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DescriptionThis project examines the television industry today, especially the field of educational programs. It includes the detailed implementation of one such show, a 30-minute demonstration of life skills, split into 3 segments. The pilot episode is also included.
ContributorsKesting, Amanda Jean (Author) / Alvarez, Melanie (Thesis director) / Snyder, Brian (Committee member) / Glaser, Ann (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2013-05
Description
Collaborative learning is a potential technique for teachers to use to meet the diverse learning needs of the students in their classrooms. Previous studies have investigated the contexts in which the benefits of collaborative learning show greater presence. The most important factor found was the quality of the interactions. Studies have suggested that high achieving students are capable of improving the quality of interactions. This bears the question if prior knowledge plays an influence in the learning outcome of students in collaborative learning. Results show that high prior knowledge students do not face a detriment in having low prior knowledge students as a partner comparing to having another high prior knowledge student and that low prior knowledge students show significantly higher learning outcome when partnered with a high prior knowledge partner than with another low prior knowledge student. It is therefore likely that having a high prior knowledge student within a dyad improves the quality of interaction, resulting in greater learning outcome through collaborative learning.
ContributorsKeyvani, Kewmars (Author) / Chi, Michelene (Thesis director) / Wylie, Ruth (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
Integrating agent-based models (ABMs) has been a popular approach for teaching emergent science concepts. However, students continue to find it difficult to explain the emergent process of natural selection. This study adopted an ontological framework–the Pattern, Agents, Interactions, Relations, and Causality (PAIR-C)–to guide the design of learning modules. This pre-posttest experimental study examines the effects of the PAIR-C module versus the Regular module on fostering students’ deep understanding of natural selection. Results show that students in the PAIR-C intervention group performed better in answering deep questions assessing the understanding of inter-level causal relationships than those in the Regular control group. Although students in both groups did not show significantly improved abilities in explaining the natural selection process for other contexts or significant differences in their abilities to explain other emergent phenomena, students in the intervention group demonstrated system-thinking perspectives and fewer misconceptions in their expressions compared to the control group. A close analysis of student misconceptions consolidates that the intervention group demonstrated drastically fewer categories and numbers of misconceptions while those in the control group did not show such drastic changes before and after the study. To precisely address misconceptions and further improve students’ learning outcomes, Epistemic Network Analysis was adopted to capture students’ misconception characteristics by examining the co-occurrences of different misconception categories as well as the relationship between misconceptions and PAIR-C features. The results of student learning outcomes and misconception characteristics collectively provide directions for improving the instructional design of the PAIR-C module. Furthermore, findings on student engagement levels during learning can also inform future design efforts. Overall, this project sheds light on applying an innovative framework to designing effective learning modules to teach emergent science concepts.
ContributorsSu, Man (Author) / Chi, Michelene (Thesis advisor) / Nelson, Brian (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
Arizona is a unique state in that rain is not a normal occurrence throughout most of the year (NWS). Arizona averages from less than three months to half a month of measurable precipitation days per year (WRCC). With that, it is important to know the public’s understanding as well as their general trend of likeness towards the weather forecasts they receive. A questionnaire was distributed to 426 people in the state of Arizona to review what they understand from the forecasts and what they would like to see on social media and television.
ContributorsHermansen, Alexis Nicole (Author) / Alvarez, Melanie (Thesis director) / Cerveny, Randall (Committee member) / Hondula, David M. (Committee member) / Walter Cronkite School of Journalism & Mass Comm (Contributor) / School of Geographical Sciences and Urban Planning (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05