Automated generators of examples and problems for studying computer algorithms A study on students’ decisions

PurposeThe purpose of this study is to investigate students’ decisions in example-based instruction within a novel self-regulated learning context. The novelty was the use of automated generators of worked examples and problem-solving exercises instead of a few handcrafted ones. According to the cog...

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Vydáno v:Interactive technology and smart education Ročník 16; číslo 3; s. 204 - 218
Hlavní autoři: Lui, Andrew Kwok-Fai, Maria Hiu Man Poon, Hong Wong, Raymond Man
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bingley Emerald Group Publishing Limited 16.09.2019
Témata:
Algorithms
Cognitive ability
Computer science
Decision making
Decisions
Educational Environment
Educational Objectives
Expected values
Experts
Generators
Learning Strategies
Machine learning
Memory
Operating systems
Paging systems
Performance measurement
Problem solving
Scaffolding
School environment
Science Curriculum
Students
Teaching Methods
ISSN:1741-5659, 1758-8510
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Shrnutí:PurposeThe purpose of this study is to investigate students’ decisions in example-based instruction within a novel self-regulated learning context. The novelty was the use of automated generators of worked examples and problem-solving exercises instead of a few handcrafted ones. According to the cognitive load theory, when students are in control of their learning, they demonstrate different preferences in selecting worked examples or problem solving exercises for maximizing their learning. An unlimited supply of examples and exercises, however, offers unprecedented degree of flexibility that should alter the decisions of students in scheduling the instruction.Design/methodology/approachASolver, an online learning environment augmented with such generators for studying computer algorithms in an operating systems course, was developed as the experimental platform. Students’ decisions related to choosing worked examples or problem-solving exercises were logged and analyzed.FindingsResults show that students had a tendency to attempt many exercises and examples, especially when performance measurement events were impending. Strong students had greater appetite for both exercises and examples than weak students, and they were found to be more adventurous and less bothered by scaffolding. On the other hand, weak students were found to be more timid or unmotivated. They need support in the form of procedural scaffolding to guide the learning.Originality/valueThis study was one of the first to introduce automated example generators for studying an operating systems course and investigate students’ behaviors in such learning environments.
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ISSN:1741-5659
1758-8510
DOI:10.1108/ITSE-10-2018-0091