Learning English as a second language has evolved through innovative methods, with robot-assisted teaching gaining traction as a promising approach. The integration of gamification and technology in language learning aims to demonstrate higher levels of success and learning gains to be adopted by education systems worldwide. To evaluate the impact of robot-assisted English language teaching, researchers from the University of Nazarbayev conducted a comparative study, comparing it to a standard teaching method.
Robot assisted language learning method used in the study:
The study focused on using picture matching tasks to teach English as a second language to children, incorporating QTrobot, an expressive, toddler-sized humanoid robot. An adaptive algorithm was developed to assess each student’s success in answering questions and tailor the sequence and number of posed questions according to their individual progress. QTrobot’s role was to interact with students, respond to their answers, and provide mental and emotional support through facial expressions, gestures, and verbal communication.
Using a gamified picture matching task led by QTrobot, and providing appropriate feedback using facial expression, gesture and verbal communication to support students emotionally and mentally in learning language
Study participants and outcome measurements:
In the experiment, 17 children aged 7 to 8 years old participated in a within-subject study, undergoing pre-test assessments before the interventions and post-test evaluations after the study. The learning outcomes were assessed based on pronunciation, spelling, and the children’s mood, measured using a funometer.
To evaluate the language gain resulted from the robot assisted intervention, Pre-test and post-test were done focusing on evaluating pronunciation and spelling.
Robot assisted language learning results and impact:
The results were promising, as children found working with QTrobot and its expressive features encouraging and cheerful. Moreover, the study revealed a statistically significant difference in learning gains between the question priority algorithm and the standard game.
Leveraging the potential of QTrobot, this research offers a dynamic and engaging approach to enhance language learning experiences, paving the way for more efficient language education methodologies. With growing evidence supporting the effectiveness of robot-assisted language learning, the study highlights the potential of prioritization techniques in educational robotics for language acquisition, bringing exciting prospects to the field of language education.
Reference:
The study was done in Department of Robotics and Mechatronics, Nazarbayev University
The results of the study were presented in HRI ’23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction March 2023 Pages 711–715
More information about the study can be found here: https://dl.acm.org/doi/abs/10.1145/3568294.3580179
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