TY - GEN
T1 - Visualization, assessment and analytics in data structures learning modules
AU - Mcquaigue, Matthew
AU - Burlinson, David
AU - Subramanian, Kalpathi
AU - Saule, Erik
AU - Payton, Jamie
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/2/21
Y1 - 2018/2/21
N2 - In recent years, interactive textbooks have gained prominence in an effort to overcome student reluctance to routinely read textbooks, complete assigned homeworks, and to better engage students to keep up with lecture content. Interactive textbooks are more structured, contain smaller amounts of textual material, and integrate media and assessment content. While these are an arguable improvement over traditional methods of teaching, issues of academic integrity and engagement remain. In this work we demonstrate preliminary work on building interactive teaching modules for data structures and algorithms courses with the following characteristics, (1) the modules are highly visual and interactive, (2) training and assessment are tightly integrated within the same module, with sufficient variability in the exercises to make it next to impossible to violate academic integrity, (3) a data logging and analytic system that provides instantaneous student feedback and assessment, and (4) an interactive visual analytic system for the instructor to see students’ performance at the individual, sub-group or class level, allowing timely intervention and support for selected students. Our modules are designed to work within the infrastructure of the OpenDSA system, which will promote rapid dissemination to an existing user base of CS educators. We demonstrate a prototype system using an example dataset.
AB - In recent years, interactive textbooks have gained prominence in an effort to overcome student reluctance to routinely read textbooks, complete assigned homeworks, and to better engage students to keep up with lecture content. Interactive textbooks are more structured, contain smaller amounts of textual material, and integrate media and assessment content. While these are an arguable improvement over traditional methods of teaching, issues of academic integrity and engagement remain. In this work we demonstrate preliminary work on building interactive teaching modules for data structures and algorithms courses with the following characteristics, (1) the modules are highly visual and interactive, (2) training and assessment are tightly integrated within the same module, with sufficient variability in the exercises to make it next to impossible to violate academic integrity, (3) a data logging and analytic system that provides instantaneous student feedback and assessment, and (4) an interactive visual analytic system for the instructor to see students’ performance at the individual, sub-group or class level, allowing timely intervention and support for selected students. Our modules are designed to work within the infrastructure of the OpenDSA system, which will promote rapid dissemination to an existing user base of CS educators. We demonstrate a prototype system using an example dataset.
KW - Algorithms
KW - Data structures
KW - Interactive textbook
KW - Learning analytics
KW - Student engagement
KW - Visualization
UR - https://www.scopus.com/pages/publications/85046103458
UR - https://www.scopus.com/pages/publications/85046103458#tab=citedBy
U2 - 10.1145/3159450.3159460
DO - 10.1145/3159450.3159460
M3 - Conference contribution
AN - SCOPUS:85046103458
T3 - SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education
SP - 864
EP - 869
BT - SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education
PB - Association for Computing Machinery, Inc
T2 - 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018
Y2 - 21 February 2018 through 24 February 2018
ER -