Matching Items (3)
Filtering by
- All Subjects: Learning
- Genre: Academic theses
- Creators: Nelson, Brian
Description
This research study investigated the effects of high fidelity graphics on both learning and presence, or the "sense of being there," inside a Virtual Learning Environment (VLE). Four versions of a VLE on the subject of the element mercury were created, each with a different combination of high and low fidelity polygon models and high and low fidelity shaders. A total of 76 college age (18+ years of age) participants were randomly assigned to one of the four conditions. The participants interacted with the VLE and then completed several posttest measures on learning, presence, and attitudes towards the VLE experience. Demographic information was also collected, including age, computer gameplay experience, number of virtual environments interacted with, gender and time spent in this virtual environment. The data was analyzed as a 2 x 2 between subjects ANOVA.
The main effects of shader fidelity and polygon fidelity were both non- significant for both learning and all presence subscales inside the VLE. In addition, there was no significant interaction between shader fidelity and model fidelity. However, there were two significant results on the supplementary variables. First, gender was found to have a significant main effect on all the presence subscales. Females reported higher average levels of presence than their male counterparts. Second, gameplay hours, or the number of hours a participant played computer games per week, also had a significant main effect on participant score on the learning measure. The participants who reported playing 15+ hours of computer games per week, the highest amount of time in the variable, had the highest score as a group on the mercury learning measure while those participants that played 1-5 hours per week had the lowest scores.
The main effects of shader fidelity and polygon fidelity were both non- significant for both learning and all presence subscales inside the VLE. In addition, there was no significant interaction between shader fidelity and model fidelity. However, there were two significant results on the supplementary variables. First, gender was found to have a significant main effect on all the presence subscales. Females reported higher average levels of presence than their male counterparts. Second, gameplay hours, or the number of hours a participant played computer games per week, also had a significant main effect on participant score on the learning measure. The participants who reported playing 15+ hours of computer games per week, the highest amount of time in the variable, had the highest score as a group on the mercury learning measure while those participants that played 1-5 hours per week had the lowest scores.
ContributorsHorton, Scott (Author) / Nelson, Brian (Thesis advisor) / Savenye, Wilhelmina (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2014
Description
Many web search improvements have been developed since the advent of the modern search engine, but one underrepresented area is the application of specific customizations to search results for educational web sites. In order to address this issue and improve the relevance of search results in automated learning environments, this work has integrated context-aware search principles with applications of preference based re-ranking and query modifications. This research investigates several aspects of context-aware search principles, specifically context-sensitive and preference based re-ranking of results which take user inputs as to their preferred content, and combines this with search query modifications which automatically search for a variety of modified terms based on the given search query, integrating these results into the overall re-ranking for the context. The result of this work is a novel web search algorithm which could be applied to any online learning environment attempting to collect relevant resources for learning about a given topic. The algorithm has been evaluated through user studies comparing traditional search results to the context-aware results returned through the algorithm for a given topic. These studies explore how this integration of methods could provide improved relevance in the search results returned when compared against other modern search engines.
ContributorsVan Egmond, Eric (Author) / Burleson, Winslow (Thesis advisor) / Syrotiuk, Violet (Thesis advisor) / Nelson, Brian (Committee member) / Arizona State University (Publisher)
Created2014
Description
Distributed self-assessments and reflections empower learners to take the lead on their knowledge gaining evaluation. Both provide essential elements for practice and self-regulation in learning settings. Nowadays, many sources for practice opportunities are made available to the learners, especially in the Computer Science (CS) and programming domain. They may choose to utilize these opportunities to self-assess their learning progress and practice their skill. My objective in this thesis is to understand to what extent self-assess process can impact novice programmers learning and what advanced learning technologies can I provide to enhance the learner’s outcome and the progress. In this dissertation, I conducted a series of studies to investigate learning analytics and students’ behaviors in working on self-assessments and reflection opportunities. To enable this objective, I designed a personalized learning platform named QuizIT that provides daily quizzes to support learners in the computer science domain. QuizIT adopts an Open Social Student Model (OSSM) that supports personalized learning and serves as a self-assessment system. It aims to ignite self-regulating behavior and engage students in the self-assessment and reflective procedure. I designed and integrated the personalized practice recommender to the platform to investigate the self-assessment process. I also evaluated the self-assessment behavioral trails as a predictor to the students’ performance. The statistical indicators suggested that the distributed reflections were associated with the learner's performance. I proceeded to address whether distributed reflections enable self-regulating behavior and lead to better learning in CS introductory courses. From the student interactions with the system, I found distinct behavioral patterns that showed early signs of the learners' performance trajectory. The utilization of the personalized recommender improved the student’s engagement and performance in the self-assessment procedure. When I focused on enhancing reflections impact during self-assessment sessions through weekly opportunities, the learners in the CS domain showed better self-regulating learning behavior when utilizing those opportunities. The weekly reflections provided by the learners were able to capture more reflective features than the daily opportunities. Overall, this dissertation demonstrates the effectiveness of the learning technologies, including adaptive recommender and reflection, to support novice programming learners and their self-assessing processes.
ContributorsAlzaid, Mohammed (Author) / Hsiao, Ihan (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / VanLehn, Kurt (Committee member) / Nelson, Brian (Committee member) / Bansal, Srividya (Committee member) / Arizona State University (Publisher)
Created2022