Enriching Students' Combinatorial Reasoning through the Use of Loops and Conditional Statements in Python

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Bibliographic Details
Title: Enriching Students' Combinatorial Reasoning through the Use of Loops and Conditional Statements in Python
Language: English
Authors: Lockwood, Elise (ORCID 0000-0002-4118-338X), De Chenne, Adaline
Source: International Journal of Research in Undergraduate Mathematics Education. Oct 2020 6(3):303-346.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 44
Publication Date: 2020
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 1650943
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Thinking Skills, Programming Languages, Computer Science Education, Introductory Courses, Computation, Mathematics Skills, Concept Formation, Problem Solving, Instructional Effectiveness, Undergraduate Students
DOI: 10.1007/s40753-019-00108-2
ISSN: 2198-9745
Abstract: When solving counting problems, students often struggle with determining what they are trying to count (and thus what problem type they are trying to solve and, ultimately, what formula appropriately applies). There is a need to explore potential interventions to deepen students' understanding of key distinctions between problem types and to differentiate meaningfully between such problems. In this paper, we investigate undergraduate students' understanding of sets of outcomes in the context of elementary Python computer programming. We show that four straightforward program conditional statements seemed to reinforce important conceptual understandings of four canonical combinatorial problem types. We also suggest that the findings in this paper represent one example of a way in which a computational setting may facilitate mathematical learning.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1269141
Database: ERIC
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