BRIDGES: Real world data, assignments and visualizations to engage and motivate CS majors

BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to ea...

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Veröffentlicht in:	Education and information technologies Jg. 29; H. 9; S. 10649 - 10675
Hauptverfasser:	Burlinson, David, Mcquaigue, Matthew, Goncharow, Alec, Subramanian, Kalpathi, Saule, Erik, Payton, Jamie, Goolkasian, Paula
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2024 Springer Springer Nature B.V
Schlagworte:	Algorithms Bridges Computer Appl. in Social and Behavioral Sciences Computer Science Computers and Education Education Educational Technology Grouping (Instructional Purposes) Information Systems Applications (incl.Internet) Quasiexperimental Design Required Courses School Holding Power Student Interests Student retention User Interfaces and Human Computer Interaction Visualization (Computers) Visualization Engagement Programming assignments Data structures Algorithms Real-world data
ISSN:	1360-2357, 1573-7608
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Abstract	BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as part of routine classroom exercises. The objective is to use the infrastructure to promote a better understanding of the data structure and its underlying algorithms. This report describes the BRIDGES infrastructure and provides evaluation data collected over the first five years of the project. In the first 2 years, as we were developing the BRIDGES projects, our focus was on gathering data to assess whether the addition of the BRIDGES exercises had an effect on student retention of core concepts in data structures; and throughout the 5-year duration of the project, student interest and faculty feedback were collected online and anonymously. A mixed method design was used to evaluate the project impact. A quasiexperimental design compared student cohorts who were enrolled in comparable course sections that used BRIDGES with those that did not. Qualitative and quantitative measures were developed and used together with course grades and grade point averages. Interest and relevance in BRIDGES programming assignments was assessed with additional survey data from students and instructors. Results showed that students involved in BRIDGES projects demonstrated larger gains in knowledge of data structures compared to students enrolled in comparable course sections, as well as long-term benefits in their performance in four follow-on required courses. Survey responses indicated that some investment of time was needed to use BRIDGES, but the extra efforts were associated with several notable outcomes. Students and instructors had positive perceptions of the value of engaging in BRIDGES projects. BRIDGES can become a tool to get students more engaged in critical foundational courses, demonstrating relevance and context to today’s computational challenges.
AbstractList	BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as part of routine classroom exercises. The objective is to use the infrastructure to promote a better understanding of the data structure and its underlying algorithms. This report describes the BRIDGES infrastructure and provides evaluation data collected over the first five years of the project. In the first 2 years, as we were developing the BRIDGES projects, our focus was on gathering data to assess whether the addition of the BRIDGES exercises had an effect on student retention of core concepts in data structures; and throughout the 5-year duration of the project, student interest and faculty feedback were collected online and anonymously. A mixed method design was used to evaluate the project impact. A quasiexperimental design compared student cohorts who were enrolled in comparable course sections that used BRIDGES with those that did not. Qualitative and quantitative measures were developed and used together with course grades and grade point averages. Interest and relevance in BRIDGES programming assignments was assessed with additional survey data from students and instructors. Results showed that students involved in BRIDGES projects demonstrated larger gains in knowledge of data structures compared to students enrolled in comparable course sections, as well as long-term benefits in their performance in four follow-on required courses. Survey responses indicated that some investment of time was needed to use BRIDGES, but the extra efforts were associated with several notable outcomes. Students and instructors had positive perceptions of the value of engaging in BRIDGES projects. BRIDGES can become a tool to get students more engaged in critical foundational courses, demonstrating relevance and context to today’s computational challenges.
Audience	Academic
Author	Burlinson, David Subramanian, Kalpathi Goolkasian, Paula Goncharow, Alec Saule, Erik Mcquaigue, Matthew Payton, Jamie
Author_xml	– sequence: 1 givenname: David surname: Burlinson fullname: Burlinson, David organization: Computer Science, UNC Charlotte – sequence: 2 givenname: Matthew surname: Mcquaigue fullname: Mcquaigue, Matthew organization: Computer Science, UNC Charlotte – sequence: 3 givenname: Alec surname: Goncharow fullname: Goncharow, Alec organization: Computer Science, UNC Charlotte – sequence: 4 givenname: Kalpathi orcidid: 0000-0002-7316-2735 surname: Subramanian fullname: Subramanian, Kalpathi email: krs@charlotte.edu organization: Computer Science, UNC Charlotte, Computer Science, Temple University – sequence: 5 givenname: Erik surname: Saule fullname: Saule, Erik organization: Computer Science, UNC Charlotte – sequence: 6 givenname: Jamie surname: Payton fullname: Payton, Jamie organization: Computer Science, Temple University – sequence: 7 givenname: Paula surname: Goolkasian fullname: Goolkasian, Paula organization: Psychological Sciences, UNC Charlotte
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Cites_doi	10.1109/TVCG.2011.185 10.1177/1745691616650284 10.1017/CBO9780511816833.029 10.1145/2157136.2157246 10.1145/2538862.2538941 10.1145/800031.808596 10.1145/1227504.1227466 10.1145/3159450.3170601 10.1145/2591708.2591752 10.1080/00461520.1991.9653139 10.1145/1592761.1592779 10.1145/1953163.1953338 10.1145/2839509.2844635 10.1145/1463673.1463684 10.1145/2676723.2677240 10.1145/2676723.2691875 10.1145/3017680.3017697 10.1145/274790.274310 10.1145/961290.961542 10.1145/971300.971328 10.1145/3408877.3439600 10.1145/1352135.1352255 10.1145/2591708.2591750
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Copyright	The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. COPYRIGHT 2024 Springer
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: COPYRIGHT 2024 Springer
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SubjectTerms	Algorithms Bridges Computer Appl. in Social and Behavioral Sciences Computer Science Computers and Education Education Educational Technology Grouping (Instructional Purposes) Information Systems Applications (incl.Internet) Quasiexperimental Design Required Courses School Holding Power Student Interests Student retention User Interfaces and Human Computer Interaction Visualization (Computers)
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