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Description
Emergent processes can roughly be defined as processes that self-arise from interactions without a centralized control. People have many robust misconceptions in explaining emergent process concepts such as natural selection and diffusion. This is because they lack a proper categorical representation of emergent processes and often misclassify these processes into the sequential processes category that they are more familiar with. The two kinds of processes can be distinguished by their second-order features that describe how one interaction relates to another interaction. This study investigated if teaching emergent second-order features can help people more correctly categorize new processes, it also compared different instructional methods in teaching emergent second-order features. The prediction was that learning emergent features should help more than learning sequential features because what most people lack is the representation of emergent processes. Results confirmed this by showing participants who generated emergent features and got correct features as feedback were better at distinguishing two kinds of processes compared to participants who rewrote second-order sequential features. Another finding was that participants who generated emergent features followed by reading correct features as feedback did better in distinguishing the processes than participants who only attempted to generate the emergent features without feedback. Finally, switching the order of instruction by teaching emergent features and then asking participants to explain the difference between emergent and sequential features resulted in equivalent learning gain as the experimental group that received feedback. These results proved teaching emergent second-order features helps people categorize processes and demonstrated the most efficient way to teach them.
ContributorsXu, Dongchen (Author) / Chi, Michelene (Thesis advisor) / Homa, Donald (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2015
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