Concept Mapping in Technology-Supported K-12 Education: A Systematic Review of Selected SSCI Publications From 2001 to 2020

Abstract

Concept maps have been verified as an effective learning tool in technology-supported learning environments, in particular, in K-12 education. To enable educational technology researchers and school teachers to thoroughly know the research foci and applications of concept maps in technology-supported learning environments for K-12 education (CM-K12), the present study conducted a systematic review of studies published in seven SSCI (Social Science Citation Index) journals of educational technology by referring to the technology-based learning model. The review results showed that concept maps mostly played the role of a personal Mindtool and a collaborative Mindtool, and were mainly applied in elementary schools. The majority of elementary school teachers employed concept maps as a collaborative Mindtool (for a learning group), a personal Mindtool (for individual students), and a learning design tool. As for secondary school teachers, concept maps were mostly adopted as an assessment and evaluation tool, personal Mindtools, and collaborative Mindtools. In terms of application issues, the most adopted medium was desk-top computers, and the most adopted application domains were science, engineering, and language. The research focus explored the most in CM-K12 research was cognition, followed by affect and learning behavior. Based on the findings, several recommendations are proposed.

Keywords

concept mapping K-12 education literature review technology-supported learning

Introduction

Rapid advances in computer and communication technologies and the growing importance of technology-based learning have led to the application of computerized concept maps in educational contexts, in particular in K-12 settings in which students need more assistance in organizing what they have learned (Chen et al., 2019; Chou et al., 2021; Hwang et al., 2013; Sun & Chen, 2016). Concept maps have been proven by researchers to be effective learning tools and learning strategies for integrating new technologies into K-12 educational practice (Chou et al., 2021; Lai & Hwang, 2015; Rosciano, 2015; Sun & Lee, 2016; Wu & Chen, 2018).

In addition, the effectiveness of concept mapping in technology-supported K-12 education has been highlighted by several researchers from diverse perspectives, including creative thinking (Hsu, 2019), problem solving (Namdar & Shen, 2018), learning achievement (Farrokhnia et al., 2019), and knowledge organizing (Chang et al., 2012). For example, Rosciano (2015) indicated that concept maps could be employed to benefit students’ reflection, knowledge building, problem solving, inquiry, and critical thinking as well as promoting their learning retention. However, high-quality concept maps combined with technologies to facilitate student learning in K-12 education are never simple tasks. In technology-supported learning in K-12 settings, it remains a challenge to successfully integrate technologies into learning activities based on the educational objective of promoting students’ learning performance (Lai & Hwang, 2015), although there is evidence confirming the potential of concept mapping in technology-supported learning environments, particularly in K-12 education (Chu et al., 2019; Hartmeyer et al., 2018; Huang et al., 2017; Schroeder et al., 2018). Therefore, a review study is needed to provide a reference regarding how concept maps can be used to facilitate teaching and learning in technology-supported K-12 educational settings. Moreover, understanding the trends and characteristics of effective concept mapping in technology-supported K-12 education could help researchers and practitioners plan for future studies or learning designs as well as providing a reference to policymakers for planning objectives and strategies for K-12 educational settings. In particular, a review study enables novice researchers to have a whole conception of the field they intend to engage in, as indicated by Hwang and Wu (2012) and Xia and Zhong (2018).

The purpose of this study was to examine research trends in concept mapping in technology-supported K-12 education (CM-K12) from 2001 to 2020. According to Hsu et al. (2012), Hwang and Tsai (2011), and Hwang and Wu (2012), seven highly-recognized technology-based learning journals were selected for the review, namely, the British Journal of Educational Technology (BJET), Computers & Education (C&E), Educational Technology & Society (ET&S), Educational Technology Research and Development (ETR&D), the Journal of Computer Assisted Learning (JCAL), Interactive Learning Environments (ILE), and Innovations in Education and Teaching International (IETI). Moreover, the following research questions were investigated in this study by referring to the Technology-Based Learning model (Lai, 2020) which suggested that, to investigate the trends of technology-based applications, it is important to review the literature from several dimensions, that is, the interaction issues (including roles of concept maps and participants), application issues (including the media of concept maps and application domains), and research issues (including research methods and data analysis methods, evaluation of learners’ performance, and research foci) in CM-K12 research. In particular, the following research questions were investigated in this study.

1. From 2001 to 2020, in terms of interaction, what were the roles and participants of CM-K12 research?

2. From 2001 to 2020, in terms of application, what were the learning environments, media of concept maps, and application domains of CM-K12 research?

3. From 2001 to 2020, in terms of research, what were the research methods and data analysis methods, evaluation of learners’ performance, and research foci of CM-K12 research?

Background of the Study

Concept maps have been regarded by researchers as a visual thinking tool which uses connections and hierarchical relationships between nodes to illustrate learners’ understanding of concepts, and hence enables learners to conduct meaningful learning (Novak & Gowin, 1984; Yue et al., 2017). In K-12 educational settings, concept mapping has been applied in science education (Chen et al., 2019; Chou et al., 2021), social science education (Lin et al., 2012), language education (Chu et al., 2019; Wu & Chen, 2018), mathematics education (Chen, 2009), anti-phishing education (Sun & Lee, 2016), and physical education classes (Huang et al., 2017). For example, Chen (2009) proposed a novel genetic-based curriculum sequencing scheme based on a generated ontology-based concept map, and planned the appropriate path of learning mathematics for individual learners. In history courses, students made use of concept mapping to learn and organize historical events according to the time of their occurrence, which improved their learning achievement (Chu et al., 2015). In anti-phishing education, Sun and Lee (2016) specified that students who adopted tablet computers and concept maps for learning enhanced their learning motivation and achievement. Huang et al. (2017) investigated the effects of collaborative learning and concept-mapping intervention on students’ critical thinking and skill learning during physical education.

Moreover, the number of applications regarding the use of mobile and communication technologies in K-12 education has increased in the past decade, implying that learning is no longer limited to the classroom (Hsu et al., 2012; Hwang & Tsai, 2011; Lai, 2020). With the development of emerging technologies, learning activities in K-12 education are becoming increasingly diverse. However, in order to avoid the possible negative effects of using technologies in K-12 educational settings, scholars have suggested the need to integrate proper learning strategies into technological learning contexts (Chou et al., 2021; Lai & Hwang, 2015; Ye et al., 2018). For example, Wu and Chen (2018) demonstrated the use of mind maps for promoting elementary school students’ reading comprehension, association skills, and positive attitude in a classical Chinese course. Wu and Chen (2018) pointed out that learning strategies through concept mapping could not only improve students’ expression and critical thinking, but also facilitate individualized learning and exploration (Novak & Gowin, 1984; Schmid & Telaro, 1990; Wu & Chen, 2018). Chou et al. (2021) adopted concept maps with an AR (Augmented Reality) technology to help students organize and understand what they had learned in an elementary school science and technology course.

Chu et al. (2015) further revealed that concept mapping could play several roles in technology-supported learning settings, such as learning guidance, assessment, and knowledge organizing. For instance, researchers have employed concept maps to help teachers summarize what they have taught (Sun & Chen, 2016), to guide students to organize what they have learned (Cui & Yu, 2019; Farrokhnia et al., 2019), and to evaluate students’ knowledge structures (Kao et al., 2017). In addition, according to Hsu et al. (2012) and Hwang and Tsai (2011), a literature review allows researchers to understand the relevant topics and trends of their research field, which is important for them to plan their future studies. However, to the best of our knowledge, only a few studies have been conducted to review the use of concept mapping in education from a specific perspective or for a particular application domain. For example, Weinerth et al. (2014) reviewed 24 papers related to concept mapping published from 1999 to 2013, and showed that most of the studies neglected the interplay between usability and test use training; Yue et al. (2017) employed a systematic review and meta-analysis to evaluate the effects of concept mapping on critical thinking in nursing education; Schroeder et al. (2018) performed a meta-analysis to show the effectiveness of applying concept mapping in STEM and non-STEM educational settings; Hartmeyer et al. (2018) reviewed nine studies related to concept mapping-based formative assessment processes in primary and secondary science education; and Machado and Carvalho (2020) reviewed the benefits and challenges of concept mapping in higher education. As described above, all of these studies provide some insights into research trends in concept mapping. To date, however, no literature review has examined the characteristics and impact of CM-K12 publications. Therefore, this study extends the framework of previous studies by examining roles of concept maps, participants, the media of concept maps, application domains, research methods, evaluation of learners’ performance, data analysis methods, and research foci, to analyze the CM-K12 research.

Research Methods

To understand how technologies assist students’ learning or teachers’ teaching through concept maps in technology-based learning in K-12 settings, this study referred to Hwang and Tsai (2011), Hsu et al. (2012), and Schroeder et al. (2018). Moreover, to perform this systematic review for CM-K12 research, this study adopted the principles and guidelines of PRISMA provided by Moher et al. (2009).

Resources

This is a systematic review of the literature that includes articles published up until December 31, 2020, from the Web of Science (WOS) database with the following keywords in their titles, abstracts, and/or keywords: “concept map*” or “concept mapping” or “mind map*” or “mind mapping” (Weinerth et al., 2014). To be included in this systematic review, each study had to meet the criteria indicated in Table 1. The search was limited to seven journals related to educational technology: BJET, C&E, ET&S, ETR&D, JCAL, ILE, and IETI. A total of 154 articles were obtained on April 15, 2021. First, editorials, correction notes, and early access articles were excluded, leaving 150 for review. Following that, a manual review process was conducted to examine the paper title and abstract of each article, and 81 studies that were not related to the use of concept maps in K-12 education were removed. Moreover, 10 non-empirical studies were deleted after manually screening the full texts. Finally, a total of 59 articles were included in this review study (see Figure 1). Among the 59 articles, 20, 11, 10, 8, 7, 2, and 1 were published by C&E, ET&S, BJET, JCAL, ILE, ETR&D, and IETI, respectively.

Table 1.

Inclusion and exclusion criteria.

Inclusion	Exclusion
a. Must involve concept mapping as a primary condition. b. Topic must include K-12 students. c. Must include the use of technology. d. Must be published between January 2001 and December 2020.	a. Editorials, correction notes, and early access articles were excluded. b. Higher education, adult learning. c. Concept mapping not used for educational purposes.

Figure 1.

Web of Science database searching steps.

Data Distribution

Figure 2 shows the distribution status of the CM-K12 articles published from 2001 to 2020. As suggested by previous review studies (Hwang & Tu, 2021; Hwang & Wu, 2012; Zheng et al., 2016), it is important to perceive trends in one research field by reviewing studies published in the past two decades (i.e., 2001–2020); furthermore, it is important to compare the findings from the first decade (i.e., 2001–2010) and the second decade (i.e., 2011–2020) in order to see the paradigm shift between these two time periods. Therefore, the current study classified the 59 CM-K12 articles published from 2001 to 2020 into two periods. A total of 21 articles were published in the first (2001–2011) period, and 38 in the second (2011–2020) period. This indicates that CM-K12 related issues have received growing attention from researchers in the field of educational technology in recent years. In addition, CM-K12 research increased in 2007, which could be due to the popularity of computer technology as well as the increased adoption of technologies in K-12 educational settings, as indicated by several researchers (e.g., Asan, 2007; Kimber et al., 2007; Tseng et al., 2007).

Figure 2.

Published literature on CM-K12 education from 2001 to 2020.

Theoretical Model, Data Coding, and Analysis

To explore research trends in CM-K12, the present study conducted coding analysis of the issues discussed among learners, concept maps, and the environments based on the technology-based learning model suggested by Lai (2020), as shown in Figure 3. The intersection between two of the three factors generates some issues, such as interaction issues (including roles of concept maps and participants), application issues (including the media of concept maps and application domains), and research issues (including research methods, evaluation of learners’ performance, data analysis methods, and research foci). Moreover, two researchers were invited to conduct the coding, which had a kappa value of 0.82. In order to ensure the quality of the analysis, the two researchers discussed the inconsistent coding until they reached consensus.

Figure 3.

Concept mapping in technology-supported K-12 education.

Table 2 illustrates the overall coding schemes of this study. Descriptions of the above dimensions are presented as follows:

1. In terms of interaction issues, two dimensions were included: “roles of concept maps” and “participants.” The coding items of the “roles of concept maps” dimension were determined by referring to the categories suggested by Hay et al. (2008). The items in the “participants” dimension were determined based on the categories suggested by Xia and Zhong (2018).

2. In terms of application issues, two dimensions were included: “media of concept maps” and “application domains.” The coding items of the “media of concept maps” dimension consisted of printed materials, desk-top computers, mobile devices, and mixed. The coding items of the “application domains” dimension were determined by referring to the categories suggested by Lai (2020).

3. In terms of research issues, four dimensions were included: “research methods,” “evaluation of learners’ performance,” “data analysis methods,” and “research foci.” The items in the “research methods” dimension were determined based on the categories suggested by Lai (2020). The coding items of the “evaluation of learners’ performance” dimension were determined by referring to the categories suggested by Xia and Zhong (2018). The items in the “data analysis methods” and “research foci” dimensions were determined based on the categories suggested by Lai (2020).

Table 2.

Coding schemes of this study.

Issues	Dimensions	Coding Items
Interaction issues	Roles of concept maps	Personal Mindtool, design tool, collaborative Mindtool, brainstorming tool for learning, knowledge map, summative tool, learning design tool, learning material design tool, assessment and evaluation tool, learning diagnosis tool, adapting learning paths, and recommending tool.
Interaction issues	Participants	Elementary school students, secondary school students, teachers, and mixed.
Application issues	Media of concept maps	Printed materials, desk-top computers, mobile devices, and mixed.
Application issues	Application domains	Science (physics, chemistry, biology), mathematics, arts, language, social studies (including history), engineering (including computer courses), health, medical and physical education, business or management, mixed courses, and non-specified.
Research issues	Research methods	Quantitative, qualitative, and mixed methods.
	Evaluation of learners’ performance	Observation, questionnaire, evaluation of artifacts, verbal interview, test/examination and self-report.
	Data analysis methods	Descriptive statistics, chi-square, t tests, one-way ANOVA/ANCOVA, two-way ANOVA/ANCOVA and advanced statistical methods, correlations, principal components analysis, regression analysis, structural equation modeling (SEM), cluster analysis, sequential pattern analysis, qualitative analysis, and data mining.
	Research foci	• Cognition, affect, learning performance (skillful), learning behavior, correlation/cause-and-effect analysis, accuracy and reliability, learning strategies, learning styles and others.
		• Cognition: learning achievement, higher order thinking performance, collaboration/communication, and quality of problem posing.
		• Affect: technology acceptance, attitude/effort, self-efficacy/belief, satisfaction/interest, cognitive load, learning anxiety, and interview or open-ended questions.

Research Results

Research question 1: From 2001 to 2020, in terms of interaction, what were the roles and participants of CM-K12 research?

Figure 4 shows that the roles of concept maps in K-12 education from 2001 to 2020 were dominated by personal Mindtools (25.42%) and collaborative Mindtools (25.42%), followed by assessment and evaluation tools (15.25%) and learning design tools (11.86%). In the first (2001–2011) period, the role as personal Mindtools (6 studies) accounted for the majority, followed by collaborative Mindtools (4 studies) and an assessment and evaluation tool (4 studies); that is, in the early technology-supported learning environment, concept maps mostly played the role of Mindtools, collaborative Mindtools, and an assessment and evaluation tool. In the second (2011–2020) period, the role as collaborative Mindtools (11 studies) accounted for the majority, followed by personal Mindtools (9 studies), a learning design tool (5 studies), and an assessment and evaluation tool (5 studies). As shown in Figure 4, the CM-K12 research related to collaborative Mindtools increased the most in the second period. Moreover, some new roles appeared during this period, for example, brainstorming tools for learning and knowledge maps (e.g., Sun & Chen, 2016; Wang et al., 2016). The roles of concept maps in K-12 education such as design tools, brainstorming tools for learning, knowledge maps, adapting learning paths, summative tools, and recommending tools were less discussed in CM-K12 research, specifically summative tools.

Figure 4.

The distribution of the roles of concept maps in the CM-K12 research.

According to the statistics in Figure 5, the participants explored the most in CM-K12 research from 2001 to 2020 was elementary school students (52.54%), followed by secondary school students (44.07%), and mixed (3.39%). From Figure 5, it is also found that teachers’ perceptions of using concept mapping in K12 education were rarely discussed; in particular, in these selected journals. This finding represents that the seven primary technology-based learning journals focus more on the effects of concept mapping on K-12 students and less on teachers’ perceptions of and attitudes toward implementing concept maps that facilitate the integration of technologies and instruction.

Figure 5.

The distribution of participants in the CM-K12 research.

Research Question 2: From 2001 to 2020, in Terms of Application, What Were the Learning Environments, Media of Concept maps, and Application Domains of CM-K12 Research?

From 2001 to 2020, the most adopted media of concept maps in CM-K12 research was desk-top computers (62.71%), followed by mobile devices (16.95%), mixed (11.86%), and printed materials (8.47%) (see Figure 6). In the first period, the most adopted media of concept maps was desk-top computers (17 out of 21 studies, 80.95%), followed by mixed (3 studies), and printed materials (1 study). In the second period, desk-top computers (20 out of 38 studies, 52.63%) were adopted the most, followed by mobile devices (10 studies), printed materials (4 studies), and mixed (4 studies). From these shifts, it is evident that the effectiveness of adopting concept maps in technology-based learning environments has been recognized by researchers. In addition, as the opportunities for mobile devices (such as tablet computers, smartphones, PDAs, and notebooks) to be employed in education increase, the media of concept maps will change accordingly.

Figure 6.

The distribution of media of concept maps in the CM-K12 research.

As shown in Figure 7, among the CM-K12 research published from 2001 to 2020, the most common application domain of concept maps was science (physics, chemistry, biology) (47.46%), followed by engineering (including computer courses) (18.64%), and language (16.95%). These three learning domains were early to integrate and implement concept maps into technology-based supported learning in K-12 settings. In addition, only four application domains of concept maps in K-12 learning activities could be found in the first period: science (physics, chemistry, biology) (9 studies), engineering (including computer courses) (7 studies), language (3 studies), and mathematics (2 studies). In the second period, the most adopted application domain was science (physics, chemistry, biology) (19 studies), followed by language (7 studies) and social studies (including history) (6 studies). During this period, there were papers investigating the learning effectiveness of applying concept maps in social studies (including history). Nevertheless, CM-K12 research did not explore the application of concept maps in the domains of arts, health, medical and physical, business or management, or across-disciplines (e.g., STEM).

Figure 7.

The distribution of the application domains in the CM-K12 research.

Figure 8 shows the distribution of the roles of concept maps by application domains in the CM-K12 research from 2001 to 2020. The most adopted roles of concept maps in science (physics, chemistry, biology) were personal Mindtools and collaborative Mindtools, followed by a learning diagnosis tool, an assessment and evaluation tool, and a learning diagnosis tool (e.g., Farrokhnia et al., 2019; Hwang et al., 2020; Lin et al., 2016). For example, in a learning activity based on a number game, Sun et al. (2015) demonstrated the elaboration potential of concept maps in facilitating students' learning of physics concepts. Ye et al. (2018) explored the effects of concept maps combined with flipping game learning on students' physical motion concepts. Moreover, the most adopted role of concept maps in engineering (including computer courses) was collaborative Mindtools (e.g., Kwon & Cifuentes, 2009; Sun & Lee, 2016), while that in language was personal Mindtools, followed by an assessment and evaluation tool (e.g., Chu et al., 2019; Cui & Yu, 2019; Hsu, 2019). For example, Kimber et al. (2007) adopted electronic concept maps to understand students’ conceptual understanding of collaborative learning in the subject of English. In the Chinese curriculum, Hwang et al. (2019) adapted a concept mapping-based summarization strategy into a flipped learning activity to improve students’ reading comprehension.

Figure 8.

Application domains and roles of concept maps.

Research Question 3: From 2001 to 2020, in Terms of Research, What Were the Research Methods and Data Analysis Methods, Evaluation of Learners’ Performance, and Research Foci of CM-K12 Research?

As demonstrated in Figure 9, the most adopted research method in the CM-K12 research published from 2001 to 2020 was quantitative (59.32%), followed by mixed methods (40.68%). Moreover, the CM-K12 study did not adopt a qualitative approach on its own. Table 3 demonstrates the distribution of adopting data analysis methods in the CM-K12 research from 2001 to 2020. As demonstrated in Table 3, the most adopted statistical method in the CM-K12 research was one-way ANOVA/ANCOVA (64.41%), followed by t tests (42.37%), and descriptive statistics (33.90%). Comparing the two periods, the adoption of two-way ANOVA/ANCOVA and advanced statistical methods, t tests, Chi-square, one-way ANOVA/ANCOVA, and qualitative analysis as the data analysis methods all experienced growth. However, several data analysis methods have not been employed in CM-K12 research, for example, principal components analysis, structural equation modeling (SEM), and data mining. In other words, the majority of CM-K12 research was experimental research. When compared to survey research, the sample sizes were small. Thus, principal components analysis, SEM, and data mining were scarcely used in the CM-K12 research.

Figure 9.

The distribution of research methods in the CM-K12 research.

Table 3.

The distribution of data analysis methods in the CM-K12 research.

	2001–2010 (n = 21)	2011–2020 (n = 38)	2001–2020 (n = 59)
Descriptive statistics	10 (47.62%)	10 (26.32%)	20 (33.90%)
Chi-square	(0%)	2 (5.26%)	2 (3.39%)
t tests	6 (28.57%)	19 (50.00%)	25 (42.37%)
One-way ANOVA/ANCOVA	10 (47.62%)	28 (73.68%)	38 (64.41%)
Two-way ANOVA/ANCOVA and advanced statistical methods	1 (4.76%)	6 (15.79%)	7 (11.86%)
Principal components analysis	(0%)	(0%)	(0%)
Regression analysis	2 (9.52%)	(0%)	2 (3.39%)
SEM	(0%)	(0%)	(0%)
Cluster analysis	3 (14.29%)	(0%)	3 (5.08%)
Sequential pattern analysis	(0%)	5 (13.16%)	5 (8.47%)
Data mining	(0%)	(0%)	(0%)
Correlations	3 (14.29%)	2 (5.26%)	5 (8.47%)
Qualitative Analysis	6 (28.58%)	14 (36.85%)	20 (33.89%)

In the CM-K12 research in 2001–2020, learners’ performance was evaluated most frequently with tests/examinations (81.36%), followed by questionnaires (59.32%), observation (44.07%), verbal interviews (22.03%), self-reports (6.78%), and evaluation of artifacts (5.08%) (see Figure 10). Except for evaluation of artifacts, the adoption of all the other evaluations of learners’ performance grew between the two periods, especially verbal interviews. Also, several CM K-12 studies have examined the effectiveness of overall learning activities by not only measuring learners’ learning achievements, but also by exploring learners’ learning experiences/perspectives and learning behaviors (e.g., Asan, 2007; Kao et al., 2017; Wu & Chen, 2018).

Figure 10.

The distribution of evaluation of learners’ performance in the CM-K12 research.

In these studies, research foci were divided into nine aspects: cognition, affect, learning performance (skillful), learning behavior, correlation/cause-and-effect analysis, accuracy and reliability, learning strategies, learning styles, and others. If the study involved more than one research focus, then each issue was recorded once under that research focus. Table 4 shows the distribution of research foci in the CM-K12 research published from 2001 to 2020. The research focus explored the most was cognition (89.83%), followed by affect (67.80%), learning behavior (35.59%), learning strategies (10.17%), learning performance (skillful) (8.47%), correlation/cause-and-effect analysis (8.47%), others (5.08%), accuracy and reliability (3.39%), and learning styles (3.39%).

Table 4.

Research issues in the CM-K12 research.

	2001–2010 (n = 21)	2011–2020 (n = 38)	2001–2020 (n = 59)
Cognition	20 (95.24%)	33 (86.84%)	53 (89.83%)
Affect	9 (42.86%)	31 (81.58%)	40 (67.80%)
Learning performance (skillful)	3 (14.29%)	2 (5.26%)	5 (8.47%)
Learning behavior	10 (47.62%)	11 (28.95%)	21 (35.59%)
Correlation/cause-and-effect analysis	3 (14.29%)	2 (5.26%)	5 (8.47%)
Accuracy and reliability	2 (9.52%)	(0%)	2 (3.39%)
Learning strategies	3 (14.29%)	3 (7.89%)	6 (10.17%)
Learning styles	(0%)	2 (5.26%)	2 (3.39%)
Others	1 (4.76%)	2 (5.26%)	3 (5.08%)

As shown in Table 4, the research issues became increasingly diverse in CM-K12 research. Except for accuracy and reliability and learning performance (skillful), there was growth in other dimensions, especially for affect and cognition. For example, Farrokhnia et al. (2019) made use of desk-top computer-supported concept maps to evaluate students’ learning, which not only enhanced their learning performance and integrity of concept maps, but also increased their collaboration and communication skills. Cui and Yu (2019) combined concept mapping and the flipped classroom to investigate students’ learning behavior related to ancient Chinese poetry, and indicated that applying concept maps to learning activities could increase the breadth and depth of students’ acquired knowledge.

In addition, in the second period, the CM-K12 research began to explore the issue of learning styles; for instance, Wang et al. (2016) proposed a question prompt-based concept mapping approach, and revealed that this approach could improve learners’ learning attitude, learning achievement, and 5C competences (i.e., communication, collaboration, critical thinking, complex problem solving, and creativity). The study also showed that the reflective-style students outperformed the active-style students in terms of learning achievement. Hwang et al. (2017) applied concept-mapping-based e-books in a junior high school law course to compare the learning effects of active, research, and learning-style students. The results implied that concept maps helped students organize the knowledge to enhance their learning achievement, especially for abstract and complicated learning content. Furthermore, other research issues in CM-K12 research consisted of science learning method, flow experience, and learning processes. For example, Gurlitt and Renkl (2008) explored the effect of highly coherent concept maps on the relationship between high school and university students’ prior knowledge and learning process. Chu et al. (2015) examined the effects of integrating a historical role-playing game and concept maps on elementary school students’ learning effectiveness, learning motivation, and self-efficacy.

The present study further explored the research issues in the cognition and affect aspect. Table 5 shows the distribution of research issues in the cognition and affect aspect in the CM-K12 research published from 2001 to 2020. In the cognition aspect, 52 studies (88.14%) could be found to investigate students’ learning achievement, followed by higher order thinking performance (13.56%), collaboration/communication (6.78%), and quality of problem posing (3.39%). In addition, some CM-K12 research started to explore the quality of problem posing in the second period. For example, Hwang et al. (2020) showed that adopting a multi-level concept mapping approach could enhance elementary school students’ learning performance and question-posing ability in a science class.

Table 5.

The cognitive and affect aspects for each period.

	2001–2010 (n = 21)	2011–2020 (n = 38)	2001–2020 (n = 59)
Cognition
Learning achievement	20 (95.24%)	32 (84.21%)	52 (88.14%)
Higher order thinking performance	2 (9.52%)	6 (15.79%)	8 (13.56%)
Collaboration/communication	1 (4.76%)	3 (7.89%)	4 (6.78%)
Quality of problem posing	(0%)	2 (5.26%)	2 (3.39%)
Affect
Technology acceptance	(0%)	7 (18.42%)	7 (11.86%)
Attitude/effort	6 (28.57%)	16 (42.11%)	22 (37.29%)
Self-efficacy/belief	(0%)	9 (23.68%)	9 (15.25%)
Satisfaction/interest	(0%)	7 (18.42%)	7 (11.86%)
Cognitive load	(0%)	8 (21.05%)	8 (13.56%)
Learning anxiety	1 (4.76%)	(0%)	1 (1.69%)
Interviews or open-ended questions	4 (19.05%)	15 (39.47%)	19 (32.20%)

In the affect aspect, a total of 22 studies (37.79%) were found to explore students’ attitude/effort, followed by interview or open-ended questions (32.20%), self-efficacy/belief (15.25%), cognitive load (13.56%), satisfaction/interest (11.86%), technology acceptance (11.86%), and learning anxiety (1.69%). In the second period, several research issues started to be examined in CM-K12 research, that is, self-efficacy/belief, cognitive load, technology acceptance, and satisfaction/interest. For instance, researchers revealed that integrating a time sequence-oriented concept map approach into games could improve students’ history learning, and significantly increase their learning performance and technology acceptance. Yet, there was no difference in their learning attitude towards the history class, learning motivation, or self-efficacy (Chu et al., 2015). Hsu (2019) investigated the effects of a concept mapping strategy on EFL learners’ attention, relevance, confidence, and satisfaction.

Discussion

Roles of Concept Maps and Participants

In 2001–2020, personal Mindtools and collaborative Mindtools accounted for the majority of roles of concept maps in CM-K12 education research, followed by an assessment and evaluation tool and a learning design tool. This indicated that most of the K-12 education research used concept maps to facilitate individual or group collaboration for organizing knowledge (e.g., Lin et al., 2016), and explored learners’ application of concept mapping to organize their knowledge (e.g., Chu et al., 2019; Hsu, 2019). Other studies made use of concept maps as a tool to evaluate learners’ learning status (e.g., Kao et al., 2017; Sun et al., 2015; Wu & Chen, 2018), while concept maps as a summative tool, design tool, brainstorming tool for learning, knowledge map, adapting learning paths, and recommending tool have been less discussed in the CM-K12 research.

Regarding participants, the majority of CM-K12 research adopted elementary school students, followed by secondary school students and mixed, while few studies discussed the use of concept mapping in K-12 education from teachers’ perspectives. Mutodi and Chigonga (2016) also pointed out that teachers incorporate concept maps into the formative assessment process; it is thus important to understand teachers' perceptions of their adoption (including applicability, strengths, and weaknesses). Further exploring the CM-K12 research, in the first (2001–2010) period, the subject explored the most was secondary school students, while that of 2011–2020 was elementary school students. Among the CM-K12 research investigating elementary school students, the most adopted role of concept maps was a collaborative Mindtool (e.g., Chiu et al., 2013), followed by personal Mindtool (e.g., Chu et al., 2019; Cui & Yu, 2019), and learning design tool (e.g., Hwang et al., 2013). In CM-K12 research which used secondary school students as subjects, concept maps were mostly adopted as an assessment and evaluation tool (e.g., Kao et al., 2017; Wu & Chen, 2018), personal Mindtools (e.g., Hsu, 2019; Ye et al., 2018), and collaborative Mindtools (e.g., Farrokhnia et al., 2019; Lin et al., 2016).

Media of Concept Maps and Application Domains

In CM-K12 research, the most adopted media of concept maps was desk-top computers, followed by mobile devices and mixed. With the development of mobile learning, researchers have increasingly applied concept maps on mobile devices, which mainly assist learners in organizing knowledge by the use of mobile device-based concept maps (e.g., Chu et al., 2019; Yang et al., 2013). In addition, Science (physics, chemistry, biology) accounted for the majority of the adopted application domains in CM-K12 research, followed by engineering (including computer courses), language, and mathematics. For example, in the application domain of science (physics, chemistry, and biology), the study employed the collaborative knowledge construction strategy to promote students’ learning behavior and learning performance in a digital game (Sung & Hwang, 2018). Another study applied individually-constructed and collaboratively-constructed computer-based concept mapping to learning activities in a computer course to examine the effects of the concept mapping method on students’ attitude and deeper conceptual understanding (Kwon & Cifuentes, 2009). As for language learning, Chu et al. (2019) employed a collaborative concept map in an online game-based learning activity to explore its effects on learners’ learning performance and learning behavior of English grammar. Hwang et al. (2019) indicated that applying concept maps with summarizing strategies in a flipped learning Chinese course can influence students’ summarizing ability and could also be indirectly related to students' reading comprehension.

Moreover, several studies started to apply concept mapping in social studies (including history) in the second period (e.g., Chu et al., 2015; Lin et al., 2012). Huang et al. (2017) and Lodewyk (2009) implied that concept mapping facilitated the development of students’ motor skills and critical thinking, and had a positive effect on their tactical knowledge. In physical education, tactical knowledge is a crucial factor of critical thinking. On the other hand, in a game requiring tactical knowledge such as a basketball game, the concept mapping method could enhance critical thinking abilities, including assumption identification, induction, deduction, interpretation, and evaluation (Huang et al., 2017). In addition, CM-K12 research has less frequently combined concept mapping with technology for application in arts, health, medical and physical, business or management, and cross-disciplinary domains (e.g., STEM).

Research Methods and Data Analysis Methods, Evaluation of Learners’ Performance, and Research Foci

The most adopted research method in CM-K12 research was quantitative, followed by mixed methods. However, qualitative methods were rarely employed alone in CM-K12 research. The evaluation of learners' performance consists mainly of tests/examinations, followed by questionnaires and verbal interviews. Comparing the two periods, except for evaluation of artifacts, it showed a growth in other evaluation of learners’ performance in CM-K12 research, especially for interviews. This revealed that the evaluation of learners’ performance is becoming gradually more diverse, and that researchers have started to pay attention to learners’ perceptions of and perspectives on concept-mapping-assisted learning. Most of the CM-K12 research adopted one-way ANOVA/ANCOVA, t tests, and descriptive statistics as the data analysis methods, which might be because CM-K12 research mostly used concept maps to assist learners in organizing knowledge or evaluating their learning status. Researchers have revealed that there may be a relationship between the adopted data analysis methods and the investigated issues. For example, with more attention paid to learners’ learning behavior and learning process, more studies can be found to adopt sequential pattern analysis (Lai, 2020).

As for the research foci, the cognition aspect accounted for the majority, followed by the affective aspect. In general, a single study usually discusses at least two issues, and most studies evaluate students’ learning performance or learning achievement since this is the direct method to examine whether a learning approach can realize the course objectives. In addition to the frequently explored research foci (e.g., learning motivation and attitude), an interesting finding in the present study is that several studies explored the effect of concept maps on active and reflective-style students' learning performances (e.g., Wang et al., 2016). Wang et al. (2016) also specified the importance of appropriate scaffolding for students to construct concept maps from substantial data. Hwang et al. (2020) indicated that scaffolding question posing with concept mapping could improve question posing quality, effective learning, and learning motivation. Furthermore, collaboration/communication, metacognition, learning anxiety, learning styles, learning strategies, and learning performance (skillful) were scarcely explored in CM-K12 research.

Conclusions

The current study reviewed the CM-K12 research published in seven primary technology-based learning journals from 2001 to 2020. The results showed that concept mapping could help learners identify the main learning concepts, positively organize their acquired knowledge, and reduce their learning anxiety during the learning process. It could also assist learners in clarifying misconceptions, and in explaining the knowledge and concepts during the thinking process, which helped them synthesize the acquired knowledge to construct their own knowledge (Chang et al., 2001; Erdogan, 2009; Riley & Åhlberg, 2004). The present study classified the research findings and summarized its contributions: (a) it analyzed the roles of concept maps and research subjects in K-12 education research; (b) it proposed the media of concept maps and application domains that were less explored in CM-K12 research; and (c) it proposed the data analysis methods, evaluation of learners' performance, and research foci that were less investigated in CM-K12 research.

Based on the findings of the study, several recommendations are made for future research related to CM-K12 research.

1. It can be considered to employ concept maps to facilitate the integration of new technologies (e.g., virtual reality/AR) into learning activities, and to examine learners’ perceptions (e.g., learning anxiety, self-efficacy, and confidence) and learning outcomes. In addition, teachers can also apply concept mapping in new technology-assisted teaching, and employ it as a summative tool, design tool, brainstorming tool for learning, knowledge map for adapting learning paths, and as a recommending tool to increase students’ learning effectiveness and 5C competences. For example, it could be valuable to explore the relationship between learners' conceptions, behaviors, and learning characteristics by analyzing their concept mapping process as well as learning achievements and perceptions for recommending appropriate learning guidance or feedback.

2. In K-12 educational settings, MOOCs (Massive Open Online Courses) have been the trend. Employing the concept mapping strategy could be considered to help students organize knowledge and to design online/physical learning activities for brainstorming or arguments, which could develop K-12 students’ autonomous learning.

3. In addition to achievement, it is also important to explore the impact of concept mapping on students’ learning outcomes in diverse dimensions, such as collaboration/communication skills, metacognition, and learning anxiety. Moreover, it is also worth investigating why concept mapping was rarely adopted in some disciplines such as mathematics, arts, health, medical and physical, business or management, and cross-disciplinary domains (e.g., STEM). For instance, is it possible to engage learners in collaborative concept mapping in a STEM activity of developing a robot?

4. Another suggestion is to investigate and explore the relationships between factors affecting students’/teachers’ use of the concept mapping learning strategy, or analyzing the online learning behavior of K-12 students with different learning styles after implementing a concept mapping learning activity.

On the other hand, some limitations of the present study need to be noted. First, the review was conducted based on seven primary journals of educational technology. Second, the analytical results highly relied on the classifications and coding schemes. Therefore, in the future, it is suggested that further studies can be conducted by taking into account articles from different academic databases (e.g., the WOS and Scopus databases) rather than just focusing on several selected journals; moreover, it would be worth analyzing the CM-K12 articles from different analytical angles.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported in part by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST-108-2511-H-011-005-MY3.

ORCID iD

Gwo-Jen Hwang

Author Biographies

Chun-Chun Chang is a PhD student at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology. Her research interests include nursing education, medical education, digital game-based learning and mobile learning.

Gwo-Jen Hwang is a chair professor at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology. His research interests include mobile learning, digital game-based learning, flipped classrooms, and AI in education.

Yun-Fang Tu is an Assistant Professor at the Department of Library and Information Science, Research and Development Center for Physical Education, Health, and Information Technology, Fu Jen Catholic University, Taiwan. Her research interests include mobile and ubiquitous learning, and bibliometric mapping analysis.

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