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Curri: A Curriculum Visualization System that Unifies Curricular Dependencies with Temporal Student Data

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Computing and Information Technology Division Technical Session 7

Tagged Division

Computing and Information Technology

Page Count

19

DOI

10.18260/1-2--34362

Permanent URL

https://peer.asee.org/34362

Download Count

567

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Paper Authors

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Stephen Michael MacNeil University of California, San Diego

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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.

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Mohsen M. Dorodchi University of North Carolina at Charlotte

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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.

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Erfan Al-Hossami University of North Carolina at Charlotte

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Erfan Al-Hossami is a Ph.D. student at UNC Charlotte. Erfan has been mentored in teaching CS1 since 2016 and then in CS education research. His work mainly focuses on predictive learning analytics. His research interests include Machine Learning, NLP, and Conversational A.I. and mental health. Recently, he's been learning more about code generation, transfer learning, and text classification.

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Aileen Benedict University of North Carolina at Charlotte

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Aileen Benedict is a Ph.D. student and GAANN Fellow at UNC Charlotte, who has been mentored in teaching since 2016. Her work mainly focuses on CS education and learning analytics, with specific interests in reflective practices and predictive analytics. More recently, she has also been learning more about various topics in machine learning, recommender systems, and mental health.

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Devansh Desai University of North Carolina at Charlotte

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Devansh Desai is a recent graduate from UNC Charlotte working at Lowe's. He worked as a teaching assistant for two years, after which he focused on educational research. His research work mainly focuses on learning and predictive analytics and is experienced in data science and machine learning. Currently, he is seeking ways to contribute his time toward education and teaching.

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Mohammad Javad Mahzoon Core Compete Inc. Orcid 16x16 orcid.org/0000-0003-4689-5842

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MJ Mahzoon received his PhD from the University of North Carolina at Charlotte where he also worked as a researcher to analyze student data. His research spans using data mining and machine learning tools to identify patterns, trends and anomalies in data.

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Abstract

The correct sequence of courses in a curriculum can ensure that students develop their knowledge and skills holistically. The challenge level can also be more evenly distributed. Creating these sequences is difficult because curriculum designers must consider multiple potentially conflicting criteria simultaneously. There currently exists a dearth of tools for analyzing the curriculum that incorporates course dependencies as defined by curriculum designers while also considering students' pathways through the curriculum. In this paper, we present Curri, a data-driven curriculum visualization system that scrapes dependencies from our university's published curriculum and leverages student academic data to determine when, on average, students take each course. We evaluate our approach with a case study and two focus groups. This work provides initial evidence that considering both dependencies and students' temporal performance leads to new analyses and insights.

MacNeil, S. M., & Dorodchi, M. M., & Al-Hossami, E., & Benedict, A., & Desai, D., & Mahzoon, M. J. (2020, June), Curri: A Curriculum Visualization System that Unifies Curricular Dependencies with Temporal Student Data Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34362

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