Quan-Thanh
Huynh
a and
Yu-Chuan
Yang
*b
aDepartment of Education and Human Potentials Development, National Dong Hwa University, Taiwan. E-mail: quanthanh.chemistry@gmail.com
bDepartment of Natural Resources and Environmental Studies, National Dong Hwa University, Taiwan. E-mail: ycyang@gms.ndhu.edu.tw
First published on 1st November 2023
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.
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.
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.
Definitions | Examples (new concept is in the grey shade) | |
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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). |
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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). |
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Superordinate learning | The new inclusive concept emerges from several less inclusive prior concepts or propositions (Ausubel, 2000, p. 91). |
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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. |
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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).
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.
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.
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.
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).
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.
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.
Topic 1 | Topic 2 | |
---|---|---|
Treatment group | 38 | 30 |
Control group | 37 | 35 |
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?
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 |
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).
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.
Group | n | M (SD) | Adj M (SE) | F | p | Effect size η2 |
---|---|---|---|---|---|---|
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. 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”.
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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.
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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.
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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. |
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.
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.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3rp00238a |
This journal is © The Royal Society of Chemistry 2024 |