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Description
Programming is quickly becoming as ubiquitous and essential a skill as general mathematics. However, many elementary and high school students are still not aware of what the computer science field entails. To make matters worse, students who are introduced to computer science are frequently being fed only part of what it is about rather than its entire construction. Consequently, they feel out of their depth when they approach college. Research has discovered that by teaching computer science and programming through a problem-driven approach and focusing on a combination of syntax and computational thinking, students can be prepared when entering higher levels of computer science education.
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.
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.
ContributorsKury, Nizar (Author) / Nelson, Brian C (Thesis advisor) / Hsiao, Ihan (Committee member) / Kobayashi, Yoshihiro (Committee member) / Arizona State University (Publisher)
Created2017
Description
The current study investigates accent effects using virtual agents in the context of a multimedia learning environment. In a 2 (voice type: human, synthetic) x 2 (voice accent: English, Russian) between-subjects factorial design, the source and accent of the agent’s voice were manipulated. Research has shown that an instructor’s accent can have an impact on learning outcomes and perceptions of the instructor. However, these outcomes and perceptions have yet to be fully understood in the context of a virtual human instructor. Outcome measures collected included: knowledge retention, knowledge transfer, and cognitive load. Perception measures were collected using the Agent Persona Instrument-Revised, API-R, and a speaker-rating survey. Overall, there were no significant differences between the accented conditions. However, the synthetic condition had significantly lower knowledge retention, knowledge transfer, and mental effort efficiency than the professional voices in the human condition. Participants rated the human recordings higher on speaker-rating and API-R measures. These findings demonstrate the importance of considering the quality of the voice when designing multimedia learning environments.
ContributorsSiegle, Robert Franklin (Author) / Craig, Scotty D (Thesis advisor) / Cooke, Nancy J (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2022