Exploring students’ navigation profile in a computer-supported collaborative learning context
1University of Oulu, Faculty of Education, Educational Sciences
|Online Access:||PDF Full Text (PDF, )|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-201805161814
|Publish Date:|| 2018-05-16
|Thesis type:||Master's thesis
As technology advances rapidly, computer-supported collaborative learning (CSCL) approaches are more and more implemented in educational contexts. However, the assessment of learning processes in CSCL is still a challenge for both teachers and students. Trace data (e.g., from log files), which is objective and can be collected in an unobtrusive way, provides great opportunity to investigate and assess learning process. The present study explored high school students’ (N = 12) navigation behavior on a web-based learning environment during an advanced physics course. The course was implemented in a collaborative learning context and was loosely scripted. Students were instructed to work collaboratively on certain tasks during each lesson session. The study investigated students’ navigation profiles at three different levels, (i.e., class level, group level, individual level), and the relationship between students’ navigation profiles and their final learning outcomes. The study used a quantitative research methodology. The log data of students’ navigation behavior was automatically recorded by the Open edX learning environment during the whole course. Log file was preprocessed (filtered and features extracted) before conducting descriptive and correlation analyses. The findings of this study suggest that overall students were following the collaborative script during the whole course and some navigation behavior (i.e., navigated to course plan chapter) manifests students’ presence of planning and monitoring behavior. It was also found that each collaborative group consisted of different combination of individual navigation profiles and there was a significant correlation between students’ total navigation frequency and their final exam grade. The implication for students’ navigation profile as an assessment tool in CSCL are discussed. The small sample size imposes a limitation of the generalizability of the results. In future research, it is suggested to investigate students’ navigation behavior from multiple dimensions (e.g., sequential pattern, linearity of navigation) rather than a single factor (navigation frequency). Some other research possibilities are also proposed.
© Min Qu, 2018. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.