| Summary: | 碩士 === 國立交通大學 === 教育研究所 === 107 === This study, based on flow (Csikszentmihalyi, 1975), was to explore whether the environmental conditions college students experienced in Systems Engineering courses (of Dept. of Electronic Engineering) were more effectively leading them to engage in learning than the environmental conditions in other courses? Learning engagement is defined by flow theory, meaning that if an individual experiences higher flow, s/he shows higher tendency of learning engagement. In this study, the external coordinates of experience and internal coordinates of campus experience of college students were collected in several school days and were analyzed to understand where (in what place of a campus) and how (doing what kind of activities, feeling what features of environmental conditions) would college students experience the highest flow? For the three environments with the highest flow experiences, the thesis explored the predictive capacity of environmental conditions and the environments on flow, and examined the theoretical model.
Shernoff and Csikszentmihalyi (2009) proposed that two environmental conditions that could predict students’ engagement in learning environments, are academic intensity and positive emotional response. In the study, the researcher selected “challenge” and “relevance” that college students experienced in the campus as the indicators of “activity intensity” and “activeness” as the indicator of “positive emotional response”. Bandura (2000) proposed that people’s psychological function is easily influenced by physical and social environments, so the environments in which college students were and activities which they engaged were selected as the factors to predict the level of flow. In educational studies, researchers usually used experimental design to examine the effectiveness of a new type of course between experimental and control groups, but in this study college students’ daily school experiences were compared (within person) with classroom experiences in the newly designed SE courses.
The data source comes from an intensive longitudinal dataset funded by Ministry of Science and Technology to the advisor (MOST projects of Engineering Education remodeling) research project of Professor Lin, and the data was collected for the four Systems Engineering courses from 2013 to 2015. Used Day Reconstruction Method (Kahneman et al., 2004) designed by Cheng (2016) to repeatedly collect 58 participants’ subjective experience over five weeks in a semester. Participants had to report each event they took part in and provide several information (tags) about the events, such as the time, the place, and the activities as well as the experiences that they felt in each event, such as environmental conditions and flow. The participants were 58 engineering students who took Systems Engineering (SE) courses. Internal coordinates of experience used in the study were “environmental conditions” and “flow” and external coordinates of experience were “the place” and “the activities”. Then, several Hierarchical Linear Modeling (HLM) were conducted to test whether the environmental conditions (i.e., challenge, relevance, and activeness) could predict flow and whether the prediction could be moderated by the place tag of the events (i.e., SE courses versus non-SE courses)? Whether there was a cross-level interaction between internal coordinates of experience (i.e., challenge, relevance, and activeness) and external coordinates of experience (i.e., SE courses versus non-SE courses)? The major findings of the research were summarized as in below.
1. The descriptive results showed that college students reported the highest number of events were taken place in Systems Engineering courses compared with other events in a school day. They experienced the highest “challenge” and “relevance” in SE courses compared with all other places, however, activeness they experienced in SE courses was only higher than “other courses” and “workplaces”. They experienced the highest flow in SE courses which was followed by other courses and computer activity. In SE courses, they experienced the highest flow when they took three activities: exams, working on assignments and experiments, and conducting creative projects.
2. For the three places with the highest flow that college students experienced, several HLM models were conducted to test whether the environmental conditions and the place (a dummy variable for the comparison of SE courses versus non-SE courses which was an integration of the places of other courses and computer activity) could predict flow. The results of the random coefficient model showed that challenge, relevance, and activeness (environmental conditions) all significantly predicted flow, meaning that there was an apparent individual difference. For some college students, environmental conditions they experienced predicted flow more successfully than others. However, in the full model, when external coordinates of experience and internal coordinates of experience were simultaneously considered, flow in SE courses versus in non-SE courses (other courses and computer activity) were not significantly different. In contrast, the between-persons variance of flow was significant, meaning that for flow experience there was a remarkable individual difference among college students. Some of them experienced higher level of flow than others, no matter in SE or non-SE course. Comparatively, higher proportion of flow variance was explained by individual difference than by places. Such individual difference might be accounted for by college students’ abilities. When their abilities matched the challenges (Csikszentmihalyi, 1975), college students tended to experience flow. However, students at the time in entering colleges must perform quite outstanding in order to pass the application screening. Why is there a great discrepant in abilities shown in the senior year in taking SE courses? There were three possible reasons. First, some college students might find SE program is interesting in the process of studying but others might not. The motivated college students might actively look for challenges to enhance their abilities but others might not. The active learning approach might form an ability gap between motivated and not-motivated students. Second, some college students couldn't keep up with the challenges in the process of studying. When their abilities reached the limit, it might form an ability gap between college students who could keep up with the challenges and those could not. Third, because most teachers expected that college students could learn actively and spontaneously, some college students didn’t meet teacher's expectation. It might form an ability gap between college students who learned actively/spontaneously versus those who did not.
The other possible reason was that we used a strict reference of other courses and computer activities (where college students obtained the second highest level of flow) as the comparison of SE courses. The difference of flow experienced in SE courses and non-SE courses was not enough apparent.
Finally, according to the above results, the researcher provided several discussions. Several suggestions for future research and for SE teachers were then given.
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