Student misconceptions of dynamic programming: a replication study

We replicated and expanded on previous work about how well students learn dynamic programming, a difficult topic for students in algorithms class. Their study interviewed a number of students at one university in a single term. We recruited a larger sample size of students, over several terms, in bo...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computer science education Jg. 32; H. 3; S. 288 - 312
Hauptverfasser: Shindler, Michael, Pinpin, Natalia, Markovic, Mia, Reiber, Frederick, Kim, Jee Hoon, Carlos, Giles Pierre Nunez, Dogucu, Mine, Hong, Mark, Luu, Michael, Anderson, Brian, Cote, Aaron, Ferland, Matthew, Jain, Palak, LaBonte, Tyler, Mathur, Leena, Moreno, Ryan, Sakuma, Ryan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Norwood Routledge 03.07.2022
Taylor & Francis Ltd
Schlagworte:
Algorithms
algorithms education
Colleges & universities
Computer Science Education
Dynamic programming
Misconceptions
Private Colleges
Problem solving
Programming
Replication (Evaluation)
Replication study
Students
Undergraduate Students
Universities
ISSN:0899-3408, 1744-5175
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We replicated and expanded on previous work about how well students learn dynamic programming, a difficult topic for students in algorithms class. Their study interviewed a number of students at one university in a single term. We recruited a larger sample size of students, over several terms, in both large public and private universities as well as liberal arts colleges. Our aim was to investigate whether the results of the previous work generalized to other universities and also to larger groups of students. We interviewed students who completed the relevant portions of their algorithms class, asking them to solve problems. We observed the students' problem solving process to glean insight into how students tackle these problems. We found that students generally struggle in three ways, "technique selection," "recurrence building," and "inefficient implementations." We then explored these themes and specific misconceptions qualitatively. We observed that the misconceptions found by the previous work generalized to the larger sample of students. Our findings demonstrate areas in which students struggle, paving way for better algorithms education by means of identifying areas of common weakness to draw the focus of instructors.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0899-3408
1744-5175
DOI:10.1080/08993408.2022.2079865