Man-KI Moon ( Ph. D)

1. A Computer-assisted Learning Model Based on The Digital Game Exponential Reward System (the latest published (Jan 2011), European journal, SSCI) Man-KI Moon (Ph. D) Major: Digital Images/ Internets/Digital Games) Computer Game Tech& Application

2. Studies based on Digital Game Entertainment Medical treatment Digital Games Economical Multi therapy Pedagogy Digital Art (Game Art) This Research

3. Summary • Technically, what we are trying to present is the main point of a research on motivated learning model methodology. • Educators tried the case where they applied the functional formula into the learning model. The ‘exponential learning model’ by Johnson & Aldridge (1985) is one of them. Johnson & Aldridge proposed the ‘exponential learning model’ regarding the ‘traits and amount of time spent’. where =learning effects, = learning time, = time spent on the subject before beginning to learn it, where = ability and = motivation, and =logarithm.

4. Summary • We were interested in the reasons why teenagers are devoted to the digital games. • In order to apply to the motivated learning model, we proposed ‘a computer-assisted exponential learning model,’ which is based on the result of modeling and analysis on comparison between educational games and games for the entertainment.

5. Abstract • The aim of this research was to construct a motivational model which would stimulate voluntary and proactive learning using digital game methods offering players more freedom and control. • The theoretical framework of this research lays the foundation for a pedagogical learning model based on digital games. • We analyzed the game reward system, which is recognized as one of the most important mechanisms to engage players in active sustainable digital game playing. • In general, the reward system is designed similar to an exponential learning model. • This paper compares the reward systems of four typical digital games which have more than 10 million school-age players around the world.

6. Abstract • Based on the results, we propose a computer-assisted exponential learning model similar to that applied in digital game based learning models. • By applying these results to educational algorithms associated with the field of artificial intelligence, we are able to motivate emergent learning. • Using the proposed method, it is possible to form a model of computer-assisted learning, adequate for all learning levels. Keywords: Digital Games, Reward System, Pedagogical Function Formula, Computer-Assisted Learning model.

7. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modelling 7. Discussion 8. Conclusions 9. Future works

8. Introduction • With advancements in digital technology, digital games are now enmeshed in popular culture (Oblinger, 2004). • The inter-activeness of digital games is not only a result of a gathering of players, it requires discovering and learning certain rules contained within the game text and actively organizing while experiencing the game (Kiili, 2005a; Koc & Bakir, 2010; Prensky, 2001; Teo, 2009b; Tutgun & Deniz, 2010). • Arguably, the young student generation forms the major age group of digital games users on a global level. • Accordingly, we reviewed the reason for their interest in digital games and the possibility of designing a learning model based on the reward system used to induce play.

9. Introduction • From this perspective, functional mechanisms of the reward system in digital games were comparatively analyzed. • Based on the exponential learning equation model proposed by educators we suggest a computer-assisted learning model design. • The first game type includes two cases of ‘education-emphasized digital games’ (EEDG) made in South Korea, which are available to all ages and have over 10 million student-age players around the world. • The other game type includes two cases representative of massive multiplayer online role-playing games (MMORPG) of ‘amusement-emphasized digital games’ (AEDG) available to those 15 years of age and older.

10. Introduction • Data from the analysis was examined through a process known as normalized evaluation. • Experimental results suggested that the pedagogical function formula and the computer–assisted learning model which digital games are based on are very similar to the active absorption factor inherent in games played voluntarily by teenagers.

11. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modelling 7. Discussion 8. Conclusions 9. Future works

12. Literature review 2.1. Learning Model Function 2.2.1 Experience point (EXP) in digital games • Experience point (EXP) is a reward system in digital games; a presentation mode of a numerical form on extrinsic motivation. • Exp is generally determined in the commitment to quest, for instance, player makes use of increased ability of character to destroy or go through monster or another obstacle. • Most digital games have been designed to challenge players milestones (koster, 2005). therefore, players will acquire points on skill only if they put greater effort in game play.

13. Literature review 2.2. 2.The digital game reward system based on an exponential mechanism • Considering that a large number of students take part voluntarily in digital games, digital pedagogy tries to figure out the best way of making use of the sense of pleasure in playing games to educate in order to maximize learning. • When it comes to the surrounding conditions that the computer–assisted learning model would be designed essentially for forms similar to level based exp applicable to the digital game domain, it is possible for the computer–assisted learning model

14. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modelling 7. Discussion 8. Conclusions 9. Future works

15. Methodology 3.1.Research design • We analyzed the reward system of digital games, which is an element that makes teenagers become immersed in game-playing. To investigate the possibility of its application in learning model applications, the following areas were examined during the experimental process: • A level-based comparison of exps between education-emphasized digital games (EEDG) and amusement-emphasized digital games (AEDG); • An analysis of major stages in the game-playing process in EEDG and AEDG; • Derivation of the functional formula of digital game reward systems;

16. Methodology • Applicability of a learning model based on EEDG; • Differences in exponential curves based on motivational parameters from the normalized data fit; • Feasibility of a computer-assisted learning model, applied to exponential-learning, based on the digital game reward system.

17. Methodology 3.2. Data collection • This work focuses on two types of digital games for the comparative analysis and experiments. • One is two cases of Educationally Emphasized Digital Games made in South Korea, which are available to all ages and also have over 10million members belonging to the student-age group around the world. • The other is two cases of representative Massive Multiplayer Online Role-Playing-Game (MMORPG) in Amusement Emphasized Digital Games.

18. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modelling 7. Discussion 8. Conclusions 9. Future works

19. Data Analysis • Digital games level up system is open to public in general for players to help game-play, or otherwise it is analyzed and open to public for general users through the expert sites related to the digital game analysis. • Level up system in these games was actually projected up to about 130 levels, but the graphs show only up to 50 levels in order to be effective in experiments. As a result, we proved that research was correct.

20. Data Analysis Educational digital games group Table 1. Maple Story Level Based EXP Sheet Table 2. Tales Runner Level Based EXP Sheet

21. Data Analysis Amusement digital games group Table 3. Suddenattack Level Based EXP Sheet Table 4. WOW Level Based EXP Sheet

22. Data Analysis 4.2. Category 1- Comparative analysis to level based EXP (a) (b) (c) (d) Figure 2. Level up curve, (a) maple story, (b) tales runner, (c)suddenattack, (d) wow.

23. Analysis of Reward system in Digital Games 4.3. category 2- Active elements in game-play • Figure 3 is expressed as a model of the step-by-step courses in playing digital games, presented in the analysis on Figure 2. • Motivation was focused on Powerful Level up Area. players experienced Motivation (a) possess Will (b) in the course of Adjustment. Players with this background are well-developed to commit playing the game (c) with their own skills knowing they are going to be faced with difficult problems to solve in certain situations. Figure 3. Active elements in game-play.

24. Analysis of Reward system in Digital Games 4.4.category 3- Experience Point Data Modeling • Figure 4 is a form of modeling in order to drive the functional formulary shown in Figure 2. It shows that it is easy to increase from level E₁ to E₃ on the x-axis, and it is seen as a gentle slope from A of E₃ on the x-axis according to the degree of difficulty. Figure 4. Experience point data modeling.

25. Analysis of Reward system in Digital Games • According to Figure 2, in the case of Figure 4 being designed with a fan-shaped structure in which the levels on y-axis increase consistently, it is very easy for players to lose interest in them such that they could fall into the conditions of anxiety or boredom. However, players who experienced E₁ to E₃ get in the state of having accumulated certain amount of skills gained from 1 to A. therefore, players are continuously challenged by means of high numerical points in will toward the performance in active. It is possible to make Figure 4 the model with a formula: = game level; = a constant: the degree of difficulty in digital games; = exponential; = Experience Point.

26. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modeling 7. Discussion 8. Conclusions 9. Future works

27. Data Fitting and Formula 5.1 Data Normalizations and Fitting • Level sheet in four cases of digital games from Table 1 to Table 4 went through the process of normalization as a percentage. • The main characteristics in each groups presented in the comparative analysis on the fitting model of four cases of Educationally Emphasized Digital Games and Amusement Emphasized Digital Games in Figure 5 are as follows:

28. Data Fitting and Formula (a) (b) (c) (d) Figure 5. Data fit, (a) Maple story, (b) Tales Runner, (c) Suddernattack, (d) WOW.

29. Data Fitting and Formula 5.2 Motivated Parameters Evaluation in Digital Games • A functional formula induced by trend line doesn't always mean the accurate normalized values in the parameters, but values and a functional formula can be induced with a certain type that makes it possible to understand well: = game level; = a constant: the degree of difficulty in digital games; = logarithm; = Experience Point; = a constant: motivated elements. Table.5 Fitted Parameters

30. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modeling 7. Discussion 8. Conclusions 9. Future works

31. The Computer-Assisted Leaning Model Modeling 6.1. Plotting Result • Figure 6 is derived from the process of plotting the functional formula induced by Table 5 using MATLAB™. The groups in Educationally Emphasized Digital Games and Amusement Emphasized Digital Games take a common form as a logarithm, however it is easy to see that there are differences in fan-shaped lines of all the groups. Figure 6.Plotting result, Educationally Emphasized Digital Games and Amusement Emphasized Digital Games groups.

32. The Computer-Assisted Leaning Model Modeling 6.2. Functional Formula • As the above mentioned, the based on digital games can be made within its formation of a functional formula in relation to an educational model wherein a schema can be defined as a log-function, and in the case of applying a functional formula derived from Figure 5 and Table 5 to it. • The functional formula can be derived from Table 5. If it means Learning Level, and , it would be turned into: = the accumulated learning level; = a constant: the degree of difficulty in the computer–assisted learning model: the amusement, learning usability level; = educational exponential; = a constant: learning motivation elements, = the accumulated learning skill.

33. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modeling 7. Discussion 8. Conclusions 9. Future works

34. Discussion • The slope of schema increase curve in Figure 7 would be determined with the values of at logarithm function. Schema of x-axis in the modeling function consists of Amusement, Learning Usability, and Pedagogical Knowledge. According to the research result in Figure 6, k line is one of the most ideal types. Figure 7. Proposed the effective the computer-assisted learning model

35. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modelling 7. Discussion 8. Conclusions 9. Future works

36. Conclusions • This research rests on a firm basis of the comparative studies on Reward System in each groups of both Educationally Emphasized Digital Games and Amusement Emphasized Digital Games, as well as certain ideas and theories proposed by some educators. Under these conditions, the computer–assisted learning model has met the requirements of a certain modeling functional formula which has been validated by an education model. • The functional formula derived from this research is essentially applicable as a standard that makes it possible to construct course works of learning adequate for different learning levels of learners, within the framework of the computer–assisted learning model.

37. Conclusions • The functional formula, can be derived from the analysis of the Reward System in digital games. It is possible for this formula to lead to various models for educational purposes which consider factors like ages, grades, and levels as values for the constant k. It is expected to appropriately control the necessary quantities of motivation for learners through different values of the constant w.

38. Contents 1. Introduction 2. Literature review 3. Methodology 4. Data Analysis 5. Data Fitting and Formula 6. The Computer-Assisted Leaning Model Modelling 7. Discussion 8. Conclusions 9. Future works

39. Future works • The method described in this paper helps other researchers to find an effective way of constructing digital pedagogy models classifying their subjects for education. Results from this research paves the way for various forms of pilot models that could be the subject of future work and experiment.

40. Q & A