Social robots and gamification for technology supported learning: An empirical study on engagement and motivation

https://doi.org/10.1016/j.chb.2021.106792Get rights and content

Highlights

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    Prototyping of a learning environment.

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    Addition of social robot and gamification elements in a plug-and-play functionality.

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    2x2 design for addition/absence of a social robot and gamification elements.

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    Investigation of effects on engagement and motivation in an interactive study.

Abstract

Enhancing learning scenarios with social robots, as well as gamification elements, has been shown to positively influence motivation, engagement, or even both. However, they have not been combined in a learning environment. For this contribution, we created a learning environment for students in higher education and implemented additions (social robot and gamification) based on guidelines for gamification in learning scenarios, and research on pedagogical agents. Using a 2x2 design for systematic investigation of gamification elements and social robots, we tested the impact of our learning environment on motivation and engagement across four conditions: with a social robot, gamification elements, both or neither. We found no significant increase in engagement or motivation when adding gamification elements or the social robot. Quite contrary to our expectations, we found an interaction effect when combining both additions, showing lower engagement. Based on our results and former research, we discuss possible reasons for this finding and potential improvements for future research.

Introduction

Self-directed learning is increasingly relevant within today's society. This is reflected in a trend to digitalize learning, resulting in an ever growing supply of technology supported learning, especially in the areas of data science, programming and web-design (Allen & Seaman, 2013; Goodman, Melkers, & Pallais, 2019; Rodriguez, 2012). These courses can convey self-selected useful skills, facilitate personal growth and might support career advancement. However, self-directed learning requires the learner to motivate themselves to complete the courses, and to engage with the learning material. Both are crucial since engagement (Rodgers, 2008) as well as motivation (Law, Lee, & Yu, 2010) are predictors for successful learning. To increase engagement and motivation in self-directed learning scenarios, different approaches were used in the past, including the addition of gamification or robotic tutors.

In this contribution, we combine social robots and gamification for the first time in an interactive learning experience. To this end, we describe the implementation of both aspects in a learning environment in plug-and-play functionality. In a controlled study, we investigate their effects on motivation and engagement, in relationship to an existing university course. Thereby, the addition of a social robot and gamification are tested in isolation, as well as in combination.

Section snippets

Related work and theoretical background

For our endeavor, we need to take an interdisciplinary approach, combining knowledge from various areas of motivational psychology, learning, entertainment and computer-science. In the following subsections, we introduce the theoretical background and related implementations, necessary for our approach.

Implementation of the learning environment

To investigate whether the integration of gamification elements and a social robot has an impact on the learner's motivation and engagement, an extensible learning environment was created. Our implementation makes use of the modeling software Visual SceneMaker (Gebhard, Mehlmann, & Kipp, 2012) tied to a learning framework including a HTML environment and a Reeti2 Robot (Deublein et al., 2018). The learning material is presented to the user on a screen and

User study

We conducted a user study with a 2 x 2 between-subject design. The absence or integration of the social robot, and absence or integration of the gamification elements, resulted in four conditions:

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    Basic Condition (BC): No addition was made to the learning environment.

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    Robot Condition (RC): The social robot was added to the learning environment.

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    Gamification Condition (GC): The gamification elements were added to the learning environment.

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    Combined Condition (CC): The social robot and the

Results

For further analysis all quantitative data was imported into the stats program ‘SPSS’ using Version 25. An alpha-level of 0.05 was applied for all statistical tests.

Discussion

There were no significant increases, in either motivation or engagement, due to either the gamification elements or the social robot alone. Therefore, our hypotheses of a positive effect on motivation or engagement by either of the additions were not supported. The learners might have been too focused on the videos, which were present in all conditions because they conveyed the learning content. The positive reactions to the control condition might indicate that the learning material itself

Conclusion

For the present contribution, we implemented a learning environment based on social robot research and guidelines for implementing gamification with the aim to enhance motivation and engagement for students in higher education. In the learning environment, we used videos to convey the learning content, multiple-choice questions to test the participants’ knowledge, and positively formulated feedback to support the learners. The learning experience can be augmented by a social robot, or

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    These authors contributed equally to this work.

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