• Can We Do Better? A Classifica...

Treffer: Can We Do Better? A Classification of Algorithm Run-Time-Complexity Improvement Using the SOLO Taxonomy

Title:
Can We Do Better? A Classification of Algorithm Run-Time-Complexity Improvement Using the SOLO Taxonomy
Language:
English
Authors:
Aronshtam, Lior (ORCID 0000-0002-7211-2795), Shrot, Tammar (ORCID 0000-0002-9611-2765), Shmallo, Ronit (ORCID 0000-0002-1783-6109)
Source:
Education and Information Technologies. Sep 2021 26(5):5851-5872.
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:
22
Publication Date:
2021
Document Type:
Fachzeitschrift Journal Articles<br />Reports - Research
Education Level:
Higher Education
Postsecondary Education
Descriptors:
Difficulty Level, Computer Software, Taxonomy, Coding, Cognitive Ability, Undergraduate Students, Engineering Education, Grades (Scholastic)
DOI:
10.1007/s10639-021-10532-0
ISSN:
1360-2357
Entry Date:
2021
Accession Number:
EJ1308861
Database:
ERIC

Weitere Informationen

Improving code while preserving its functionality is a common task in the hi-tech industry. Yet students have difficulties improving an algorithm's run-time complexity by an order of magnitude. The paper focuses on assessing students' abilities in this area. We designed a Structure of the Observed Learning Outcome (SOLO) taxonomy, using software quality factors, to assess students' cognitive ability while improving complexity. The research was conducted with college students studying for their bachelor's degree in engineering. We established a classification based on their solutions for a given task. Later, we used the same task to validate our classification with another group of engineering students. We then compared the previous average grades of the second group of students with their SOLO levels. The results show that the higher the students' previous average grades, the greater the probability that their solutions would be classified at higher taxonomic levels. These results indicate that our SOLO classification is indeed accurate. The paper presents our novel SOLO taxonomic levels for tasks improving run-time complexity and offers several suggestions to assist students and enhance the teaching process.

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