ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation

Abstract

Under the vigorous development of global anticipatory computing in recent years, there have been numerous applications of artificial intelligence (AI) in people’s daily lives. Learning analytics of big data can assist students, teachers, and school administrators to gain new knowledge and estimate learning information; in turn, the enhanced education contributes to the rapid development of science and technology. Education is sustainable life learning, as well as the most important promoter of science and technology worldwide. In recent years, a large number of anticipatory computing applications based on AI have promoted the training professional AI talent. As a result, this study aims to design a set of interactive robot-assisted teaching for classroom setting to help students overcoming academic difficulties. Teachers, students, and robots in the classroom can interact with each other through the ARCS motivation model in programming. The proposed method can help students to develop the motivation, relevance, and confidence in learning, thus enhancing their learning effectiveness. The robot, like a teaching assistant, can help students solving problems in the classroom by answering questions and evaluating students’ answers in natural and responsive interactions. The natural interactive responses of the robot are achieved through the use of a database of emotional big data (Google facial expression comparison dataset). The robot is loaded with an emotion recognition system to assess the moods of the students through their expressions and sounds, and then offer corresponding emotional responses. The robot is able to communicate naturally with the students, thereby attracting their attention, triggering their learning motivation, and improving their learning effectiveness.

The Influence of Academic Emotions on Learning Effects: A Systematic Review

“…Except for one observational study [ 53 ], both interventions agreed that, under the intervention of different learning materials and learning environments, the learning effect is significantly improved [ 52 , 53 ]. Previous studies have shown that, compared with other aspects of academic emotions, adaptive assisted learning systems tailored to learners’ characteristics have a more significant beneficial impact on students’ learning effects [ 62 , 63 ]. Most of the experiments involved high school and college students, and five of six intervention studies and three of four observational studies showed that academic emotional regulation improved learning effects.…”

The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm

“…Deep learning is a training method of machine learning and is the main mode of operation of artificial intelligence today. In this era of automation, the application of deep learning can be seen everywhere [12].…”

The Influence of Robot-Assisted Learning System on Health Literacy and Learning Perception

“…Liew et al [ 46 ] pointed out that the enthusiastic teaching model of virtual teachers can significantly enhance learning motivation and students’ willingness to use multimedia materials. Hsieh [ 33 ] considers that, compared with the traditional teacher-centered model, adding robot-assisted teaching can effectively increase the motivation of students to continue learning.…”

“…Compared with the traditional teacher-oriented mode, the appropriate use of robots for teaching aids in the teaching process can help learners improve their learning interest, attitude, motivation, and effectiveness [ 32 , 33 ]. The four steps of the ARCS motivation model [ 14 ] include: Attention.…”