Development of the concept-process model and metacognition via FAR analogy-based learning approach in the topic of metabolism among second-year undergraduates

1 Introduction

Biochemistry serves as a foundational discipline within the pure sciences, holding significant relevance across diverse fields, including medicine, biotechnology, food technology, and other scientific industries. ^{1 , 2} Despite its importance, biochemistry education presents unique challenges due to the abstract and complex nature of its content. ^{3 , 4} Students often struggle to grasp the underlying mechanisms and intricate patterns inherent in biochemical systems. As a result, learners frequently resort to rote memorization, which hinders the development of a deep conceptual understanding of the material. ^{3 , 5 , 6} Addressing this learning issue requires exploring innovative strategies that move beyond memorization and aim to foster genuine comprehension.

Analogy-based learning effectively addresses challenges in biochemistry education by linking unfamiliar biochemical processes (targets) to familiar phenomena (analogs). ^{7 , 8 , 9} Analogies are prevalent in biochemistry textbooks, appearing more frequently than in general science or secondary chemistry resources. ⁹ However, their instructional effectiveness is often hindered by insufficient explanations or explicit mapping between analogs and targets, which can lead to misconceptions when students overextend similarities. ⁷

To address these issues, Treagust and colleagues ^{10 , 11} proposed the Focus, Action, Reflection (FAR) framework, which has been highly effective in teaching complex concepts and fostering students’ model-building skills. ^{10 , 11} Achieved by simplicity to use since FAR guide contain only 3 steps, it is easier to execute than other methods ^{9 , 12 , 13} such as Teaching-With-Analogies Model (TWA), ^{14 , 15 , 16} and General Model of Analogy Teaching (GMAT). ¹⁷ This iterative process not only deepens understanding of scientific phenomena but also cultivates critical thinking and problem-solving skills, making the FAR method a valuable approach in science education. ^{11 , 18}

Developing scientific modeling skills, often grounded in analogy and analogical reasoning, is a key focus in biochemistry education. ^{9 , 19 , 20} Scientific models, ranging from microscopic to macroscopic, are essential for visualizing abstract biochemical processes. ^{21 , 22} Among the eight types of scientific models categorized by Harrison and Treagust, ¹⁸ concept-process models (CPMs) stand out for their emphasis on dynamic processes, such as enzyme kinetics and chemical reactions, rather than static representations like structures of enzymes or organelles. ²³ CPMs are particularly effective in depicting the dynamic nature of biochemical systems, such as metabolic pathways or energy transfer during cellular respiration. ²⁴ The integration of analogies into these models helps students connect observed phenomena with scientific principles, fostering deeper conceptual understanding. ⁹

It has been reported that during the construction of models, analogies play a role not only clarify complex biochemical processes but also promote conceptual change by guiding learners from surface-level understanding to a more comprehensive grasp of the subject, as demonstrated in Mason’s ⁸ study on the human circulatory system. In anology based modeling, learners use analogs as scaffolding that guide learners through the initial stages of understanding abstract phenomena, laying the foundation for creating more detailed and accurate scientific models. ¹¹ In term of CPMs, analogies provide dynamic representations which students can use as a foundation for constructing process-based models. ^{9 , 12}

Mason ⁸ emphasizes the critical role of metacognitive awareness in supporting students’ ability to monitor and integrate new knowledge while understanding the instructional purpose of analogies. Students with high metacognitive awareness effectively identify gaps in their knowledge, recognize misconceptions, and adjust their beliefs by applying analogical reasoning to scientific models like the circulatory system. This process involves self-regulation and integration of new concepts, which are essential for building accurate mental models; ⁸ . Similarly, Papaevripidou, Constantinou, and Zacharia ²⁵ highlight that self-regulated learners not only require cognitive skills but also metacognitive knowledge about the modeling process. Those with well-developed metacognitive abilities regulate their learning more effectively and achieve deeper conceptual understanding. Louca and Zacharia ²⁶ further argue that successful modeling in science relies on both cognitive and metacognitive contributions. Aligning these skills can enhance biochemistry education by fostering a reflective approach to understanding biochemical models. ²⁷

Given these factors, this study posits that employing the FAR-based teaching approach, underpinned by analogical principles, can improve learners’ ability to construct concept-process models while simultaneously enhancing their metacognitive skills. This approach holds promise for advancing students’ comprehension of complex biochemical processes and bridging the gap between theoretical knowledge and practical application.

1.1 Theoretical framework

This study explores the interplay between analogy-based learning, CPMs, and metacognitive awareness to enhance biochemistry education. Scientific modeling involves iterative steps: analyzing, relational reasoning, synthesizing, and testing/debugging. ²⁸ CPMs are essential tools for visualizing dynamic biochemical processes, such as metabolic pathways, by linking observable phenomena with abstract molecular interactions. Analogies facilitate this by connecting familiar concepts to complex biochemical targets, such as enzymes as “locks and keys” or lipid bilayers as “sandwiches”. ^{29 , 30} These connections provide an accessible cognitive scaffold for understanding processes that are otherwise difficult to conceptualize. ¹²

The FAR method ¹¹ offers a structured approach to integrating analogies into learning, making it particularly well-suited for CPM construction. In the Action phase, students actively map macro-level analogs (e.g., everyday phenomena) to submicro-level biological processes, fostering conceptual change between the observable and theoretical. ³¹ This phase engages students in hands-on tasks like constructing molecular models or simulating metabolic pathways, enabling them to test and refine their understanding dynamically. The Reflection phase encourages iterative evaluation, where students revisit their analogies and models, address inconsistencies, and deepen their comprehension of biochemical concepts. By facilitating these transitions between familiar and abstract, FAR not only promotes better model construction but also supports meaningful learning and critical thinking. ¹²

Metacognitive awareness plays a pivotal role in this framework, acting as a scaffold for self-regulated learning during the modeling process. ^{27 , 32 , 33} Schraw and Dennison’s ³⁴ model identifies two key components of metacognition: knowledge (declarative, procedural, and conditional) and regulation (planning, monitoring, debugging, and evaluation). These metacognitive skills are crucial for helping students manage their cognitive processes, reflect on their understanding, and adjust their learning strategies. ³⁵ The FAR method’s Reflection phase aligns closely with metacognitive practices, allowing students to monitor and critique their work as they refine their models.

Metabolism, an area often underexplored in model-based learning ³⁶ provides an ideal context for applying this framework. Dynamic and sequential pathways, such as glycolysis and the Krebs cycle, require iterative modeling practices to represent the processes accurately. ^{37 , 38} The FAR method’s structured phases and metacognitive scaffolding make it particularly effective for bridging the gap between macro-level observations and submicro-level mechanisms in metabolism. By examining how analogy-based learning and metacognition influence CPM development in this domain, this study aims to address gaps in biochemistry education research and provide actionable insights for improving teaching practices as illustrated in the conceptual framework diagram shown in Figure 1.

Figure 1:

Conceptual framework diagram depicting analogy-based learning as the independent variable, concept-process model construction as the dependent variable, and metacognitive awareness as a moderating variable. The framework hypothesizes that analogy-based learning directly influences the development of concept-process models, with metacognitive awareness potentially enhancing or altering this relationship. The moderating effect of metacognitive awareness reflects its role in facilitating deeper comprehension and effective integration of analogical reasoning during model construction.

Thus, the study seeks to answer the following research questions:

1. How does FAR-based analogy learning improve biochemistry students’ ability to construct conceptual process models?

2. What is the relationship between students’ metacognitive skills and their performance in constructing CPMs after learning through the FAR method?

2 Methodology

2.4 Data analysis

Learning progress was evaluated using normalized gain scores. Post-intervention, students’ metacognitive awareness was measured and normalized by the criteria based on Asy’ari et al. ³⁹ Data analysis including paired t-test and correlation test, performed with JASP software, examined significant differences and correlations between concept-process modeling scores and metacognitive awareness.

Figure 2:

Conceptual comparison using FAR-based analogy: (a) illustrates the concept-process model of glycolysis, detailing the biochemical steps and key molecular reactions; (b) provides an analogous representation using a rice-cooking assembly line, where rice cookers linked by conveyor belts represent the stepwise flow and regulation of substrates and enzymes in glycolysis.

Figure 3:

Comparative conceptual models using FAR-based analogy: (a) depicts the concept-process model of electron transport in analogy to a water-bucket relay, illustrating the flow and transfer mechanisms; (b) presents a comparable analogy, using a security guard and gate to represent shuttle mechanisms involved in biochemical processes.

3 Results

The pre-test and post-test scores on students’ ability to construct concept-process models show a significant improvement (Table 1). The total score increased from an average of 9.097 in the pre-test to 13.710 in the post-test, out of a possible 40 points. The normalized gain score (g) was calculated to be 0.165, indicating that, on average, students improved by 16.5 % after participating in the instructional intervention. This improvement in average scores suggests a positive impact of analogy-based learning on enhancing students’ ability to construct conceptual models of biochemical processes.

The results from the inferential statistics revealed a statistically significant improvement in post-test scores compared to pre-test scores (t(136) = 7.690, p < 0.001), with a large effect size (Cohen’s d) of 0.657. This indicates that the FAR analogy-based learning method had a notable positive influence on students’ conceptual modeling abilities in biochemistry. An illustration of the comparison between the pre-test and post-test concept-process models can be seen in Figure 4, demonstrating how students’ understanding of the biochemical processes improved through the instructional intervention.

Figure 4:

Examples of student responses illustrating the concept-process model for the electron transport chain. (a) The pre-test response reveals an incomplete understanding of electron flow and inhibitor function within the process, lacking clarity and coherence in depicting how these elements interrelate. (b) The post-test response, however, shows significant improvement. The student accurately depicts the correct flow of electrons in the respiratory chain, effectively integrating the role of inhibitors and predicting the consequences of system failure.

The metacognitive awareness scores of the students are presented in Table 2, with each component evaluated on a scale of 4 points, and classified into four categories: very high (above 3.33), high (2.33–3.33), moderate (1.33–2.33), and low (below 1.33) according to the criteria from Asy’ari et al. ³⁹ The results indicate that the majority of students displayed high to very high levels of metacognitive awareness across most components.

These results reflect a generally high level of metacognitive awareness among the students, particularly in areas like comprehension monitoring and debugging strategies, which are essential for effective learning and problem-solving in biochemistry.

The correlation analysis explored the relationship between students’ metacognitive skills and their ability to construct conceptual process models (Table 3). The results showed a low but statistically significant positive correlation between students’ pre-test and post-test scores (r = 0.262, p < 0.01), indicating that students who performed well on the pre-test also tended to perform well on the post-test. However, no significant correlations were found between pre-test scores and any subcomponents of metacognition, suggesting that metacognitive skills did not strongly influence students’ initial abilities in conceptual modeling.

In contrast, the post-test scores were positively correlated with several metacognitive subcomponents, including declarative knowledge (r = 0.21, p < 0.05), procedural knowledge (r = 0.19, p < 0.05), information management strategies (r = 0.22, p < 0.01), and debugging strategies (r = 0.18, p < 0.05), as well as overall metacognitive awareness (r = 0.19, p < 0.05). This suggests that metacognition became more relevant to students’ conceptual modeling abilities after the instructional intervention.

Further analysis revealed that the normalized gain score (g) had a strong positive correlation with post-test scores (r = 0.89, p < 0.001) and a low positive correlation with certain metacognitive subcomponents, specifically information management strategies (r = 0.20, p < 0.05) and debugging strategies (r = 0.20, p < 0.05). Additionally, the subcomponents of metacognitive awareness showed strong internal consistency, with high and significant correlations among them (p < 0.001).

These findings highlight the intricate relationships between students’ metacognitive skills, their ability to construct conceptual models, and the impact of the analogy-based learning intervention. Specifically, the results demonstrate that while metacognition plays a limited role before learning, it becomes increasingly important after students have engaged with the FAR-based instructional model.

4 Discussion

The study demonstrates that the FAR method enhances students’ ability to construct Concept-Process Models (CPMs) and deepens their conceptual understanding of biochemical processes. The improvement in test scores, from an average of 9.097–13.710, reflects students’ development in connecting theoretical knowledge to practical modeling tasks, with a normalized gain of 16.5 %. This aligns with constructivist learning theory, which posits that learners build new knowledge through active engagement and connections with prior experiences. ^{12 , 40} The FAR method facilitates this constructivist process by helping students link new scientific concepts to familiar, everyday experiences. Analogies such as “conveyor belts” or “bucket brigades” are instrumental in this process, functioning as cognitive scaffolds that link familiar experiences to abstract biochemical phenomena. ⁷ For instance, understanding enzyme inhibition through a conveyor belt analogy enables students to visualize how a blockage in one component affects upstream and downstream reactions, thereby clarifying system-wide dynamics. Such analogies promote not only comprehension but also the ability to represent these processes within CPMs, reinforcing the iterative relationship between understanding and modeling. ^{7 , 9}

CPMs are tools that combine static representations and dynamic processes, bridging abstract scientific concepts with their functional roles in systems. ⁹ Constructing CPMs requires students to synthesize information, identify relationships among variables, and represent system dynamics accurately. This cognitive demand aligns with the development of conceptual understanding, where students progress from recognizing isolated facts to integrating and applying knowledge within complex frameworks. ⁴¹

Metacognitive strategies play a pivotal role in enabling students to construct and refine CPMs, bridging theoretical understanding and practical application. Metacognition involves self-awareness and regulation of one’s cognitive processes, including planning, monitoring, and evaluating one’s learning. ^{35 , 42} These strategies empower students to reflect on their conceptual understanding, identify gaps, and iteratively improve their models.

The FAR framework integrates metacognitive practices into each stage of analogy-based learning. During the Focus phase, students identify key components of the biochemical system and link them to the analogous representation, fostering awareness of what they understand and what needs further exploration. In the Action phase, they actively apply their knowledge to construct CPMs, testing the analogies against biochemical principles. Finally, the Reflection phase prompts students to evaluate their models, compare them to real-world systems, and refine their understanding through iterative cycles. ⁴³

For example, in studying the electron transport chain, students may initially misinterpret the movement of electrons and intermediates. Through reflective practices, they identify discrepancies between their CPMs and actual biochemical processes, leading to model refinement. Analogies like a “bucket relay system” clarify why certain redox centers are fully reduced or oxidized, helping students align their models with observed biochemical phenomena.

The FAR method effectively integrates analogy-based learning with metacognitive strategies to enhance students’ ability to construct Concept-Process Models and deepen their conceptual understanding of biochemistry. By fostering iterative cycles of reflection and refinement, this approach promotes meaningful learning and equips students with the cognitive tools necessary for tackling complex scientific problems.

Despite the effectiveness of the FAR method in integrating conceptual understanding, CPM development, and metacognitive skills, educators must address its limitations. Analogies, while useful, may oversimplify complex biochemical phenomena, potentially leading to misconceptions. ⁹ To address this, instructors should highlight the limitations of analogies and provide guidance to help students critically assess their applicability and relevance to real biochemical systems. Further, educators should design CPM activities that explicitly include “before-and-after” representations, helping students visualize dynamic changes. Integrating metacognitive prompts into each stage of the FAR framework can ensure students not only construct accurate models but also develop skills for independent learning and problem-solving. This comprehensive approach will better prepare students to tackle complex and dynamic scientific challenges.