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The 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.

ASolver, 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.

Results 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.

This 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.