Supplementary data for study: Understanding the Relation Between Study Behaviors and Educational Design (Study 4 and 5)
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Important learning happens outside organized lectures and labs, but much of the interaction between these educational design constructs and the study behavior of computing students is unknown. This data is part of a PhD project and relates to Studies 4 and 5. In these studies we follow a group of computing students through their year semester in order to explore these dependencies. Through weekly reports, students tracked their study behaviors. An exploratory cluster analysis was performed, mapping the students' organization, independent study, planning and priorities, time engagement, and use of different study environments. By comparing these aspects of student behavior to design parameters at both the program and course levels we get a holistic understanding of the student-driven learning environment. The results of this analysis con' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["computer science education","computing education","self reports"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Important learning happens outside organized lectures and labs, but much of the interaction between these educational design constructs and the study behavior of computing students is unknown. This data is part of a PhD project and relates to Studies 4 and 5. In these studies we follow a group of computing students through their year semester in order to explore these dependencies. Through weekly reports, students tracked their study behaviors. An exploratory cluster analysis was performed, mapping the students' organization, independent study, planning and priorities, time engagement, and use of different study environments. By comparing these aspects of student behavior to design parameters at both the program and course levels we get a holistic understanding of the student-driven learning environment. The results of this analysis con' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Important learning happens outside organized lectures and labs, but much of the interaction between these educational design constructs and the study behavior of computing students is unknown. This data is part of a PhD project and relates to Studies 4 and 5. In these studies we follow a group of computing students through their year semester in order to explore these dependencies. Through weekly reports, students tracked their study behaviors. An exploratory cluster analysis was performed, mapping the students' organization, independent study, planning and priorities, time engagement, and use of different study environments. By comparing these aspects of student behavior to design parameters at both the program and course levels we get a holistic understanding of the student-driven learning environment. The results of this analysis con
computer science education; computing education; self reports