Using simulation system for collaborative learning to enhance learner’s performance

Abstract This study focuses on adoption of simulation system in rapidly changing technology and information flow. Given the prevalent popularity of simulation system, it is important to understand and adopt simulation system to develop future educational plans. This paper addresses how simulation system enhances student collaborative learning and learner performance using Technology Acceptance Model. Results were analyzed using Structure Equation Modeling technique; this study established that perceived usefulness, perceived ease of use, and perceived enjoyment all have a significant positive relationship with simulation system. The results indicate that simulation system serves as a dynamic tool to accelerate the progress of learning environments by encouraging collaboration and communication among students which strengthen their learning abilities and increase performance because students practically perform all the theories in a risk-free environment. In the competitive world, simulation system should be implementing at education level so that students can learn more before entering into a real-life career.


PUBLIC INTEREST STATEMENT
In today's changing and dynamic environment and economic conditions, there is a need for re-orientation and modification of educational programs, where learning progression should focus on strengthening knowledge and skills in accordance with needs and wants of the market. Conventional methods of teaching are not supportive in coping with unpredicted market dynamics and complex organization conditions. It is the requirement of time to alter teaching and educational methods. Now, many universities are hiring professors, whose main task is not only to give knowledge but also to motivate students to choose their professions according to their learning. New generation is brought up with mobiles devices, access to internet, laptops, and video games. Educational system should be modified and simulation method of teaching should be adopted in accordance with current generation habits and pleasing method of learning.

Introduction
Learning methods have changed; they have moved from traditional teaching model to learner-centered model that involves the learner in an active role (Garris, Ahlers, & Driskell, 2002). Now methods of learning are shifting from learning by listening to learning by doing. Kolb (1984) defined Experimental Learning Theory (ELT) that individual's learning process is the combination of experience, cognition, behavior, and perception. Use of simulation system (games, applications, and situation) has grown significantly over the last 20 years, that enhances the learner's capability to solve the problems (Douglas, Miller, Kwansa, & Cummings, 2007;Jones, 1998;Rosen, 2008).The simulation system is an artificial environmental situation in which learners perform their duties in the real world scenarios (Gredler, 2004;Jones, 1998). Interacting with one another is a collaboration; it is a fact that cooperation enhances knowledge, resources, and capabilities to perform tasks. A number of researchers found that collaborative learning has a significant positive effect not only on the academic performance of the student (Bossert, 1988;Cohen, 1994;Johnson, 1984), but also supportive in learning psychological and social aspects (Kreijns, Kirschner, Jochems, & Buuren, 2007;Sadeghi & Kardan, 2015). Researchers have agreed that simulation system enhances collaboration and performance of the students. To have learner's maximum attention towards the task, it is necessary to indulge learners in enjoyment-related activities. Bostan (2009) andBruner andKumar(2005) added the variable of perceived enjoyment in TAM to measure consumer's internet use.
This study examined the perceived learner performance from the use of simulation system in a collaborative environment using Technology Adoption Model (TAM). In previous studies, TAM was used to measure the intention to adopt the simulation.

Model adoption
Technology Acceptance Model (TAM) was proposed by (Davis, 1989) and is a commonly used theory on the practice and acceptance of new technology (Cheung & Vogel, 2012). Technology Adoption Model (TAM) is a multi-dimensional model which is used in different fields to analyze and interpret research results i.e. pure sciences (Chaurasia et al., 2016), operation research (Chena & Ma, 2016), computer sciences (Chuah et al., 2016), public health (Zakaria & Yusof, 2016), environmental sciences (Rai & Robinson, 2015), industrial engineering (Guo & Zhu, 2016), social sciences (Arifina & Frmanzah, 2015), and many more. Many research studies applied TAM to envisage the user intent to adopt technology (Pando-Garcia, Periañez-Cañadillas, & Charterina, 2015). These studies proved that this model considerably influenced the technology adoption. Sukkar and Hasan (2005) and Benbasat and Bark (2007) inquired about the appropriateness of TAM model in the case of developing countries and students' intention in adopting technology and propose modification in TAM model for making it more appropriate in this case. However, no studies have been found in the previous literature to explore the use of simulation system for collaborative learning to enhance learner's performance using modified TAM.
Technology Acceptance Model (TAM) is derivative of Theory of Reasoned Action (Ajzen & Fishbein, 1980) illustrate that the behavior of individual changes after adoption of new technologies. TAM has basically two main constructs: perceived usefulness and perceived ease of use, which assist user to build the intention to adopt new technology. For this study, perceived enjoyment is also used because of interesting and entertaining features of using simulation systems. As such, earlier studies performed by Csikszentmihalyi (1975) and Moon and Kim (2001) used perceived enjoyment as construct in modified TAM. For this study, constructs like perceived enjoyment, perceived usefulness and perceived ease of use are used to investigate the impact simulation system on collaborative learning and learner's performance using modified TAM (Figure 1).

Perceived usefulness
Perceived usefulness is defined as "the degree to which a person believes that using a particular system may enhance his or her job performance" (Davis, 1989). Perceived usefulness is the main construct of Technology Adoption Model (TAM), which predicts the behavioral intention of a person to use technology (Davis, Bagozzi, & Warshaw, 1992;Hart & Porter, 2004). According to Alrafi (2007), this construct can predict system acceptance, user intentions and continual use of the system. Perceived usefulness has frequently been used by many researchers (Alrafi, 2007;Dulcica, Pavlicb, & Silicc, 2012;Fagan, Kilmon, & Pandey, 2012;Liu & Huang, 2015); they argue that perceived usefulness enhances attitude and encourage people to adopt simulation system in future for better understanding and learning. Researchers explore that there is a positive impact of perceived usefulness on user satisfaction. In this research, perceived usefulness is viewed as an instrumental need to accomplish tasks like learning, education, research and other experimental tasks (Bhattacherjee, 2001;Chea & Luo, 2008;. By above discussion following research hypotheses are suggested: H1: There is a significant positive relationship between perceived usefulness and Simulation system H2: There is a significant positive relationship between perceived usefulness and collaborative learning

Perceived ease to use
Perceived ease of use is defined as "the degree to which a person believes that using a particular system would be free of effort" (Davis, 1989). From the definition, ease means freedom with great effort or difficulty. The effort is a limited resource which a person can allocate for activities for which he/she is answerable (Radner & Rothschild, 1975). If the use of the system is easy, people are willing to learn it and have positive intention to use in future (Chiu & Wang, 2008). Designing of games or system should be user-friendly and easy to use, it has a positive impact on the intention of individuals to learn from that system (Hamida, Razak, Bakar, & Abdullah, 2016). Perceived ease of use will affect the individual's satisfaction which leads user's loyalty i.e. repeated usage or continuous buying of product (Zhao, Chen, & Wang, 2016).There is a non-linear relationship between the ease of use and behavior intention of individual in case of simulation system (i.e. sports, simulation system, Strategic games, adventure and fighting games). If simulation system is easy to use, it has no challenging effect and user may get bored; and if the system is too difficult to use, the user may get offended and not willing to learn from it. If the system is too easy or too difficult, learner is unable to learn from simulation system (Davis & Lang, 2012).
H3: There is a significant positive relationship between perceived ease of use and simulation system H4: There is a positive significant relationship between perceived ease of use and collaborative learning.

Perceived enjoyment
Perceived enjoyment is defined as "the degree to which the activity of using technology is perceived to be enjoyed in its own, right apart from any performance consequences that may be anticipated" (Davis et al., 1992). Perceived enjoyment has a significant positive effect on individual behavior towards his/her task (Heijden, 2003;HsuL & Lin, 2008;Pe-Than, Goh, & Lee, 2014). It can be concluded that perceived enjoyment has a positive significant behavioral intention to use the hedonic system. According to Davis et al. (1992), perceived enjoyment is similar to intrinsic motivation which boosts up the performance of an individual. Venkatesh and Speier (2000) compared two methods of training, i.e. traditional training methods (lectures, theoretical knowledge) and simulation-based training methods (simulation system) and they found simulation system-based training enhances intrinsic motivation which results in the behavioral intention of the individual in gaining knowledge from this system again and again. Hung, Tsai, and Chou (2016) stated that users perceive less enjoyment when they interact with a system having utilitarian benefits as compared to the system which has high hedonic characteristics (Nysveen, Pedersen, & Thorbjørnsen, 2005;Xu, Lin, & Chan, 2012). There is still a question that simulation system provides learner enjoyment and helps them in collaborative learning. From the above discussion, following research hypotheses are proposed: H5: There is a significant positive relationship between perceived enjoyment and simulation system H6: There is a positive significant relationship between perceived enjoyment and collaborative learning.

Simulation system
The simulation system is designed by constructivist learning theory, as user learns or constructs new knowledge from his/her conceptual understanding (Dewey, 1938;Vygotsky, 1978). Simulation is a reliable and exciting system which provides students learning-based experience which motivates and encourage them to learn. This system offers students information-rich environment where they work together in groups, brainstorm and share their ideas, and make a decision in certain situations while working in the risk-free but practical environment. Simulation system has many dependent variables, the main aim of simulation system is that all participants have some roles and all have to react according to the situation and give their feedback to achieve the desired goal (Gredler, 2004). Simulation system has some set of rules and roles which students need to follow which gave them an essence of working in the real-based scenario but risk-free (Leemkuil, Jong, & Ootes, 2000).
Simulation systems are based on theories and application which need to be implemented by students to enhance their learning performance by engaging themselves in a real situation. Mawhirter and Garofalo (2016) stated that simulation systems are the creative and innovative way to increase students' interest in learning. Simulation system not only helps students in reducing stress but also assist in knowledge retention (Popil & Dillard-Thompson, 2015). According to Blakely, Skirton, Cooper, Allum, and Nelmes (2010), simulation system in education helps the student to enhance learning with enjoyment, interest, and interacting with other students. According to (Thavikulwat (2004), simulation system let students step into the case with the complicated real-life situation. Gredler (2004) defines essential characteristics of simulation: (1) student actively interacts with complicated real-life condition; (2) clear rules, regulation, responsibilities, and position is defined to student; (3) students can apply different strategies by keeping themselves within limitations outlined earlier;(4) problems and situations change while performing the simulation system task so that students can deal with changes. Hughes and Scholtz (2015) stated that simulation system provides students an experimental way of learning, simulation system creates opportunities for students to gain more knowledge by keeping themselves in a real-world context. Students digest the knowledge which they have experienced through simulation system. Simulation system can be classified as gaming, training, discrete, continuous or combined events, workshops, and modeling (Lean, Moizer, Towler, & Abbey, 2006).
The scope of simulation system is in many fields like life sciences, information technology, mathematics, business studies, physics, aerospace, engineering, language, culture, emergency studies, and many more (Chittaro & Sioni, 2015;Ebner & Holzinger, 2007;Mawhirter & Garofalo, 2016;Strahilov & Damrath, 2015). Conventional teaching methods like lectures, case studies, and discussions provide student theoretical insights of topics but lack in providing real-world experience and the feel of actual practices and processes. Simulation system plays a role in the development of a student's learning skills, as the student has to make decisions by looking at the environment and other students' decision-making choices (Lainemaa & Nurmib, 2006). Simulation system works as the bridge between theory and practice, which helps the student to enhance their technical and critical thinking skills (Bell & Loon, 2015). Simulation system increases motivation (Fripp, 1997), experimental learning (Adobor & Daneshfar, 2006), develops critical thinking (Doyle & Brown, 2000), works on real-world scenario but in the risk-free environment (King & Newman, 2009), develops time management, and working-in-team skills (Doyle & Brown, 2000).
H8: There is a significant positive relationship between simulation system and learner performance

Collaborative learning
Collaborative learning can be defined as two or more people working together to create or achieve a particular objective. In many fields or disciplines including business, management, medicine, sports, entertainment, and much more, collaborative learning is permitted (Hmelo-Silver, Chinn, Chan, & O'Donnell, 2013). Collaborative learning encourages institutes to think out of the box and also help people to learn and innovate .There is a difference between collaborative learning and cooperative learning; their outcome of performance and learning also differ; in cooperative learning, members in group divide or split their work into subtasks and then join their work to form a result (Sadeghi & Kardan, 2015). According to Lytras et al. (2015), collaborative learning is multi-dimensional; the world is measuring the performance of the students through static, monolithic approach (Dascalu et al., 2015;Zhuhadar, Yang, & Lytras, 2013).The world should adopt new grading system in which students should not only evaluated by memorization of concepts but also tested on critical collaborative thinking. Simulation-based collaborative learning enhances students' learning in the real-world scenario. It is observed that students show better performance when they collaboratively learn through simulation system as compared to the non-simulation system (Ke & Carafano, 2016). From above, following hypothesis is presented: H9: There is a significant positive relationship between collaborative learning and learners' performance

Learner performance
Many studies show that simulations improve the learning performance of individuals (Gaba, Howard, & Fish, 2001;Grantcharov et al., 2004;Shapiro et al., 2004). Simulation system prepares learners to deal with unanticipated events, which in turn increase their confidence in real-world work performance Lateef (2010). Simulation system is a multidisciplinary system in which individuals with different technical responsibilities collaborate with each other to solve the given problem efficiently and effectively. Simulation system provides learner an eye to recognize the problem and adopts a proactive approach to solving it. According to Junco, Heiberger, and Loken (2011), there is a significant positive relationship between collaborative learning, learning performance, and engagement with work. Learners can learn very fruitful concepts by indulging themselves in simulations. Madge, Meek, Wellens, and Hooley (2009) urged that use of this technology can work as a bridge between learners, instructors, and other participants, where all can learn from each other by sharing their ideas and experiences.

Research methodology and data collection
This research measures the effect of perceived enjoyment, collaborative learning from simulation system which results in learner performance using Technology Acceptance Model. To perform statistical analysis, data were collected from multi-national under-graduate, graduate, and post-graduate students of four different disciplines i.e. business management students, pure sciences, information technology, and engineering from University of Science and Technology China, Tsinghua University and Shanghai Jiao Tong University, who know stimulation games/system. 400 questionnaires were circulated among students out of which 360 surveys were found accurately filled. Questionnaire was adapted from different research articles, such as perceived usefulness and perceived ease of use (Davis, 1989), perceived enjoyment (Moon & Kim, 2001), collaborative learning (Fraser & Treagust, 1986), and learner performance (Nale, Rauch, Wathen, & Barr, 2000), with modifications of words and sentences in accordance with the current study. Seven Likert scales are used where one strongly disagrees, four neutral, and seven strongly agree.

Data analysis
Data was collected from the classified respondents, according to gender, education, the field of study and the students who know simulation system/systems. Gender was classified as male and female; respondents were 55.8 and 44.2%, respectively. Education level was classified as undergraduate, graduate, and post-graduate, respondents were 26.38, 28.05, and 45.55%, respectively, and respondents of the field of study were 35.27% management sciences, 19.72% pure sciences, 18.61% information technology, 20.83% Engineering, and 20% others shows in Table 1. All these respondents have knowledge and have at least used simulation system once.
For the examination of measurement and structural model, structural equation model (SEM) employing the AMOS-21 was used. AMOS is a powerful technique which coalesces Principle component Analysis and regression simultaneously from the assessment of measurement and structural model. AMOS is better in dealing with evaluation of formative measurement and in testing moderating relationship. AMOS software not only can originate developmental model for a latent variable but also can show the graphical representation of interpretation. For these strong reasons, AMOS-21 software was used for the examination of measurement and structural model (Anderson & Gerbing, 1988).

Measurement model
For the examination of the measurement model, stepwise processes were followed. First, Explanatory factor analysis (EFA) was performed to examine factor loading using SPSS whether the benchmark values are above 0.4 (Hair, Anderson, Tatham, & Black, 1998). EFA is used to confirm the accuracy of items and if factors are matching and are ready for factor relationship analysis. Results verify the validity of hypothesized model as the indices of factor loading are between 0.719 and 0.902. All values of indices are above the recommended values which shows lack of concern about crossloading except for three items which were excluded from the data.
Next step, to perform CFA tests to measure the convergent validity, factor loading, Cronbach α, Average Variance Extracted (AVE), and composite reliability (CR) (Flynn, Sakakibara, Schroeder, Bates, & Flynn, 1990). CFA results indicate that loading of all items is above 0.7. To examine the reliability and validity of data tests like Cronbach α, AVE and CR values are extracted using SPSS. Cronbach α and CR values should be above than 0.7, which make data considerable for further analysis. Results indicate that Cronbach α values are between 0.778 and 0.932 and CR values are between 0.784 and 0.909. AVE value should be equal to or higher than 0.5. In this research study, AVE value ranged between 0.554 and 0.728. Results indicate that there are no convergent validity and reliability issues with data (see Table 2). Next step is to measure the discriminant validity of the hypothesized measurement model; it is measured by comparing the AVE Square root of each construct with inter-correlation of constructs.
Values of AVE square root should be higher than inter-correlation of constructs (Hair et al., 1998). Results indicate that discriminant validity is established. Hence, data is fit for further analysis of the structural model. Meanwhile, we also measure variance inflation factor (VIF) to examine the multicollinearity concerns. Results show that the values of VIF ranges between 1.53 and 2.715 which is below the threshold value of 10 (Hair et al., 1998). Thus, results show no multi-collinearity concerns (see Table 3).

Measurement and structural model analysis
Structural Equation Modeling (SEM) has become one of the important techniques for researchers across disciplines, and social sciences researchers are also adopting these methods to validate their research results (Hooper, Coughlan, & Mullen, 2008). To see if the model fit, researchers need to examine fitness indices like chi-square, GFI, AGFI, TLI, CFI, and RMSEA. Barrett (2007) and Hu and Bentler (1999) recommend value for goodness of model Chi-square should not be less than 2.0 or higher than 5.0 (Tabachnick & Fidell, 2007) depending upon the sample size. RMSEA value should be less than < 0.10 (Arbuckle, 2003) and according to Steiger (2007), upper limit is 0.07, and according to McQuitty (2004), upper limit should be less than 0.08. According to Miles and Shevlin (2007) GFI and AGFI indices values can be >0.90 and >0.80, respectively (Arbuckle, 2003). Hu and Bentler (1999) and Arbuckle (2003) state that CFI and TLI indices values can be >0.90 and >0.95, respectively. In this research paper, SEM technique is used along with AMOS 24 and CFA (Confirmatory Factor Analysis) to measure the goodness-of-fit model using indices. According to above references, indices for this research show the goodness-of-fit model where values of chi-square, GFI, AGFI, CFI, TLI, and RMSEA are correct shown in Tables 4 and 5.

Hypotheses testing
After examining the validity of the structural model, next step is to test hypotheses relationship using SEM Figure 2. Results show that perceived usefulness (β = 0.343, p < 0.001, perceived ease of use (β = 1.60, p = 0.001), and perceived enjoyment (β = 0.213, p < 0.001) have the positive effect of the use of simulation. Therefore, H1, H2, and H3 are supported. Similarly, perceived usefulness, perceived ease of use, and perceived enjoyment have the positive effect on collaborative learning. Therefore H4, H5, and H6 are supported. Use of simulation system has (β = 0.249, p < 0.001) which shows that the use of simulation system has a positive impact on collaborative learning and H7 is supported. Use of simulation system and collaborative learning has (β = 0.174, p < 0.001) and (β = 0.206, p < 0.001) respectively which shows the use of simulation system and collaborative learning has a positive impact of learning performance of student; thus H8, H9 are also supported Table 6. Figure 2 show that 28.7% of variance exists in use of simulation system and 37.3% of variance exists in collaborative learning, and 28.9% of variance exists in learning performance of students.

Conclusion and future implication
The primary aim of this study is to examine the impact of simulation system on the learning performance of the students. As traditional methods of teaching just focus on class lectures, giving lectures on theories and concepts, simulation systems allow students to work practically but in a risk-free environment. The research was performed on students who belonged to different fields to determine the learning performance of the students with a simulation system. The result of the study examined whether student learning performance improves by using simulation system, by involving them in collaborative studies in groups, easy to use system, useful and fun learning environment. In this paper, Technology Adoption Model (TAM) is used to examine learning performance of the students who applied or have some know-how about simulation system. Data was collected