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Student Misconceptions of Dynamic Programming: A Replication Study

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Title: Student Misconceptions of Dynamic Programming: A Replication Study
Language: English
Authors: Shindler, Michael (ORCID 0000-0002-3365-1729), Pinpin, Natalia, Markovic, Mia, Reiber, Frederick, Kim, Jee Hoon, Carlos, Giles Pierre Nunez, Dogucu, Mine (ORCID 0000-0002-8007-934X), Hong, Mark, Luu, Michael, Anderson, Brian, Cote, Aaron, Ferland, Matthew, Jain, Palak, LaBonte, Tyler, Mathur, Leena, Moreno, Ryan, Sakuma, Ryan
Source: Computer Science Education. 2022 32(3):288-312.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 25
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Misconceptions, Programming, Computer Science Education, Replication (Evaluation), Algorithms, Undergraduate Students
DOI: 10.1080/08993408.2022.2079865
ISSN: 0899-3408
1744-5175
Abstract: Background and Context: 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. Objective: Our aim was to investigate whether the results of the previous work generalized to other universities and also to larger groups of students. Method: 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. Findings: 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. Implications: 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.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1358061
Database: ERIC
FullText Text:
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PubType: Academic Journal
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  Data: Student Misconceptions of Dynamic Programming: A Replication Study
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  Data: <searchLink fieldCode="AR" term="%22Shindler%2C+Michael%22">Shindler, Michael</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3365-1729">0000-0002-3365-1729</externalLink>)<br /><searchLink fieldCode="AR" term="%22Pinpin%2C+Natalia%22">Pinpin, Natalia</searchLink><br /><searchLink fieldCode="AR" term="%22Markovic%2C+Mia%22">Markovic, Mia</searchLink><br /><searchLink fieldCode="AR" term="%22Reiber%2C+Frederick%22">Reiber, Frederick</searchLink><br /><searchLink fieldCode="AR" term="%22Kim%2C+Jee+Hoon%22">Kim, Jee Hoon</searchLink><br /><searchLink fieldCode="AR" term="%22Carlos%2C+Giles+Pierre+Nunez%22">Carlos, Giles Pierre Nunez</searchLink><br /><searchLink fieldCode="AR" term="%22Dogucu%2C+Mine%22">Dogucu, Mine</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-8007-934X">0000-0002-8007-934X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hong%2C+Mark%22">Hong, Mark</searchLink><br /><searchLink fieldCode="AR" term="%22Luu%2C+Michael%22">Luu, Michael</searchLink><br /><searchLink fieldCode="AR" term="%22Anderson%2C+Brian%22">Anderson, Brian</searchLink><br /><searchLink fieldCode="AR" term="%22Cote%2C+Aaron%22">Cote, Aaron</searchLink><br /><searchLink fieldCode="AR" term="%22Ferland%2C+Matthew%22">Ferland, Matthew</searchLink><br /><searchLink fieldCode="AR" term="%22Jain%2C+Palak%22">Jain, Palak</searchLink><br /><searchLink fieldCode="AR" term="%22LaBonte%2C+Tyler%22">LaBonte, Tyler</searchLink><br /><searchLink fieldCode="AR" term="%22Mathur%2C+Leena%22">Mathur, Leena</searchLink><br /><searchLink fieldCode="AR" term="%22Moreno%2C+Ryan%22">Moreno, Ryan</searchLink><br /><searchLink fieldCode="AR" term="%22Sakuma%2C+Ryan%22">Sakuma, Ryan</searchLink>
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: <searchLink fieldCode="DE" term="%22Misconceptions%22">Misconceptions</searchLink><br /><searchLink fieldCode="DE" term="%22Programming%22">Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+Education%22">Computer Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Replication+%28Evaluation%29%22">Replication (Evaluation)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink>
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  Data: 10.1080/08993408.2022.2079865
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  Data: 0899-3408<br />1744-5175
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  Data: Background and Context: 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. Objective: Our aim was to investigate whether the results of the previous work generalized to other universities and also to larger groups of students. Method: 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. Findings: 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. Implications: 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.
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