# Simulation-Based Learning

Dr. Said Ali Al -Hadhrami

Head of statistics section

DOMPS- College of Arts and Sciences

Simulation-based learning has become a popular approach that mimics real-world environments and provides learners with practical insights. It helps learners to cope with many problems easily and with fun. It also increases learner’s interest and motivation in their studies. It gives the learner a chance to test their knowledge and detect gaps in their understanding of concepts before applying them in real-life circumstances. The learner is given a chance to design and explore different scenarios, rather than just do calculations. Simulation can make abstract science phenomena more accessible and visible to learners. It helps learners visualize the phenomenon that might otherwise be difficult to depict. It can animate dynamic changes in scientific processes that are difficult to infer from static illustrations found in textbooks. This approach promotes better comprehension, allowing learners to figure out the fundamentals without becoming overwhelmed by complexity.

Simulation improves several skills for the learner. It improves learners’ computer skills and programming as computer programming is a platform for different types of simulation. It promotes critical thinking, enhances creativity, and develops decision-making and problem-solving skills. The learner becomes more engaged and interested in this approach and can work independently or within a group. Students can learn by trial and error without having to worry about actual consequences. Simulation enhances discovery learning which leads to better understanding and long-term retention than does the traditional approach. Students learn best when they actively construct their knowledge.

Simulation is used in various fields such as health care, aviation, agriculture, engineering, physics, mathematics, statistics and others. In healthcare, simulation has been a part of training for a long time. It places learners in situations where they can put their classroom knowledge into practice and it provides freedom to learn from mistakes without the need of intervention from experts.

In the aviation industry, simulation is an effective way to improve the skills and knowledge of pilots as they can practice different manoeuvres and procedures. It can handle unexpected situations, and reduce the risk of accidents and other scenarios that would be too risky or costly to recreate in the real world. It can test new aircraft designs and procedures, helping to identify potential problems before they occur in real-world operations. It also provides fun hours of enjoyment and satisfaction.

Crop growth simulation is developed to study the interaction of agronomic, environmental and hydrologic factors on crop growth. It shows the crop growth under different types of soil fertility, water availability, climatic and other factors.

In engineering, simulation investigates the system's behaviour before building the actual one. It also optimizes different components in the production process to get faster products and higher profits.

In Physics, simulation can be used to visualize the natural forces and study different combinations of forces acting on objects. In quantum mechanics, the forces are invisible and too small to observe. Thus, simulation is used to test the theories about the interaction of subatomic particles. Simulations may be used to show learners scientific phenomena that cannot be observed easily in real time like the motion of lightning, cell division, and earth revolution. Simulation is utilised in situations that require several repetitions of an experiment.

In mathematics, Mote Carlo simulation can approximate the integration. Learners can also approximate simple integration and compare the result with the analytical finding. Simulation can be also used to approximate some popular numbers like , e and . Optimization either for the hard or easy problems can be obtained and compared.

In some cases, statistical concepts, theories and ideas are misunderstood by learners or hard to figure out the meaning and importance. However, Computer simulation is one of the techniques used to solve hard problems or those which have no analytical solutions. But even simple problem can be solved using simulation which deepens the understanding, develops better statistical reasoning and add fun to learning.

Conducting simulation requires understanding the problem to be solved, and the possible models. Then designing the way of simulation and replication. This can be implemented by generating random numbers from a specific distribution with a particular sample size. These numbers are then used to calculate the statistic. Then the process is repeated several times to produce several values of the statistics which then can be plotted to notice its distribution. Eventually, the proposed distribution can be tested using one of the goodness of fit techniques. By applying this technique, estimations can be investigated and the properties of the estimators such as biases, consistency and efficiency are considered. Central limit theorem as well as some popular theorems and inequalities can be illustrated by repeating generation large samples from a specific distribution. It also approximates the probability, expectation and standard error of an estimator, visualizes sampling distribution, constructs confidence interval, tests hypotheses and obtains the power of the statistical test.

When generating numbers from a particular distribution, the learner takes a chance to understand the role of the distribution’s parameters in controlling the distribution and check whether they are location, scale, shape or rate parameters. Learners also can investigate the behaviour of the distribution when the sample size increases and observe the limiting distribution. For instance, several distributions get closer to the normal distribution under some conditions such as increasing degrees of freedom or sample size.

simulation helps illustrate the interpretation of confidence intervals. Confidence intervals can be generated for any statistics such as the mean, proportion, variance, the difference of means, difference of proportions and the ratio of two variances. It gives a chance to see whether the interval encloses the true parameter or not. It also gives a chance to see whether the interval encloses the true parameter. One can calculate the relative frequency of the cases when the parameter is falling in the confidence interval and how sample size affects the length of the interval.

It can also be applied in regression to estimate and draw inferences about regression coefficients. The process starts with choosing a deterministic true model given by relating two variables. Then random errors from the standard normal distribution which are then added to the values of the dependent variable and considered the data as real data and used for inference.

Simulations have the potential to make learning abstract concepts more concrete. it’s a faster and cheaper effective way to improve the learner’s skills and competencies. Technologies help integrate simulation into the learning systems. It provides a powerful approach that can be understood without a deep technical background. Simulation-based learning can be introduced partially and gradually to learners or design a full course on simulation to the academic plan. However, simulation-based classes do not mean ignoring the analytical solutions to problems. Both analytical and simulated solutions enhance learning.