Integrating Q.bo Robot in ESL: Developing Robot-Assisted Pedagogies for Enhanced Language Learning
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This paper explores innovative robotics applications within English as a Second Language (ESL) education through the development of three robot-assisted learning scenarios. The Q.bo robot, a consumer-oriented, open-source robotics platform made by TheCorpora, has an open-source design that can enable multisensory instruction in language learning and provide a flexible approach to implementing robotics in different teaching methods. In this regard, three learning scenarios are developed to show how the robot's versatility can potentially help in augmenting ESL instruction. The first scenario is vocabulary acquisition using ArUco card detection, in which Q.bo detects unique cards to display AR-based media. The second scenario is action-based instructions, which make the robot do actions using action-related cue cards. Lastly, an interactive quiz in ESL designed in Python allows the learners to interact with the robot and thus receive instant feedback. Using the robot's basic features as a starting point, despite the lack of recent technical updates and modern feature support from the developers, we successfully developed ESL-driven programs from scratch and demonstrated how we tested these scenarios. We also analyzed each learning scenario regarding educational effects, implementation complexity, and adaptability and compared each scenario's scalability, adaptability, and potential improvements. Our findings suggest that integrating robotics opens new ways for interaction and immersive opportunities in teaching ESL.
Abstract
This paper explores innovative robotics applications within English as a Second Language (ESL) education through the development of three robot-assisted learning scenarios. The Q.bo robot, a consumer-oriented, open-source robotics platform made by TheCorpora, has an open-source design that can enable multisensory instruction in language learning and provide a flexible approach to implementing robotics in different teaching methods. In this regard, three learning scenarios are developed to show how the robot's versatility can potentially help in augmenting ESL instruction. The first scenario is vocabulary acquisition using ArUco card detection, in which Q.bo detects unique cards to display AR-based media. The second scenario is action-based instructions, which make the robot do actions using action-related cue cards. Lastly, an interactive quiz in ESL designed in Python allows the learners to interact with the robot and thus receive instant feedback. Using the robot's basic features as a starting point, despite the lack of recent technical updates and modern feature support from the developers, we successfully developed ESL-driven programs from scratch and demonstrated how we tested these scenarios. We also analyzed each learning scenario regarding educational effects, implementation complexity, and adaptability and compared each scenario's scalability, adaptability, and potential improvements. Our findings suggest that integrating robotics opens new ways for interaction and immersive opportunities in teaching ESL.
참고문헌 (18)
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