Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: Educational technology
- Creators: Chi, Michelene
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
Collaborative Video Viewing (CVV) transforms passive video-based learning into an engaging, active process. While collaborative modes have different affordances that could potentially influence knowledge co-construction, no study has directly assessed the impact of collaborative modes in CVV activities. Therefore, this current study seeks to investigate how collaborative modes influence learning outcomes, learning engagement, group interaction and the co-construction process.The study utilized a within-subject, counterbalanced experimental design, in which each participating undergraduate student was paired in dyads. These dyads were assigned to engage in two separate CVV sessions: one using synchronous voice-based collaborative mode (SV) and the other using asynchronous text-based collaborative mode (AT). After each session, participants completed a test consisting of retention and application questions. ANCOVA was utilized to analyze the test scores.
To ascertain if the different scores were a result of varying levels of learning engagement, dyad discussions were coded using ICAP coding (Chi & Wylie, 2014). Furthermore, to delve deeper into the group interaction mechanism in SV and AT, a codebook was developed to analyze the discourse that occurred during dyad interaction. Sequential analysis and thematic narrative analysis were employed to visualize interaction patterns and the co-construction process.
The findings indicated that, generally, SV dyads performed better on application scores and have significantly higher interactive learning engagement than AT dyads. In line with ICAP predictions, the higher-score groups in both SV and AT engaged in more generative processes, leading to more constructive and interactive comments than lower-scoring groups. In terms of group interaction, both SV and AT primarily use descriptive discourse for co-explanation. However, the SV groups exclusively introduce discourse expressing uncertainty, which subsequently leads to group negotiation. The study identified distinct knowledge co-construction phases, including (a) co-explanation, (b) negotiation, and (c) application. Although the co-explanation phase is the most frequent in all dyad scores in both SV and AT, the negotiation phase appears to differentiate low-high score dyads from high-high score dyads.
These findings hold research implications for understanding learning engagement and group interaction in various online collaborative modes, as well as for the instructional design of active video-based learning through collaborative video viewing.
ContributorsTechawitthayachinda, Ratrapee (Author) / Chi, Michelene (Thesis advisor) / Hong, Yi-Chun (Thesis advisor) / Nelson, Brian (Committee member) / Arizona State University (Publisher)
Created2023