Bias Sekar Avi Shena School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung, Indonesia avishena1993@gmail.com Benhard Sitohang School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung, Indonesia benhard@stei.itb.ac.id Satrio Adi Rukmono School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung, Indonesia satrio@informatika.org Abstract— Evidence-centered Design (ECD) framework has been used to design an effective assessment plan for student. The assessment process often uses game as a tool to make the activity more active, goal-oriented and could give a direct feedback. A difficulty level in the game need to be suitable with student’s skill to create a fun, comfortable assessment environment. The result of assessment process usually need some times to be processed and the student could not experience it right away. This paper applies Dynamic Difficult Adjustment (DDA) to create a dynamic game content suitable with the student’s skill based on the result of assessment process within the game which is designed using ECD. The presented scheme produce winning rate value of 67%. The students who were participated in the test also find the game to be fun and the dynamic content inside the game could motivate them to use the game to help them learn. Keywords—skill assessment, ECD, dynamization, DDA, game. I. INTRODUCTION One of the challenge on providing a successful teaching method is to give suitable teaching environment based on student’s knowledge and skill [1]. Some researches stated that a good teaching method consists of an active, goal-oriented, interesting activity, [2], [3], [4], [5] gives a direct feedback and also gives challenge suitable with student’s knowledge and skill [6]. The last two characteristics (direct feedback and challenge) are also owned by an activity called game [7], [8]. This is the reason of why game often used to support teaching activity which is called game based learning. In the age where technology is growing rapidly, game based learning is often developed as a digital game where students play it using a computer or any kind of console that could support digital games. These games could improve student’s learning performance by 51% [9] and also improve their skills and behaviour [10]. To gain a successful learning activity using a digital game, the design of game base learning become an important factor [11]. A game developed for learning has to be able to combine fun and education which can help the student learn while feeling the fun provided by the game. The learning content in the game also has to fit with student’s knowledge and skill to create an effective learning activity. To gain the information about student’s knowledge, an evaluation could be done while students are playing the game. This process is done by analysing their action and behaviour in the game [12]. Recent research on designing and evaluate student skill using game has been done by Argasinski and Wegrzyn in 2018. They developed a fire simulation game using a framework called Evidence-centered Design (ECD) [13]. This framework provide a uniform and systematic design pattern to evaluate student’s skill effectively. The result of assessment process usually take some time to be processed as learning content and the student couldn’t experience it right away. A mechanism which could use the evidence of student’s knowledge and skill for dynamically adjust the content of the game is needed so it’ll fit with the student’s skill while they play. With the help of evidence, a more effective and accurate dynamization process can be done. Based on aforementioned problem, the contribution of this paper is to use ECD framework’s evidence as an input to create a dynamic game content using Dynamic Difficulty Adjustment (DDA). To choose the suitable dynamization method for this paper, surveys have been made on some researches about DDA method in game. An experiment by Kok Wai Wong, Chun Che Fung, Arnold Depickere and Shri Rai has created a dynamic difficulty mechanism in game using Backpropagation Neural Networks (BPNNs) where difficulty level is divided into three kinds : easy (20%), normal (50%) and difficult (95%) [14]. Another experiment on DDA is done by Peizhi Shi and Ke Chen using Learning Constructive Primitives. In this experiment, difficulty in the game is divided into 5 level of difficulty [15]. A dynamic difficulty in game’s attribute and tactic has been experimented by Pieter Spronck, Ida Sprinkhuizen-Kuyper and Eric Postma using a Difficulty Scaling of game AI [16]. A simple and quick method of DDA also introduced by Lach [17]. This method use a dynamic adjustment value based on the player’s skill. Based on the surveys, the method used for dynamization in this paper is Full Dynamic Difficulty Adjustment for a Computer Player (FDDACP) by Lach. The reason for choosing this method is because the value used to adjust the difficulty level using this method is dynamic and is based on the player’s condition and attribute value in the game. Another reason is the method’s success rate on creating the dynamic game content is higher than the one with AI because AI techniques are complex and sometime couldn’t be used in real time. This method works by processing the data from the player and the enemy in the game and change the difficulty level based on the player’s skill. The attribute of the player and enemy in the game is changed to create a dynamic content. If the player is skilful, the game will provide a limited resources and a harder enemy to be defeated and if the player is in the verge of losing, the game will help the player by giving weaker enemy and more resources. 978-1-7281-4992-9/19/$31.00 ©2019 IEEE 2019 International Conference on Data and Software Engineering (ICoDSE) Application of Dynamic Difficulty Adjustment on Evidence-centered Design Framework for Game Based Learning Authorized licensed use limited to: Institut Teknologi Bandung. Downloaded on May 19,2021 at 14:36:11 UTC from IEEE Xplore. Restrictions apply.