2018 IEEE Frontiers in Education Conference (FIE), 2018
This research to practice full paper provides preliminary evidence that integrating reflections i... more This research to practice full paper provides preliminary evidence that integrating reflections is a significant feature to identify at-risk students early in a semester as verified and validated by a sequence-based learning analytics model. We’ve devised an active learning classroom model which incorporates reflection at multiple points in students’ learning experience. This active learning model adopts Kolb’s Learning model to provide a coherent and connected set of activities before, during, and after the class. Unlike periodic assessment through testing, reflections can provide nearly-real-time information about student’s experiences in class. We extract sentiment feature vectors to capture students’ affect from written reflections. These features typically aren’t assessed on tests or during in-class activities. These features were extracted automatically using LIWC (Linguistic Inquiry and Word Count) is a tool for applied natural language processing) which is less cumbersome to implement than manually reading the written reflections. We find that using these sentiment feature vectors extracted from the reflections in our learning model increased accuracy while decreasing time-to-detect at-risk students significantly.
Stephen's research focuses on how people collaboratively make sense of complex, 'wicked' problems... more Stephen's research focuses on how people collaboratively make sense of complex, 'wicked' problems. Wicked problems are dynamic and constantly changing. They involve multiple stakeholders, often with conflicting requirements. To address these challenges, Stephen develops sociotechnical systems that collect, organize, and use data to support reflection and collective action. He received his Ph.D. at UNC in Charlotte and is currently a postdoctoral researcher in the Design Lab at UC San Diego. Dr. Mohsen M Dorodchi, UNC, Charlotte Dr. Dorodchi has been teaching in the field of computing for over 30 years of which 20 years as educator. He has taught majority of the courses in the computer science and engineering curriculum over the past 20 years such as introductory programming, data structures, databases, software engineering, system programming, etc. He is involved in multiple NSF supported research projects including Learning and Predictive Analytics Research, Research Practitioner Partnership, Implementing Teaching Methods to help Students learn more efficiently in active learning, etc.
— This research to practice full paper provides preliminary evidence that integrating reflections... more — This research to practice full paper provides preliminary evidence that integrating reflections is a significant feature to identify at-risk students early in a semester as verified and validated by a sequence-based learning analytics model. We've devised an active learning classroom model which incorporates reflection at multiple points in students' learning experience. This active learning model adopts Kolb's Learning model to provide a coherent and connected set of activities before, during, and after the class. Unlike periodic assessment through testing, reflections can provide nearly-real-time information about student's experiences in class. We extract sentiment feature vectors to capture students' affect from written reflections. These features typically aren't assessed on tests or during in-class activities. These features were extracted automatically using LIWC (Linguistic Inquiry and Word Count) is a tool for applied natural language processing) which is less cumbersome to implement than manually reading the written reflections. We find that using these sentiment feature vectors extracted from the reflections in our learning model increased accuracy while decreasing time-to-detect at-risk students significantly.
2018 IEEE Frontiers in Education Conference (FIE), 2018
This research to practice full paper provides preliminary evidence that integrating reflections i... more This research to practice full paper provides preliminary evidence that integrating reflections is a significant feature to identify at-risk students early in a semester as verified and validated by a sequence-based learning analytics model. We’ve devised an active learning classroom model which incorporates reflection at multiple points in students’ learning experience. This active learning model adopts Kolb’s Learning model to provide a coherent and connected set of activities before, during, and after the class. Unlike periodic assessment through testing, reflections can provide nearly-real-time information about student’s experiences in class. We extract sentiment feature vectors to capture students’ affect from written reflections. These features typically aren’t assessed on tests or during in-class activities. These features were extracted automatically using LIWC (Linguistic Inquiry and Word Count) is a tool for applied natural language processing) which is less cumbersome to implement than manually reading the written reflections. We find that using these sentiment feature vectors extracted from the reflections in our learning model increased accuracy while decreasing time-to-detect at-risk students significantly.
Stephen's research focuses on how people collaboratively make sense of complex, 'wicked' problems... more Stephen's research focuses on how people collaboratively make sense of complex, 'wicked' problems. Wicked problems are dynamic and constantly changing. They involve multiple stakeholders, often with conflicting requirements. To address these challenges, Stephen develops sociotechnical systems that collect, organize, and use data to support reflection and collective action. He received his Ph.D. at UNC in Charlotte and is currently a postdoctoral researcher in the Design Lab at UC San Diego. Dr. Mohsen M Dorodchi, UNC, Charlotte Dr. Dorodchi has been teaching in the field of computing for over 30 years of which 20 years as educator. He has taught majority of the courses in the computer science and engineering curriculum over the past 20 years such as introductory programming, data structures, databases, software engineering, system programming, etc. He is involved in multiple NSF supported research projects including Learning and Predictive Analytics Research, Research Practitioner Partnership, Implementing Teaching Methods to help Students learn more efficiently in active learning, etc.
— This research to practice full paper provides preliminary evidence that integrating reflections... more — This research to practice full paper provides preliminary evidence that integrating reflections is a significant feature to identify at-risk students early in a semester as verified and validated by a sequence-based learning analytics model. We've devised an active learning classroom model which incorporates reflection at multiple points in students' learning experience. This active learning model adopts Kolb's Learning model to provide a coherent and connected set of activities before, during, and after the class. Unlike periodic assessment through testing, reflections can provide nearly-real-time information about student's experiences in class. We extract sentiment feature vectors to capture students' affect from written reflections. These features typically aren't assessed on tests or during in-class activities. These features were extracted automatically using LIWC (Linguistic Inquiry and Word Count) is a tool for applied natural language processing) which is less cumbersome to implement than manually reading the written reflections. We find that using these sentiment feature vectors extracted from the reflections in our learning model increased accuracy while decreasing time-to-detect at-risk students significantly.
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