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- W170592007 abstract "The current generation is much accustomed to new technologies which enable them to perform many activities online. More importantly, these technologies have been used by students even for learning. In this research we focused on student initiated learning online. Because students have control over their own learning, they are not bounded by a syllabus or a specific learning task. Apart from learning related activities however, it is possible for them to engage in non-learning related activities. From the data gathered, the k-Means algorithm was used to discover five behaviors exhibited by students as they learned online relative to how they transitioned between viewing learning and non-learning related websites. Since emotion was previously reported to have an effect on how students learned online, differences in emotion transitions for each of the online learning behaviors were also observed. The analysis of these transitions provided possible reasons for why students exhibited these behaviors. Possible interventions were then suggested which can be used for supporting students as they learn online. Systems can later be developed to utilize the developed model for predicting the type of behavior exhibited by a student and provide appropriate support mechanisms for their learning." @default.
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- W170592007 date "2012-01-01" @default.
- W170592007 modified "2023-09-22" @default.
- W170592007 title "Categorizing and Comparing Behaviors of Students Engaged in Self-initiated Learning Online" @default.
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- W170592007 doi "https://doi.org/10.1007/978-4-431-54106-6_11" @default.
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