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Today, the development of a modern video game draws upon multiple areas of expertise. Moreover, its development cost could be as high as tens of millions of dollars. Consequently, we should carefully schedule its jobs so as not to increase the total cost. However, project leaders traditionally treat developers alike or even schedule all the jobs manually. In this study, we consider a versatile-developer scheduling problem. The objective is to minimize the makespan of a game project. We propose a branch-and-bound algorithm (B&B) to generate the optimal schedules for small problem instances. On the other hand, an imperialist competitive algorithm (ICA) is proposed to obtain approximate schedules for large problem instances. Lastly, computational experiments are conducted to show the performances of both algorithms. When the problem size is small (e.g., n ≤ 12), B&B can generate the optimal schedules within 5 s. For some large problem instances (e.g., n = 600), near-optimal schedules can be obtained by ICA within 10 min. The final results imply that both algorithms converge quickly and are of high solution quality.

As people attach increasing importance to the learning environment and design of teaching materials in STEM, how to design teaching materials that enable students to seamlessly integrate the learning of scientific investigation, engineering design, mathematical reasoning, and technical skills has become increasingly important. The purpose of this research was to explore the behavioral intention of GAR-STEM teaching application users. The results showed that the best predictor of practicability, entertainment, and media interactivity for intention towards GAR-STEM app design was usage attitude (R2= 0.547), followed by intent to use (R2 = 0.528), practicability (R2 = 0.408), and entertainment (R2 = 0.186). The proposed model explained 52.8% of the variance in behavioral intention. The overall findings suggested that usage attitude and intent to use may augment its function as a key factor for the procedural intention toward GAR-STEM app design. Keywords: augmented reality, gamification apps, importance-performance matrix analysis, STEM education.

In 2019, the United Nations released its 10-year policy of “ESD for 2030”. Many schools around the world have actively participated in the transformation and have included the United Nations Sustainable Development Goals (SDGs) in Education for Sustainable Development (ESD). Many developed countries hope to combine the concept of STEAM education (Science, Technology, Engineering, Art, and Mathematics) with interdisciplinary learning, and apply it to ESD. This study intended to integrate the sustainability concept into Virtual Reality (VR) system-aided STEAM education, in order to provide school children with integrated interdisciplinary STEAM education. A literature review was conducted and the research hypotheses were proposed. The empirical method and questionnaires were used as research methods to investigate the influence of the proposed system on the students’ satisfaction, self-efficacy, and learning outcomes after cognition learning. The results of this study showed that the combination of STEAM education, with VR-aided experience courses, could help to improve the learning satisfaction and outcomes of students and to arouse their learning motivation. However, the proposed system needs an ESD that is based on students’ traditional culture (such as designing a role-playing game), so that they can improve their self-efficacy through playing a VR game. Although VR games are interesting, they are difficult for some students to play. Therefore, it is necessary to adjust the teaching materials, methods, and strategies appropriately by using information technology, so that ESD can be achieved by inspiring students to explore continuously. The findings can serve as a reference for further relevant studies.

Adaptive Neuro Fuzzy Inference System (ANFIS) used to be applied to finance, engineering, material design, and decision-making management in past research, but seldom to predict educational learning performance. In recent research, gamification learning material design is often applied to reinforce learning performance, while the prediction of gamification learning performance is seldom discussed. This study therefore applies Rough set theory to extract Core Set and generating rule, ANFIS for learning achievement predication. In order to evaluate the performance of proposed model, the VCCSEGLS dataset are collected as experimental dataset and compared with other models. The results show that the proposed method outperforms the listing models in accuracy. The three key factors are extract, (G7) Time spent on game-based learning, (L1) Examination, normal drugs and treatment, and (L2) Integration ability (time scoring, stability scoring, strain capacity, completeness scoring).The proposed model also can offer accurate predictions and provide some simple decision rules, which can be accurately used by decision-makers and game designers.

This study focuses on serious virtual reality games, and how users can increase their understanding of the sustainable innovation learning (SIL) model and their familiarity with sustainable development strategies. &ldquo, Users&rdquo, of serious games consists of all possible target groups that are interested in attaining knowledge of sustainability through the use of games that are designed for a purpose beyond entertainment, in this case, for sustainable education. This research investigates the sustainable innovation experiential learning model by using a virtual chemistry laboratory to affect academic achievement. A questionnaire was completed by students who had used the virtual lab, and structural equation modeling (SEM) was applied for analysis. Importance-performance matrix analysis (IPMA) was able to help expand the basic partial least square (PLS)-SEM result with the fraction. The results show that experiential learning significantly affects learning motivation and academic achievement. Cognitive load and self-efficacy significantly affect learning motivation. Involvement significantly affects academic achievement. The virtual chemistry laboratory significantly affects academic achievement. Students who used the sustainability innovation experiential learning model obtained a better understanding of the chemical concepts. Moreover, a virtual lab promotes students&rsquo, motivation in regard to chemistry.

Forecasting stock price is a hot issue for stock investors, dealers and brokers. However, it is difficult to find out the best time point to buy or sell stock, since many variables will affect the stock market, and stock dataset is time series data. Therefore many time series models have been proposed for forecasting stock price; furthermore the previous time series methods still have some problems. Hence, this paper proposes a novel ANFIS (Adaptive Neuro Fuzzy Inference System) time series model based on integrated nonlinear feature selection (INFS) method for stock forecasting. Firstly, this study proposed an integrated nonlinear feature selection method to select the important technical indicators objectively. Secondly, it used ANFIS to build time series model and test forecast performance, then utilized adaptive expectation model to strengthen the forecasting performance. In order to evaluate the performance of proposed model, the TAIEX and HSI stock market transaction data from 1998 to 2006 are collected as experimental dataset and compared with other models. The results show that the proposed method outperforms the listing models in accuracy, profit evaluation and statistical test.

Changes in the way people use information come from the concept of the most primitive human migration rule. Scholars hold that migration can be considered as a switching of people&rsquo, s place of residence, but the migration of people is not limited to the migration of residence. The marketing field also borrows the population migration theory to further explore the switching behavior of customers, educationalists also discuss migration of learning through migration theory. In this regard, the migration of human beings is a process of historical evolution, though it takes several decades to understand history from the perspective of such evolution. Hence, if there is a decision-making system that simulates evolution and estimation through 3D graphics, then players may better understand the impact of environmental migration and changes on humanity. This study adopts Push-Pull-Mooring (PPM) theory to explain the migration of human beings. Push is generated from people&rsquo, s dissatisfaction with their place of residence, Pull is from attractions of the new environment, and personal mooring produces the intention of switching. A set of Gamification Environmental Education Applications (GEEA) and a migration scale for such apps are developed herein to explain the state where computer gamers switch to mobile app games. The results show that all four hypotheses are supported, and the path coefficient of each hypothesis is highly significant. The R2 values of five intrinsic constructs are Push (R2 = 0.321), Mooring (R2 = 0.574), Pull (R2 = 0.413), Switching intention (SI) (R2 = 0.552), and SB (R2 = 0.626), where the R2 value of SB is quite high, indicating that the relationship between the switching behavior (SB) and the four precursors exhibits great explanatory power. Hence, the explanatory power of this model is convincing, and the teaching materials designed through gamification education deliver better learning outcomes.

With the development and popularization of virtual reality and motion capture technology, physical therapists have had a new option to improve patient rehabilitation effectiveness. However, there were few studies in the past focusing on total knee replacement (TKR) rehabilitation solutions and many restrictions on the assistance devices. This paper aims to explore how users' motivation and perception of system usability affect rehabilitation performance in a 3D game-based rehabilitation environment. Further, we examine the feasibility of applying Kinect-based method in rehabilitation activities to improve performance. A system called the Physical Rehabilitation System (PRS) was developed and deployed in a one-week TKR rehabilitation process. 27 TKR rehabilitants were divided into a control group (Nź=ź11), and an experimental group, which was rehabilitated with a PRS (Nź=ź16), and had, also, to fill out a questionnaire. The group with higher performances generally had stronger motivation and a better evaluation on a PRS (pź

Research on gamification of learning has been very popular in the past years; especially, the learning effectiveness in applying games to the education of natural science in elementary and junior high schools has been proven. Aiming at the human blood circulation unit, which is rather difficult to comprehend, in the biology materials for junior high school students, Mobile Meaningful Blood Circulation Learning System, called MMBCLS game-based learning, is developed. The players could comprehend the functions of systemic circulation and pulmonary circulation through games. In the study, the instructional design is based on Meaningful Learning and follows the principles of digital game-based learning models to design the after-class multimedia materials, which allow learners enjoying learning with fun. The quasi-experimental design is utilized for the learning assessment, where the experimental group applies MMBCLS, while the control group uses general instruction for the teaching materials. The experimental results show significant difference of the experimental group in the learning effectiveness and better post-test results than the control group. The research outcomes could be the reference of material design for teachers and provide educators with the reference of mobile as meaningful media material design.

Researchers of game-based learning have often suggested that learner’s motivation beliefs about learning achievement would likely affect their use of game-based learning strategies and ultimate success in learning. However, research which has empirically documented the link between gamification, motivation, and cognitive load is scant. Therefore, the aim of this article attempts to explore how gamification, motivation, cognitive load will affect learning achievement, and their use of Gamification Software Engineering Education Learning System (GSEELS) are related. This research involved empirical study, comprised of four sets of questionnaires concerning gamification, motivation; cognitive load and learning achievement. Tow intact class of 107 college students participated in the study. The structural equation modeling (SEM) analysis was conducted through descriptive statistics, measurement model evaluation and structured model evaluation in order to indicate the direction and relationship between the four sets of variables. The research result shows that 5 out of 6 hypotheses are supported and merely 1 hypothesis is false. Well-design GSEELS will affect student learning motivation and achievement. To conclude, this study may be important in explaining the relationship between gamification, motivation, cognitive load, as well as in suggestions for teaching and learning four aspects with a better understanding of how teacher’ beliefs about SE education related to their strategy use.

Few practice-oriented courses are currently integrated into online learning platforms, such as OpenCourseWare, Khan Academy, and Massive Open Online Courses (MOOCs). It is worthwhile to explore how learners respond to information technology and new teaching methods when practice-oriented course are placed online. Therefore, this study probes learner willingness to participate in a practice-oriented course distributed through a MOOC platform, investigating relationships among perceptions, behavioural intentions, and actual behaviour. The current research framework integrates the Technology Acceptance Model and the Theory of Planned Behavior as its core theoretical basis. Empirical data were collected through a cross-section survey. All participants were students of 2D Animation Production, with a total of 272 respondents. The questionnaire data used Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis. Results show: (a) attitude exerted the greatest influence on learners’ behavioural intention; (b) learners’ perceived behaviour control, subjective norm, and attitude, which directly and positively influence their behavioural intention; (c) behavioural intention exhibited dual mediation effects; (d) behavioural intention positively influenced actual behaviour in the C-TAM-TPB model, with a high level of overall model fit.

This study presents a hybrid methodology for solving the serious game design evaluation in which evaluation criteria are based on meaningful learning, ARCS motivation, cognitive load, and flow theory (MACF) by rough set theory (RST) and experts’ selection. The purpose of this study tends to develop an evaluation model with RST based fuzzy Delphi-AHP-TOPSIS for MACF characteristics. Fuzzy Delphi method is utilized for selecting the evaluation criteria, Fuzzy AHP is used for analyzing the criteria structure and determining the evaluation weight of criteria, and Fuzzy TOPSIS is applied to determine the sequence of the evaluations. A real case is also used for evaluating the selection of MACF criteria design for four serious games, and both the practice and evaluation of the case could be explained. The results show that the playfulness (C24), skills (C22), attention (C11), and personalized (C35) are determined as the four most important criteria in the MACF selection process. And evaluation results of case study point out that Game 1 has the best score overall (Game 1 > Game 3 > Game 2 > Game 4). Finally, proposed evaluation framework tends to evaluate the effectiveness and the feasibility of the evaluation model and provide design criteria for relevant multimedia game design educators.

With the global wave and evolution of Cultural and Creative Industries, Law for the Development of the Cultural and Creative Industries was successfully promulgated in 2010 in Taiwan. It encourages relevant craft industries transforming to Cultural and Creative Industries and establishing private brands. In order to promote Cultural and Creative Industries, the government also reinforces the integration of art creation with culture preservation, culture, and technology. Consequently, the application of cultural and creative products is moving towards diversification. Particularly, digital creative APP (Application) becomes a popular entertainment and interaction platform for modern youngsters. In this case, understanding the cultural advantage and integrating creativity and design are the key factors in the reinforcement of Cultural and Creative Industries with cultural and creative products in Taiwan. Understanding the digital creative APP usage intention and attitudes therefore becomes a primary research issue. This study aims to develop a cultural and creative APP as well as the strict and empirically meaningful digital creative APP usage intention and attitudes scale for digital creative APP users. The characteristics of cultural and creative APP are first generalized the usefulness, hedonism, and media interaction through literature review, expert interview, purification and prediction of measurement items, and the reliability and validity evaluation of formal questionnaire. Churchill (1979) and DeVellis (1991) as well as the suggestions from research with relevant scales are referred to measure the digital creative APP usage intention and attitudes. The results show that the best predictor of practicability, entertainment and media interactive for intention toward cultural creative APP design is use attitude (R2= 0.547), followed by intend to use (R2 = 0.528), practicability (R2 = 0.408), entertainment (R2 = 0.186). This study proposes a general extended technology acceptance model for Intention toward Cultural Creative APP Design. Our proposed model explained 52.8% of the variance in behavioral intention. Overall findings suggest that use attitude and intend to use may augment its function as a key factor for procedural Intention toward cultural creative APP design.

As RS (recommendation system) presents the advantage of adaptive recommendation, it is gradually applied to e-learning systems to recommend learners for the next learning content. Problems exist in current learning recommender system available to student in that they are often of a general learning content not offering a personalized service. To overcome this, An adaptive learning path recommendation system (ALPRS) is proposed comprising: (a) Fuzzy Delphi Method (FDM) is applied to acquire the key factors in learning content; (b) Fuzzy Interpretive Structural Model (ISM) is further utilized for establishing the adaptive learning path hierarchical structure; and (c) repertory grid technology (RGT) is further used for acquiring the effects of the recommender element attributes of the adaptive learning path. The result show that the learning outcome with ALPRS is better than it with general learning course guided recommendation mechanism, and the scores of system satisfaction with ALPRS and personal service are higher than 90; recall (95%), precision (68%), F1 index (45%), and MAE (8%) in ALPRS outperform other approaches. Finally, three contributions are organized in this study. (1) The novel hybrid ALPRS is proposed and tested the practicability. (2) A prototype gamification geometry teaching material module is developed for the promotion in MSTE (Mathematics, Science, and Technology Education) areas. (3) The adaptive geometry learning path diagram generated with ISM based on learning styles could offer reference for successive studies.

This study aims to explore the factors in a patient’s rehabilitation achievement after a total knee replacement (TKR) patient exercises, using a PCA-ANFIS emotion model-based game rehabilitation system, which combines virtual reality (VR) and motion capture technology. The researchers combine a principal component analysis (PCA) and an adaptive network-based fuzzy inference system (ANFIS) to present a predicting, artificial emotion model with a Plutchik emotional wheel in a 3D Gesture gamification rehabilitation system, and illustrate the value of the Plutchik emotional wheel. Also, this study tries to improve a rehabilitant’s self-efficacy, based on interesting games and the use of Kinect technology to capture a rehabilitant’s motion. This quasi-experimental design required two months to collect the PRS rehabilitation treatment data from the study’s participants, who were divided into experimental and control groups. The experimental group, rehabilitated with a PRS, filled out a questionnaire and evaluated whether the system operations and self-efficacy changes affected their rehabilitation achievement. These findings could be referenced for related researchers designing auxiliary tools and for helping physical therapists improve rehabilitants’ performance. Meanwhile, these findings recommend that patients have the operation in the early stages and that they form regular exercise habits for a better rehabilitation outcome.

This paper practically develops a game-based learning system to improve self-efficacy for student's learning. The game-based learning is combined with educational and information technology. From the e-learning carried on, the game-based learning is getting more attention. In game-based learning, the course content is mapped into the game to provide a scenario environment of learning, the repeated self-learning, and the ongoing interaction and feedback can increase the learning interest and motivation. Hence, game-based learning could reach the goal of learning effectively. For evaluating the learning effects, this paper uses 3D game development tools and the course content corresponding to the game level content. In the questionnaire, the questionnaire design is based on the ARCS Motivation Model, and the questionnaire items have been revised by the experts’ opinions. This quasi-experimental teaching is executed for system analysis course (at third-year undergraduate level), the subjects (students) have an information management background. The students are separated into two groups for quasi-experimental design: one is the experimental group, the other is the control group. After the implementation of game-based learning system, the achievement scores and questionnaire of the experimental group are collected practically, and the difference in learning achievement between the experimental and control groups is examined. The results show that the learning motivations of students have significant impact on the learning achievement, and the learning achievements of students with game-based learning are better than those who use the traditional face-to-face teaching. And the results could provide the related educators as references.

To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment decisions; and (2) the time complexity of the algorithms to produce forecasting outcomes is very high. Therefore, to provide understandable rules for investors and to reduce the time complexity of forecasting algorithms, this paper proposes a novel model for the forecasting process, which combines two granulating methods (the minimize entropy principle approach and the cumulative probability distribution approach) and a rough set algorithm. The model verification demonstrates that the proposed model surpasses the three listed conventional fuzzy time-series models and a multiple regression model (MLR) in forecast accuracy.

Background:Game-based learning is an effective learning method, whose performance depends on the quality of the educational game. Due to versatile game environments with complex backgrounds, evaluations are not easy to implement.Materials and methods:This study designs a Flow dimension questionnaire in which some domain experts are asked to re-examine the attributes of the Flow dimension by using the Delphi method. The extracted attributes of Flow dimension are then employed to build the proposed model for evaluating a given educational game. For practical implementation, this study developed an Evaluating Mobile-Learning Game System (EMGS), based on the proposed model. In the case study, several experts were requested to use the developed system for evaluating three different types of educational games.Results:The results show that the developed system is faster and more convenient than the traditional evaluation method, and the developed system could effectively recommend the best qualified educational game for educators in different situations.Conclusions:The case study and system satisfaction questionnaire verified that the evaluation process provided by this system is fast and convenient, and that it can give an accurate recommendation to assist educators in selecting the most appropriate games for learning.

Background:Aiming at the human blood circulation unit, which is rather difficult to comprehend, in the biology materials for junior high school students, Mobile Meaningful Blood Circulation Learning System, called MMBCLS gamification learning, is developed.Materials and methods:In the study, the instructional design is based on Meaningful Learning and follows the principles of digital game-based learning models to design the after-class multimedia materials, which allow learners enjoying learning with fun. With quasi-experimental design, Kolb’s Learning Styles Scale and Meaningful Learning Scale are utilized as the research instruments. Taking G8 class as the subject, 46 valid copies of questionnaires are received.Results:The research findings show more Divergent styles in mobile game-based learning styles, students with convergent styles regarding the highest curriculum design in Meaningful Learning, students with distinct gender presenting no significant difference in curriculum design and learning achievement in Meaningful Learning, students with different learning styles revealing remarkable differences in learning achievement, and students in the experimental group appearing higher learning achievement than the ones in the control group, with notable differences.Conclusions:The research outcomes could be the reference of material design for teachers and provide educators with the reference of mobile as meaningful media material design.

Emotions have a very important impact on human’s beliefs, motivations, actions, and physical states. Emotions predicting and its application in intelligent system can improve the interaction between humans and machines. Current research in artificial emotion focuses on how to measure, calculate, or compute it. However, the transfer of emotion is often too complicated to present full emotion states and changes. This paper combines with emotional dimension and theory of variable fuzzy sets to present a predicting artificial emotion model and shows illustrated example of it. This study shows that any raw data from input can be computed with variable fuzzy set. It provides a mathematical method for representing emotion quantitative, gradual qualitative, and mutated qualitative change. This framework improves calculation methods and mechanisms, closer to real emotional changes.

This paper employed an SPANI (Shooting Pose Adjustment with Nature Interactions) learning system with achievement prediction model of meaningful learning. Motivation, cognitive, and PLS (Partial Least Square) method was used to analyze the results. Proposed model is focused on information and communication technology teaching mode, meaningful learning, learning motivation, cognitive load, and learning achievement. Theories of SPANI achievement prediction model investigated the learner’s degree of meaningful learning, learning motivation, cognitive loading, and learning achievement (Huang et al. 2012, Crede and Phillips 2011, Deleeuw and Mayer 2008, and Peterson et al. 2010). Questionnaire and systems tests were used with 107 valid samples in the samples’ record to conduct narrative statistics, inspection of reliability and validity, and PLS of Structural Equation Modeling (SEM). The results show that the developing system is very helpful to learner’s learning motivation and learning achievement. And learner’s learning motivation, which influences the degree of the cognitive load and learning achievement, has a high relationship. It means, the designers of teaching materials can start with digital content to improve learning motivation and also handle the two important parts which are learning strategy and learning motivation. It can be very helpful in improving teaching quality.

In this paper, we present a game-based tutoring system with learning performance, to assist Taiwanese students learning English. Based on the programmed instruction, a speech recognition game for eco-pond context is applied as content for English learning, in which learners take lessons in English from chapters of biological food chain and biological evolution. Also, learners can interact with organisms by speech through microphone. Indeed, they learn from English situational dialogues, pronunciation practice, story reading and vocabulary games play through the biology scenarios by tutoring system. For evaluating the learning performance of game-based tutoring system, this study thus adopt listening, translation, and revision as the main factors for measuring the language learning performance. It found that the listening, translation, and revision are the significance on the language learning. Moreover, this study found that the students improve the performance of language learning with the tutoring system.

Since people cannot predict accurately what will happen in the next moment, forecasting what will occur mostly has been one challenging and concerned issues in many areas especially in stock market forecasting. Whenever reasonable predictions with less bias are produced by investors, great profit will be made. As the emergence of artificial intelligence (AI) algorithms has arisen in recent years, it has played an important role to help people forecast the future. In the stock market, many forecasting models were advanced by academy researchers to forecast stock price such as time series, technical analysis and fuzzy time-series models. However, there are some drawbacks in the past models: (1) strict statistical assumptions are required; (2) objective human judgments are involved in forecasting procedure; and (3) a proper threshold is not easy to be found. For the reasons above, a novel forecasting model based on variation and trend pattern is proposed in this paper. To verify the forecasting performance of the proposed model, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) of the year 2000, is used as experimental dataset and two past fuzzy time-series models are used as comparison models. The comparison results have shown that the proposed model outperforms the listed models in accuracy and stability.

This study develops and evaluates a game-based learning system for a software engineering course. The game-based system adopts a role-playing strategy proposed by a digital game-based learning model and is designed based on the educational theory of Technological Pedagogical Content Knowledge that integrates pedagogical knowledge, content knowledge, and technological knowledge. By using the system, students learn about the process of software development in a team-based environment. In this learning environment, learners play different characters, such as a project leader, a system analyst, a system designer, or a programmer. The learners are also able to conduct collaborative activities with other team members during the software development process. To evaluate the effectiveness of the system, thirty-four undergraduate students in a software engineering course were recruited to evaluate the system via a questionnaire. The survey results indicated that students had a positive learning attitude toward the system usage.

A novel global ERP system architecture based on open grid service architecture (OGSA) is proposed in this paper, called GridERP, to solve the problem of noneffective sharing with distributed resources and interoperability on the global deployment of enterprises. With the OGSA concept, GridERP architecture that takes advantage of Web services and grid computing to efficiently integrate several core services. GridERP architecture possesses benefits of Web services, including standard protocols (e.g. XML, SOAP etc.), cross-heterogeneous platforms, flexible-functional integration (e.g. loosely coupled services), low cost of system establishment. Also, GridERP architecture owns capabilities of grid computing, such as effective integration of large enterprise resources, deployment of distributed resources and self-performance management of resource network. Besides, the GridERP architecture can facilitate the effective collaboration and reuse service components among global enterprise resources. Finally, a prototype system is developed according to the GridERP architecture in this work as an effective practice of a global ERP system to integrate all enterprise services on the Internet. We believe introducing the GirdERP architecture can provide the novel development of a new generation of ERP system.

Online games have emerged as an important and interesting research area in a variety of academic and official settings. This study adopts ethnographic decision tree modelling (EDTM) which here integrates qualitative and quantitative paradigms to model the decision-making process of whether or not to play online games by male undergraduate students. The model is built based on qualitative data gathered through in-depth interviews of 29 respondents, where 18 criteria for playing online games are elicited. The model is tested using qualitative and quantitative data from interviews, and a structured questionnaire involving 241 respondents. The predictability of the model is 90.04%. Armed with the results, this study provides online game industries an understanding of the decision-making process of male undergraduate players in Taiwan, and reveals methodology and theory standpoints for the researchers with similar interests.

This paper aimed to investigate how gamified learning approach influence science learning achievement through a context- aware mobile learning environment and to explain effects on student learning outcome. A mobile learning environment has been developed based on MILS (Mobile Insect Learning System) gamified learning activity. A series of gamified learning activities based on MILS was developed and implemented in an elementary school science curriculum to improve student learning achievement and help students to actively engage in learning activities. A quasi-experimental design was used to investigate the effectiveness of gamification approach by combining game elements with the use of MILS in a real-world scenario. The response to questionnaire indicates that students valued the outdoor learning activities made possible by use of the smartphone and its functions. Pre- and post-test results demonstrated that incorporating mobile and gamification technologies into botanical learning process could achieve a better learning achievement than using non-gamified mobile learning and traditional instruction. In managerial implications, the results could provide parents, teachers, educational organizations to make related educational decision.