1. Introduction
The Microelectronic Circuits course is a cornerstone of any electrical and computer engineering curriculum, providing students with foundational knowledge for understanding and designing electronic systems. This course covers essential topics, such as transistor modeling, analog and digital circuit design, and integrated circuit production, that are critical for applications in computing, telecommunications, and embedded systems. Despite its importance, however, the course often poses significant challenges for students due to its abstract nature and the difficulty of linking theoretical concepts to practical applications (Dori et al., 2014).
Microelectronic circuits, ranging from basic logic gates to advanced integrated circuits, underpin modern technology, including smartphones, supercomputers, and embedded systems. The ability to design, analyze, and solve issues in microelectronic circuits is an essential skill for aspiring electronics engineers. Yet, traditional lecture-based approaches often fall short in fostering the active engagement and deeper comprehension necessary for mastering these complex topics. These conventional methods primarily emphasize theoretical delivery and memorization, which can lead to a fragmented understanding and difficulties in applying knowledge to solve real-world problems (Biggs & Tang, 2011; Prince, 2004).
Key concepts in microelectronics, such as small-signal modeling, frequency response, and transient analysis, are particularly challenging to grasp through static diagrams and equations alone. Students often struggle to visualize how changes in one part of a circuit impact the entire system. This disconnect between theory and practice can hinder students’ ability to develop problem-solving skills and apply their knowledge effectively in real-world contexts (Felder & Brent, 2005; Hussain, 2012).
To address these challenges, innovative teaching approaches are necessary to enhance student learning and engagement. One promising strategy is the integration of simulation tools like LTspice, a widely used circuit simulation software. LTspice enables students to design, simulate, and analyze circuits in a virtual environment, offering real-time feedback on circuit behaviors. This interactive approach helps bridge the gap between theoretical principles and practical application by allowing students to visualize and experiment with dynamic circuit interactions (Dickerson & Clark, 2018; Mohindru & Mohindru, 2021). Although prior studies have demonstrated the effectiveness of LTspice in improving students’ conceptual understanding and problem-solving abilities (Pouncey & Lehr, 2015), many of them treat simulations as standalone interventions, neglecting the potential of combining them with other pedagogical practices. In addition to simulation-based learning, reflective practices that are grounded in constructivist theories of learning can further enhance students’ understanding. Reflection encourages metacognitive awareness, critical thinking, and the evaluation of one’s learning process. By prompting students to identify challenges, assess their progress, and refine their strategies, reflective practices play a vital role in fostering deeper learning (Dewey, 1933; Schön, 2017). Despite their potential, however, reflective practices are rarely incorporated into technical courses like those on microelectronic circuits.
This study investigates the integration of LTspice simulations and structured reflective practices within a PBL framework to address both cognitive and metacognitive aspects of student learning. By embedding simulation-based assignments and reflective activities into a semester-long capstone project, the course encourages students to actively engage with the material, connect theoretical concepts with practical applications, and develop critical problem-solving skills.
This research contributes to the growing body of literature on active and student-centered learning methodologies in engineering education. By analyzing the combined effects of LTspice simulations, reflective practices, and PBL, the study provides insights into innovative strategies for improving educational outcomes and fostering critical engineering competencies.
1.1. Research Objective and Questions
The Microelectronic Circuits course plays a pivotal role in preparing students for the complexities of modern electronic systems. However, its abstract nature and reliance on traditional lecture-based instruction often hinder students’ ability to connect theoretical knowledge with practical applications. Addressing these challenges requires innovative pedagogical strategies that promote active learning, critical thinking, and real-world problem-solving.
This study evaluates the integration of LTspice simulations, structured reflective practices, and PBL as a cohesive pedagogical approach in a Microelectronic Circuits course. The goal is to investigate how this combination of tools influences students’ conceptual understanding, problem-solving skills, and engagement, while also examining the challenges and benefits of implementing such an approach. To guide this investigation, the study addresses the following research questions: How do LTspice simulations and structured reflective practices influence students’ conceptual understanding of microelectronic circuits? How do these practices impact students’ problem-solving abilities when engaging in circuit design and analysis? How does the integration of simulations, reflections, and PBL affect students’ engagement and motivation in the course? What challenges and benefits arise from incorporating LTspice simulations, reflective practices, and PBL into the curriculum?
1.2. Theoretical Background
1.2.1. Challenges in Teaching Microelectronic Circuits
The Microelectronic Circuits course is a cornerstone of electrical and computer engineering curricula, yet it presents significant conceptual and pedagogical challenges. Topics such as small-signal modeling, frequency response, and transient analysis are essential but abstract, making them difficult for students to grasp (Dori et al., 2014; Felder & Brent, 2005). Traditional lecture-based instruction relies heavily on static diagrams and equations, often failing to convey the dynamic nature of circuit behavior (Hussain, 2012). As a result, students struggle to visualize how components interact within a system and apply their learning to real-world problems (Prince, 2004). Memorization is often prioritized over critical thinking and engagement, leading to fragmented understanding (Biggs & Tang, 2011).
1.2.2. Simulation Tools in Engineering Education: Benefits and Limitations
Simulation tools have become integral in addressing the theory–practice gap in engineering education. From MATLAB and PSpice to LTspice and Simulink, these platforms allow students to model and visualize complex systems in real time (Chernikova et al., 2020; Fares et al., 2012; Mannan, 2017). LTspice in particular has gained popularity for its intuitive interface, accurate analog/digital simulation capabilities, and accessibility (Mladenović, 2015; Ash & Hu, 2024). Simulation-based learning environments offer several pedagogical advantages: They visualize dynamic circuit behavior, such as waveform propagation, logic transitions, and signal delays (Rivera-Reyes et al., 2017). They provide a risk-free environment to test ideas and experiment with designs without damaging physical components (Tenzin et al., 2023). Immediate feedback allows iterative refinement and supports students’ problem-solving skills (Dormido et al., 2008). LTspice’s free and lightweight design promotes flexible learning outside of scheduled lab hours (Coller & Scott, 2009).
However, challenges persist. Students may rely too heavily on simulations without understanding underlying principles—a phenomenon known as “black box thinking” (Itagi & Sushma, 2016; Wang et al., 2018). Others encounter technical barriers due to unfamiliarity with simulation syntax or interface limitations (Mladenović, 2015). Furthermore, integrating simulations meaningfully into curricula requires thoughtful instructional design, not simply software access (Prince, 2004).
Emerging directions in simulation-based education include the integration of virtual and augmented reality to improve immersion (Tenzin et al., 2023), and artificial intelligence to offer real-time scaffolding or adaptive feedback (Dai & Ke, 2022).
1.2.3. Simulation-Based Learning in Active Learning Pedagogies
Simulation tools align naturally with active learning methodologies, which emphasize participation, construction of knowledge, and reflection (Piaget, 1985; Vygotsky, 1978; Biggs & Tang, 2011). In engineering education, active learning typically includes flipped classrooms, problem-based learning, and collaborative design challenges (Kolmos et al., 2008). When simulations are embedded in these approaches, they foster engagement, deeper understanding, and transferable problem-solving skills.
LTspice simulations, when used in active learning settings, allow students to test designs, analyze outcomes, and revise their work iteratively—practices that support the development of engineering intuition (Mladenović, 2015; Dormido et al., 2008). Rather than passively absorbing content, the students actively apply theory in a controlled environment, reflecting constructivist principles (Schön, 2017). Recent studies have highlighted the advantages of simulation-enhanced active learning: Magana and de Jong (2018) and Vlachopoulos and Makri (2017) showed improved student engagement and motivation. Rivera-Reyes et al. (2017) demonstrated enhanced conceptual understanding in LTspice-supported analog design courses. Harris and McPherson (2017) found that students using PSpice developed stronger analog troubleshooting skills.
Still, implementation challenges remain, such as resource requirements, lack of instructor training, or a disconnect between technical tool use and conceptual understanding (Itagi & Sushma, 2016; Tenzin et al., 2023).
1.2.4. Toward an Integrated Framework: Simulation, Reflection, and PBL
Reflection is a central component of metacognitive development. It enables students to evaluate their learning processes, identify misconceptions, and refine their reasoning (Dewey, 1933; Kolb, 2014). Despite its recognized value, reflective writing remains relatively rare in engineering curricula—particularly in technical subjects such as microelectronic circuit design—where the focus is often on procedural and analytical proficiency (Schön, 2017).
PBL offers a promising pedagogical framework to embed reflection and simulation into authentic learning contexts. When LTspice simulations are used as part of a larger design project, the students engage with engineering concepts in a way that mirrors real-world problem-solving. Structured reflections, situated within the design cycle, prompt learners to articulate their decisions, assess the effectiveness of their strategies, and relate simulation results to theoretical principles introduced in class.
The combination of simulation tools (e.g., LTspice), structured reflection, and PBL supports three complementary domains of student learning: Cognitive: LTspice simulations facilitate the visualization of circuit behavior and support the development of system-level thinking (Rashid, 2024; Salovirta, 2024). Metacognitive: Reflective writing encourages students to monitor and regulate their thought processes, recognize patterns of error, and develop more strategic approaches to learning (Dickerson & Clark, 2021; Magana & de Jong, 2018). Collaborative: PBL fosters teamwork, communication, and the co-construction of engineering knowledge through group decision-making and shared troubleshooting (Issa et al., 2023).
While prior studies have explored each of these pedagogical tools in isolation, few have examined their synergistic integration in the context of analog and digital circuit education. The existing LTspice-focused literature, such as by Rashid (2024), Ptak (2021), and Salovirta (2024), often emphasizes the tool’s technical functionality or its utility in remote or asynchronous learning environments. Emerging work has also begun to highlight how circuit simulation examples can enhance student engagement and comprehension even in pre-university settings, suggesting a broader applicability of this instructional strategy (Ash & Hu, 2024). However, these studies rarely address LTspice as a scaffold for reflective practice or as part of a broader instructional framework that supports deeper learning, metacognitive development, and collaborative problem-solving. Similarly, while simulation has been increasingly adopted in engineering curricula, the alignment between simulation use and structured reflection practices remains underexplored (Dickerson & Clark, 2021; Ptak, 2021).
This study builds on this emerging body of work by investigating how the integration of LTspice simulation, structured reflection, and project-based learning impacts students’ conceptual understanding, problem-solving ability, and engagement in a third-year Microelectronic Circuits course.
2. Materials and Methods
This study employed a qualitative case study approach (Yin, 2009), focusing on a single undergraduate Microelectronic Circuits course as a bounded case. To analyze the data, we used thematic analysis (Braun & Clarke, 2006), supported by frequency counts of theme occurrences across student reflections, researcher observations, and assignment reports. This quantified thematic analysis enabled pattern identification while preserving the richness of the qualitative insights. The research was approved by the institutional review board (IRB) of the Ruppin Academic Center, under approval number 245, dated 10 November 2024.
2.1. Participants
The study included 49 undergraduate students who were enrolled in a mandatory Microelectronic Circuits course as part of a degree in electrical engineering. The participants were in their third year of study and had a solid understanding of electronic concepts, making them ideal candidates for investigating the integration of LTspice simulations, structured reflective practices, and PBL into their courses.
The participant group, ranging in age from 20 to 30 years old, was also diverse in terms of gender (26 male, 13 female) and academic background, ensuring a comprehensive representation of student perspectives. The students’ various levels of prior exposure to circuit design and simulation tools enabled the study to capture a diverse variety of viewpoints on the usefulness of LTspice simulations, structured reflective practices, and PBL.
2.2. Course Design and Intervention
The course was designed to address both theoretical and practical elements of microelectronic circuits and included topics such as analog-to-digital converters (ADCs), digital-to-analog converters (DACs), and CMOS transistor design. The course design incorporated LTspice simulation exercises alongside theoretical topics covered in lectures, ensuring that the simulations reinforced the principles discussed each week. The course emphasized both theoretical instruction and applied design activities, where the students engaged with both circuit analysis and simulation tasks using LTspice. The project-based nature of the course encouraged the integration of learned theory with hands-on experimentation.
The course consisted of four academic hours (180 min) per week, structured as a 90 min traditional lecture followed by a 90 min design-focused session dedicated to developing the ADC converter project. The course was redesigned to incorporate LTspice simulations, reflective practices, and PBL as core instructional elements. The intervention was structured around the following components: LTspice simulations: The students completed simulation assignments involving circuit design, analysis, and optimization. These assignments focused on key concepts such as small-signal modeling, frequency response, and transient analysis. Each simulation assignment was a step in the development of a comprehensive final project. Reflective practices: After completing each simulation assignment, the students submitted written reflections analyzing their progress. Reflection prompts encouraged the students to evaluate their understanding of circuit behavior, identify challenges, describe their problem-solving strategies, and connect their work to the overall project goals. These reflections aimed to enhance metacognitive awareness and critical thinking. PBL framework: The assignments were embedded within a semester-long capstone project that required the students to design, simulate, and analyze a complex circuit system, simulating real-world engineering challenges. The PBL framework emphasizes iterative improvement, collaboration, and the practical application of theoretical knowledge. Curricular adjustments: Lecture time was reduced to accommodate interactive learning sessions. Simulation labs, group discussions, and reflection activities allowed the students to actively engage with the material, fostering deeper learning and skill development.
Figure 1 shows a Gantt chart that was created to visualize the integration of instructional topics and project-based assignments. This timeline illustrates the gradual scaffolding of design, from high-level system design to low-level component integration.
2.3. Data Collection
Data were collected throughout a 10-week semester, during which the students participated in practical simulation projects using LTspice. The key data sources were the students’ self-reflections, researcher observations, and final project presentations (exam) at the end of the course: Reflective journals: After completing each LTspice simulation assignment, the students were required to submit written reflections, which were intended to promote metacognitive thinking and self-assessment (Shekh-Abed, 2024). Reflection prompts were designed to assist the students in explaining their learning experiences, obstacles they encountered, problem-solving tactics they used, and insights gained from the simulations. Participant observations: The course instructor, who was also the primary researcher, engaged in participant observation throughout the project session. Field notes were systematically recorded during lab sessions, team discussions, and project presentations. These observations focused on student engagement, collaborative behavior, debugging strategies, and expressions of understanding or misconceptions. Assignment reports: The students were required to submit four project-related assignment reports over the course of the semester. These reports documented each phase of the design process, including simulation setup, circuit testing, iterative design changes, and final implementation. The reports provided valuable technical artifacts for analyzing the evolution of the students’ reasoning and problem-solving across the project timeline. Final project presentations (exam): At the end of the semester, the students presented their final projects, which were based on the accumulated knowledge and abilities learned throughout the course, including the use of LTspice. During this presentation, the students had to display their projects and respond to questions posed by the course instructor. This final exam functioned as both an assessment of the students’ comprehension and an opportunity for them to communicate and share their problem-solving processes and design decisions.
2.4. Procedure
Throughout the semester, the students were assigned a series of LTspice simulation projects that were strategically placed to correspond to the weekly lecture themes. The assignments included the following: Building a 4-bit DAC: The students were charged with developing and simulating a 4-bit DAC using LTspice, allowing them to put theoretical concepts into practice. Implementing ADC components at a high system level: This assignment required the students to design and simulate ADC components at the system level, helping them better grasp high-level circuit design. Integrating ADC components at a high system level: The students were asked to integrate previously built ADC components into a bigger system, which helped them comprehend system-level design and integration. Implementing transistor-level components and integrating them into ADCs: This assignment required the students to focus on transistor-level design and integration into ADC systems, bridging the gap between low-level component design and high-level system operation.
After completing each LTspice simulation assignment, the students were asked to write self-reflections that addressed the following questions: What key principles did you learn from this simulation assignment? Describe any difficulties you faced. How did you handle these challenges? How do you think this simulation assignment improved your grasp of the theoretical concepts covered in the lectures?
These introspective assignments were designed to enhance deeper knowledge and self-awareness, helping the students internalize the content and refine their problem-solving techniques.
2.5. Data Analysis
The data were analyzed using a thematic analysis approach, which is suitable for discovering and analyzing patterns in qualitative data (Braun & Clarke, 2006). A hybrid coding strategy was applied, integrating both inductive and deductive methods to provide a comprehensive understanding of student experiences. The analysis procedure included several critical steps: Data familiarization: The first stage was to read the students’ reflections, researcher observations, and assignment reports several times to become familiar with the topic. This technique enabled the researcher to detect initial patterns and repeating themes in the data. First cycle—Open coding: Using an inductive strategy, the researcher manually assigned codes to meaningful segments of the text without relying on a predefined coding structure. The codes captured recurring phrases, concerns, strategies, and learning insights. Second cycle—Focused coding: A deductive approach was then employed, drawing on relevant theoretical constructs from the simulation-based and active learning literature (Yin, 2009). The codes were refined and grouped into broader categories aligned with the study’s research objectives—such as conceptual understanding, problem-solving strategies, metacognitive reflection, and student engagement. Theme development: The codes were organized into larger themes that reflected the core of the students’ educational experiences. Themes such as “enhanced conceptual understanding,” “improved problem-solving skills,” “increased engagement and motivation,” and “challenges in using LTspice” were created to arrange the data. Theme refinement: The themes were examined and adjusted to ensure they accurately reflected the facts. This stage entailed re-examining the coded material within each topic to ensure consistency and coherence. Themes that were overlapping or too broad were separated or combined to provide a more complete picture of the findings. Peer review and trustworthiness: To enhance credibility, an external researcher with expertise in engineering education independently coded a random 25% sample of the reflections. An inter-coder agreement of approximately 82% was reached. Discrepancies were resolved through discussion, which led to final adjustments in the thematic structure. Interpretation and reporting: The final phase of the analysis entailed interpreting the themes in light of the study questions and the available literature. The interpretation focused on how the combination of LTspice simulations affected the students’ learning outcomes, specifically conceptual understanding, problem-solving abilities, and engagement. The findings were then presented, with example extracts from the students’ reflections and final project presentations to corroborate the analysis.
2.6. Validity and Reliability
Multiple strategies were used to assure the validity and reliability of the study: Triangulation: Using various data sources—self-reflections, researcher observations, assignments reports, and final project presentations—provided a holistic picture of the students’ learning experiences, increasing the validity of the findings (Patton, 1999). Peer review: A peer with experience in qualitative research assessed the data analysis procedure. This review contributed to the rigorous coding and theme creation processes, as well as to the credibility of the findings (Creswell & Miller, 2000). Reflexivity: Throughout the study, the researcher kept a reflective record, documenting any potential biases or assumptions that may have influenced the data interpretation. This method helped reduce researcher bias and maintain objectivity throughout the analysis (Yin, 2009).
3. Results
This section presents the findings organized around key themes that emerged from the data analysis. These themes correspond to the four research questions outlined in Section 1.1. To enhance clarity, each subsection below includes a reference to the relevant research question.
3.1. Thematic Analysis Results
To address the study’s four research questions, a thematic analysis was conducted on 49 student reflection submissions across the four LTspice-based assignments. Each submission contained two structured reflections, guided by prompts aligned with the course’s learning objectives. This resulted in a dataset of 136 structured reflections, from which 196 distinct insights were coded after eliminating duplicates and grouping entries thematically.
Using an inductive coding approach, the responses were categorized under five overarching themes (T1–T5), each corresponding to a key area of focus: conceptual understanding, problem-solving, engagement and collaboration, and challenges and benefits of curricular integration. These themes emerged from a grounded reading of the students’ narratives and align with the study’s pedagogical framework: T1: Theory–Practice Link—Reflections coded under this theme emphasized how the students used LTspice simulations to connect abstract theoretical content—such as analog-to-digital conversion—with observable circuit behavior. T2: Iterative Strategy—This theme captured the students’ trial-and-error approaches, parameter tuning, and resimulation strategies to improve circuit functionality. T3: Metacognitive Awareness—Entries in this theme reflected the students’ recognition of their own mistakes, reasoning processes, and learning strategies—indicative of higher-order thinking. T4: Peer Learning and Engagement—This category included reflections on collaboration, mutual feedback, and the emotional dimensions of teamwork and simulation-based learning. T5: Integration Reflections—Reflections in this category dealt with challenges and benefits of combining LTspice simulation, structured reflection, and PBL into the curriculum. The students highlighted moments of frustration as well as insights gained from working through integrated project tasks.
These themes informed both the narrative findings and the development of structured visualizations (Table 1 and Table 2) that cross-reference student reflections with additional sources of evidence such as researcher observations and final project artifacts.
Table 1 presents the key themes that emerged from the qualitative analysis of the students’ reflective responses, aligned with the study’s four research questions. Each theme corresponds to a specific focus area—conceptual understanding, problem-solving strategies, student engagement, and curriculum integration. The table includes representative insights from the students, highlighting how the structured reflection and LTspice simulations supported their learning experiences. The frequency of each theme’s appearance across the 136 unique student reflections is also reported to indicate prevalence.
Table 2 presents the convergence of findings across three qualitative data sources: student reflections, researcher observations, and final project presentations. Each row corresponds to a key theme (T1–T5), with observed frequencies and examples of evidence that illustrate alignment across sources. Convergence levels (High, Medium, Low) indicate the strength of agreement among sources.
To enhance the validity of the qualitative findings, a triangulation matrix (Table 2) was constructed by aligning thematic insights from the student reflections with corresponding patterns in the researcher observations and final project presentations. The table summarizes convergence levels across the three sources for each of the five identified themes (T1–T5). The high convergence for themes such as Theory–Practice Link (T1) and Iterative Strategy (T2) underscores the robustness of the findings, while the lower convergence in themes like Peer Learning and Engagement (T4) suggests areas for further instructional scaffolding.
3.2. Enhanced Conceptual Understanding (RQ1)
A notable outcome of the study was the significant improvement in the students’ conceptual understanding of microelectronic circuits. As shown in Table 1, the most frequently occurring theme (T1: Theory–Practice Link) appeared in 59 out of 136 coded reflections (40%), underscoring how the LTspice simulations facilitated the students’ ability to connect theoretical knowledge with observable circuit behavior.
The integration of LTspice simulations into the course allowed the students to visualize complex circuit behaviors that are often difficult to grasp through traditional theoretical instruction alone. The ability to experiment with real-time circuit simulations enabled the students to directly examine how different components interact and how these interactions impact overall circuit performance.
Figure 2 shows that one key example of this enhanced understanding is the students’ improved comprehension of the Successive Approximation Register (SAR) ADC process. The SAR ADC works by converting an analog input signal into a digital output using a binary search algorithm. The SAR ADC architecture exemplifies the integration of fundamental blocks such as DACs, comparators, and digital control logic, making it ideal for demonstrating ADC operation at both the conceptual and circuit levels.
Through the LTspice simulations, the students were able to graphically observe how the SAR ADC interacts with the comparator and DAC.
One student noted the following:
“Before the simulation, I struggled to understand how the SAR ADC worked. Seeing the SAR update step-by-step in response to the comparator’s output, helped me visualize how digital conversion happens in real time.”
Another reflection reinforced this insight:
“I never realized how important the DAC’s role is in refining the ADC’s accuracy until I saw how changing the reference voltage affected the resolution of the output.”
The researcher observations corroborated this theme, with 12 recorded notes of the students demonstrating “aha” moments while interpreting the ADC waveform results. Table 2 shows that 19 out of 25 project teams included labeled waveform plots that demonstrate stair-step ADC behavior in their final presentations, which indicates that this understanding moved from individual simulations to the overall project results.
As the students progressed, they demonstrated improved understanding of trade-offs in ADC design. An example reads as follows:
“Before running the simulation, I assumed my ADC would capture the sine wave input perfectly. But when I saw the step-like output, it finally clicked why higher bit resolution reduces quantization error.”
“The simulation showed how a slow sampling rate leads to aliasing, something I understood only mathematically before.”
These reflections are consistent with research that highlights the benefits of visualization and active experimentation in engineering learning environments (Rivera-Reyes et al., 2017; Kolb, 2014; Tenzin et al., 2023).
The researcher observations confirmed that the students engaged deeply with the analysis of simulation results, comparing theoretical expectations to actual waveforms and refining their designs accordingly.
The students also appreciated the opportunity to experiment with different ADC architectures, including SAR and two-step Flash ADC, and to observe how design decisions influenced performance. By adjusting reference voltage, clock speed, and component values, the students could immediately assess conversion accuracy and speed trade-offs. One student wrote as follows:
“Seeing how the clock speed affects conversion rate and resolution in the SAR ADC helped me understand why high-speed ADCs are used in some applications while lower-speed ADCs work for others.”
This convergence between the student reflections, researcher observations, and final project artifacts reinforces the validity of the learning outcomes associated with conceptual understanding.
3.3. Enhanced Problem-Solving Abilities (RQ2)
The ADC project served as a multifaceted problem-solving challenge, requiring the students to diagnose and troubleshoot circuit behavior through multiple design iterations. As reflected in Table 1, two key themes emerged related to problem-solving development—T2: Iterative Strategy and T3: Metacognitive Awareness—which were present in 39 (29%) and 29 (21%) of the student reflections, respectively.
3.3.1. Iterative Strategy: Practical Problem-Solving Engagement
The students initially employed trial-and-error approaches but gradually adopted more structured debugging strategies. The LTspice simulations provided rapid feedback loops that helped them identify, isolate, and correct design flaws in real time. One student reflected as follows:
“I initially made an error in my resistor divider, which threw off my reference voltage. Seeing the incorrect waveform in the simulation helped me diagnose the problem quickly.”
Another noted the following:
“I realized that changing a comparator’s threshold had a bigger impact on ADC accuracy than I expected. The simulation helped me fine-tune it systematically.”
These examples illustrate how iterative design became an effective learning strategy, reinforcing what prior research describes as a core competency in engineering problem-solving (Dormido et al., 2008; Mladenović, 2015).
The researcher observations supported these trends, with seven documented cases of the students conducting re-simulations or iterating component parameters to resolve errors (Table 2). Moreover, 13 of the 25 project teams explicitly described such parameter tuning processes in their final presentations, demonstrating the convergence between reflection, instructional feedback, and project artifacts.
3.3.2. Metacognitive Awareness: Reflection on Thinking and Learning
Beyond tactical troubleshooting, the students also developed metacognitive insight—Theme T3—by analyzing their own decision-making processes. The structured reflection prompted them to monitor errors, assess their strategies, and identify recurring issues:
“Writing down my thought process after each simulation helped me realize patterns in my mistakes. Instead of making the same error multiple times, I started anticipating problems before they occurred.”
“Using LTspice taught me that designing an ADC involves more than just applying formulas; I needed to understand how adjusting one component, such as a comparator or resistor, could impact the entire conversion process and overall performance.”
These reflections reinforce the role of metacognitive regulation in effective engineering design, as echoed in the literature on simulation-based and experiential learning (Magana & de Jong, 2018; Tenzin et al., 2023).
The instructors also documented four instances of the students verbalizing and correcting errors during live debugging sessions, while eight project teams included slides or commentary acknowledging lessons learned from failed attempts. The consistent appearance of T2 and T3 themes across the reflections, instructor notes, and student deliverables underscores a medium to high convergence in the development of the students’ problem-solving capabilities (Table 2).
The reason for maintaining these as two separate themes in Table 1 is to preserve the nuanced differences between what the students did to solve problems (T2) and how they thought about their learning (T3). However, their interaction is evident: iterative practice often led to deeper reflection, and metacognitive insights often informed subsequent design iterations.
3.4. Enhanced Engagement and Motivation (RQ3)
The interactive nature of the LTspice simulations significantly contributed to enhancing student engagement and motivation. This was captured in Theme T4: Peer Learning and Engagement, which appeared in 14 reflections (10%), as shown in Table 1. Although the frequency was lower than for other themes, the reflections revealed meaningful engagement, particularly when the students collaborated or received peer feedback during debugging and simulation activities.
The students frequently expressed appreciation for the ability to explore circuit behavior hands-on in a safe, low-risk environment. One student wrote as follows:
“The ability to experiment with various designs and instantly observe the outcomes consistently intrigued and pushed me to continue my studies.”
This supports prior research emphasizing the motivational power of active learning and simulation tools (Kolmos et al., 2008; Prince, 2004). The dynamic and visual feedback offered by the simulations enabled the students to maintain curiosity and sustained effort outside of traditional lecture contexts.
Moreover, the reflections highlighted a growing sense of ownership and control over the learning process. As the students tested different designs, they reported increased confidence and autonomy in their decision-making. One student commented as follows:
“The simulations made me feel like I was really in charge of the design. I could try things and immediately see what worked.”
This perceived agency aligns with the concept of intrinsic motivation and supports findings that interactive, exploratory tools like LTspice promote deeper learning by offering students control and visible progress (Wang et al., 2018).
Despite these positive reflections, the convergence across data sources was relatively low for this theme (see Table 2). Only two researcher observations captured peer-teaching moments, and just 3 out of 25 project teams explicitly documented engagement strategies or collaboration in their presentations. This suggests that while engagement was meaningful for some students, it was not as widespread or consistently observable as other learning outcomes.
Nonetheless, when engagement occurred, it often reinforced collaborative learning and problem-solving. Peer feedback helped the students debug circuits more efficiently, and several students noted that emotional support and shared discovery during simulations made the learning experience more enjoyable. As one student summarized:
“My teammate’s explanation helped me finally understand what was wrong with our DAC. We figured it out together.”
These insights affirm the importance of designing simulation-based learning environments that not only promote individual exploration but also intentionally foster collaboration, especially when aiming to support both independent and team-based motivation.
3.5. Addressing Implementation Challenges (RQ4)
While the study revealed several positive learning outcomes, it also surfaced a range of challenges associated with using LTspice simulations—especially during the initial phases of the course. These challenges were captured in Theme T5: Struggle Turning into Insight, which appeared in 25 reflections (18%) and was supported by instructor notes (5) and presentation content from 6 teams (see Table 1 and Table 2). These findings suggest that many students experienced difficulty but ultimately grew through those moments of frustration.
A common initial obstacle was the steep learning curve of the LTspice platform. Students unfamiliar with circuit simulation or lacking prior experience in design tools described feeling overwhelmed. As one student shared:
“Initially, I encountered considerable difficulty using LTspice. The abundance of options and settings overwhelmed me, leaving me unsure of where to begin. The experience was exasperating, as I found myself devoting more time to deciphering the software rather than gaining knowledge about circuits.”
This challenge is consistent with prior findings that identify cognitive overload and tool complexity as significant barriers to learning using simulations (Itagi & Sushma, 2016; Mladenović, 2015).
Another important issue was over-reliance on simulations without conceptual grounding, a phenomenon often referred to as “black box thinking.” Several students admitted to tweaking parameters until the simulations “worked” without fully understanding the reasons. One student reflected as follows:
“Occasionally, I would persistently adjust the parameters of the SAR ADC simulation until it produced the desired conversion results, yet I didn’t always fully understand the underlying principles that made it work.”
This kind of superficial engagement risks creating false confidence, where students succeed in simulation tasks but struggle to apply concepts in novel or real-world contexts. This limitation has been discussed in the literature as a caution against simulation-based learning environments that lack structured reflection or theoretical anchoring (Wang et al., 2018).
Despite these difficulties, the theme analysis showed that many students transformed early struggles into insight, especially when supported by iterative design and structured reflection. These moments of growth were documented in the project presentations and instructor feedback. For example, students who initially struggled with waveform accuracy later demonstrated mastery in tuning reference voltages or clock speeds, as evidenced by annotated graphs and technical explanations.
Thus, while the challenges were real and occasionally demotivating, they also played a critical role in fostering deeper learning for a significant portion of the students. Designing simulation-based experiences that intentionally scaffold early learning and embed metacognitive reflection may help turn initial frustration into meaningful insight.
4. Discussion
The findings of this study provide valuable insights into the impact of integrating LTspice simulations, structured reflective practices, and PBL in a Microelectronic Circuits course. The discussion focuses on how this approach enhances conceptual understanding, problem-solving skills, and student engagement while also addressing the challenges and implications for instructional design in engineering education.
4.1. Enhanced Conceptual Understanding
The study found that the students gained a significantly deeper conceptual understanding of microelectronic circuits by interacting with the LTspice simulations and engaging in structured reflection. Traditional approaches often struggle to connect abstract circuit theory to dynamic behavior. In contrast, the ability to simulate analog-to-digital converter (ADC) circuits allowed the students to visualize and manipulate variables like reference voltage and sampling rate in real time, reinforcing their comprehension of conversion processes and signal integrity.
These findings support prior research on simulation-based learning as a catalyst for deeper conceptual engagement (Dormido et al., 2008; Magana & de Jong, 2018). The addition of reflective journaling prompted the students to identify and correct misconceptions, a strategy that aligns with metacognitive theory and constructivist learning models (Bransford et al., 2000).
4.2. Better Problem-Solving Abilities
The students showed a clear transition from trial-and-error troubleshooting to iterative problem-solving and metacognitive self-regulation. Themes T2 and T3—Iterative Strategy and Metacognitive Awareness—demonstrate how the students learned to debug systematically and reflect on their learning trajectories. This development mirrors findings by Jonassen (2000) on meaningful learning in ill-structured domains and Magana and de Jong (2018) on engineering design iteration.
LTspice facilitated rapid prototyping and testing, enabling the students to build a mental model of circuit interactions through experimentation. The structured reflection further supported metacognitive monitoring and adaptation, aligning with Zimmerman’s (2002) framework on self-regulated learning. The triangulated evidence—student reflections, researcher observations, and presentation artifacts—adds credibility to the improvement in problem-solving skills.
4.3. Greater Engagement and Motivation
The simulation-based learning also enhanced student motivation and engagement. The students reported increased autonomy and ownership of learning, echoing self-determination theory (Deci & Ryan, 1985), which emphasizes the importance of autonomy and competence in fostering motivation. The opportunity to test hypotheses and receive immediate feedback through LTspice made the learning process more interactive and rewarding.
Peer collaboration further enriched engagement, though it appeared in a smaller subset of the cohort. This is consistent with the literature on collaborative inquiry-based learning, which suggests that shared problem-solving promotes deeper understanding (Hmelo-Silver et al., 2007). However, as peer learning was not a dominant theme, further instructional support may be needed to foster this dimension.
4.4. Addressing Implementation Challenges
Despite the positive outcomes, challenges were evident. A steep learning curve with LTspice initially hindered some students. Similar issues have been reported in other simulation-based environments where technical barriers limit engagement (Paganotti et al., 2024). Additionally, the risk of “black box thinking”—adjusting parameters without understanding underlying concepts—surfaced in the student reflections. This aligns with earlier concerns raised in the simulation literature about superficial learning (Ramberg & Karlgren, 1998).
To mitigate these issues, scaffolded tutorials and formative assessment tools are essential. Embedding reflective prompts that require students to justify design decisions and link simulation results to theoretical principles can encourage deeper cognitive processing.
4.5. Limitations and Future Research
This study is limited by its focus on a single cohort of 49 students within one academic institution, which constrains the generalizability of the findings to broader educational contexts. While the cohort included diversity in age (20–30 years), gender (26 male, 13 female), and academic background, no formal analysis was conducted on how these demographic factors or prior exposure to circuit design and simulation tools may have influenced learning outcomes. Additionally, the absence of a control group or comparison condition limits the ability to isolate the effects of the pedagogical intervention.
The study also relies primarily on subjective data sources, such as student reflections, researcher observations, and assignment reports. Although these provided rich qualitative insights into student learning and engagement, the lack of objective performance measures—such as standardized assessments or project rubric scores—limits the ability to quantify gains in conceptual understanding or problem-solving ability.
Future research should consider incorporating pre- and post-course assessments, simulation accuracy metrics, and structured rubrics to triangulate qualitative findings with quantitative indicators. Studies conducted across multiple institutions with varied instructional settings could further validate the integrated framework. In addition, exploring peer learning dynamics, long-term retention of circuit concepts, and the role of digital tools in promoting equity and access in engineering education would enrich the understanding of this pedagogical approach.
5. Conclusions
This study explored the integration of LTspice simulations, structured reflective practices, and PBL in a Microelectronic Circuits course, revealing their role in enhancing conceptual understanding, problem-solving abilities, and student engagement. The findings demonstrate that simulation-based learning environments provide an effective means of bridging the gap between theory and practice, helping students develop both technical proficiency and analytical thinking skills essential for engineering education.
The novelty of this study lies in its integration of LTspice simulations with structured reflective practices within a PBL framework—an approach that has not been sufficiently explored in the existing LTspice-related literature. While previous studies often emphasize the role of simulations in visualizing circuit behavior or supporting technical skill development, they rarely examine how simulations can be used to foster metacognitive awareness and iterative problem-solving within real-world design contexts. This study bridges that gap by combining LTspice with systematic reflection activities and triangulated evaluation methods, including student reflections, researcher observations, and final project presentations. This multi-layered approach provides new insights into how students internalize conceptual knowledge, develop structured troubleshooting strategies, and engage more deeply in the learning process—offering a richer, more holistic model of simulation-based engineering education.
The findings underscore the need for strategic implementation of simulation-based learning to maximize its benefits while mitigating potential drawbacks. Key recommendations include the following: Designing structured learning pathways for LTspice proficiency to help students overcome initial technical barriers and develop confidence in using simulation tools. Embedding reflective learning exercises that encourage students to analyze and articulate their troubleshooting strategies, reinforcing deeper conceptual understanding. Encouraging collaborative problem-solving activities, enabling students to engage in peer discussions and share insights, in turn fostering a more dynamic and interactive learning environment. Integrating simulations with hands-on lab work and theoretical instruction to create a balanced and holistic educational approach that supports the development of both analytical and practical skills.
While this study demonstrates the effectiveness of simulation-based learning, certain challenges remain. Technical barriers and the risk of over-reliance on digital tools require targeted pedagogical interventions to ensure students actively engage with underlying engineering principles rather than treat simulations as black box solutions.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Ruppin Academic Center (protocol code 245 and date of approval 11 November 2024).
Informed consent was obtained from all subjects involved in the study.
The data presented in this study are available on request from the corresponding author due to ethical reasons.
The author declares no conflicts of interest.
Footnotes
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Figure 1 Gantt chart outlining the weekly sequence of lectures and assignments in the course.
Figure 2 Block diagram of SAR ADC.
Thematic Results Aligned with Research Questions.
| RQ Focus | Theme ID | Thematic Label | Reflection Example | Reflection Count (%) |
|---|---|---|---|---|
| RQ1: Conceptual Understanding | T1 | Theory–Practice Link | “Simulating the SAR update step-by-step helped me understand how digital conversion works.” | 54 (40%) |
| RQ2: Problem-Solving | T2 | Iterative Strategy | “We adjusted Vref in steps, observed errors, and found the optimal range for our ADC.” | 39 (29%) |
| RQ2: Problem-Solving | T3 | Metacognitive Awareness | “I learned to explain where I got stuck, and what I would do differently next time.” | 29 (21%) |
| RQ3: Engagement | T4 | Peer Learning and Engagement | “A teammate’s explanation helped me debug the circuit. We solved it together.” | 14 (10%) |
| Q4: Implementation Challenges | T5 | Integration Reflections | “The project was hard at first, but LTspice made debugging easier and showed me how real circuits behave.” | 25 (18%) |
Cross-Source Triangulation Matrix.
| Theme ID | Reflections (freq.) | Instructor Note | Project Presentation (Exam) | Convergence Level |
|---|---|---|---|---|
| T1 | 59 (40%) | 12 researcher observations of students connecting theoretical ADC principles with simulation outcomes | 19 out of 25 project teams included labeled waveform plots showing stepwise ADC output behavior | High |
| T2 | 39 (29%) | 7 notes on resimulation and problem iterations | 13 out of 25 teams discussed parameter tuning or simulation-debug iterations | High |
| T3 | 29 (21%) | 4 instances of self-explanation of errors | 8 out of 25 teams added slides reflecting on how they overcame design challenges or learned from mistakes | Medium |
| T4 | 14 (10%) | 2 peer-teaching observations | 3 out of 25 teams shared strategies in final report or slides | Low |
| T5 | 25 (18%) | 5 notes on student frustration turning into insight | 6 project teams discussed LTspice as helpful for integration work | Medium |
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1 Department of Electrical and Computer Engineering, Faculty of Engineering, Ruppin Academic Center, Emek Hefer, Hadera 4025000, Israel; [email protected], The Center for Research in Technological and Engineering Education, Ruppin Academic Center, Emek Hefer, Hadera 4025000, Israel