Paper Number

1576

Paper Type

Short

Description

E-Learning, as a prevalent instructional approach in the midst of the COVID-19 pandemic, is often criticized for reducing motivation and increasing mental fatigue among learners. Despite the attractiveness of various gamification designs to resolve these issues, there still exists a lack of comprehensive and integrated understanding of the pedagogic effectiveness of gamification rewards. Motivated thus, this study assesses and compares four different types of gamification rewards: unexpected-hedonic rewards, expected-hedonic rewards, unexpected-utilitarian rewards, and expected-utilitarian rewards. Drawing from self-determination theory and opportunity cost model of subjective effort and task performance, this study evaluates the effect of gamification reward type on learning motivation and mental fatigue. The effect of gamification reward type will be examined in a longitudinal field experiment in an introductory undergraduate computer science course.

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Dec 12th, 12:00 AM

The Effects of Gamification Rewards in E-Learning: A Longitudinal Field Study on Motivation and Mental Fatigue

E-Learning, as a prevalent instructional approach in the midst of the COVID-19 pandemic, is often criticized for reducing motivation and increasing mental fatigue among learners. Despite the attractiveness of various gamification designs to resolve these issues, there still exists a lack of comprehensive and integrated understanding of the pedagogic effectiveness of gamification rewards. Motivated thus, this study assesses and compares four different types of gamification rewards: unexpected-hedonic rewards, expected-hedonic rewards, unexpected-utilitarian rewards, and expected-utilitarian rewards. Drawing from self-determination theory and opportunity cost model of subjective effort and task performance, this study evaluates the effect of gamification reward type on learning motivation and mental fatigue. The effect of gamification reward type will be examined in a longitudinal field experiment in an introductory undergraduate computer science course.

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