarily reflect the views of the Ministry of Education and Ministry of Science and Technology, Republic of China (Taiwan). This manuscript is a reworking of the master's thesis of the first author.

References

1. Adams W. K. and Wieman C. E., (2011), Development and validation of instruments to measure learning of expert-like thinking, Int. J. Sci. Educ., 33(9), 1289–1312.
2. Aguiar J. G. and Correia P. R. M., (2016), Using concept maps as instructional materials to foster the understanding of the atomic model and matter–energy interaction, Chem. Educ. Res. Pract., 17(4), 756–765.
3. Anzovino M. E. and Bretz S. L., (2016), Organic chemistry students’ fragmented ideas about the structure and function of nucleophiles and electrophiles: a concept map analysis, Chem. Educ. Res. Pract., 17(4), 1019–1029.
4. Ausubel D. P., (2000), The acquisition and retention of knowledge: a cognitive view, Netherlands: Dordrecht Springer.
5. Berger R. and Hänze M., (2015), Impact of expert teaching quality on novice academic performance in the jigsaw cooperative learning method, Int. J. Sci. Educ., 37(2), 294–320.
6. Best R., Ozuru Y. and McNamara D. S., (2004), Proceedings of the 6th International Conference on Learning Sciences, California: International Society of the Learning Sciences.
7. Blankenship J. and Dansereau D. F., (2000), The effect of animated node-link displays on information recall, J. Exp. Educ., 68(4), 293–308.
8. Briscoe C. and LaMaster S. U., (1991), Meaningful learning in college biology through concept mapping, Am. Biol. Teach., 53(4), 214–219.
9. Brondfield S., Seol A., Hyland K., Teherani A. and Hsu G., (2019), Integrating Concept Maps into a Medical Student Oncology Curriculum, J. Cancer Educ., 1–7.
10. 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), 53–66.
11. Byers B. and Eilks I., (2009), The need for innovations in higher level chemistry education – A pedagogical justification, in Eilks I. and Byers B. (ed.), Innovative methods of teaching and learning chemistry in Higher Education, London: RSC Publishing, ch. 1, pp. 5–22.
12. Carden S. W., Holtzman N. S. and Strube M. J., (2017), CAHOST: an excel workbook for facilitating the Johnson–Neyman technique for two-way interactions in multiple regression, Front. Psychol., 8, 1293.
13. Chang K.-E., Sung Y.-T. and Chen S.-F., (2001), Learning through computer-based concept mapping with scaffolding aid, J. Comput. Assist. Learn., 17(1), 21–33.
14. Chiou C., (2008), The effect of concept mapping on students’ learning achievements and interests, Innov. Educ. Teach. Int., 45(4), 375–387.
15. Chu H.-C., Wang C.-C. and Wang L., (2019), Impacts of concept map-based collaborative mobile gaming on english grammar learning performance and behaviors, Educ. Technol. Soc., 22(2), 86–100.
16. Cohen J., (1992), A power primer, Psychol. Bull., 112(1), 155.
17. Crippen K. J. and Brooks D. W., (2009), Applying cognitive theory to chemistry instruction: the case for worked examples, Chem. Educ. Res. Pract., 10(1), 35–41.
18. Crippen K. J., Schraw G. and Brooks D. W., (2005), Using an interactive, compensatory model of learning to improve chemistry teaching, J. Chem. Educ., 82(4), 637.
19. Eppler M. J., (2006), A comparison between concept maps, mind maps, conceptual diagrams, and visual metaphors as complementary tools for knowledge construction and sharing, Inf. Vis., 5(3), 202–210.
20. Fraenkel J. R., Wallen N. E. and Hyun H. H., (2011), How to design and evaluate research in education, New York: McGraw-Hill.
21. Francisco J. S., Nicoll G. and Trautmann M., (1998), Integrating multiple teaching methods into a general chemistry classroom, J. Chem. Educ., 75(2), 210.
22. Freeman S., Eddy S. L., McDonough M., Smith M. K., Okoroafor N., Jordt H. and Wenderoth M. P., (2014), Active learning increases student performance in science, engineering, and mathematics, Proc. Natl. Acad. Sci. U. S. A., 111(23), 8410–8415.
23. Hake R. R., (1998), Interactive-engagement versus traditional methods: a six-thousand-student survey of mechanics test data for introductory physics courses, Am. J. Phys., 66(1), 64–74.
24. Jang S., (2010), The impact on incorporating collaborative concept mapping with coteaching techniques in elementary science classes, Sch. Sci. Math., 110(2), 86–97.
25. Johnson T. R., (2016), Violation of the homogeneity of regression slopes assumption in ANCOVA for two-group pre-post designs: Tutorial on a modified Johnson–Neyman procedure, Quant. Meth. Psych., 12(3), 253–263.
26. Kinchin I. M., (2000), Concept mapping in biology, J. Biol. Educ., 34(2), 61–68.
27. Kober N., (2015), Reaching students: What research says about effective instruction in undergraduate science and engineering, Washington DC: National Academies Press.
28. Laight D. W., (2004), Attitudes to concept maps as a teaching/learning activity in undergraduate health professional education: influence of preferred learning style, Med. Teach., 26(3), 229–233.
29. Lawless C., Smee P. and O’Shea T., (1998), Using concept sorting and concept mapping in business and public administration, and in education: an overview, Educ. Res., 40(2), 219–235.
30. Machado C. T. and Carvalho A. A., (2020), Concept mapping: benefits and challenges in higher education, J. Contin. High. Educ., 68(1), 38–53.
31. Markow P. G. and Lonning R. A., (1998), Usefulness of concept maps in college chemistry laboratories: students’ perceptions and effects on achievement, J. Res. Sci. Teach., 35(9), 1015–1029.
32. Martínez G., Pérez Á. L., Suero M. I. and Pardo P. J., (2013), The effectiveness of concept maps in teaching physics concepts applied to engineering education: experimental comparison of the amount of learning achieved with and without concept maps, J. Sci. Educ. Technol., 22(2), 204–214.
33. Mitee T. L. and Obaitan G. N., (2015), Effect of mastery learning on senior secondary school students’ cognitive learning outcome in quantitative chemistry, J. Educ. Pract., 6(5), 34–38.
34. Nesbit J. C. and Adesope O. O., (2013), Concept maps for learning, in Schraw G., McCrudden M. T. and Robinson D. (ed.), Learning through visual displays, Information Age Publishing, pp. 303–328.
35. Novak J. D., (1990), Concept mapping: a useful tool for science education, J. Res. Sci. Teach., 27(10), 937–949.
36. Novak J. D., (1993), The Proceedings of the third international seminar on misconceptions and educational strategies in science and mathematics, Ithaca NY: Misconceptions Trust.
37. Novak J. D., (1998), Learning, creating, and using knowledge: Concept maps as facilitative tools in schools and corporations, Routledge.
38. Novak J. D. and Cañas A. J., (2006a), The origins of the concept mapping tool and the continuing evolution of the tool, Inf. Vis., 5(3), 175–184.
39. Novak J. D. and Cañas A. J., (2006b), The theory underlying concept maps and how to construct them, Technical Report IHMC CmapTools 2006-01, Florida Institute for Human and Machine Cognition, pp. 1–31.
40. Novak J. D. and Gowin D. B., (1984), Learning how to learn, Cambridge University Press.
41. O’donnell A. M., Dansereau D. F. and Hall R. H., (2002), Knowledge maps as scaffolds for cognitive processing, Educ. Psychol. Rev., 14(1), 71–86.
42. Pickering M., (1990), Further studies on concept learning versus problem solving, J. Chem. Educ., 67(3), 254.
43. Ruiz-Primo M. A., Schultz S. E., Li M. and Shavelson R. J., (2001), Comparison of the reliability and validity of scores from two concept-mapping techniques, J. Res. Sci. Teach., 38(2), 260–278.
44. Schau C., Mattern N., Zeilik M., Teague K. W. and Weber R. J., (2001), Select-and-fill-in concept map scores as a measure of students’ connected understanding of science, Educ. Psychol. Meas., 61(1), 136–158.
45. Schroeder N. L., Nesbit J. C., Anguiano C. J. and Adesope O. O., (2018), Studying and constructing concept maps: a meta-analysis, Educ. Psychol. Rev., 30(2), 431–455.
46. Schwendimann B. A., (2019), Concepts maps as versatile learning, teaching, and assessment tools, Learning, Design, and Technology, Springer International Publishing, pp. 1–64.
47. Seel N. M., (2012), Assimilation theory of learning, Encyclopedia of the Sciences of Learning, US: Springer, pp. 324–326.
48. Seery M. K., (2009), The role of prior knowledge and student aptitude in undergraduate performance in chemistry: a correlation-prediction study, Chem. Educ. Res. Pract., 10(3), 227–232.
49. Su K. D., (2013), Using concept maps and animations to support college students’ problem-solving ability in molecular chemistry learning, Chin. J. Sci. Educ., 103, 37–60.
50. Talbert L. E., Bonner J., Mortezaei K., Guregyan C., Henbest G. and Eichler J. F., (2020), Revisiting the use of concept maps in a large enrollment general chemistry course: implementation and assessment, Chem. Educ. Res. Pract., 21(1), 37–50.
51. Turan-Oluk N. and Ekmekci G., (2018), The effect of concept maps, as an individual learning tool, on the success of learning the concepts related to gravimetric analysis, Chem. Educ. Res. Pract., 19(3), 819–833.
52. Wang C. X. and Dwyer F. M., (2004), Effects of three concept mapping strategies and prior knowledge in a Web-based learning environment, J. Educ. Technol. Syst., 32(4), 377–397.
53. Wang Z., Adesope O., Sundararajan N. and Buckley P., (2020), Effects of different concept map activities on chemistry learning, Educ. Psychol., 1–16.
54. Wong R. M., Sundararajan N., Adesope O. O. and Nishida K. R. A., (2020), Static and interactive concept maps for chemistry learning, Educ. Psychol., 1–18.
55. Ye L., Eichler J. F., Gilewski A., Talbert L. E., Mallory E. and Litvak M. et al., (2020), The impact of coupling assessments on conceptual understanding and connection-making in chemical equilibrium and acid-base chemistry, Chem. Educ. Res. Pract., 21(3), 1000–1012.
56. Zubaidah S., Mahanal S., Sholihah M., Rosyida F. and Kurniawati Z. L., (2019), Using Remap RT (Reading–Concept Mapping–Reciprocal Teaching) learning model to improve low-ability students’ achievement in biology, Cent. Educ. Policy Stud. J., 10(3), 117–144.
57. Zumdahl S. S., Zumdahl S. A. and DeCoste D. J., (2017), Chemistry, Cengage Learning.

---

Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3rp00238a

---