Assessing the Effectiveness of Case-Based Learning and Concept Mapping Approaches in Developing Theoretical and Metacognitive Skills in Medical Students

doi:10.21203/rs.3.rs-4373060/v1

This research utilizes a mixed-method approach, integrating quasi-experimental design and quantitative triangulation methods to determine the impact of Case-Based Learning (CBL) and CBL supported by Concept Maps (CMs) on the theoretical knowledge comprehension abilities and metacognitive skills of medical students. In the first phase of the two-stage evaluation process, researchers administered a pre-test/post-test achievement test focusing on students' comprehension levels of theoretical knowledge; the data were analyzed using two-way ANOVA. The analysis results indicated a statistically significant increase in the average achievement scores across all learning environments; however, it was determined that this increase was not attributable to differences specific to the learning environments. In the second phase of the assessment, a new test was administered to not only assess the students' theoretical knowledge but also how they apply this knowledge in new and uncertain situations. The data obtained from this test, which targeted students' metacognitive skills and was administered after the experimental process, were analyzed using One-Way ANOVA to determine the effectiveness of the learning environments. The analysis results revealed that CBL significantly impacts metacognitive skills, and this effect is enhanced when supported by CMs. The findings demonstrate that CBL and CMs-supported educational approaches play a significant role in enhancing medical students' ability to effectively apply theoretical knowledge in the face of new and uncertain situations. These findings highlight the extensive utility of CBL and CM-supported approaches in enhancing critical metacognitive skills, essential for medical education. Furthermore, they suggest practical ways for integrating these methodologies into future curricula and refining educational strategies.

Case-Based Learning

Concept Mapping

Metacognitive Skills

Theoretical Knowledge Application

Medical Students

The integration of innovative teaching methodologies in medical education is crucial for preparing future health professionals to make effective and ethical decisions within complex healthcare systems. In this context, particularly in continuously evolving and developing areas such as health information security and informatics ethics, it is essential to provide students with applicable in-depth knowledge and skills for real-world scenarios. Such knowledge and skills assist students in enhancing their ability to apply theoretical information within ethical standards and support them in tackling challenges that may arise in today's healthcare environments. The current trend in medical education is moving away from traditional passive learning environments towards more active and engaging learning exercises (Slieman & Camarata, 2019). These methods have a greater potential to enhance students' knowledge and skills compared to traditional teaching approaches. In this regard, Problem-Based Learning (PBL), a long-successfully applied educational methodology that can be integrated with innovative teaching approaches, serves as a concrete example of how medical education can strengthen students' problem-solving abilities and deepen their clinical knowledge. Moreover, the undergraduate level, where clinical research skills complement clinical training, is particularly suited for a longitudinal PBL model (Wyer, 2019).

In medical education, the PBL methodology is widely favored for both enhancing students' problem-solving skills and enabling them to acquire necessary information through clinical cases in a comprehensive and integrated manner. This methodology supports students in holistically assimilating knowledge gained from clinical cases and advancing their problem-solving capabilities to a higher level (Barrows & Tamblyn, 1980). In this context, strategies such as Case-Based Learning (CBL) bolster the PBL methodology by providing opportunities for students to integrate theoretical knowledge with practical scenarios while also contributing to the development of clinical thinking and decision-making skills. CBL allows students to handle real-life situations they will encounter in the healthcare environment within the framework of innovative teaching approaches, facilitating the application of the knowledge and skills acquired. Moreover, as a strategic extension of the PBL methodology, CBL presents a comprehensive and integrated framework for medical education, promoting the integration of theoretical and practical knowledge. This approach also aims to equip students with critical thinking skills necessary for analyzing complex health issues and focusing on the development of diagnostic and clinical reasoning skills (Weiss & Levison, 2000).

Also known as the case method, CBL is an effective student-centered strategy for integrating theory into practice and enhancing critical thinking and problem-solving skills through focus on patient cases (McLean, 2016; Peñuela-Epalza & De la Hoz, 2019; Thistlethwaite et al., 2012). Offering practical learning opportunities through real-life cases, CBL provides unique advantages in medical education by aiding students in overcoming challenges and developing critical thinking skills. Furthermore, it enables students to deepen their understanding of theoretical knowledge by applying it in real-life situations and supports their ethical decision-making processes. CBL adopts an inquiry method aligned with defined learning objectives and offers a more systematic structure compared to the PBL methodology (Srinivasan et al., 2007). With these features, CBL holds the potential to be an important learning strategy that helps medical students adapt to the dynamics of healthcare services and develop effective problem-solving capabilities. Particularly, the development of students' abilities to comprehend theoretical knowledge and enhance metacognitive skills during this process is critically important. In this context, Martinez (2006) defines metacognition as "the monitoring and control of thought," a definition that closely aligns with the simplified expression "thinking about thinking." This perspective reveals the fundamental nature of metacognitive processes and highlights how critical thinking skills can be developed through greater awareness and control over one's own cognitive activities (Martinez, 2006).

In addition to the practical learning experiences provided by CBL, Concept Maps (CMs) also emerge as an effective tool for enhancing knowledge and understanding. CMs are graphical tools that represent a set of concepts arranged in propositions (Novak & Cañas, 2008). These tools facilitate the active construction of individual knowledge structures, moving beyond passive information transfer from experts to individuals (Peñuela-Epalza & De la Hoz, 2019). Due to their usefulness in organizing and synthesizing information, CMs contribute to meaningful learning and are utilized across various levels and fields of medical education (Cruz & Fierrros, 2006; Peñuela-Epalza & De la Hoz, 2019; Rendas et al., 2006; Vink et al., 2016). Besides simplifying the complex aspects of medicine, CMs are noted for enhancing student motivation and active participation, making the learning process interactive, and supporting deep learning (Shrivastava & Shrivastava, 2022). Specifically, the use of CMs in analyzing and reflecting on the practical experiences gained through CBL enriches the learning process. The integration of these two approaches provides students with opportunities to effectively apply theoretical knowledge and to comprehensively understand and integrate this knowledge (Peñuela-Epalza & De la Hoz, 2019). This integration, by blending theory with practice in-depth, can offer a more holistic learning experience for students. Therefore, incorporating CMs into CBL sessions, particularly for novice clinical students encountering patient health issues for the first time and feeling lost in these situations, is seen as a strategic approach to strengthen the deep integration of theory and practice (Peñuela-Epalza & De la Hoz, 2019).

In medical education, the use of real or realistic patient scenarios is crucial for enhancing students' abilities to learn new medical procedures, develop skills, and create treatment plans. However, students often struggle to apply the theoretical knowledge gained in class to their performance in practical exams and clinical settings. As an effective alternative to overcome these challenges, the CBL strategy offers robust support to students in transforming theoretical knowledge into practice, developing clinical skills, and preparing for real-life scenarios. CMs, on the other hand, help students visualize their knowledge structures, organize their thoughts, and integrate what they have learned. The combined use of these approaches has the potential to provide students with opportunities for in-depth learning and developing critical thinking skills. Evaluating the effectiveness of this integration is a critical step in developing strategies for the education of future health professionals. Furthermore, equipping future health professionals with advanced knowledge and skills in new medical procedures, application, and treatment planning is of great importance. In this context, the study aimed to determine the impact of case-based learning and concept mapping-supported case-based learning approaches on the levels of theoretical knowledge comprehension and metacognitive skills of medical students. The following research questions play a critical role in assessing the effectiveness of educational strategies and developing methods that better meet the needs of future health professionals:

Is there a statistically significant difference in academic achievement scores related to the comprehension of theoretical knowledge according to the applied learning approaches?
Is there a statistically significant difference in academic achievement scores related to metacognitive skills according to the applied learning approaches?

Research design

This study was conducted using an approach that integrates a quasi-experimental design with quantitative triangulation methods. The quasi-experimental design adopted in this study allowed for the examination of the effects of interventions on natural groups, while the quantitative triangulation method combined multiple quantitative research techniques and analysis methods to expand the accuracy and scope of the data obtained. This strategic approach has strengthened the internal validity of the research and enhanced the generalizability and reliability of the findings, seeking detailed and multi-faceted answers to complex research questions (Creswell, 2014; Shadish et al., 2002; Tashakkori & Teddlie, 2003). Furthermore, this methodological integration aimed to minimize limitations by merging the structural discipline provided by the quasi-experimental design with the methodological richness of quantitative triangulation, leveraging the strengths of both approaches. The implementation process and overview of this comprehensive methodology are presented in Table 1.

Table 1. Quasi-Experimental Design: Comparative Analysis of Educational Approaches in Medical Training*

Groups		Pre experimental	Experimental process	Post experimental
Control	CG	Pretest (AT)	Traditional Educational Environment	Posttest (AT + CBCTT)
Experimental	EG1		Case-Based Learning Approach
Experimental	EG2		Case-Based Learning Approach Supported by Concept Mapping
*Note. AT: Achievement Test; CBCTT: Case-Based Critical Thinking Test; Independent variable: Educational Environment Approaches (Traditional, CBL, CBL with CMs); Dependent variable: Achievement. The groups evaluated in this study include the Control Group (CG), the CBL Experimental Group (EG1), and the CBL Supported by CMs Experimental Group (EG2).

In addition to the teaching programs applied to students, a two-stage evaluation process was followed to determine the effectiveness of the CBL approaches supported by CBL and CMs. In the first phase of evaluation, a pre-test/post-test format achievement test was administered to measure the level of comprehension of theoretical knowledge. During this process, the measurement tool developed by researchers was repeated after an eight-week interval, and the changes in students' academic achievements were examined in detail. In the second phase of evaluation, a customized test prepared on a new case, independent of the cases studied, was administered. This stage aimed not only to assess the students' theoretical knowledge but also how they applied this knowledge to new and uncertain situations. Thanks to this multi-layered evaluation approach, the effectiveness of the learning strategies examined was analyzed in a multi-faceted way, attempting to increase the generalizability and reliability of the results obtained.

Study group

Participants for the study conducted with medical school students were selected using purposive sampling methods, specifically criterion sampling. Attention was paid to meet the criteria established according to the study's objectives, and several factors were considered in selecting the study groups: ensuring continuity of experimental procedures, ease of access to subjects, similar pre-course knowledge levels among participants, and voluntariness. Accordingly, students from three different groups enrolled in an elective course on Security and Ethics in Medical Informatics, taught by the same instructor, formed the study groups. A pre-test was administered to the predetermined three groups to assess the homogeneity and normality of their baseline knowledge distribution. Based on the test results, the students were evaluated in the following groups:

Control Group (CG): This group received no intervention and continued with traditional educational methods.
Case-Based Learning Experimental Group (EG1): This group was educated with CBL methods.
Case-Based Learning Supported by Concept Mapping Experimental Group (EG2): This group received education with both CBL methods and concept mapping techniques.

Table 2. Gender Distribution of the Study Group

Study groups		Gender				Total
		Female		Male		Total
		N	%	N	%	N	%
Control Group	CG	14	51.9	13	48.1	27	34.2
Experimental Groups	EG1	13	50.0	13	50.0	26	32.9
Experimental Groups	EG2	14	53.8	12	46.2	26	32.9
Total		41	51.9	38	48.1	79	100.0

As detailed in Table 2, the study included a total of 79 students: 27 in the Control Group (CG) with 14 females and 13 males, 26 in the Case-Based Learning Experimental Group (EG1) with 13 females and 13 males, and 26 in the Case-Based Learning Supported by Concept Mapping Experimental Group (EG2) with 14 females and 12 males. The study was concluded with the participation of all students in the post-test applications.

Educational Methods and Standardization

This study was conducted under the approval of Çanakkale Onsekiz Mart University Ethics Commission, with the authorization number 2023-YÖNP-0614. All participating students received face-to-face instruction from the same lecturer, who adhered to a standardized curriculum throughout the duration of the research.

Creation and implementation of the achievement tests

This study was conducted within the scope of the "Security and Ethics in Medical Informatics" course, which focuses on the development of skills related to information security and ethical issues in the use of computing technologies in the medical field. This course, categorized as an elective in the medical education curriculum, was designed to measure each learning outcome through at least 3 questions prepared by field experts, totaling 40 questions. Before a preliminary trial of the questions, their clarity, understandability, and appropriateness to the course outcomes were reviewed by three faculty members specialized in medical informatics, ethics, and information security, as well as an academician expert in assessment and evaluation methodologies. The clarity and understandability of these multiple-choice questions were assessed from an interdisciplinary perspective and confirmed to meet academic standards. The questions, designed to be clear and have only one correct answer according to the course outcomes, underwent item analysis after validation of content validity by experts. This analysis involved 93 students from the university where the study was conducted and two other universities at the same educational level. Each multiple-choice question offered five options, with correct answers scored as 1 point and incorrect or unanswered questions scored as 0. The internal consistency of the test scores was examined using the Kuder Richardson-20 (KR-20) reliability, and the relationship between the scores obtained from the test items and the total test score was calculated using item-total correlation. It is indicated that items with an item-total correlation of .30 or higher discriminate well between individuals, and a test reliability coefficient of .70 or higher is generally sufficient for the reliability of the test scores (Fraenkel, & Wallen, 2011; Nunnally, & Bernstein, 1994). The analyses resulted in item-total correlations ranging from .32 to .65, a Mean Discrimination Index of .431, and a KR-20 reliability value of .818 for the achievement test. Students in the experimental and control groups were not involved in the development process of the achievement test and encountered these questions for the first time during the pre-test phase.

In the second phase of the assessment, the Case-Based Critical Thinking test was administered to measure students' metacognitive skills such as critical thinking, problem solving, and adaptation to new and uncertain situations. In the post-test phase of the study, analyses were conducted on the resolutions made by all groups based on a new case presented to the students. This test, comprising short-answer questions, was designed to evaluate how students could apply what they learned to previously unencountered situations, and was developed in collaboration with the same faculty experts who contributed to the creation of the first assessment tool. The short-answer questions used in the research were chosen to measure the academic success of students' metacognitive skills. This approach not only demonstrated the students' ability to link theoretical knowledge with practical scenarios and develop independent solutions, but also allowed for a more accurate assessment of their deep understanding and application skills. To evaluate the reliability and validity of the questions, a pilot test was administered to a small group of 13 students with similar characteristics to the study groups. Based on the results of this test, the clarity and measurement effectiveness of the questions were reviewed, and the discriminative power of the questions was determined through the analysis of student responses. At this stage, the questions were scored objectively by an expert group, and the scores obtained were used to assess the overall reliability of the test and its suitability as a measurement tool. Each question was structured to have one correct answer related to the final decision on the case.

Case Development and Integration into the Process

This study meticulously prepared cases focusing on the development of skills related to information security and ethics in the medical field, taking into account the outcomes aimed at enhancing these skills. Every step of this process was designed to reflect real-world scenarios that students might encounter, maximizing the applicability of theoretical knowledge to practical situations. Initially, core topics appropriate to the course's learning objectives and the curriculum's demands were selected. These core topics were determined to encompass various situations students might face in fields such as medical informatics, information security, and ethics. Following this, the existing literature related to the chosen topics was thoroughly reviewed, and real-world cases were investigated. At this stage, the opinions of expert faculty members who contributed to the development of the assessment tools were also sought. This expert group assessed the educational value, realism, and alignment with learning objectives of the cases, guiding their preparation. Based on the group's recommendations, detailed case scenarios for each topic were developed. These scenarios were designed to test students' critical thinking, problem-solving, and decision-making abilities and were organized to reflect realistic situations. The prepared cases were revisited by the expert group for content accuracy and educational value, and necessary revisions were made. Subsequently, a pilot test was conducted on a small group of students to evaluate the clarity of the cases, student interaction, and educational effectiveness. Feedback from the students provided the basis for the final adjustments to the cases. The revised cases were presented as part of the course, and the responses provided by the students were evaluated against predetermined criteria. This evaluation aimed to analyze the educational effectiveness of the cases and their impact on learning outcomes. When necessary, further improvements were made to the cases for future implementations. Throughout this process, the invaluable insights of the expert faculty members facilitated a multidisciplinary approach to the development of cases that met academic standards.

Implementation and Evaluation of Learning Methods

The course instructor administered an achievement test during the first week of the course, focused on the concepts covered in the curriculum, to determine the students' prior knowledge levels. The data from this pre-test were used as a basis for forming the study groups. No interventions were made in the control group during the process, and they continued with the existing educational programs. In the experimental groups where CBL (EG1) and CBL supported by CMs (EG2) were implemented, comprehensive information about the process and its evaluation was provided in the second week of the course (see Figure 1).

Figure 1. Comparative Flowchart of Educational Interventions for Experimental and Control Groups

In the third week of the course, the information session continued; to the EG2 group, in addition to the training conducted for the EG1 group, comprehensive training on concept mapping techniques was provided. During this training session, the relationship between the case and CMs was discussed, and strategies on how to create CMs (either manual or digital) were demonstrated with examples and presented to the students (for more details, see Appendix A1). At the end of this informational process, the first case expected to be evaluated in the fourth week of the course was provided with instructions (for an example, see Appendix A2). Each student was expected to submit their analysis of the first case, provided in print, before the next class session (either in print or digital format). In the EG2 group, unlike the EG1 group, students were asked to use the concept mapping strategy in their case analyses based on the information acquired during the course process and to submit the CMs they created along with their case analyses (for an example, see Appendix A3). During the class, discussions were held over the analyses prepared by the students, and in-class analyses were conducted. While the EG1 group focused on the cases, the EG2 group also included analyses made with concept mapping. At the end of the class, the case to be discussed in the next session was presented to the students in print, and the students were asked to submit their analyses of this case before the next class. This arrangement and sequencing led to a case analysis being conducted each week for a total of five weeks. In the study, where pre-tests and post-tests were conducted eight weeks apart, the achievement test containing the questions asked in the pre-test phase was administered to all groups the week after the case analyses were completed. Afterwards, the Case-Based Critical Thinking test developed for the second evaluation phase was administered to all groups, thus completing the study.

Data collection

In the study, a two-stage evaluation was conducted to observe changes in students' understanding of information security and computer ethics. In the first stage, an achievement test focusing on the concepts covered in the course was administered to the study groups at the beginning and end of the educational processes simultaneously. In the second stage, the Case-Based Critical Thinking test, which focuses on measuring metacognitive skills, was administered at the end of the educational processes, and the data collection process was completed. Achievement tests were administered to all study groups in a controlled classroom environment, following ethical standards and after obtaining informed consent. The data collection was conducted using standardized procedures to ensure equal conditions for each student.

Data analysis

At the beginning and end of our reserach study, the achievement test containing multiple-choice questions awarded 1 point for each correct answer and 0 points for incorrect or unanswered questions. Given the homogeneity among the groups studied, the assumption of normal distribution was met, and the three groups had similar levels of prior knowledge, parametric tests were used for data analysis. To test the effectiveness of the environments, a 3x2 split plot design was utilized, and a two-way ANOVA for repeated measures was performed to analyze this research question. Similarly, in the Case-Based Critical Thinking test administered during the second evaluation phase, 1 point was awarded for each correct answer, with 0 points for incorrect or unanswered questions. The data were analyzed using a One-Way ANOVA to compare the effectiveness of the three different learning environments. The significance of differences between group mean scores was interpreted at the .05 level, and post-hoc tests were used to determine the source of differences where necessary. Additionally, Cohen's d effect size analysis was performed to determine the magnitude of the learning environments' impact on student success. According to benchmarks set by Cohen (1988), effect sizes are typically classified as small (d=0.2), medium (d=0.5), or large (d=0.8). These evaluations help to determine the practical significance of the research findings and quantitatively demonstrate the impact of the learning environments on student success.

Prior to the study, we conducted a pretest to establish the initial knowledge levels of the groups, and to test for homogeneity and normality of the distribution. After administering the pretest, homogeneity among the groups was confirmed (Levene's Test F=1.453, p>.05). Normality tests also showed that the pretest scores of students in the CG (Skewness=.211, Kurtosis=-.872), EG1 (Skewness=-.70, Kurtosis=-1.087), and EG2 (Skewness=-.75, Kurtosis=-.881) groups were normally distributed. A One-Way ANOVA test was conducted to determine if there were significant differences in pretest scores among the three groups prior to the experimental procedure, and the analysis found no significant differences in the mean scores (F(2,76)=.024, p>.05). All these results made it possible to use parametric tests to evaluate the effectiveness of the educational interventions. A 3x2 split plot design was used to test the effectiveness of the educational environments in improving students' comprehension of theoretical knowledge, and a two-way ANOVA was performed within this framework. In this design, the first factor represents three different learning environments (Traditional Educational Environment, Case-Based Learning, and Case-Based Learning Supported by Concept Mapping), and the second factor represents the pre- and post-experiment measurements (pretest-posttest). The mean and standard deviation of the pretest-posttest scores for the environments where the students were educated are provided in Table 3.

Table 3. Learning Environments Mean and Standard Deviation Values (AT)

Groups		N	Pretest(AT)		Posttest (AT)		Effect size
Groups		N	Mean	Sd	Mean	Sd	Cohens’d	Level
Control	CG	27	19.59	4.05	31.81	3.61	2.912	Large
Experimental	EG1	26	19.42	5.45	33.35	3.50	3.041	Large
Experimental	EG2	26	19.31	4.71	34.23	3.15	3.723	Large
Note: All scores have been evaluated on a 40-point scale.

According to the data obtained, students in the CG who were taught in a traditional educational environment had a pre-experiment average achievement score of 19.59, which increased to 31.81 post-experiment (Cohen’s d=2.912). In the CBL group EG1, students' average achievement score rose from 19.42 before the experiment to 33.35 afterward (Cohen’s d=3.041). In the group where CBL supported by CMs EG2 was implemented, the pre-experiment average achievement score of 19.31 increased to 34.23 post-experiment (Cohen’s d=3.723). These results demonstrate significant and large-scale effects on student achievement scores. The significance and magnitude of the effects of the respective learning strategies have been evaluated using a two-factor ANOVA, the results of which are presented in Table 4.

Table 4. ANOVA Results regarding Learning Environments

Source of variance	Sum of squares	df	Mean squares	F	p	Eta(η²)
Between Subjects	109165.855	79
Intercept	109134.906	1	109134.906	4320.379	<.001*	.983
Group (Individual/Group)	30.949	2	15.475	0.613	.545	.016
Error	1919.798	76	25.260
Within Subjects	7449.550	79
Measurement (Pretest-Posttest)	7400.008	1	7400.008	828.059	<.001*	. 916
*GroupMeasurement**	49.542	2	24.771	2.772	.069	.068
Error	679.179	76	8.937
Total	119214.382	158
*p<.05

According to the results obtained, while there was an increase in achievement scores from pre-test to post-test among students in three different learning environments, this increase was not due to differences inherent to the learning environments. In other words, the combined effects of different learning environments and repeated measures did not show a statistically significant impact on student achievement (F(2,76)=2.772, p=.069, η²=.068). This low effect size indicates that the interaction between learning environments and measurement times had limited influence on student outcomes. Additionally, the influence of the group factor on the total variance was minimal (F(2,76)=0.613, p=.545, η²=0.016), confirming that different learning environments did not have a significant effect on student success. Although there was an observed increase in achievement scores across different learning environments (F(1,76)=828.059, p<.001, η²=.916), this increase was not attributable to the unique characteristics of these environments. Specifically, the strong and statistically significant relationship between pre-test and post-test average achievement scores was uniformly associated with the overall level of success (Intercept) across all environments (F(1,76)=4320.379, p<.001, η²=.983). This finding indicates that the overall increase in achievement was observed independently in all groups and that the learning environments did not play a decisive role in this change. These results demonstrate that different learning environments did not significantly impact students' ability to comprehend theoretical knowledge. Furthermore, the second phase of the evaluation focused on the impact of learning environments on students' metacognitive skills, examining these skills in detail. During this phase, the Case-Based Critical Thinking test was administered to assess students' abilities in critical thinking, problem-solving, and adaptation to novel situations. This test, designed to measure how students could solve problems they had never encountered before, provided data on the average scores and standard deviations of the groups, which are presented in Table 5.

Table 5. Average Scores and Standard Deviations of Achievement Scores Among Control and Experimental Groups

Study groups		N	Mean	Sd
Control Group	CG	14	11.07	1.90
Experimental Groups	EG1	13	13.08	1.26
Experimental Groups	EG2	14	13.73	1.15
Total		79	12.61	1.86
Note: All scores have been evaluated on a 15-point scale.

According to the results of the Case-Based Critical Thinking test, students in CG scored the lowest with an average of 11.07 points. Conversely, students in EG1 achieved an average score of 13.08, and those in EG2 scored an average of 13.73, indicating higher performance. These results demonstrate that the interventions applied to the experimental groups significantly outperformed CG. Following these findings, the results of the Case-Based Critical Thinking test, aimed at measuring students' metacognitive skills, were analyzed using a One-Way ANOVA and detailed in Table 6.

Table 6. ANOVA results for the posttest achievement scores of the students in the control group and multiple experimental groups

	Sum of Squares	df	Mean Square	F	p	Post Hoc*
Between Groups	102,022	2	51,011	23,241	<.001*	CG-EG1 CG-EG2
Within Groups	166,813	76	2,195
Total	268,835	78

*p<.05

According to the results, there were statistically significant differences in the achievement scores of students across three different learning environments (F(2,76)=23,241, p<.001). This finding indicates that various educational interventions had a significant impact on student performance, which was confirmed through variance analysis. To determine the source of these differences among the groups, the Tukey HSD post-hoc test, which fully meets the assumptions, was employed (Field, 2009; Tukey, 1949). The results showed statistically significant differences between CG and both EG1 and EG2 (p<.001), suggesting that the experimental groups significantly outperformed the control group. Consequently, this analysis demonstrates the impact of different learning environments on student achievement and supports the effectiveness of the pedagogical interventions applied to the experimental groups. However, although the average scores of students in EG2 were higher than those in EG1, this difference was not found to be due to the unique characteristics of the learning environments (p>.05).

Discussion and Conclusion

A rigorous approach integrating quasi-experimental design and quantitative methods was employed to determine the effects of CBL and CBL supported by CMs on the understanding of theoretical knowledge and metacognitive skills among medical students. In addition to their existing curriculum, a two-stage evaluation process was applied to ascertain the effectiveness of the CBL and CMs-supported CBL approaches. In the first evaluation phase, a success test focusing on medical-specific knowledge of security and ethics was administered as a pre-test and post-test. The data obtained from the success test developed by researchers were analyzed using a two-factor analysis of variance (ANOVA) to determine the effectiveness of the learning environments. According to the analysis results, there was a statistically significant increase in the average success scores of students in all learning environments between the pre-test and post-test; however, this increase was not found to be due to differences specific to the learning environments. This finding indicates that the general increase in success was observed independently across all groups and that the learning environments did not play a decisive role in this change. Therefore, these results show that different learning environments did not have a significant impact on students' abilities to understand theoretical knowledge. Similarly, a study by Li et al. (2021) demonstrated that while CBL can improve specific clinical skills compared to traditional teaching methods, the effect on acquiring theoretical knowledge was less clear. These findings further clarify the complex effects of CBL and CMs on understanding and applying theoretical knowledge. Despite these general findings, the analysis results presented a ranking that should not be ignored based on the success scores between learning environments. In this ranking, the CBL approach supported by CMs achieved the highest success, followed by the traditional CBL approach, with the traditional teaching method showing the lowest performance. These findings indicate that the support of CMs, in addition to the CBL approach, particularly contributes to students' abilities to understand theoretical knowledge. Additionally, this situation suggests that the interactive and integrative structure of learning environments could positively contribute to student success. While the observed differences are not statistically significant, they reflect the potential effects of the structural and functional features of learning methods on student performance.

In the second phase of the study, we developed a new test not only to assess students' theoretical knowledge but also their ability to apply this knowledge in novel and uncertain situations. Crafted based on expert feedback, this test was specifically designed to comprehensively evaluate the effects of various learning environments on students' metacognitive skills. Data collected from the test administered after the experimental process were analyzed using a One-Way ANOVA to determine the effectiveness of these environments. The analysis indicated that the interventions implemented in the experimental groups notably outperformed the control group, showing positive impacts. Furthermore, it was revealed that these effects were due to specific differences between the learning environments. Subsequent analyses aimed at identifying the sources of these differences demonstrated that both experimental groups, which utilized CBL and CBL supported by CMs, achieved significantly higher success scores than the control group. These results substantiate the impact of different learning environments on student achievement and affirm the efficacy of the pedagogical interventions applied. Other studies have echoed these findings, showing that CBL not only strengthens the bridge between theory and practice but also positively affects critical thinking, problem solving, and clinical decision-making skills among medical students (Ahmed et al., 2012; Dubey & Dubey, 2017; Forsgren et al., 2014; Li et al., 2021; Yoo & Park, 2014). These research outcomes align with our results, providing robust evidence that CBL can enhance learning processes and effectively implement theoretical knowledge in practice. Despite these positive findings, no significant statistical differences were observed between the two experimental groups employing CBL and CBL supported by CMs. This indicates that although the average success scores were higher in the CBL group supported by CMs compared to the standard CBL group, these differences were not attributable to the distinctive characteristics of the learning environments. Moreover, the analyses highlighted that CBL significantly boosts metacognitive skills, an effect that is amplified when CMs are incorporated. Consistently, Peñuela-Epalza and De la Hoz (2018) found that integrating CMs into CBL training helps medical students effectively integrate existing knowledge with new clinical information. This integration greatly aids in developing diagnostic, problem-solving, and clinical decision-making skills (Peñuela-Epalza & De la Hoz, 2018). Furthermore, Slieman and Camarata (2019) concluded that the use of CMs during the CBL process enhances the integration of basic medical sciences with clinical applications, significantly boosting students' critical thinking abilities. These findings, which align with our research outcomes, offer valuable insights into how the integration of CBL and CMs in learning processes can bolster students' advanced cognitive and practical skills.

These findings demonstrate that the interactive and integrative structure of learning environments can positively contribute to student success. Although the effectiveness of learning environments on students' ability to comprehend theoretical knowledge appears limited, the potential effects of the structural and functional features of learning methods on student performance should not be overlooked. Moreover, particularly the support of CMs alongside the CBL approach has clearly shown its impact not only on theoretical knowledge but also on the success of applying this knowledge to new and uncertain situations. This situation provides valuable insights into how students' metacognitive skills such as critical thinking, problem-solving, and clinical decision-making can be enhanced. At the same time, the interaction of different pedagogical strategies shows that positive effects can be created on student success, and such integrative approaches should be widely adopted in educational practices. Despite promising findings, some limitations of this study should not be overlooked. Although this study employed a comprehensive quasi-experimental design, it might not offer as effective a control mechanism as randomized controlled trials. Moreover, although the measurement tools used in this study were carefully developed and tested, they might have inherent limitations in terms of validity or reliability. Considering these limitations, it is advised that the information provided by the study be carefully evaluated and that caution be exercised regarding the applicability of the results to other educational settings.

Suggestions

In light of the observed findings, the following recommendations are proposed:

The expansion of programs supported by CBL and CMs is recommended to enhance the integration of interactive learning models into medical education.
Educators should be provided with supportive training to effectively use innovative learning approaches such as CBL and CMs, and these methods should be integrated with traditional teaching techniques.
As an alternative to traditional teaching methods, the use of various learning approaches should be encouraged to support student success and cognitive skills.
More research is needed to evaluate the long-term academic and clinical effects of CBL and CMs applications.
Increasing the sample size and diversity will strengthen the generalizability of the research findings, allowing for more detailed and reliable results.
Conducting follow-up studies that assess the long-term effects of pedagogical interventions will enable the continuous improvement of teaching approaches.

Compliance with Ethical Standards

Competing interests

The authors declare no conflict of interest.

Funding

No funding was received for conducting this study.

Ethics approval and consent to participate

This study was approved by the Ethics Commission of Çanakkale Onsekiz Mart University (approval number: 2023-YÖNP-0614) in 2023. All procedures were carried out in accordance with the relevant guidelines and regulations. Informed consent was obtained from all study participants.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Data Availability

All data generated and analyzed during this study are included in this published article.

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

AppendixA.pdf

Assessing the Effectiveness of Case-Based Learning and Concept Mapping Approaches in Developing Theoretical and Metacognitive Skills in Medical Students

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Abstract

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Introduction

Method

Results

Discussion, Conclusion and Suggestions

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References

Additional Declarations

Supplementary Files

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