Computer simulations have emerged as powerful tools for teaching STEM subjects in high schools. Unlike static textbooks or lectures, simulations provide interactive, visual, and hands-on experiences that can make abstract concepts tangible. Educators and researchers have increasingly explored how these digital tools can enhance learning in mathematics, physics, and chemistry classrooms. Pioneering thinkers like Andrea diSessa, Harold Abelson, and Stephen Wolfram have long argued that computing and simulation can fundamentally transform education[51†L236-L244][6†L228-L235]. This review examines evidence from peer-reviewed studies, meta-analyses, and industry reports on the effectiveness of simulation-based learning versus traditional methods, through the lens of both learning theory and practical outcomes.
High school STEM subjects often deal with complex or invisible phenomena – from subatomic particles to gravitational fields – which are hard to experience directly. Computer simulations offer several key benefits:
Simulations create dynamic visual models of mathematical and scientific ideas, helping students “see” and manipulate elements of a problem. For example, dynamic geometry software lets students drag the vertices of shapes and instantly observe how angles and areas change, reinforcing geometric relationships through exploration. Studies confirm that using such software yields improved student achievement, especially on higher-level reasoning tasks[49†L74-L82]. In algebra, tools like function simulators or the SimCalc program (which links motion simulations to graphs) allow students to experiment with variables and see immediate outcomes, bolstering their understanding of calculus and algebraic concepts.
In physics and chemistry, simulations can replicate laboratory experiments that might be too dangerous, expensive, or time-consuming to perform in a school lab. For instance, a chemistry simulation can let students virtually mix chemicals to observe reactions without any safety risk. A 2014 meta-analysis of 59 studies found that across K–12 science and math settings, computer-based simulations had an overall significant positive effect on student learning outcomes compared to instruction without simulations[20†L331-L339]. Simulations also let students run experiments multiple times, speed up or slow down processes (e.g. simulating years of evolution in minutes), and explore phenomena at different scales[16†L13-L21][16†L15-L23] – experiences that traditional labs or lectures often cannot provide.
Because they are interactive and often game-like, simulations tend to captivate students’ interest. Research syntheses indicate that digital games and simulations yield higher student engagement, critical thinking, and problem-solving skills, making them valuable supplements to traditional instruction[45†L69-L77][45†L75-L79]. In one study, Ethiopian 10th-graders learned chemistry through an inquiry cycle (the “7E” model) with and without simulations; the class using simulations showed substantially greater engagement (behavioral, emotional, and cognitive) than those with the same method minus simulations[28†L808-L817][30†L1-L4]. Qualitative feedback from these students indicated increased understanding and a sense of relevance as they connected academic concepts to everyday contexts. Such outcomes align with reports from the U.S. Department of Education that game-based simulations build “creativity, systems thinking, and perseverance” in learners[45†L75-L79].
Interactive simulations often provide instant feedback. A student adjusting the slope of a line in a graphing sim sees the line tilt in real time, or a student adding weight to a virtual spring sees it stretch accordingly. This immediate cause-and-effect feedback helps students test their predictions and correct misunderstandings on the spot, embodying a learning-by-doing approach. Constructivist learning theory emphasizes that learners build knowledge actively; simulations support this by letting students form hypotheses, try out ideas, and learn from the simulated results in a low-stakes environment.
In physics and chemistry education, computer simulations and virtual labs have been widely studied as alternatives or supplements to traditional hands-on experiments. The evidence consistently shows that well-designed simulations can match or even exceed the learning gains from traditional methods on conceptual understanding:
Interactive simulations like those from the PhET project (University of Colorado) have been used to replace real lab equipment in topics like electric circuits. Remarkably, students who used a circuit simulation learned the concepts as well as or better than students who used real wires, bulbs, and batteries[38†L40-L48]. In a controlled study, a PhET Circuit Construction Kit simulation led to improved conceptual learning about electrical circuits “in the best cases,” and at worst produced learning equivalent to the traditional lab[38†L40-L48]. The advantage of the simulation was that it focused students on core principles – e.g. bulb brightness and current flow – by stripping away distractions like tangled wires or imperfect connections[38†L44-L49]. Students could quickly reconfigure circuits with a click, test multiple scenarios, and avoid frustrating mechanical issues, thereby spending more time on reasoning. (Notably, while real labs teach practical skills, the simulation users in one study were later able to assemble physical circuits faster than those trained on real equipment, likely because they had a clearer conceptual model to guide them[38†L73-L82].)
Many physics concepts (gravity fields, gas molecules moving, forces on an object) are invisible or hard to observe directly. Simulations make these visible. A classic example comes from Logo programming in the 1980s: Andrea diSessa created a “dynaturtle” (dynamic turtle) that obeys Newton’s laws of motion, extending Seymour Papert’s Logo environment to physics[1†L7-L15][1†L23-L31]. Elementary students using the dynaturtle simulation learned through play—giving the turtle “kicks” (forces) and watching its motion change—eventually overcoming the common misconception that “things always go in the direction you push them.” The feedback from the microworld helped students realize that a push adds momentum and typically deflects an object’s path, exactly as Newton’s laws predict[1†L21-L29][1†L31-L39]. Even university physics students showed similar misconceptions that the dynaturtle helped to rectify[1†L51-L59]. This early work demonstrated how simulations can confront intuitive misconceptions by providing an experiential bridge to formal scientific understanding.
Simulations in science are often used within inquiry-based pedagogies. Rather than being given facts, students can manipulate variables and observe outcomes – mirroring the work of scientists. Modern curricula aligned with the Next Generation Science Standards (NGSS) use simulations to introduce scientific phenomena and drive student-led inquiry. For example, an NGSS-aligned lesson might start with a digital simulation of a climate system or an ecosystem as a “phenomenon” for students to explore. This gives learners a self-guided opportunity to test their theories in a designed digital world, engaging in a process of sense-making[45†L103-L111]. A recent systematic review (Kefalis et al., 2025) of studies from 2019–2024 found that inquiry-based learning combined with interactive simulations is one of the most common and effective strategies in STEM classrooms[23†L49-L57][23†L53-L61]. These studies reported improvements in students’ conceptual understanding of science topics and increased ability to apply scientific reasoning, thanks to the rich, exploratory learning environment that simulations provide.
Beyond test scores, simulations help build scientific skills such as forming and testing hypotheses, interpreting data, and working through scientific processes. They also encourage “what-if” thinking: students can ask, What if I change this angle? What if gravity were stronger? and immediately see the answer. According to a whitepaper by the Smithsonian Science Education Center, such tools have “marked benefits on the development of critical thinking, problem-solving, systems thinking, and creativity skills — all of which are crucial to science education”[45†L73-L79]. These are exactly the kind of higher-order skills that traditional cookbook labs or lectures sometimes fail to cultivate.
Mathematics education has also benefited from computer-based simulations and interactive software, albeit in different ways. While a math problem itself doesn’t “behave” like a physical experiment, simulations and dynamic software help students explore mathematical ideas dynamically and visually:
Dynamic Geometry Software (DGS) like GeoGebra or Cinderella allows students to construct geometric figures and then drag points to see how properties change in real time. This turns geometry into an experimental science – students can conjecture (e.g., perhaps the sum of angles in a triangle is constant), test multiple cases by dragging the vertices, and observe the invariant (the angles always sum to 180°). A review of studies on DGS found a positive effect on students’ achievement in geometry, especially for tasks requiring higher-level thinking and visualization[49†L74-L82]. Students using DGS developed better spatial reasoning and were more engaged, though the research also emphasized the need for teacher guidance to maximize these benefits[49†L79-L87]. In algebra, graphing simulators enable students to manipulate parameters in functions and immediately see how the graph shifts or stretches, reinforcing their understanding of function families and transformations. This immediacy helps make abstract algebraic relationships more concrete.
Through simulations, students can tackle more complex, real-world math problems that would be impractical to solve by hand. Stephen Wolfram and others advocate for “computer-based math,” where students use tools like Wolfram Mathematica or Wolfram Alpha to offload tedious calculations and instead focus on setting up and interpreting problems. Wolfram’s vision is that by embracing computation and simulation, math education can shift toward deeper conceptual understanding and real-world problem solving. As Conrad Wolfram (Stephen’s brother) puts it, curricula should be rebuilt “assuming computers exist,” integrating programming and simulations to explore rich problems[11†L9-L16]. In practice, high school classes have used software to simulate statistical experiments (e.g. running thousands of trials of a probability experiment to observe distributions) or to model real datasets in algebra. Wolfram Research’s Demonstrations Project is one example of industry support for this approach – it offers thousands of free interactive demonstrations of mathematical and scientific concepts, from calculus visualizations to interactive physics experiments[14†L47-L54]. These allow students to tweak parameters and observe outcomes, effectively playing with math ideas to build intuition.
Even before modern GUIs, pioneers like Harold Abelson and Andrea diSessa showed how computers could transform math learning. In the 1980 book Turtle Geometry, Abelson and diSessa introduced the idea of using the Logo programming language’s “turtle” as an agent to explore geometry. Students would program the turtle to move and turn, drawing shapes on the screen. This approach “demonstrates how the effective use of personal computers can profoundly change the nature of a student’s contact with mathematics,” allowing learners to discover geometric properties by guiding an imaginary turtle through space[6†L228-L235]. High school students (and even younger children) could learn about angles, lengths, and even differential geometry concepts by experimentation in this programmed microworld. This constructivist approach – where students construct geometric knowledge by writing programs and observing the outcomes – was a forerunner to today’s interactive math simulations. It showed that when students actively create or control elements in a math environment, they engage more deeply and “learn with ‘pleasure and commitment,’” as diSessa later described[51†L230-L238].
A central question for educators is how simulation-based learning compares to more traditional teaching methods (such as textbook exercises, lectures, or physical labs). Research suggests that blending simulations with good pedagogy yields superior results in many cases, while wholly replacing traditional methods requires careful implementation:
A comprehensive review by Rutten et al. (2012) of a decade of research found “robust evidence that computer simulations can enhance traditional instruction, especially as far as laboratory activities are concerned.”[36†L65-L72]. In other words, students taught with a mix of simulations and teacher guidance tended to learn as much or more than those taught with traditional lectures and hands-on labs alone. Importantly, the best results occurred when simulations were not used in isolation but embedded in a well-designed lesson. For example, a physics teacher might use a simulation to let students explore a concept (like projectile motion) and then lead a discussion or problem-solving session to generalize their findings. The meta-analysis by SRI International (D’Angelo et al., 2014) similarly concluded that, overall, classes using simulations outperformed those with no simulations[20†L331-L339]. The effect was seen across science and math topics, though evidence was strongest in physics and chemistry education. Crucially, the SRI review noted that adding instructional supports or enhancements to simulations (e.g. guiding questions, scaffolds) produced even better outcomes than unguided simulation use[20†L331-L339]. This suggests that simulations are most effective when paired with constructivist teaching strategies—teachers facilitating inquiry, asking students to predict and explain results, etc.
Simulations excel at building conceptual understanding and transferable skills, often outshining traditional methods on those fronts[38†L40-L48][45†L75-L79]. However, for certain procedural skills or hands-on techniques, traditional methods may still be needed. For instance, a virtual chemistry lab can teach the theory of titration and let students practice virtually, but actually handling lab equipment is a different skill that virtual environments only partially address. Educators often adopt a blended approach: using simulations to prep students on the concepts and experiment design, then following up with a real lab for practical skills. That said, when resources are limited, simulations can effectively stand in for physical labs – providing science learning opportunities that would otherwise be impossible in many high schools. During the COVID-19 pandemic, teachers reported relying heavily on simulations for remote science labs, maintaining student learning despite the lack of physical lab access[45†L67-L75].
One notable advantage of simulations is their adaptability. Students can learn at their own pace – replaying an animation, adjusting difficulty levels, or exploring extension activities if they grasp the basics quickly. This can help differentiate instruction for diverse learners. Some studies also suggest simulations can benefit both high-achieving and lower-achieving students by providing more visual, engaging pathways into the material[49†L74-L82]. However, not all students are equally comfortable with open-ended exploration; thus, balancing guidance and freedom is important. Ensuring access to technology is another practical consideration: not all schools have sufficient devices or reliable internet for extensive simulation use, though the growing availability of low-cost devices and offline simulations is easing this issue.
The success of simulations in education is underpinned by theories of learning that emphasize active engagement and construction of knowledge. Constructivism, rooted in the work of Piaget and Vygotsky, posits that learners build new understanding based on their experiences and prior knowledge. Simulation-based learning aligns naturally with this philosophy:
Seymour Papert, a pioneer of constructionist learning (a branch of constructivism), famously wrote that “the computer is the Proteus of machines. Its essence is its universality, its power to simulate.”[43†L1-L4] By this he meant that computers can take on myriad forms and roles, enabling children to learn by creating and exploring within computer-simulated worlds. In Papert’s vision (outlined in Mindstorms, 1980), students would learn math and science much as they learn a language – by immersion and experimentation in a rich environment (which he called “microworlds”). The Logo turtle was one such microworld: a simple simulation where kids could learn geometry by guiding the turtle. This approach embodied the constructivist mantra that “learners construct knowledge actively” rather than receiving it passively[39†L5-L13]. Decades of follow-up work, including diSessa’s Boxer environment and various “learn to code” initiatives in schools, have carried this idea forward – letting students construct simulations themselves as a mode of learning. For example, diSessa and colleagues showed that even high schoolers could collaboratively design a computer program to model Newton’s laws, and in doing so engage in deep discussions and inquiry about physics[44†L58-L66][44†L67-L73]. The act of building a simulation (via programming) required students to articulate their understanding formally, get immediate feedback from the program’s behavior, and debug their ideas, which is a profoundly constructivist learning process.
A well-known challenge in STEM education is that students come in with strong intuitions or preconceptions (e.g. “heavier objects fall faster” or “you divide to make numbers smaller”). Traditional instruction might try to overwrite these with formulas and facts, but often the intuitions persist unless actively confronted. Simulations offer a way to bridge intuitive knowledge and formal concepts by allowing students to experience contradictions and resolve them. Andrea diSessa highlights that computers can engage students’ intuitive knowledge as a platform for building scientific understanding[51†L238-L244]. In a simulation, when students’ intuitive prediction fails (say, their virtual bridge collapses despite their expectations), it creates a powerful teachable moment. Constructivist theory tells us that this cognitive dissonance, followed by guided reflection, leads to genuine conceptual change. Research in physics education, for instance, has used simulations of force and motion to confront misconceptions: students visibly see that a continual force is not needed to maintain motion (newtonian dynamics), which challenges their everyday intuition that constant motion requires constant force. With appropriate guidance, students reconstruct their mental models to align with the scientific model. In essence, simulations make the invisible laws of nature visible and interactive, helping learners migrate from naïve ideas to scientifically accurate concepts through exploration and feedback.
Many interactive simulations also support collaborative learning – students working in pairs at a computer or discussing outcomes as a class. Vygotskian theory emphasizes the social construction of knowledge; simulations can serve as a shared reference point for discussion. For example, two students using a geometry sim might debate why a shape behaves a certain way when dragged, negotiate strategies to achieve a goal in a game-like simulation, or collectively troubleshoot a simulation that isn’t producing expected results. These interactions promote language use, argumentation, and reflection, which are crucial for deep learning. In the classroom, teachers often facilitate whole-group conversations around a simulation (projecting it for everyone), asking students to predict outcomes, explain observed behaviors, and connect the simulation to formal theory – essentially using the sim as an interactive visual aid around which collective sense-making happens.
Simulation-based learning in STEM owes much to visionary figures who recognized early on the potential of computers in education:
The education industry and policy organizations have increasingly recognized the value of simulations in STEM education, leading to greater support and integration in curricula:
The U.S. National Research Council (NRC) and Department of Education have highlighted simulations and digital labs as promising practices for STEM. Reports note that simulations can present phenomena that students “normally would not be able to experience firsthand” and allow investigations at different scales and timeframes[16†L13-L21][16†L15-L18]. The NRC has suggested that simulations, by making the unseen visible and allowing repeated trials, can address gaps in traditional science instruction (NRC, 2011). Likewise, the Next Generation Science Standards include science and engineering practices (like using models and simulations) as key skills – effectively encouraging teachers to incorporate simulation-based activities to meet those standards[45†L103-L111].
Industry-sponsored research and independent meta-analyses provide evidence backing simulations. A systematic review funded by the Gates Foundation (2014) quantitatively summarized decades of studies and concluded that “simulations have a beneficial effect over treatments in which there were no simulations.”[20†L331-L339] The same review found that providing instructional scaffolding around simulations (such as prompts or guided inquiry worksheets) enhanced their effectiveness even more[20†L331-L339]. A 2018 literature review by S. de Freitas (referenced by the Smithsonian) similarly found broad support for game-based and simulation-based learning as an enhancement to traditional pedagogy[45†L69-L77]. These publications, often circulated as whitepapers or executive summaries, have helped persuade school administrators of the legitimacy and value of investing in simulation software and teacher training.
There has been a boom in educational technology products focusing on simulations. PhET Interactive Simulations (free from University of Colorado) are now commonly used worldwide in high school science classes. Labster, a company providing virtual reality science labs, reports increased motivation and learning when students use its 3D lab simulations as a supplement to class[52†L1-L8]. For example, a recent study in Germany found that Labster’s virtual biology labs improved 10th graders’ understanding of experimental design and boosted their confidence in learning science (Hutt et al., 2021)[52†L15-L23]. The Wolfram Alpha computational engine is integrated into many math classrooms to help students check work and explore extensions, effectively acting as a simulation tool for mathematics queries in real time. Additionally, many textbook publishers now include simulation-based interactive content with their high school STEM books (for instance, interactive diagrams or virtual lab activities accessible online). All of this points to a growing ecosystem of industry support for simulation-based learning, making it easier for teachers to adopt these methods.
Alongside the tools, industry and non-profits have recognized that teachers need training to effectively use simulations. Companies and organizations offer workshops, online courses, and resources to help teachers integrate simulations into lesson plans. Teachers learn how to facilitate rather than lecture – asking students to predict outcomes of a sim, encouraging exploration, and guiding reflection. When teachers are well-prepared, simulations are used not as novelties but as integral parts of the learning sequence. For example, an effective sequence might be: introduce a concept with a real-world problem, let students explore it in a simulation (with guiding questions), then have students present their findings and connect back to theory or formulas. This approach capitalizes on the engagement of the simulation while ensuring alignment with learning objectives and assessments.
Computer simulations have proven to be a highly effective and innovative approach to teaching STEM in high school. Research over the past two decades converges on a few key findings:
In conclusion, computer simulations represent a transformative tool in high school STEM education, enabling more effective learning by engaging students in active, discovery-oriented experiences. As Andrea diSessa affirmed in his research, the computer can be “the basis for a new literacy” in science and math learning[51†L236-L244] – one where interactive models and simulations become as fundamental to learning as books and lectures have been for centuries. Harold Abelson and others demonstrated early on that even young learners, given the right tools, can become explorers and builders of knowledge in mathematical worlds[6†L228-L235]. Today, with high-quality simulations available for nearly every STEM topic and a generation of “digital-native” students, the potential to improve learning is greater than ever. Both academic studies and industry reports concur that simulation-based learning, when implemented well, not only improves test scores and content mastery, but also nurtures the deeper skills and enthusiasm for STEM that students need in the 21st century[45†L73-L79][20†L331-L339].