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- All Subjects: game
- Creators: Kobayashi, Yoshihiro
- Creators: Nelson, Brian
- Member of: Theses and Dissertations
- Member of: ASU Electronic Theses and Dissertations
This thesis describes the design, development, and early user testing of a theory-based virtual world for computer science instruction called System Dot. System Dot was designed to visually manifest programming instructions into interactable objects, giving players a way to see coding as tangible entities rather than text on a white screen. In order for System Dot to convey the true nature of computer science, a custom predictive recursive descent parser was embedded in the program to validate any user-generated solutions to pre-defined logical platforming puzzles.
Steps were taken to adapt the virtual world to player behavior by creating a system to detect their learning style playing the game. Through a dynamic Bayesian network, System Dot aims to classify a player’s learning style based on the Felder-Sylverman Learning Style Model (FSLSM). Testers played through the first half of System Dot, which was enough to test out the Bayesian network and initial learning style classification. This classification was then compared to the assessment by Felder’s Index of Learning Styles Questionnaire (ILSQ). Lastly, this thesis will also discuss ways to use the results from the user testing to implement a personalized feedback system for the virtual world in the future and what has been learned through the learning style method.
Web-Based Programming Grading Assistant: An Investigation of the Role of Students Reviewing Behavior
This paper explores the inner workings of algorithms that computers may use to play Chess. First, we discuss the classical Alpha-Beta algorithm and several improvements, including Quiescence Search, Transposition Tables, and more. Next, we examine the state-of-the-art Monte Carlo Tree Search algorithm and relevant optimizations. After that, we consider a recent algorithm that transforms Alpha-Beta into a “Rollout” search, blending it with Monte Carlo Tree Search under the rollout paradigm. We then discuss our C++ Chess Engine, Homura, and explain its implementation of a hybrid algorithm combining Alpha-Beta with MCTS. Finally, we show that Homura can play master-level Chess at a strength currently exceeding that of our backtracking Alpha-Beta.
different currency rates. In the game, the player can control a group of heroes against another
set of heroes. In this project, two different currency rates are examined. The player can get
money more easily in a lower currency rate. Two groups of players are formed, and there are 5
players in group A and group B respectively. Players in group A are assigned to play the idle
game with a higher currency rate and players in group B are assigned to play the game with a
lower currency rate. The idle game is created by using Unity and C# language. The feedback
from the players is collected by asking them to finish an 11-question survey. The analysis is
based on the game’s currency rate and survey results. It is concluded that a higher currency rate
lowers players’ enjoyment of the idle game.
Unity simulation tool by implementing political policies or adjusting values via sliders, buttons, etc., which will alter the values in the framework. The user can then use the simulation interface to view different estimated population values for categories of people, such as regional differences, education levels, and more.