Example-based learning: Effects of different types of examples on student performance, cognitive load and self-efficacy in a statistical learning task

ABSTRACT

Previous research has indicated the disconnect between example-based research focusing on worked examples (WEs) and that focusing on modeling examples. The purpose of this study was to examine and compare the effect of four different types of examples from the two separate lines of research, including standard WEs, erroneous WEs, expert (masterly) modeling examples, and peer (coping) modeling examples, on student performance (knowledge retention, near transfer, and far transfer), cognitive load, and self-efficacy. One hundred and sixteen students participated in the study by undergoing computer-based instruction in one of the four versions differing in how examples were provided. The results showed that, overall, expert modeling examples were most effective in promoting knowledge retention, near transfer, and far transfer, while peer modeling examples were shown to be superior in fostering self-efficacy among the four different types of examples.

KEYWORDS:

• Example-based learning
• worked examples
• modeling examples
• cognitive load theory
• self-efficacy

Acknowledgments

The author would like to thank Tamara Van Gog for her valuable feedback on an early version of the paper. The author would also like to thank Justin Mathews, Anissa Pugh, and John Reynolds for their assistance in instructional material development and/or data collection.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes on contributor

Xiaoxia Huang is an assistant professor in Instructional Design at Western Kentucky University. Her research has been focused on designing optimum learning environments in various formal and informal settings across disciplines through research-based learning strategies and technologies. Her work has covered topics such as cognitive load theory, self-efficacy, distance education, and instructional design and technology.

Additional information

Funding

This work was supported by Office of Sponsored Program, co-supported by School of Teacher Education and College of Education and Behavioral Sciences, Western Kentucky University [RCAP#14-8014].