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- Member of: ASU Electronic Theses and Dissertations
Description
Computational thinking, the fundamental way of thinking in computer science, including information sourcing and problem solving behind programming, is considered vital to children who live in a digital era. Most of current educational games designed to teach children about coding either rely on external curricular materials or are too complicated to work well with young children. In this thesis project, Guardy, an iOS tower defense game, was developed to help children over 8 years old learn about and practice using basic concepts in programming. The game is built with the SpriteKit, a graphics rendering and animation infrastructure in Apple’s integrated development environment Xcode. It simplifies switching among different game scenes and animating game sprites in the development. In a typical game, a sequence of operations is arranged by players to destroy incoming enemy minions. Basic coding concepts like looping, sequencing, conditionals, and classification are integrated in different levels. In later levels, players are required to type in commands and put them in an order to keep playing the game. To reduce the difficulty of the usability testing, a method combining questionnaires and observation was conducted with two groups of college students who either have no programming experience or are familiar with coding. The results show that Guardy has the potential to help children learn programming and practice computational thinking.
ContributorsWang, Xiaoxiao (Author) / Nelson, Brian C. (Thesis advisor) / Turaga, Pavan (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Electronic books or eBooks have the potential to revolutionize the way humans read and learn. eBooks offer many advantages such as simplicity, ease of use, eco-friendliness, and portability. The advancement of technology has introduced many forms of multimedia objects into eBooks, which may help people learn from them. To help the readers understand and comprehend a concept that is put forward by the author of an eBook, there is ongoing research involving the use of augmented reality (AR) in education. This study explores how AR and three-dimensional interactive models are integrated into eBooks to help the readers comprehend the content quickly and swiftly. It compares the reading activities of people when they experience these two visual representations within an eBook.
This study required participants to interact with some instructional material presented on an eBook and complete a learning measure. While interacting with the eBook, participants were equipped with a set of physiological devices, namely an ABM EEG headset and eye tracker during the experiment to collect biometric data that could be used to objectively measure their user experience. Fifty college students participated in this study. The data collected from each of the participants was used to analyze the reading activities of people by performing an Independent Samples t-test.
This study required participants to interact with some instructional material presented on an eBook and complete a learning measure. While interacting with the eBook, participants were equipped with a set of physiological devices, namely an ABM EEG headset and eye tracker during the experiment to collect biometric data that could be used to objectively measure their user experience. Fifty college students participated in this study. The data collected from each of the participants was used to analyze the reading activities of people by performing an Independent Samples t-test.
ContributorsJuluru, Kalyan Kumar (Author) / Atkinson, Robert K. (Thesis advisor) / Chen, Yinong (Thesis advisor) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Students seldom spontaneously collaborate with each other. A system that can measure collaboration in real time could be useful, for example, by helping the teacher locate a group requiring guidance. To address this challenge, the research presented here focuses on building and comparing collaboration detectors for different types of classroom problem solving activities, such as card sorting and handwriting.
Transfer learning using different representations was also studied with a goal of building collaboration detectors for one task can be used with a new task. Data for building such detectors were collected in the form of verbal interaction and user action logs from students’ tablets. Three qualitative levels of interactivity were distinguished: Collaboration, Cooperation and Asymmetric Contribution. Machine learning was used to induce a classifier that can assign a code for every episode based on the set of features. The results indicate that machine learned classifiers were reliable and can transfer.
Transfer learning using different representations was also studied with a goal of building collaboration detectors for one task can be used with a new task. Data for building such detectors were collected in the form of verbal interaction and user action logs from students’ tablets. Three qualitative levels of interactivity were distinguished: Collaboration, Cooperation and Asymmetric Contribution. Machine learning was used to induce a classifier that can assign a code for every episode based on the set of features. The results indicate that machine learned classifiers were reliable and can transfer.
ContributorsViswanathan, Sree Aurovindh (Author) / VanLehn, Kurt (Thesis advisor) / Hsiao, Ihan (Committee member) / Walker, Erin (Committee member) / D' Angelo, Cynthia (Committee member) / Arizona State University (Publisher)
Created2020