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. 2026 Jan 30;103(2):920–929. doi: 10.1021/acs.jchemed.5c01385

Assessing Student Socio-Cognitive Outcomes from Virtual Experiment Simulators in Polymer Science Education

Yu Wang †,‡,*, Christine Fry Wise , Tom McKlin , Manyu Li §,*
PMCID: PMC12895404  PMID: 41693736

Abstract

This study tested students’ socio-cognitive outcomes in using the Open Virtual Experiment Simulator Education Tool (OVESET), a series of virtual experiment simulators designed for undergraduate polymer science education. The educational tool, covering core polymer science concepts (e.g., molecular weight distribution and polymerization kinetics), was implemented across two consecutive years in an upper-level undergraduate macromolecules course. Guided by Self-Determination Theory (SDT), this pretest–post-test study measured changes in students’ self-regulation, self-efficacy, sense of belonging, and intention to pursue a career in polymer science after using the virtual modules. In the first year, two modules were used across 3 weeks with 16 participating students; in the second year, seven modules were used over 12 weeks with 20 students. Results showed that OVESET modules significantly enhanced students’ self-efficacy in polymer science, with medium effect sizes, while changes in self-regulation, belonging, and intention to pursue a career in polymer science were not significant. This study highlights the implementation and evaluation of virtual laboratory tools in polymer science education and underscores the importance of considering student perceptions and engagement.

Keywords: virtual laboratory, chemical education research, polymer science, undergraduate education

Introduction

Students’ perceptions and engagement are crucial in the learning process. When designing educational tools or learning interventions, it is thus important to consider whether the tools enhance students’ positive perceptions of the subject. This study tested students’ socio-cognitive outcomes in using the Open Virtual Experiment Simulator Education Tool (OVESET), which is a series of simulation tools developed to teach undergraduate polymer science concepts through interactive virtual experiments. Guided by self-determination theory, this study compared changes in four key aspects of students’ self-perceived socio-cognitive outcomes: self-regulation, self-efficacy, sense of belonging, and intention to pursue a career in polymer science.

Virtual Laboratories in STEM Education

Computer simulations and virtual laboratories offer significant benefits to STEM learning, providing interactive and engaging environments that enhance conceptual understanding, inquiry skills, and problem-solving abilities in science education. − Virtual laboratories in science classrooms have been found to complement traditional hands-on laboratories by increasing students’ domain knowledge and developing inquiry skills. Specifically, students exposed to a combination of hands-on and virtual lab environments showed a greater reduction in misconceptions than those in virtual-only or hands-on-only conditions. Interestingly, hands-on-only and virtual-only conditions were found to be equally effective at correcting misconceptions. In another study implementing virtual simulation learning in science classrooms, students reported that simulations were easier to visualize and more enjoyable than real equipment, fostering more spontaneous experimentation. Simulations were also perceived as a safe environment for exploration, reducing students’ fear of breaking equipment or making mistakes, leading to more active engagement and discussion among peers.

In chemistry education, virtual laboratories have been shown to effectively complement traditional hands-on laboratory experiences, such as through enhancing students’ conceptual understanding and self-efficacy prior to performing actual experiments. Additionally, virtual laboratories were also found to promote the development of problem-solving and inquiry skills. Particularly, the provision of immediate feedback in virtual environments has been identified as valuable for fostering deeper conceptual learning. Although virtual laboratories cannot replace physical laboratories, students reported that virtual laboratories were helpful in increasing safety, and reducing anxiety and cognitive load when completing the physical laboratory tasks later.

Polymer science courses, particularly at the undergraduate level, have mostly relied on a lecture-based model where teaching is focused on concepts and evaluations are primarily conducted through midterm and final examinations. When an in-person lab course is offered, such as a new polymer science course for advanced undergraduates, students are provided with lab manuals containing step-by-step instructions. The lab section may involve having students complete prelab questions, record observations, and submit lab reports. Digital tools have been developed to provide students with technology-enhanced education experience, although materials centered on polymer science are relatively limited when compared to the vast virtual materials available in chemistry. These tools include virtual reality (VR) environments to visualize polymer structures, video-based laboratories, and online simulations.

Theoretical Background: Self-Determination Theory

Self-Determination Theory as a Pedagogical Framework

Self-determination Theory (SDT) posits that human motivation is driven by three fundamental psychological needs: autonomy (feeling in control), competence (feeling effective), and relatedness (feeling connected). According to SDT and educational research, when these needs are met, students exhibit higher intrinsic motivation, behavioral engagement, and persistence. ,

In chemical education, interventions grounded in SDT, such as Process Oriented Guided Inquiry Learning (POGIL) or flipped classrooms, have successfully showed evidence of increased self-regulation, self-efficacy, and sense of belonging. − For example, measuring students’ self-efficacy in lower-level chemistry courses, researchers found that self-efficacy toward the end of the semester predicted students’ final course outcomes. Similarly, both early semester and late-semester sense of belonging appeared to correlate with students’ exam outcomes in a general chemistry course. Training self-regulation skills were also found to help students stay engaged. Furthermore, longitudinal studies suggest that fulfilling these needs correlates strongly with course outcomes and exam performance. ,

Autonomy: Self-Regulation

Autonomy refers to self-initiated and self-endorsed behavior, which manifests as self-regulation in educational contexts. Self-regulation involves setting realistic goals and determining the necessary steps to achieve them. Research consistently demonstrates that students with higher levels of self-regulation achieve better learning outcomes than those with lower levels. −

The OVESET modules support students’ sense of autonomy through features that provide students with opportunities to make choices and self-direct their learning during the virtual experiments. For example, students can choose different experimental conditions, such as temperature, concentration, and reaction time, to observe how these factors affect the polymerization process. Past research on a virtual simulator for engineering students found that students’ frequency of behaviors in the tools, such as adjusting the setting, computing the tools, and observing the results, were related to their self-regulation scores. Additionally, the OVESET modules are openly accessible without requiring students to log in or download any software. This design choice provides flexibility, allowing instructors to assign modules as preclass preparation or postclass review and allowing students to revisit the modules at their own pace and convenience outside of class time.

Competence: Self-Efficacy

The second basic need proposed by the SDT, competence, manifests in the educational context as self-efficacy which refers to students’ perceived confidence in their ability to understand complex concepts and solve problems. Self-efficacy, also supported by the classic Social Cognitive Theory, is the belief in one’s ability to learn and perform well in a specific subject area. In chemistry education, it encompasses students’ belief in their ability to conduct experiments and solve problems in the field. Research has shown that students who have a higher self-efficacy are more likely to persist in learning and achieve better learning outcomes.

OVESET targets students’ self-efficacy by providing them with interactive and engaging features (e.g., drop-down menus, sliders, and buttons) and immediate feedback. Interactive features are capable of making students feel ownership of the knowledge, , thus increasing students’ motivation and engagement in the learning process.

Relatedness: Belonging

The third component, relatedness, or sense of connection, refers to students’ perceived connectedness with or belongingness in the learning community. While autonomy and competence are crucial in students’ self-perceptions, relatedness (or the need to belong) is equally important to motivation. When students feel they belong in the learning environment, their sense of ownership increases and they are more engaged in learning activities. ,

OVESET targets students’ sense of belonging by providing opportunities for collaboration and interaction with peers. Classroom implementation ideas and practice questions that encourage students to work together and discuss their findings are provided. Belonging is context-specific. Students from different backgrounds and institutional types may need different support to feel belonged. Instructors may thus need to adapt OVESET to fit their educational contexts. To facilitate this, OVESET is licensed openly to allow instructors to modify the tools and publish their adaptations. The modifications made may further contribute to making the tools better, more accessible, and diverse in topics and usage. For example, instructors may edit the language and wording to fit their students’ reading levels by simply double-clicking the text box and editing it. Instructors may add their introduction based on how they teach the course by adding a new text box at the top of the module. The text box also allows the inserting of images, links, and videos; therefore, instructors may add welcome videos to the virtual simulator. Additionally, instructors may modify the practice questions to fit their course learning objectives.

Intention to Pursue a Career in Polymer Science

When subjects are challenging, learning motivation is at risk. Therefore, having high self-regulation (autonomy), perceived self-efficacy (competence), and belonging (relatedness) can potentially protect students from giving up or quitting. , When the needs for autonomy, competence, and relatedness are met, students are more likely to persist in challenging fields, and students’ motivation to persist and stay engaged also increases. Research findings supported this notion, showing that a high score in one or more of the three aspects of SDT relates to higher intention in pursuing the subjects. , In the context of chemical or polymer science education, the three SDT components are particularly important due to the complexity of the topics and the high cognitive demands required for understanding. This study will focus on students’ intention to pursue a career in polymer science.

Evaluating OVESET with SDT Components

Given the robust findings in the literature, we evaluated the implementation of OVESET using components associated with the SDT. A previous implementation of OVESET tested the effectiveness in terms of content knowledge improvement using a control-experimental design with pretest and post-test administered. The results showed that students who used OVESET had higher content knowledge scores than students who did not use OVESET but were taught with the same content using PowerPoint lectures. For this study, content knowledge is not the focus of the evaluation. Instead, we focused on testing students’ perceived changes in the SDT components, including self-regulation, self-efficacy, sense of belonging, and intention to pursue a career in polymer science. Since the sample size is small and cannot effectively test subgroups, we did not have a control group in our research design. Instead, we administered pretest and post-test to measure the change of students before and after the use of OVESET modules in the classroom.

Our evaluation questions include the following:

  • 1.

    To what extent did students’ self-regulation (SDT component of autonomy) change after using the OVESET modules?

  • 2.

    To what extent did students’ perceived polymer science self-efficacy (SDT component of competence) change after using the OVESET modules?

  • 3.

    To what extent did students’ sense of belonging in polymer science (SDT component of belonging) change after using the OVESET modules?

In addition to the three SDT components, we also evaluated students’ intention to pursue a career in polymer science, as such intention to persist is an important outcomes of the SDT framework. Therefore, the fourth evaluation question is as follows: 4. To what extent did students’ intention to pursue a career in polymer science change after using the OVESET modules?

Methods

The implementation and the research conducted on the implementation were approved by the Institutional Review Board at the University of Louisiana at Lafayette (Approval Number: IRB-21-068-OTHR-OTHR). The evaluation procedure was designed and conducted independently by external evaluators (second and third authors). Informed consent was presented to the students prior to the administration of each survey. The materials were implemented in an undergraduate-level macromolecules course that teaches students introductory concepts of polymer and materials science. It covers topics such as polymer synthesis, polymer properties, and polymer applications. The course is typically taken by junior or senior students.

OVESET Modules and Qualitative Pilot Study

OVESET Modules

The links to the OVESET modules were posted in a dedicated webpage, accessible via a web browser (https://wangyu16.github.io/PolymerScienceEducation). Each module begins with a concise introduction to the topic, followed by clearly stated learning objectives, classroom implementation suggestions, and practice questions. Figure shows some of the top sections of the homepage of each module, including a link to the simulator and the learning objectives.

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Module objectives are shown on the front page of each experimental simulator.

By default, an example experimental setup is provided for students to start with. Students can then modify the experimental conditions using interactive features to explore different scenarios and observe the effects on the results. The virtual simulator Web site also includes explanations of the concepts and principles behind the experiments as well as guidance on how to interpret the results.

In each module, classroom implementation ideas and practice questions are provided on the main module page. For example, using OVESET Module 1, students were asked to experiment with the basic statistics of step growth polymerization and living chain growth polymerization and calculate the number-average and weight-average molecular weights. In Module 2, students were then instructed to perform a series of experiments to understand the kinetics of step growth polymerization. Before the virtual experiments were conducted, students were asked to predict the results of the experiments. After the experiments were conducted, students were asked to compare their predictions with the actual results. Practice questions were also given to allow students to learn step-by-step. For example, in Module 1, students were asked to calculate the number-average molecular weights with a given mole of a specific type of molecules. In Module 2, students were asked to complete a more advanced calculation relating to the degree of polymerization. Each module was completed in a class session, which was approximately 50 min. The Supporting Information provides a sample workflow.

Pilot Study

During our pilot study in 2023, two polymer science modules (Molecular Weight Distribution and Step Growth Polymerization) were piloted to gather open-ended feedback. Specifically, the first module, Molecular Weight Distribution, introduces students to the basic statistics of step growth polymerization and living chain growth polymerization. The second module, Step Growth Polymerization, allows students to conduct a series of experiments to understand the kinetics of step growth polymerization. The two modules are connected, with Module 1 being an introduction and Module 2 being a deeper dive into the details of one of the methods.

The course enrollment was 16. A total of 6 students consented and completed the open-ended feedback form. Among the 6 students, 3 were females (50%), 2 were males (33.3%), and 1 was nonbinary (16.7%). Participants consisted of 3 White students (50%) and 2 Black students (33.3%). One did not state the race/ethnicity (16.7%). A total of 3 students were chemistry majors (50%) and 3 were biology majors (50%). A majority of students were seniors (n = 5, 83.3%), followed by one junior (n = 1, 16.7%).

Pilot Study Questionnaire

For the pilot study in 2023, students were administered a postuse survey containing four open-ended questions related to logical reasoning, instructional comparison, classroom collaboration, and confidence in tutoring. The specific questions provided to the students included, “In what ways do the simulation tools help you learn the logical reasoning behind the concepts?”, and “Compared to a traditional PowerPoint slides lecture, does using the simulation tools help you learn the logic behind the concepts better? Why or why not?”, “Now, consider the classroom experiences. Does discussing the contents with your classmates during class help with your learning? Why or why not?”, and “If you are asked to use the tools to tutor or teach future students the logical reasoning behind the topics, how confident are you to do so?”

Pilot Findings

The open-ended feedback from the pilot study provided a rich context for student perceptions. We summarized the students’ feedback below.

Enhancement of Autonomy through Visual and Manipulative Features

The most prominent theme was the role of the simulator as a visual and manipulative aid in conceptual mastery. Students strongly favored visual feedback, noting that the tool helped them “to actually see how the reactions are happening rather than just using the equations”. The simulation was seen as a necessary complement to theory, assisting with the application of knowledge: “The slides do well with showing the step-by-step process...but...the simulators assist with this issue by allowing me to tweak variables and see how they affect the outcome”. However, while students appreciated the simulation for visualization, some felt it was not always the best method for abstract concepts, suggesting a balance with traditional lectures is necessary: “Sometimes yes [the simulation is better]. Polymerization processes are better with the simulation but other aspects such as important concepts are better in slides”.

Students further linked the control of the virtual experiment to better logical reasoning, supporting the goal of enhancing autonomy as well as the confidence of understanding the concepts. The ability to manipulate variables provided an active learning experience: “I am a visual learner, and being able to manipulate the factors myself, to obtain results, helps me grasp the concept better”. This control led to a feeling of active engagement, as one student stated that the tools helped them “be more active in class as its hands on”.

The Value of Relatedness (Collaboration)

Although they were not directly associated with the tools, students commented on the classroom discussion experiences. The feedback consistently affirmed the critical role of peer discussion, which informs why intentional implementation of collaboration is necessary to boost relatedness. Students found discussion vital for clarification and psychological safety: “Discussing contents with my classmates during class helps me...because it allows me to get a second opinion when I am hesitant about something”. Furthermore, peer discussions were noted for their clarifying effect: ″If another student can help them by discussing, it can help clear up some blurred lines”. However, one student noted a preference for working alone to maintain focus, which could be a point of friction during mandatory group work: “Sometimes yes [discussing helps]. It is nice to exchange ideas, but I do enjoy learning by myself to completely understand and focus on the process”.

Confidence in Communicating the Concepts

Responses related to tutoring confidence highlighted a discrepancy: while students felt competent in the concept, they were often hesitant to teach it due to limited exposure. One student, despite noting the simulation made topics easy to understand, still lacked confidence: “I wouldn’t feel confident in teaching any other students...I don’t feel confident in my ability to convey information in a way that someone other than me will understand”. Conversely, two out of six students felt that the tool itself facilitated instruction: “I am fairly confident that I would be able to tutor students using these tools”.

The 2023 pilot findings confirmed the potential of the OVESET tools. However, the feedback also revealed a need for more structured implementation and evaluation to understand how students change before and after use of the tools. Therefore, we proceeded with two rounds of formal, quantitative evaluation guided by SDT to measure changes in self-regulation, self-efficacy, sense of belonging, and intention to pursue a career in polymer science across two subsequent implementations in 2024 and 2025.

Formal Implementations of OVESET Modules

In this section, we describe the setup and implementation of OVESET modules across two academic years. All implementations took place in upper-level undergraduate polymer science courses at a four year college in the southern United States. Enrollment in the course required successful completion (grade C or better) of the General Chemistry sequence and Organic Chemistry I.

During the first implementation in 2024, two modules (same as pilot, i.e., Molecular Weight Distribution and Step Growth Polymerization) were implemented across 3 weeks. The total course enrollment for the 2024 implementation was 35 students.

During our second implementation (2025), seven modules were developed and implemented, adding to key polymer science topics that include conventional radical polymerization, atom transfer radical polymerization, radical copolymerization, network formation, single chain conformation, self-avoiding chains, and star and branched polymers. All modules were reviewed independently and single-anonymously (i.e., the developers did not know the reviewers’ identities, but the reviewers knew the developers’ identities) by polymer science instructors before implementation. The total number of courses in this implementation was 23 students.

Participants

Our ethical procedure allowed students to opt out of the research study or withdraw at any point. During the first implementation, attendance was also not mandatory, resulting in a large portion of students absent from class (n = 19) and thus not exposed to the modules or the surveys. Out of the 35 students enrolled, only 16 students consented and completed the two modules and the pretest and post-test surveys. Among the 16 participants, 10 were females (63%) and 6 were males (37%). Participants were predominantly White (n = 14, 88%), followed by Hispanic (1, 6%) and multiethnic (1, 6%). A total of 14 students were chemistry majors (87.5%), and 2 were social sciences students (12.5%). Students spread across different years in college, with 2 sophomores (12.5%), 8 juniors (50.0%), and 6 seniors (37.5%).

For the second implementation, out of the 23 students enrolled, three students dropped the class before the introduction of the second module and thus were not exposed to a majority of the modules. For the 20 students who completed the modules, one did not fill out the postsurvey and was thus removed from the data. The final sample consisted of a total of 19 students who consented and completed the seven modules and the pretest and post-test surveys. A total of 14 were females (74%) and 5 were male (25%). Partipants were mostly White (n = 8, 42.1%), with the rest spreading across African American/Black (n = 3, 15.8%), Asian (n = 4, 21.1%), and multiracial (n = 4, 21.1%). A majority of students were chemistry majors (n = 18, 94.7%), and one was a biology major (5.3%). A majority of students were seniors (n = 11, 57.9%), followed by juniors (n = 8, 42.1%).

Measures

Measure Selection and Analytic Approach

Because of the short attention span of students, we kept the survey as brief as possible to lower the attrition rate. Therefore, instead of using full scales (which typically contain 8–20 items per construct), the second and third authors (professional education evaluators) selected 2–4 items from existing scales to measure each construct. The selection process focused on the face validity of the items in the context of polymer science learning and the evaluation. Because we were unable to use the full, validated scales (due to constraints on survey length), analyses of the main hypotheses focused on analyzing the distributions and prepost changes of individual item scores.

All data analyses were conducted using R version 4.1.0. To provide a foundation for future research and demonstrate the quality of our item selections, we report the internal consistency (reliability) and factor structure (construct validity) of the item sets by using the full combined sample (N = 35). The EFA was conducted using Principal Axis Factoring. We used the Kaiser criterion (eigenvalues greater than 1) as the basis for determining the number of factors. , We confirmed a single-factor structure for all measures by noting that the second eigenvalue was consistently below the criterion of 1.

The reporting of the Cronbach’s alpha (to observe internal consistency) and Exploratory Factor Analysis (EFA) score (to observe construct validity) serves merely as a reference for future researchers. These scale-level metrics were not used for inferential comparison. Instead, the final hypotheses were tested by using the individual item scores.

Descriptive statistics (means, standard deviations) were computed to directly compare pretest and post-test values. Nonparametric tests, namely, the Wilcoxon Signed-Rank tests for paired pretest and post-test scores were conducted using the rstatix package (version 0.7.2). Given the sample size and presence of ties, the p-value was computed using the normal approximation with a continuity correction. The effect size, r (rank-biserial correlation), and its confidence interval were obtained using the wilcox_effsize function in the rstatix package. Pairs with zero difference were excluded from the analyses. Results will be reported separately for the first and second implementations.

Self-Regulation

Self-regulation were assessed using five items from the Personal Growth Initiative Scale-II, rated on a 5-point Likert scale from strongly disagree (1) to strongly agree (5). Only items relating to self-regulated behavioral changes were included. Sample items are, “I set realistic goals for what I want to change about myself”, and “I figure out what I need to change about myself.” Cronbach’s alpha was 0.77 (95%CI = [0.64, 0.83]). EFA showed a one-factor structure (eigenvalue 1 = 2.08; eigenvalue 2 = 0.42), demonstrating that the individual items appear to fall onto the same construct.

Perceived Self-Efficacy in Polymer Science

The study assessed students’ perceived self-efficacy in polymer science before and after the modules. Perceived polymer science self-efficacy was measured using four items adapted from the Chemistry Self-Efficacy Scale to target the different aspects of polymer science learning. Participants rated on a 5-point scale from very poorly (1) to very well (5). Only items relating to the context of the study were included. Specifically, participants were asked to rate their perceived ability to describe/explain polymer science knowledge (e.g., describing properties of polymers, structure of polymers, or explaining/interpret kinetics and mechanism of polymerization reactions). Cronbach’s alpha was 0.89 (95%CI = [0.84, 0.93]). EFA demonstrated a single-factor structure (eigenvalue 1 = 2.71; eigenvalue 2 = 0.68), indicating that the individual items appear to fall onto the same construct.

Sense of Belonging in Polymer Science

Sense of belonging was measured using six items curated by the evaluators (second and third authors), focusing on students’ perceived membership in the polymer science community, as well as their feeling of involvement in participating in different academic activities. These include two items taken from the membership subscale of the Math Sense of Belonging scale (e.g., “I feel that I belong to the polymer science community.”), and four items on their feeling of involvement in undergraduate research, reading research papers, attending a seminar, and working in the polymer science field. These items were rated on a 5-point Likert scale from strongly disagree (1) to strongly agree (5). Cronbach’s alpha was 0.91 (95%CI = [.88, 0.94]). EFA revealed a single-factor structure (eigenvalue 1 = 3.85; eigenvalue 2 = 0.64), indicating that the individual items appear to fall onto the same construct.

Intention to Pursue a Career in Polymer Science

Two items were used to measure participants’ intention to pursue polymer science career, including “I intend to devote my career to an area related to polymer science” and “I intend to find a job that uses polymer science knowledge”, rated on a 6-point scale from very untrue of me (1) to very true of me (6). Cronbach’s alpha was 0.92 (95%CI = [0.87, 0.95]). EFA revealed a single-factor structure (eigenvalue 1 = 1.70; eigenvalue 2 = 0.85), indicating that the individual items appear to fall onto the same construct.

Results

Self-Regulation

Descriptive statistics for each item of the Personal Growth Initiative Scale-II are presented in Table . Wilcoxon sign-rank test results are presented in Table . Results showed that none of the items showed significant change from pretest to post-test in either implementation.

1. Descriptive Statistics for the Personal Growth Initiative across Two Implementations.

  Year 1
 Year 2
  Pretest
 Post-test
 Pretest
 Post-test
Items Mean SD Mean SD Mean SD Mean SD
Set goals to adjust behaviors 3.88 0.81 3.88 0.62 4.05 1.03 3.95 0.97
Know whst one needs to adjust behaviors 4.25 0.68 4.12 0.50 4.16 0.50 4.00 1.00
Know when one needs to adjust behaviors 3.81 0.83 4.00 0.63 4.05 0.62 3.84 0.96
Use resources to grow 3.94 1.06 4.12 0.72 4.26 0.81 4.32 1.00
Seek help to adjust behaviors 3.25 1.24 3.50 1.10 3.42 0.84 3.47 1.07
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2. Wilcoxon Test for Personal Growth Initiative across Two Implementations.

  Year 1
 Year 2
Items V p r [95%CI] V p r [95%CI]
Set goals to adjust behaviors 10.5 1.000   37.0 0.548  
Know what one needs to adjust behaviors 10.5 0.588   18.0 0.624  
Know when one needs to adjust behaviors 23.0 0.516   25.0 0.492  
Use resources to grow 45.0 0.644   50.5 0.740  
Seek help to adjust behaviors 34.5 0.495   29.0 0.915  
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Effect size (r) and 95% Confidence Interval (CI) are only provided when p < 0.05.

Perceived Self-Efficacy in Polymer Science

Descriptive statistics for each item of the polymer science self-efficacy scale are presented in Table . Wilcoxon sign-rank test results are presented in Table . Results showed that two items showed significant increases from pretest to post-test in both implementations. Specifically, students’ perceived self-efficacy in “describing properties of polymers” (for both implementations) and “explaining kinetics and mechanism of polymerization reactions” (for Year 2 implementation only) significantly increased after learning with the OVESET modules in both implementations, with medium effect sizes.

3. Descriptive Statistics for Perceived Polymer Science Self-Efficacy across Two Implementations.

  Year 1
 Year 2
  Pretest
 Post-test
 Pretest
 Post-test
Items Mean SD Mean SD Mean SD Mean SD
Interpret equations 2.56 1.03 2.94 0.93 2.21 0.98 2.84 0.83
Describe properties of polymers 2.75 0.77 3.50 0.63 2.47 0.77 3.21 0.92
Describe structure of polymers 3.00 1.03 3.44 0.73 2.47 0.77 3.00 0.82
Explain kinetics and mechanism of polymerization reactions 2.50 0.89 2.88 1.15 1.95 0.78 2.74 0.87
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4. Wilcoxon Test for Perceived Polymer Science Self-Efficacy across Two Implementations.

  Year 1
 Year 2
Items V p r [95%CI] V p r [95%CI]
Interpret equations 49.5 0.120   81.0 0.072  
Describe properties of polymers 55.0 0.004 0.47 [0.21,0.69] 98.0 0.028 0.42 [0.13,0.66]
Describe structure of polymers 45.0 0.059   78.5 0.098  
Explain kinetics and mechanism of polymerization reactions 33.5 0.197   94.0 0.007 0.46 [0.15,0.70]
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Effect size (r) and 95% Confidence Interval (CI) are only provided when p < 0.05.

Figures and visually compare the pre- and post-test scores on the two items that showed significant increases across the two implementations. As shown, students’ perceived self-efficacy in describing polymer properties and explaining polymerization kinetics and mechanism increased after using the OVESET modules in both implementations. Students’ pretest scores were slightly lower in the second implementation than in the first implementation. However, students’ post-test scores were similar across the two implementations.

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Comparison of pre- and post-test scores on self-efficacy in describing polymer properties across two implementations.

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Comparison of pre- and post-test scores on self-efficacy in explaining polymerization kinetics and mecahnism across two implementations.

Sense of Belonging in Polymer Science

Descriptive statistics for each item of the polymer science sense of belonging scale are presented in Table . Wilcoxon sign-rank test results are presented in Table . Results showed that only one item, “want to attend polymer science seminars”, showed a significant decrease from pretest to post-test in the second implementation, with a small effect size. No other items showed significant changes in either implementation.

5. Descriptive Statistics for Polymer Science Sense of Belonging across Two Implementations.

  Year 1
 Year 2
  Pretest
 Post-test
 Pretest
 Post-test
Items Mean SD Mean SD Mean SD Mean SD
Feel belonged to the polymer science community 2.44 0.89 2.38 0.96 2.61 0.98 2.11 1.20
Feel connected to the polymer science community 2.50 0.97 2.25 1.06 2.58 1.02 2.21 1.23
Want to participate in polymer science research 3.25 1.18 2.81 1.33 3.58 0.96 3.21 1.36
Want to read polymer science research 2.88 1.36 2.50 1.32 3.21 1.03 2.53 1.26
Want to attend polymer science seminars 3.00 1.10 2.75 1.18 3.47 1.02 2.74 1.33
Want to work in fields relating to polymer science 2.44 1.15 2.50 1.26 3.11 1.10 2.53 1.47
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6. Wilcoxon Test for Polymer Science Sense of Belonging across Two Implementations.

  Year 1
 Year 2
Items V p r [95%CI] V p r [95%CI]
Feel belonged to the polymer science community 6.0 0.766   34.0 0.138  
Feel connected to the polymer science community 7.0 0.240   40.5 0.262  
Want to participate in polymer science research 9.0 0.222   34.0 0.238  
Want to read polymer science research 10.5 0.158   27.5 0.108  
Want to attend polymer science seminars 6.5 0.457   15.0 0.029 0.29 [0.02, 0.58]
Want to work in fields relating to polymer science 24.5 0.851   37.5 0.202  
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Effect size (r) and 95% Confidence Interval (CI) are only provided when p < 0.05.

Intention to Pursue a Career in Polymer Science

Descriptive statistics for each item of the intention to pursue a career in polymer science are presented in Table . Wilcoxon sign-rank test results are presented in Table . Results showed that none of the items showed significant change from pretest to post-test in either implementation.

7. Descriptive Statistics for the Intention to Pursue a Career in Polymer Science across Two Implementations.

  Year 1
 Year 2
  Pretest
 Post-test
 Pretest
 Post-test
Items Mean SD Mean SD Mean SD Mean SD
Devote career to fields relating to polymer science 2.88 1.41 2.94 1.53 2.89 1.29 2.84 1.50
Find job that uses polymer science knowledge 2.38 1.36 2.38 1.36 2.42 1.46 2.21 1.44
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8. Wilcoxon Test for Intention to Pursue a Career in Polymer Science across Two Implementations.

  Year 1
 Year 2
Items V p r [95%CI] V p r [95%CI]
Devote career to fields relating to polymer science 21 0.714   51.0 0.949  
Find job that uses polymer science knowledge 15 0.930   41.5 0.804  
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Effect size (r) and 95% Confidence Interval (CI) are only provided when p < 0.05.

Discussion

Moderate Effect on Aspects of Perceived Self-Efficacy

In this study, we evaluated the implementation of OVESET, a series of polymer science virtual experimental simulators. The results from the two implementations showed promising results in increasing perceived self-efficacy in describing properties of polymers (Years 1 and 2) and explaining the mechanism and kinetics of polymerization reactions (Year 2). These findings align with previous research that showed the effectiveness of virtual laboratories in enhancing students’ perceived self-efficacy in chemistry education. ,

A critical question remains about whether the perceived self-efficacy developed in a virtual setting fully transfers to real-world laboratory performance. Self-efficacy is context-dependent. OVESET effectively enhances perceived self-efficacy related to conceptual understanding and procedural aspects of experiments, but it may not replicate the complexity of the hands-on work. Virtual settings use simplified, controlled interfaces that minimize real-world challenges, such as equipment malfunctions, measurement errors, or safety concerns. Therefore, the self-efficacy gained may be more tied to conceptual mastery and digital tool confidence than to the practical skills and resilience needed in a physical lab.

Future research may explore how this virtual self-efficacy translates to hands-on laboratory performance. Investigating hybrid approaches, which combine virtual modules with hands-on experiences, could determine whether this leads to a more comprehensive development of self-efficacy across both conceptual and practical domains.

Limited Effect on Other Sociocognitive Aspects

The other two SDT components, self-regulation (autonomy) and the sense of belonging (relatedness), did not show the expected increases. Consequently, the anticipated outcome of SDT, the intention to pursue a career in polymer science, also did not increase.

A slight, unexpected decrease in students’ sense of belonging was observed, particularly in the second implementation. This may be interpreted as a natural process of self-selection. Introducing a new discipline prompts students to reflect more critically on their interests and future goals. As they gain a clearer understanding of polymer science, some may realize that their sense of fit is not as strong as initially perceived.

Our null findings suggested that virtual laboratories, while effective at increasing self-efficacy in specific content areas, may not be as impactful in fostering broader sociocognitive constructs such as self-regulation, belonging, or the intention to pursue a career in polymer science. These constructs often develop through sustained interpersonal interactions, mentorship, and authentic engagement within a disciplinary community, experiences that may be less accessible in a virtual or simulation-based environment.

The structure of OVESET, which emphasizes individual exploration and self-paced learning, may not offer sufficient opportunities for the community-building necessary for belonging. Although students were encouraged to work in groups, their focus may have remained on individual devices. This lack of hands-on, real-world challenges, and direct peer interaction could limit the development of autonomy and relatedness. Instructors who implement the modules may consider incorporating more collaborative elements, such as shared workspace, shared-group-based simulation inputs, or the use of embedded discussion prompts that require synchronous peer input to proceed, transforming the activity into a communal learning experience to better foster relatedness. Additionally, future iterations could integrate features for asynchronous reflection or group data sharing to further operationalize relatedness.

Limitations and Future Directions

Several limitations should be acknowledged. First, the small and predominantly female and White sample sizes limit the generalizability of the results. Future studies require larger and more diverse populations for validation. Additionally, the short three week duration in the first implementation and a semester-long duration in the second implementation may not capture long-term effects. Longitudinal studies are needed to assess the sustained impact.

A second limitation is the self-reported nature of the measures. Future research should complement these with objective assessments, such as performance tasks or observational data. Furthermore, while practical, the use of selected survey items may not fully capture the intended constructs. Using full-scale instruments or developing new, tailored instruments is recommended for future research.

Further, although we gathered initial insights from a small pilot questionnaire, future research may employ more robust qualitative approaches (e.g., student interviews, focus groups, or detailed open-ended questions) to better capture the nuanced experiences and motivations that are often missed by quantitative assessments.

From a pedagogical perspective, virtual laboratories offer flexibility and interactive learning opportunities that can complement traditional teaching methods. The virtual laboratories are a good option when resources do not allow sufficient laboratory hours, but they do not replace the benefits of an in-person laboratory completely. Balancing virtual and in-person instruction is essential to ensuring comprehensive educational outcomes.

Conclusion

Despite these limitations, this study demonstrates the potential of virtual laboratories to enhance self-efficacy in polymer science education. The interactive and engaging nature of OVESET appears to empower students, fostering a greater sense of competence and confidence in their ability to grasp complex polymer science concepts. Future research should explore the integration of additional interactive features and assess their impact on different domains of learning. Additionally, investigating the scalability of OVESET in larger educational settings and its adaptability across various scientific disciplines could further establish the tool’s utility in enhancing STEM education.

Supplementary Material

ed5c01385_si_001.pdf (632.6KB, pdf)

Acknowledgments

The authors thank the financial support from the National Science Foundation awarded to Y.W. and M.L. (award number: 2142043), and the Open Education Group, awarded to Y.W.

The Supporting Information is available at https://pubs.acs.org/doi/10.1021/acs.jchemed.5c01385.

  • Details regarding the OVESET simulator (PDF)

The authors declare no competing financial interest.

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Supplementary Materials

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