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

sustainability

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

Yi-Zeng Hsieh 1,2,3 , Shih-Syun Lin 4,* , Yu-Cin Luo 1, Yu-Lin Jeng 5 , Shih-Wei Tan 1,*, Chao-Rong Chen 6 and Pei-Ying Chiang 7

1 Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan; [email protected] (Y.-Z.H.); [email protected] (Y.-C.L.) 2 Institute of Food Safety and Risk Management, National Taiwan Ocean University, Keelung City 202, Taiwan 3 Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan 4 Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan 5 Department of Information Management, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan; [email protected] 6 Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan; [email protected] 7 Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106, Taiwan; [email protected] * Correspondence: [email protected] (S.-S.L.); [email protected] (S.-W.T.)

Received: 3 March 2020; Accepted: 17 June 2020; Published: 12 July 2020

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.

Keywords: sustainable learning; robot-assisted teaching; ARCS model; anticipatory computing; artiﬁcial intelligence; emotional big data

Sustainability 2020, 12, 5605; doi:10.3390/su12145605 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 5605 2 of 17

1. Introduction Anticipatory computing (AC) has become increasingly popular in technological applications and computer science. The development of AC can be dated back to 1985 and the work of Robert Rosen [1]. Anticipatory system applications consist of sensors, human–computer interactions (HCI), artificial intelligence (AI), and content estimation [2–6]. There are four major AC capabilities: sensing, context prediction, content estimation, and smart behavior [7–10]. The authors of [10] proposed a machine learning approach for controlling the flow of products from their respective storage locations to the packing area in a retail distribution center. Education is the most important factor in realizing sustainable life, through which people gain new knowledge and thrive in academic achievements. Education has led to the rapid global development of science and technology, the popularization of the internet, and the extensive application of robotics and AC. The authors of [11] presented an innovative approach to promote AI in Education 4.0. The authors of [12] studied an anticipatory agent based on the Anticipatory Learning Classifier System (ACS) for learning in changing environments. The authors of [13] proposed a fast machine-learning-based reading algorithm to identify the important elements of a story that are handed down despite temporal and spatial differences. The authors of [14] demonstrated that machine learning can be used as a tool to successfully conduct interdisciplinary education at the middle school level. The authors of [15] proposed a small piece of a set of modern tools for informatics and electrical engineering education. The authors of [16] presented impressive steps of the use of data analysis and prediction. The authors of [17] showed the outcomes of a research and development project aimed at a setup for predictive learning analytics. Robots are being used for education more frequently in recent years. In addition, robotics has moved toward universal education, such as robots for sign language counseling [18] and robotics for kids [19]. These are only parts of the ongoing research in recent years, but representing robotics research, invention, and application of technology in the scope of education. Robotics involves a wide range of knowledge, technology, and comprehensive aspects, thus resulting in a different robotics education. According to robot education experts’ research and practice, the applications of robot education can be divided into five types, which will be detailed in the following sections.

1.1. Robot Subject Instruction (RSI) Robot Subject Instruction (RSI) refers to robotics as a science, at all levels of various types of education and specialized courses, so that all students generally grasp the basic knowledge of robotics and its related basic skills. The teaching objectives are as follows: Knowledge goals: To understand the basic knowledge of robot software engineering, hardware structure, functions, and applications. Skill goals: To be capable of robot programming and compilation, the assembly of a variety of robots with practical functions, the use and maintenance of robots and smart appliances, and the independent development of software-controlled robots. Emotional goals: to cultivate an interest in AI technology, and to truly recognize the role of intelligent robots in social progress and economic development [20]. Robot education has become part of the subject curriculum, especially for the teaching experience of primary and secondary school teachers, as well as their equipment, venues, and activity funding, and it thus presents great challenges.

1.2. Robot-Assisted Instruction (RAI) Robot-Assisted Instruction (RAI) refers to robots as the main teaching media and tools for teaching and learning activities. Compared with the robot courses, robot-assisted teaching is characterized by the fact that it is not the main teaching tool, but rather, an auxiliary tool [21]. It acts as a helper, a companion role that ordinary teaching aids cannot play, due to their lack of intelligence. Similar to RAI, an environment, or intelligent equipment, it plays a teaching are the concepts of Robot-Assisted Sustainability 2020, 12, x FOR PEER REVIEW 3 of 18

Robot-Assisted Learning (RAL), Robot-Assisted Training (RAT), Robot-Assisted Education (RAE), and Robot-Based Education (RBE).

1.3. Robot-Managed Instruction (RMI) Robot-Managed Instruction (RMI) refers to the role that is played by robots in the planning, organization, coordination, direction, and control of classroom teaching, educational affairs, Sustainability 2020, 12, 5605 3 of 17 finances, personnel, equipment, and other teaching management activities [22]. Robot management is characterized by its automation and intelligence is related to its organizational forms, its organizationalLearning efficiency, (RAL), Robot-Assisted etc. It fills Training a kind (RAT), of Robot-Assistedauxiliary management Education (RAE), function. and Robot-Based Therefore, robot Education (RBE). management teaching has become a welcome companion for many educators. 1.3. Robot-Managed Instruction (RMI) 1.4. Robot-RepresentedRobot-Managed Routine Instruction (RRR) (RMI) refers to the role that is played by robots in the planning, Robotsorganization, have an coordination, analogous direction, type of and human control wisdom of classroom, as teaching,well as educationala partly human affairs, finances,function; thus, personnel, equipment, and other teaching management activities [22]. Robot management is they cancharacterized replace teachers by its automation and students and intelligence in dealing is related with tosome its organizational things, other forms, than its organizational classroom teaching [18], sucheffi asciency, the etc.borrowing It fills a kind of ofbooks, auxiliary note-taking, management meals, function. playing Therefore, games, robot managementetc. The purpose teaching of using the agencyhas becomefunctions a welcome of robots companion is to forimprove many educators. their learning-related functions and to promote the improvement of learning efficiency and quality. 1.4. Robot-Represented Routine (RRR)

1.5. Robot-DirectedRobots Instruction have an analogous (RDI) type of human wisdom, as well as a partly human function; thus, they can replace teachers and students in dealing with some things, other than classroom Theteaching highest [18 level], such of as robot the borrowing application of books, is in note-taking, the field meals, of education. playing games, At this etc. Thelevel, purpose the robot of is, in many ways,using theno agency longer functions a supporting of robots isactor; to improve instea theird, learning-relatedit becomes the functions master and toof promotethe organization, the implementationimprovement and of management learning efficiency of teaching. and quality. Robotics is the object of our study. AI, combined with virtual reality1.5. Robot-Directed technology, Instruction and multimedia (RDI) technology, makes it a possible alternative for human teachers. It Theis highestextremely level ofimportant robot application to determi is in the fieldne ofhow education. it can At thisbecome level, themore robot in is, inline many with the developmentways, no of longer education. a supporting actor; instead, it becomes the master of the organization, implementation Accordingand management to the five of teaching. robot education Robotics is classificatio the object ofns our above, study. AI,our combined system adopted with virtual the reality RAI method to assist technology,the teacher and in the multimedia classroom technology, and to detect makes itand a possible identify alternative the emotions for human of students. teachers. Our It is research adopts aextremely big data important emotional to determine dataset how (the it canGoogle become facial more expression in line with the comparison development dataset), of education. combined According to the five robot education classifications above, our system adopted the RAI method with theto AI assist emotion the teacher software in the classroomto design and an toemotion detect and detection identify the system. emotions We of aim students. to improve Our research the learning environmentadopts of a bigstudents data emotional by improving dataset (the their Google intere facialst, expression motivation comparison and effective dataset), learning. combined withOur system adopts thethe AI AI emotion software software, to design which an consists emotion of detection foundational system. data We aim such to as improve 9 M + thefaces, learning 87 countries, 5.1 B facialenvironment frames and of students six years’ by improvingof video data, their interest, and it is motivation described and as e fffollows:ective learning. firstly, Our the systemfacial features are detected,adopts and the AI secondly, emotion software, emotion which recognition consists of ad foundationalopts deep data learning such as 9to M identify+ faces, 87 human countries, emotions. 5.1 B facial frames and six years’ of video data, and it is described as follows: firstly, the facial features The emotionare detected, AI lists and all secondly, the emotion emotion recognitionscores, such adopts as deepanger, learning contempt, to identify disgust, human emotions.fear, happiness, neutrality,The sadness emotion AI and lists allsurprise, the emotion and scores, the suchhighest as anger, score contempt, reflects disgust, the user’s fear, happiness, emotions, neutrality, as shown in Figure 1.sadness and surprise, and the highest score reflects the user’s emotions, as shown in Figure1.

FigureFigure 1. 1.OurOur emotion emotion AIAI software software results. results

Moreover, in recent years, many schools have adopted digital teaching platforms for curriculum Moreover,assistance in [23 recent]. The years, purpose many is to schools use the curriculumhave adopted to enable digital teachers teaching and studentsplatforms to employfor curriculum assistancecommunication [23]. The purpose and information is to transmissionuse the curricul outsideum the classroom,to enable as teachers well as to facilitateand students time-saving. to employ communication and information transmission outside the classroom, as well as to facilitate time- saving. However, there is still no effective way of improving the motivation of the class and the Sustainability 2020, 12, 5605 4 of 17

Sustainability 2020, 12, x FOR PEER REVIEW 4 of 18 However, there is still no effective way of improving the motivation of the class and the effectiveness ofeffectiveness learning in theof learning physical in classroom the physical unless classroom the teacher’s unless classroom the teacher’s teaching classroom method teaching is interesting method and is attractiveinteresting enough and attractive for the students enough tofor keep the focused;students ifto a keep teacher focused; cannot ifdo a teacher this, most cannot students do this, may most feel thatstudents the class may isfeel boring, that the or theyclass willis boring, not understand or they will the not purpose understand of learning, the purpose which of willlearning, lead towhich low participationwill lead to low in theparticipation classroom in and the poor classroom learning and eff ectiveness.poor learning effectiveness. ToTo promotepromote thethe interactioninteraction ofof collaborativecollaborative learninglearning inin thethe classroomclassroom andand thethe adaptationadaptation and understandingunderstanding ofof thethe classroomclassroom content,content, we intend to design a robotrobot that can be used for teaching, andand thatthat can can cooperate cooperate with with the the teachers teachers in the in classroom the classroom and interact and interact with students, with students, by using by di ffusingerent pronunciationsdifferent pronunciations and intonations and intonations to assist them to assist in their them class in their learning class [ 24learning]. The robot[24]. The that robot we use that is thewe ASUSuse is Zenbothe ASUS robot Zenbo and Zenbo robot scratchand Zenbo programming scratch programming (Figure2). Zenbo (Figure programming 2). Zenbo programming is scratch-based is codingscratch-based that is usedcoding for that children is used and for it children is easy to and program it is easy by graphto program block. by The graph students block. can The use students Zenbo Scratchcan use toZenbo code Scratch their own to code Zenbo their interactive own Zenbo animations. interactive animations. Figure2 shows Figure our 2 interactiveshows our interactive animated Zenboanimated scratch Zenbo code. scratch In this code. system, In this we system, also added we thealso T&S added (Teacher the T&S & Students) (Teacher mobile& Students) platform mobile and theplatform ARCS and teaching the ARCS method teaching that wasmethod proposed that was by prop Kellerosed [8]. by The Keller goal [8]. is The for thegoal robot is for to the help robot both to thehelp teachers both the in teachers the classroom in the classroom or the students or the studen after class.ts after Robots class. can Robots be used can be to used teach to middle teach middle school students,school students, where teachers where canteachers use them can use to organize them to their organize teaching their courses teaching and courses let students and interactlet students with them,interact to with enhance them, their to enhance interest andtheir willingness interest and to wi learnllingness mathematics. to learn Themathematics. final goal The of our final approach goal of isour for approach students tois for improve students their to motivation improve their and motivation learning outcomes. and learning Our proposedoutcomes. digital Our proposed learning system,digital learning which is system, based on which emotion is based big data, on emotio can helpn big the data, teacher can to help notice the the teacher student’s to notice learning the performance.student’s learning Our work performance. is significant Our in work the followingis significant four in ways: the following Firstly, according four ways: to bigFirstly, data according feedback, ourto big system data canfeedback, improve our the system performance can improve of both the the pe teachersrformance and of the both students. the teachers Secondly, and the according students. to theSecondly, feedback according of students to the and feedback the big data, of students the teacher and can the analyze big data, each the student’s teacher learning can analyze statement. each Thirdly,student’s teachers learning are statement. able to track Thirdly, the students’ teachers learningare able patterns.to track the Finally, students’ our system learning can patterns. retrieve betterFinally, insights our system from can the retrieve big data better learning insights process. from Our the systembig data adopts learning Emotion process. AI Our as a system feedback adopts tool toEmotion detect theAI as students’ a feedback facial tool expressions, to detect the and studen it recordsts’ facial the expressions, students’ facial and expressions,it records the as students’ well as theirfacial learning expressions, features as well data. as Iftheir we canlearning collect features more anddata. more If we data can oncollect learning more features, and more this data could on improvelearning thefeatures, educator’s this could teaching improve model, the and educat we couldor’s teaching predict the model, students’ and learningwe could performance, predict the usingstudents’ machine learning learning performance, or the deep using learning machine tool. learning or the deep learning tool.

FigureFigure 2.2. TheThe ZenboZenbo scratchscratch programming.programming

2.2. RelatedRelated WorkWork InIn thethe pastpast fewfew years,years, muchmuch researchresearch hashas focusedfocused onon collaborativecollaborative e-learning,e-learning, asas itit hashas becomebecome anan importantimportant cornerstone of the the e-learning e-learning system. system. The The digital digital learning learning platform platform enables enables students students to study together, regardless of their geographic location or time limits [25,26]. These educational environments are designed and committed to encouraging both personal and group work in all areas Sustainability 2020, 12, 5605 5 of 17 Sustainability 2020, 12, x FOR PEER REVIEW 5 of 18 to study together, regardless of their geographic location or time limits [25,26]. These educational of teaching, as well as to make collaborative learning more efficient, while modern learning has also environments are designed and committed to encouraging both personal and group work in all areas begun to use AI as a suitable solution. There are currently quite a few collaborative learning systems of teaching, as well as to make collaborative learning more eﬃcient, while modern learning has also that are used to solve problems; for example, in English teaching in primary schools, in high school begun to use AI as a suitable solution. There are currently quite a few collaborative learning systems vocational courses, and even in the execution of business operations [27]. However, most of these that are used to solve problems; for example, in English teaching in primary schools, in high school studies focus on electronic collaborative learning platforms in order to make them a conduit for users vocational courses, and even in the execution of business operations [27]. However, most of these to learn online, to have discussions with each other and to access new knowledge. Today, there are studies focus on electronic collaborative learning platforms in order to make them a conduit for users already well-functioning and user-friendly digital learning management systems, such as Moodle, to learn online, to have discussions with each other and to access new knowledge. Today, there are Edmodo and Blackboard, and Massively Open Online Courses (MOOCs) [26] (see Figure 3). already well-functioning and user-friendly digital learning management systems, such as Moodle, Edmodo and Blackboard, and Massively Open Online Courses (MOOCs) [26] (see Figure3).

FigureFigure 3.3. Digital learning platform:platform: Moodle [[26]26].

A robot is usedused byby thethe AichiAichi NagoyaNagoya University School of Engineering in Japan as a classroom teachingteaching aid [[24].24]. It It uses uses the the voice voice of of the robot, intonation and facial expression changes to assist students and teachersteachers withwith classroomclassroom interaction.interaction. This This will will change change the the traditional traditional model model of of the classroom that has has always always been been led led by by teachers teachers in in th thee past; past; instead, instead, the the robot robot will will help help the the teacher teacher to toteach teach and and the the students students to tolearn learn in in the the classroom. classroom. Ro Robotsbots are are also also used used to to assist assist pupils pupils with with their languagelanguage exercisesexercises [28[28].]. These These cases cases have have been been statistically statistically analyzed, analyzed, and and the the results results have have proven proven that suchthat such practices practices can attract can attract the attention the attention of the studentsof the students and improve and improve their attitudes their attitudes towards learning.towards Thelearning. big data The is big integrated data is into integrated the human into context the huma in manyn context applications in many on applications the internet. onLytras the etinternet. al. [29] analyzedLytras et theal. [29] main analyzed aspects ofthe big main data aspects and data of big analytics data and research, data analytics and provided research, metaphors and provided for the future.metaphors Drawing for the from future. the detailed Drawing big datafrom analysis the detail of theed outcomesbig data ofanalysis an international of the outcomes pilot study of [ 30an], theyinternational show-cased pilot even study the [30], most they educated show-cased users of even smart the city most services. educated The multimodalusers of smart emotion city services. analysis isThe a very multimodal popular emotion research topicanalysis that is is a based very popular on big data research methods, topic semantic that is based rules andon big machine data methods, learning, bysemantic using dirulesﬀerent and techniques machine learning, [31]. by using different techniques [31]. Currently, there there are are convenient convenient online online platforms platforms that help that students help students to learn after to learn class. after However, class. However,if classroom if classroomteaching involvesteaching robot involves participation, robot participation, which is our which plan, isit would our plan, change it would the status change of theteachers status and of teachers students and in students the classroom, in the classroom, and it would and it increase would increase the students’ the students’ sense senseof fun of in fun the in theclassroom classroom as well as well as a as feeling a feeling of ofnovelty. novelty. We We look look forward forward to to using using robots robots to to increase increase classroom participation,participation, toto assistassist teachers teachers in in helping helping students studen tots answerto answer questions, questions, to solve to solve problems problems in the in class, the andclass, to and enhance to enhance learning learning motivation moti andvation eﬀ ectiveness.and effectiveness. Therefore, Therefore, based on based the digital on the teaching digital teaching platform thatplatform can be that used can for be learning used for and learning having and discussions having discussions with, and among,with, and students among, outside students the outside classroom, the andclassroom, with the and help with of classroom-assistedthe help of classroom-assisted robots, we aim robots, to combine we aim the to two,combine in order the totwo, create in order a better to waycreate for a studentsbetter way to enhancefor students their to willingness enhance their to learn willingness and to improveto learn theirand to learning improve outcomes. their learning outcomes.

3. The Research Methods Sustainability 2020, 12, x FOR PEER REVIEW 6 of 18

The goal of this research is to apply anticipatory computing to robots, based on AI, to improve children’s learning by increasing their motivation in the classroom. Robots will become teachers’ assistants and help them to teach in the classroom. The integration of robots in the classroom can provide interactive teaching, with the teachers asking questions, discussing problems and helping students to find answers to their problems, stimulating the students’ enthusiasm and motivation for learning, improving the long-standing teacher-oriented teaching with passive students in the past, Sustainability 2020, 12, 5605 6 of 17 and developing the their high-level thinking skills. With the help of robots, teachers can use the class time to teach more3. The effectively. Research Methods Students can also improve the ways and attitudes toward self-learning because of changes in teaching methods, namely, the interaction between people and robots [3], The goal of this research is to apply anticipatory computing to robots, based on AI, to improve thereby improvingchildren’s the learning learning by increasing outcomes their motivationand havi inng the classroom.a positive Robots impact will become on future teachers’ education and research. assistants and help them to teach in the classroom. The integration of robots in the classroom can provide interactive teaching, with the teachers asking questions, discussing problems and helping students to find answers to their problems, stimulating the students’ enthusiasm and motivation 4. A Mobile Networkfor learning, Exchange improving thePlatform long-standing teacher-oriented teaching with passive students in the past, and developing the their high-level thinking skills. With the help of robots, teachers can use Because ofthe the class widespread time to teach more and eff developedectively. Students network can also today improve and the ways the andincreasing attitudes toward number of people who have one self-learningor more smartphones, because of changes we in teaching use the methods, T&S mobile namely, the platform interaction as between a communication people and platform robots [3], thereby improving the learning outcomes and having a positive impact on future education for teachers andand students research. to publish messages, discuss courses, communicate, interact with robots, and more. We use the network platform, called Tronclass, which also has an app to provide users 4. A Mobile Network Exchange Platform with more convenient learning resources and the environment. The functions of the platform are also Because of the widespread and developed network today and the increasing number of people quite complete,who including have one or moreimportant smartphones, learning we use theindi T&Scators, mobile such platform as as fragmented, a communication autonomous, platform guided and systematicfor learning teachers and (Figure students to4). publish It not messages, only discuss helps courses, teachers communicate, to understand interact with robots,student’s learning and more. We use the network platform, called Tronclass, which also has an app to provide users process, but alsowith for more teachers convenient and learning students resources to and use the an environment. online communication The functions of the platform channel, are alsoto view the class content onlinequite (Figure complete, 5 and including Figure important 6), to learning view indicators, student such and as fragmented,teacher online autonomous, interaction guided and and feedback, to release messages,systematic etc. learning The (Figureinterface4). It not is onlyquite helps simple, teachers easy to understand to understand student’s learning and use, process, which can help but also for teachers and students to use an online communication channel, to view the class content teachers to understandonline (Figures the5 and classroom6), to view studentsituation and teacherand to online save interaction time, and and feedback,it is also to coupled release with online learning, discussions,messages, a etc. test The question interface is bank, quite simple, and easyother to understandways of helping and use, which students can help practice teachers and learn. At to understand the classroom situation and to save time, and it is also coupled with online learning, present, quite discussions,a few domestic a test question universities bank, and other have ways adop of helpingted this students system, practice in and order learn. Atto present,save teachers more time for otherquite teaching a few domestic tasks. universities Because have our adopted research this system, focuses in order on to savethe teachers design more of time interactive for robots, teachers must otherspend teaching much tasks. more Because effort our research on class focuses design on the design and ofinteraction interactive robots, in the teachers teaching must of robotics, spend much more effort on class design and interaction in the teaching of robotics, while using the while using theplatform platform can save can teachers save time teachers in the program. time in the program.

FigureFigure 4. Tronclass 4. Tronclass digitaldigital platform. platform Sustainability 2020, 12, x FOR PEER REVIEW 7 of 18 SustainabilitySustainability2020 2020,,12 12,, 5605 x FOR PEER REVIEW 77 of of 18 17

Figure 5. Tronclass UI FigureFigure 5.5. Tronclass UI.UI

Figure 6. Tronclass announcement.announcement Figure 6. Tronclass announcement 5.5. DesigningDesigning an Interactive Teaching RobotRobot 5. Designing an Interactive Teaching Robot TheThe robotrobot design design and and hardware hardware equipment equipment that that we usewe isuse the is ASUS the ASUS Zenbo. Zenbo. For the For software the software design, thedesign, methodThe the robot method proposed design proposed byand the hardware Nagoya by the Nagoya Universityequipment Univer Institutethatsity we Instituteuse of Engineeringis the of ASUS Engineering [Zenbo.24] is used. [24]For theis The used. software method The proposedmethoddesign, theproposed in method the interactive in proposed the interactive teaching by the ofteaching Nagoya the robot, of Univer th thee robot, expressionsity Institute the expression of theof Engineering transformation, of the transformation, [24] asis wellused. as The theas voicewellmethod as intonation theproposed voice and intonationin wordingthe interactive and usage, wording teaching can eﬀ ectivelyusage, of th ecan enhancerobot, effectively the the expression learning enhance attention,of th thee learningtransformation, willingness attention, and as well as the voice intonation and wording usage, can effectively enhance the learning attention, SustainabilitySustainability 20202020,, 1212,, 5605x FOR PEER REVIEW 8 of 18 17 Sustainability 2020, 12, x FOR PEER REVIEW 8 of 18 willingness and interaction of students, thereby achieving good results. We have specifically interaction of students, thereby achieving good results. We have speciﬁcally compiled easy-to-learn willingnesscompiled easy-to-learn and interaction software of students,into the ro therebybot, for example,achieving “scratch” good results. (Figure We7). have specifically softwarecompiled into easy-to-learn the robot, software for example, into “scratch”the robot, (Figure for example,7). “scratch” (Figure 7).

Figure 7. Zenbo scratch Figure 7. Zenbo scratch.scratch Along with a speech recognition system, which it can be easily, quickly and effectively understoodAlong with andwith aused, speecha speech the recognition robot recognition also has system, networkingsystem, which which it capa can bilities. beit easily,can Inbe quickly other easily, words, and quickly eﬀ itectively can and act understoodaseffectively a mobile andunderstoodcomputer used, thethat and robot responds used, also the hasand robotnetworking accesses also has the networking capabilities. Internet by capa voice In otherbilities. or words,by In issuing other it can words,programmatic act as it acan mobile act instructions, as computer a mobile thatcomputerso that responds teachers that andresponds do not accesses feel and overly theaccesses Internet confused the byInternet by voice their by or use. voice by Therefore, issuing or by programmaticissuing Zenbo programmatic focuses instructions, on the instructions, expression, so that teacherssotone that and teachers do vocabulary not do feel not overly offeel the overly confused interaction confused by theirbetween by use. their the Therefore, use. teachers Therefore, Zenbo and Zenbo students. focuses focuses on Zenbo the on expression, thegives expression, a happy tone andtoneexpression vocabularyand vocabulary and ofa thepleasant interactionof the interactiontone between when betweenstudents the teachers the answer teachers and students.the andquestions, students. Zenbo givesand Zenbo there a happy gives are expression soothinga happy andexpressionexpressions a pleasant andand tone encouraginga pleasant when students tone words when when answer students students the questions, answer do not knowthe and questions, therethe answers; are soothingand in thisthereexpressions way, are Zenbo soothing andcan encouragingexpressionscommunicate and words more encouraging like when an studentsaverage words person, dowhen not students knowso that the the do answers; interactionnot know in the thisbetween answers; way, te Zenboachers in this can and way, communicate students Zenbo cancan morecommunicatebe improved, like an average more which like person,attracts an average so the that students’person, the interaction so attent that theion between interaction and increases teachers between andtheir studentste classroomachers and can beparticipationstudents improved, can whichbe(Figure improved, attracts 8). thewhich students’ attracts attention the students’ and increases attention their and classroom increases participation their classroom (Figure participation8). (Figure 8).

FigureFigure 8.8. Zenbo expression reaction (top left to bottom bottom right right were were shy, shy, stunned, confused, confused, interested, interested, Figure 8. Zenbo expression reaction (top left to bottom right were shy, stunned, confused, interested, happy,happy, tired).tired) happy, tired) TheThe design interacts with the teaching in the cl classroom,assroom, and the teacher’s questions and answers duringduringThe the design classclass interacts help thethe with studentsstudents the teaching to betterbetter in understandunderst the classroom,and what and is goingthe teacher’s on. It can questions also be and designed answers to deliberatelyduringdeliberately the class oofferffer help the the wrong students answer, to better toto allowallow underst the studentstandudent what to correctis going it, on. and It can to confirmconfirm also be whetherdesigned the to studentdeliberatelystudent hashas understoodunderstoodoffer the wrong the classroomclassroomanswer, to content. allow the When student the teacher to correct asks it, question questions,and to confirms, the robot whether can alsothe givestudentgive the has students understood reminders, the classroom give directions content. or promptpr Whenompt the the teacher students asks to find find question the answers. s, the robot After After can school, school, also robotsgiverobots the cancan students alsoalso bebe reminders, usedused byby studentsstudents give directions to practice or pr inompt the test the bank.students When to find you you the answer answer answers. correctly, correctly, After it school, gives praiserobotspraise viaviacan thethealso tabletable be used lineline andby students sound. Whento practice you answanswerin theer test incorrectly, bank. When it encourages you answer you correctly, and gives it givessome tipspraisetips toto viahelphelp the you you table solve solve line the the and problem. problem. sound. IfWhen If you you keep you keep makingansw makinger incorrectly, mistakes, mistakes, the it the robotencourages robot makes makes you a joke. aand joke. By gives usingBy usingsome this approach,tipsthis toapproach, help students you students solve can the do can problem. more do after-schoolmore If you after-school keep exercises making exercises to mistakes, enhance to enhance thethe erobotffectiveness the makes effectiveness ofa joke. their By learning,of using their asthislearning, the approach, robotic as the responsesstudents robotic responsescan attract do studentsmore attract after-school andstudents enhance andexercises theirenhance learning;to enhancetheir learning; even the students effectiveness even students with Touretteof theirwith Syndromelearning,Tourette Syndromeas can the experience robotic can responsesexperience a happy andattract a happy comfortable students and co learningandmfortable enhance environment. learning their learning; environment. Therefore, even the studentsTherefore, focus of with thisthe Tourette Syndrome can experience a happy and comfortable learning environment. Therefore, the Sustainability 2020, 12, 5605 9 of 17 Sustainability 2020, 12, x FOR PEER REVIEW 9 of 18 researchfocus of this is the research interactions is the ofinteractions teachers, students, of teachers, and students, robots in and the robots classroom, in the which classroom, will also which play will an importantalso play an part important in improving part in the improving students’ the motivation students’ and motivation learning and outcomes. learning outcomes.

6.6. Robot for Teaching DesignDesign InIn termsterms ofof thethe teachingteaching methodmethod design,design, wewe focusfocus onon thethe interactionsinteractions ofof teachers,teachers, students, and robots.robots. BecauseBecause of of the the pressure pressure of theof learningthe learning environment, environment, students students are less are likely less to likely take theto initiativetake the toinitiative ask questions to ask questions during classroom during classroom instruction, instru evenction, if the even teacher if the wants teacher to wants know ifto theknow students if the understand.students understand. Therefore, Therefore, when the when teacher the explains teacher aexplains new theory a new or theory problem-solving or problem-solving method, themethod, robot canthe askrobot the can teacher ask the questions teacher questions on time, andon time, the teacher and the responds teacher responds to the robot’s to the problem, robot’s problem, in order toin achieveorder to interaction achieve interaction and enable and the enable students the to studen understandts to understand the problem. the The problem. teacher The also teacher finds some also studentsfinds some in thestudents classroom in the to classroom answer the to questions. answer th Moste questions. of the students Most of maythe students be confused may becausebe confused they dobecause not know they thedo answer.not know At the this answer. time, the At robotthis time, gives the some robot tips gives and some helps tips the studentsand helps to the answer students the questions.to answer the Of course,questions. all theOf questionscourse, all andthe questi answersons in and the answers classroom in mustthe classroom be designed must by be the designed teacher andby the asked teacher by the and robot, asked in order by tothe elicit robot, answers. in order The to class elicit adopts answers. the ARCS Themotivation class adopts model, the soARCS that studentsmotivation can model, clearly so understand that students the problem,can clearly find un thederstand answers, the analyze problem, the find results the and answers, apply themanalyze in thethe variousresults and fields apply during them their in study.the various Finally, fields this duri methodng their can study. be used Finally, to train this the method high-level can thinking be used skillsto train of students,the high-level so that thinking they can skills begin of tostudents, think independently so that they can and begin find the to think answers, independently instead of being and providedfind the answers, directly withinstead the of answers. being provided directly with the answers. Finally,Finally, withwith thethe methodmethod thatthat wewe have designed,designed, combined with the system of teachers, teachers, students, students, robotsrobots andand digitaldigital platformsplatforms (refer(refer toto FigureFigure9 9),), the the students’students’ motivationmotivation andand eeffectivenessffectiveness in learning shouldshould bebe improved,improved, soso thatthat theythey areare ableable toto solvesolve thethe above-mentionedabove-mentioned educational educational problems. problems.

FigureFigure 9.9. SystemSystem conceptconcept illustration.illustration

IntroducingIntroducing thethe functionfunction ofof eacheach partpart ofof thisthis system:system: Network:Network: With With today’s academic collaborativecollaborative learning, which emphasizes the system’ssystem’s convenienceconvenience andand the the current current high high coverage coverage of of wireless wireless networks networks in Taiwan,in Taiwan, the the Internet Internet uses uses Wi-Fi Wi-Fi as a mediumas a medium for data for data transmission transmission and reception.and reception. DigitalDigital platform:platform: The The Tronclass Tronclass service service is used is used as an as interactive an interactive platform platform for teachers for teachers and students, and bothstudents, in- and both outside in- and the outside classroom. the classroom. ARCSARCS MotivationMotivation mode:mode: During the course,course, attention,attention, relevance,relevance, conﬁdence,confidence, and satisfactionsatisfaction serveserve asas aa benchmarkbenchmark forfor thethe evaluationevaluation ofof thethe measuresmeasures thatthat aim to cultivate the high-level thinking abilityability ofof students,students, andand toto enhanceenhance theirtheir learninglearning motivation.motivation. Robot:Robot: TheThe use use of of robots robots to assistto assist teachers teachers in classroom in classroom teaching teaching and presentation and presentation is done throughis done dialoguethrough dialogue and interaction, and interaction, which adds which to the adds classroom to the classroom fun and enhances fun and enhances the will to the learn. will to learn. InteractiveInteractive teaching:teaching: In In class, class, teachers, teachers, students, students, robots robots and and interactive interactive teaching teaching methods methods are used are toused increase to increase the students’ the students’ participation participation in the in classroom the classroom [22]. [22]. ACAC basedbased onon AI:AI: TheThe AIAI setset for the robot, namely, the emotion AI that detects the students’ emotions,emotions, enablesenables corresponding corresponding interactions interactions and responses,and responses, with interactivewith interactive ways of askingways of questions asking questions and helping students to discuss the problem and find the answers in the classroom. Our system, using emotion AI, is the human emotion detection method that is based on a standard SustainabilitySustainability 20202020,, 1212,, 5605x FOR PEER REVIEW 10 of 18 17

webcam on a robot. The emotion AI first detects human faces from images. The deep-learning and helping students to discuss the problem and find the answers in the classroom. Our system, using algorithms (such as the Convolutional Neural Network, CNN) are adopted, to analyze the facial emotion AI, is the human emotion detection method that is based on a standard webcam on a robot. expressions. The emotion AI is trained by six million faces and the deep-learning method can achieve The emotion AI first detects human faces from images. The deep-learning algorithms (such as the high performance and accuracy. Convolutional Neural Network, CNN) are adopted, to analyze the facial expressions. The emotion AI is trained by six million faces and the deep-learning method can achieve high performance and accuracy. 7. Planning to Enhance Learning Motivation and Effectiveness 7. PlanningWe use to the Enhance ARCS LearningMotivation Motivation Model proposed and Effectiveness by Keller (Figure 10) [28] to improve the motivationWe use and the ARCSeffectiveness Motivation of students. Model proposed The meth byod Keller emphasizes (Figure 10 that)[28 learners] to improve must the be motivationmotivated andby four effectiveness elements ofto students.stimulate their The methodlearning emphasizes motivation. thatIt suggests learners that must the be systematic motivated teaching by four elementsdesign must to stimulate be strengthened their learning and that motivation. students must It suggests be encouraged that the systematic to participate teaching and interact design mustwith beeach strengthened other, while and providing that students the theory must beand encouraged practical application to participate [8], andcombined interact with with a eachvariety other, of whiletechniques providing to assist the theorywith their and learning, practical applicationas well as to [8 ],solve combined incoherent with alearning variety ofand techniques knowledge to assistconflicts. with their learning, as well as to solve incoherent learning and knowledge conflicts.

FigureFigure 10.10. ArcsArcs motivationmotivation mode.mode 8. ARCS Mode Features 8. ARCS Mode Features 1. Attention to motivation and feelings of affection. 1. Attention to motivation and feelings of affection. 2. It can complement other theories of teaching design steps, by combining them. 2. It can complement other theories of teaching design steps, by combining them. 3. It not only focuses on improving teaching effectiveness, but it also pays special attention to 3. It not only focuses on improving teaching effectiveness, but it also pays special attention to learning interest, through a series of strategies, to enhance the interest of learners and to achieve learning interest, through a series of strategies, to enhance the interest of learners and to achieve learning effectiveness. learning effectiveness. AccordingAccording toto ARCS ARCS motivation motivation (Figure (Figure9) to achieve9) to achieve learning learning outcomes outcomes [ 25], this [25], method this combines method currentcombines theories current of learningtheories motivation,of learning suchmotivation, as the cognitive, such as the humanistic, cognitive, social humanistic, learning social and behavioral learning theories,and behavioral etc. (Table theories,1), by analyzingetc. (Table and1), summarizingby analyzing newand explanations.summarizing Thisnew motivationexplanations. model, This combinedmotivation with model, various combined perspectives with of various the motivation perspect theory,ives canof the provide motivation teachers theory, with the can appropriate provide teachingteachers strategies.with the appropriate teaching strategies. It can be seen from the above that planning and design of the ARCS model uses a series of strategies to enhance the interest of learners,Table and 1. Supporting to promote teaching their learning strategy table. effectiveness. Therefore, we divide the robot into the four elements (ARCS), as discussed below: Learning Meaning in Principles that Enhance Corresponding ARCS Attention: In order to attract the interest of the students, as well as to stimulate their curiosity, Mechanism Education Students’ Motivation Mode we put the teaching robot, Zenbo, in the classroom, with its rich expression and self-introducing voice 1. Empowering 1. Use situational issues, and timely questions, to attract the students. Another important pointAttention—Learning is the provision of change,first learning materials symbols, pictures, and in orderCognitive to stimulate the students’ need for knowledge. In class, ZenboArouse helps the students attention to answerof 2. Scaffolding analysis to elicit students’ questions and encourages them to be more confident and courageous,learners by asking and answering theory application attention questions in the classroom. 1. Demand Relevance: Personal relevance is established.1. Teachers Althoughshould pay attention and curiosity are necessary, hierarchy Relevance—Content needs they cannot be sufficient learning conditions;attention students to whether need to the be aware of the need to pursue their Humanity 2. Student-centered to be related to student life goals, to develop a learning style, and tobasic connect needs them of withstudents their are past learning experiences. In class, 3. Meaningful (Student-centered) met. learning Sustainability 2020, 12, 5605 11 of 17 the program’s curriculum is based on the middle school basic mathematics for teaching; we use Zenbo to play videos and communicate with students, so that they can identify whether they are interested in learning about math, and how they relate to what is going on in their lives, and how to achieve a clearer learning motivation.

Table 1. Supporting teaching strategy table.

Learning Principles that Enhance Meaning in Education Corresponding ARCS Mode Mechanism Students’ Motivation 1. Empowering learning 1. Use situational issues, Attention—Learning first materials symbols, pictures, and Cognitive Arouse the attention of 2. Scaffolding theory analysis to elicit students’ learners application attention 1. Teachers should pay attention to whether the 1. Demand hierarchy basic needs of students are Relevance—Content needs to Humanity 2. Student-centered met. be related to student life 3. Meaningful learning 2. Cultivate students’ (Student-centered) self-development or self-realization 1. Confident to complete 1. Imitation and cognition are Confidence—Learners can Social the task behavioral changes confidently complete tasks learning 2. Learn from people with 2. Model learning (Self-efficacy) successful experience 1. Give students 1. External control incentive Satisfaction—Behavioral reinforcement and turn Behavior 2. Reward greater than penalty performance is rewarded and external enhancement into 3. Means of purpose satisfied self-enhancement

Confidence: A clear learning goal builds confidence. In order to build the students’ confidence in learning, the teachers arrange online tests, most of which are not too difficult, but very close to current events or topics related to the students, and Zenbo helps them to solve problems. When the students are successful in a problem-solving session, they could understand how a problem is solved and how it can prove to be useful in the real world, so that their confidence in learning is increased, they become more motivated, and have a sense of belonging. Satisfaction: In order for students to actively engage in the learning experience, to feel satisfied and to apply what they have learned, we use the test method. The content, as well as the problem-solving skills and the methods used, are all offered in class. It is not difficult for students to answer the questions, nor is it difficult to encourage them to learn and get a good score, and to feel satisfied. Not only can it be used in real-life situations, but students also become proud and satisfied with their learning outcomes during the semester, as they finally achieve a sense of self-satisfaction and their learning is effectively enhanced. The following corresponding strategy shown in Table2 lists the use of the elements of ARCS motivation: Sustainability 2020, 12, 5605 12 of 17

Table 2. Supporting teaching strategy table.

Motivation Purpose Definition Teaching Strategy Element Using the characteristics of the learning platform to attract the attention of 1. Sensory attraction Attract students’ interest learners, thereby increasing the visibility 2. Inquiry into the A: Attention and stimulate student and learning of the textbook. problem curiosity The robot uses a teaching interaction 3. Maintain with change mode (expression and sound) to interact with students to avoid boredom. 1. Similar events 2. Establishment of goal Meet the individual The use of robots to convey knowledge so orientation needs and goals of the that students can apply what they have R: Relevance 3. Cooperate with the students, to develop a learned to daily life, and feel that the learner’s movement positive learning attitude teaching theme is “relevant”. Machine demand In classroom teaching, teachers, students, Help students build 1. Essential conditions and robots are used to create a more confidence in their for learning relaxed and stress-free classroom C: confidence success and believe that 2. Provide opportunities environment, so that students are brave they can master their for learning success enough to engage in challenging tasks tasks. and improve their confidence in learning. The robot encourages students to feel the Students can get internal 1. Natural result freedom of learning with encouragement and external drumsticks S: Satisfaction 2. Positive result and fun interaction, so that they can and rewards for their 3. Maintain justice improve their learning motivation after achievements. satisfying their sense of accomplishment.

9. Experiment Method The research method was conducted by the “teacher, who is the researcher” and who carried out the ongoing research experiment. The Tronclass learning platform and the teaching robot are used as classroom aids to confirm whether the students’ motivation and learning effectiveness can be improved in the teaching of sustainable learning. Our proposed robot-assisted instruction learning system adopted AI to recognize the students’ emotions when they are learning. The assisted learning system is based on AI. The class is divided into two classes, namely, Class A, the experimental group, and Class B, the control group (Table3), to compare the e ffectiveness of the two classes and to confirm whether robot-assisted teaching can enhance the students’ willingness to learn and achieve. The experimental group is a class that implements digital platform and robot teaching. The control group teaches traditionally, without robot-assisted instruction. In order to get the most objective and accurate results, both classes must pass the pre-test, in order to confirm that the scores do not differ greatly. The average score is compared. The number of classes are three hours per week, and each class is 50 min long. The content of the textbooks is the same and the two sides do not know each other’s teaching situation. At the end of the semester, the two classes take the post-test on arithmetic and problem-solving, based on the basic sustainable learning of the semester. Finally, they fill in the motivation scale and questionnaire; the teacher will then conduct a statistical analysis and report to see if the students had any help in the new class or from the robot (Figure 11). Our study focused on robot-assisted teaching in the traditional classroom, using the digital platform. Therefore, the students were divided into two groups, namely, the robot-assisted group and the non-robot-assisted group. Our system adopted the robot and the digital platform-assisted group in the traditional classroom. After school, the students can solve their question by asking the robot in front of the rest of the class. Our system has definitely verified that the robot interacts with the students, which improves their learning motivation. Sustainability 2020, 12, x FOR PEER REVIEW 13 of 18

Table 3. Experimental group and control group teaching content and methods

Teaching Content Experimental Group Control Group and Methods 50 min/per class, 3 classes/per Course time 50 min/per class, 3 classes/per week week Course Contents Middle school instruction Middle school instruction Using teaching robots and ARCS Using traditional slides to Teaching methods motivation model as the classroom teach interaction method Tronclass digital platform, computer, Tronclass digital platform, Software and video recorder, researcher, robot computer, video recorder, hardware equipment (Zenbo) researcher Practice question Digital platform test bank practice, Traditional digital platform bank and test robot interactive practice, pre-test, post- test bank practice, pre-test, method test post-test.

The experimental group is a class that implements digital platform and robot teaching. The Sustainabilitycontrol group2020, 12teaches, 5605 traditionally, without robot-assisted instruction. In order to get the13 most of 17 objective and accurate results, both classes must pass the pre-test, in order to confirm that the scores do not differ greatly. The average score is compared. The number of classes are three hours per week, Table 3. Experimental group and control group teaching content and methods. and each class is 50 min long. The content of the textbooks is the same and the two sides do not know eachTeaching other’s Content teaching and Methods situation. At the Experimental end of the Groupsemester, the two classes Control take Groupthe post-test on arithmeticCourse and problem-solving, time based 50 min /onper the class, basic 3 classes sustainable/per week learning 50 min of/ perthe class,semester. 3 classes Finally,/per week they fill in theCourse motivation Contents scale and questionnaire; Middle school the instructionteacher will then conduct Middle a statistical school instruction analysis and report to see if the students had anyUsing help teaching in the robots new andclass ARCS or from the robot (Figure 11). Our study focusedTeaching on robot-assisted methods teachingmotivation in the traditional model as the classroom, classroom using theUsing digital traditional platform. slides Therefore, to teach the students were divided into two groups,interaction namely, method the robot-assisted group and the non-robot- assistedSoftware group. and hardwareOur system adoptedTronclass the digital robot platform, and computer,the digital Tronclassplatform-assisted digital platform, group computer, in the traditionalequipment classroom. After school,video the recorder, students researcher, can solve robot their (Zenbo) questionvideo by asking recorder, the researcher robot in front ofPractice the rest question of the bankclass. and Our test systemDigital has platform definitely test bank verified practice, that robot the robotTraditional interacts digital with platform the students, test which improvesmethod their learning motivation.interactive practice, pre-test, post-test bank practice, pre-test, post-test.

FigureFigure 11. 11.Research Research methodmethod ﬂowflow chart.chart

10. Statistical Results and Analysis of Experimental Results The results of the test are determined by the teacher. The statistics include the average of the pre-test scores and the post-test scores and the total average of the progress score, to assess whether the learning effectiveness of the students improved with the interactive teaching of robots. Our learning system was adopted by middle school students and the learning study top group is about the middle school mathematics domain. We tested the experimental group and the control group. The sample numbers are 25 and 25, respectively. The t-value and p-value are shown in Table4, where we find that the four factors (ARCS) are extremely significant when using the anticipatory computing and emotion big data for sustainable education learning. Our research investigation is mainly on teaching-assisted robots for classroom teaching, as well as the satisfaction and learning motivation of the teachers and students. This study focused on the students involved in this course, and the Student Learning Motivation Scale questionnaire design was used shown in AppendixA (Table A1). The design of the questionnaire was adapted from Keller (IMMS, Textbook Motivation Scale) [9], which is a tool for the ARCS model that is used to determine the learner’s motivation. The questions contained four subscales, which were used to measure the learners’ attention, as well as the relevance, confidence, satisfaction and learning motivation of the teaching material, using the Likert five-point rating scale, from being very much disapproved (1) to very agreeable (5): the higher the score, the stronger the motivation, Sustainability 2020, 12, 5605 14 of 17 and vice versa. The topic is divided into four parts, namely, there are seven questions on Attention, seven questions on Relevance, seven questions on Confidence, and five questions on Satisfaction.

Table 4. T value and p value of Experimental Group Control Group.

Experimental Group Control Group T Value p Value Mean Std. Mean Std. Attention Score 3.35 0.71 1.8 0.54 8.688 0.000 ** Reverse question Score 1.72 0.54 1.68 0.57 Relevance Score 3.46 0.86 2.12 0.57 6.493 0.000 ** Reverse question Score 1.57 0.532 1.62 0.53 Conﬁdence Score 3.77 0.68 2.43 0.56 7.605 0.000 ** Reverse question Score 1.56 0.57 1.67 0.55 Satisfaction Score 3.36 0.72 2.02 0.68 6.765 0.000 ** Reverse question Score 1.62 0.7 1.45 0.602 ** p < 0.01.

11. Conclusions Digital learning on big data relies on the following: (1) From the big data extracting the efficient learning features, teachers can improve their teaching processes. (2) To predict the student’s performance, the teachers can identify the learning difficulties of the students. (3) The students can help to extract learning features from the big data. The goal of this research is to apply anticipatory computing to robots and to assist teachers in their teaching, which is shown in Figure 12. We adopted the robot and digital platform-assisted system in the traditional classroom. After school, the students can solve their questions by asking the robot in front of the class. Our system has definitely verified that the robot interacts with the students, which improves their learning motivation. The most important purpose is to increase students’ sustainable learning motivation and to improve their learningSustainability outcomes, 2020, 12, x andFOR PEER interactive REVIEW teaching is provided to stimulate the their enthusiasm for learning 15 of 18 and their motivation, to improve the past teacher-oriented teaching methods, to integrate the robot into the classroom,classroom, to participate in the curriculum and and to help the students to to find find solutions to to problems and cultivate high-order thinking skills, based on anticipatory computing and emotion big data. This will improve the learning outcomes and have a positive impact on future education and fieldsfields of research. Our proposed sy systemstem adopts digital learning in the big data discussion discussion on on emotion emotion AI, andand ourour systemsystem cancan improveimprove thethe students’students’ performanceperformance and the teacher’steacher’s teaching techniques according toto thethe bigbig data. data. Our Our proposed proposed digital digital system system can can detect detect the the student’s student’s emotions emotions when when they they are learning,are learning, and theand robotthe robot can give can immediategive immediate feedback feedback on their on emotions. their emotions. It is suitable It is forsuitable all students, for all basedstudents, on theirbased emotional on their emotional classification, classification, and it builds and the it assisted-teachingbuilds the assisted-teaching platform to platform help the teachers to help inthe class. teachers in class.

Figure 12. The interaction of the Zenbo Robots with the middle school students inin class.class

Funding: This research was funded by the Ministry of Science and Technology (contracts MOST-109-2221-E-019 -057 -, MOST-108-2221-E-019-038-MY2, MOST-107-2221-E-019-039-MY2, MOST-108-2634F-019-001, MOST-108- 2634-F-008-001, MOST-109-2634-F008-007, and MOST-109-2634-F-019-001) of Taiwan. This research was also funded by the University System of Taipei Joint Research Program (contract USTP-NTUT-NTOU-106-02, USTP- NTUT-NTOU-107-04, USTP-NTUT-NTOU-109-01, USTP-NTOU-TMU-108-01 and USTP-NTUT-NTOU-108-04) of Taiwan.

Conflicts of Interest: The authors declare no conflict of interest.

Appendix

Table A1. Questionnaire

Author Contributions: Conceptualization, Y.-Z.H.; methodology, Y.-Z.H.; software, Y.-C.L.; validation, Y.-Z.H.; formal analysis, Y.-Z.H.; investigation, Y.-Z.H.; resources, Y.-Z.H.; data curation, S.-S.L.; writing—original draft preparation, Y.-Z.H.; writing—review and editing, Y.-Z.H. and Y.-C.L.; visualization, Y.-C.L. and S.-S.L.; supervision, Y.-Z.H.; project administration, Y.-Z.H.; funding acquisition, Y.-Z.H., S.-S.L., Y.-L.J., S.-W.T., C.-R.C. and P.-Y.C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Ministry of Science and Technology (contracts MOST-109-2221-E-019-057-, MOST-108-2221-E-019-038-MY2, MOST-107-2221-E-019-039-MY2, MOST-108-2634F-019-001, MOST-108-2634-F-008-001, MOST-109-2634-F008-007, and MOST-109-2634-F-019-001) of Taiwan. This research was also funded by the University System of Taipei Joint Research Program (contract USTP-NTUT-NTOU-106-02, USTP-NTUT-NTOU-107-04, USTP-NTUT-NTOU-109-01, USTP-NTOU-TMU-108-01 and USTP-NTUT-NTOU-108-04) of Taiwan. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Appendix A

Table A1. Questionnaire.

Question Attention [1] It’s very interesting to hear the robot presentation. [2] It’s very interesting to interact with the robot. [3] It’s very interesting to see the robot help me answering question. [4] It’s boring to interact with the robot. [5] The robot can help me to pay attention in class. [6] The robot assists me doing more exercises. [7] The interactive learning helps me to focus more. Relevance [1] The interactive learning method helps me to understand the content. [2] The robot can help me understanding the question. [3] The robot’s suggestion is very helpful. [4] The interactive learning cannot help me understanding the content. [5] The robot can help me to be more constructive. [6] The interactive learning can let me feel more related to the contents. [7] The robot can help me quickly find the answer. Confidence [1] The content in the class is not difficult for me. [2] The robot can help me more feel comfortable in class. [3] The interactive learning helps me more to be creative. [4] After interactive learning, I can find the best solution. [5] After interactive learning, I do not feel I have learnt new knowledge. [6] After interactive learning, I often discuss with my classmates. [7] After interactive learning, I can perform well. Satisfaction [1] After interactive learning, I feel more satisfied with my learning. [2] I’m satisfied with my homework. [3] I would like the robot assisting me to find answer. [4] I don’t like interacting with the robot. [5] I’m satisfied with my learning activity in class. [6] I’m satisfied with team work in class. [7] I’m satisfied with my exam score in class.

References

1. Rosen, R. Anticipatory Systems; Philosophical Mathematical and Methodological Foundations; Pergamon Press: New York, NY, USA, 1985. 2. Chen, G.; Kotz, D. A Survey of Context-Aware Mobile Computing Research. 2000. Available online: https://pdfs.semanticscholar.org/a9d0/b687da10c70bbbcc73d3657c96d92af6ada2.pdf?_ga=2.246235847. 276034082.1594098662-71393538.1593663719 (accessed on 1 December 2019). Sustainability 2020, 12, 5605 16 of 17

3. Butz, M.V.; Sigaud, O.; Gérard, P. Anticipatory behavior: Exploiting knowledge about the future to improve current behavior. In Anticipatory Behavior in Adaptive Learning Systems; Springer: Berlin, Germany, 2003; pp. 1–10. 4. Lin, C.; Zhao, G.; Wu, Y.J.; Li, H. Anticipatory computing for human behavioral change intervention: A systematic review. IEEE Access 2019, 7, 103738–103750. [CrossRef] 5. Nadin, M. (Ed.) Anticipation and Computation: Is Anticipatory Computing Possible? In Anticipation Across Disciplines. Cognitive Systems Monographs; Springer: Cham, Switzerland, 2016; p. 29. 6. Burbey, I.; Martin, T.L. A survey on predicting personal mobility. Int. J. Pervasive Comput. Commun. 2012, 8, 5–22. [CrossRef] 7. Pantic, M.; Pentland, A.; Nijholt, A.; Huang, T.S. Human Computing and Machine Understanding of Human Behavior: A Survey. In Artifical Intelligence for Human Computing; Springer: Berlin, Germany, 2007. 8. Lu, H.; Frauendorfer, D.; Rabbi, M.; Mast, M.S.; Chittaranjan, G.T.; Campbell, A.T.; Gatica-Perez, D.; Choudhury, T. StressSense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, New York, NY, USA, 5–8 September 2012; pp. 351–360. 9. Pejovic, V.; Mehrotra, A.; Musolesi, M.; Nadin, M. Anticipatory Mobile Digital Health: Towards Personalized Proactive Therapies and Prevention Strategies. In Anticipation and Medicine; Springer: Cham, Switzerland, 2017; pp. 253–267. 10. Baniwal, V.; Kayal, C.; Shah, D.; Ma, P.; Khadilkar, H. An Imitation Learning Approach for Computing Anticipatory Picking Decisions in Retail Distribution Centres. In Proceedings of the 2019 American Control Conference (ACC), Philadelphia, PA, USA, 10–12 July 2019; pp. 4186–4191. 11. Ciolacu, M.; Tehrani, A.F.; Binder, L.; Svasta, P.M. Education 4.0—Artificial Intelligence Assisted Higher Education: Early recognition System with Machine Learning to support Students’ Success. In Proceedings of the 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME), Iasi, Romania, 25–28 October 2018; pp. 23–30. 12. Hossain, M.; Alam, S. Intelligent anticipatory agents for changing environments. In Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, 9–12 October 2016; pp. 004597–004602. 13. Jeon, H.; Oh, H.; Lee, J. Machine Learning based Fast Reading Algorithm for Future ICT based Education. In Proceedings of the 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea, 17–19 October 2018; pp. 771–775. 14. Sakulkueakulsuk, B.; Witoon, S.; Ngarmkajornwiwat, P.; Pataranutaporn, P.; Surareungchai, W.; Pataranutaporn, P.; Subsoontorn, P. Kids making AI: Integrating Machine Learning, Gamification, and Social Context in STEM Education. In Proceedings of the 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Wollongong, NSW, Australia, 4–7 December 2018; pp. 1005–1010. 15. Dolezel, P.; Dvorak, M. Computer game as a tool for machine learning education. In Proceedings of the 2017 21st International Conference on Process Control (PC), Strbske Pleso, Slovakia, 6–9 June 2017; pp. 104–108. 16. Ciolacu, M.; Tehrani, A.F.; Beer, R.; Popp, H. Education 4.0—Fostering student’s performance with machine learning methods. In Proceedings of the 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME), Constanta, Romania, 26–29 October 2017; pp. 438–443. 17. Uskov, V.L.; Bakken, J.P.; Byerly, A.; Shah, A. Machine Learning-based Predictive Analytics of Student Academic Performance in STEM Education. In Proceedings of the 2019 IEEE Global Engineering Education Conference (EDUCON), Dubai, UAE, 8–11 April 2019; pp. 1370–1376. 18. Akalin, N.; Uluer, P.; Kose, H.; Ince, G. Humanoid robots communication with participants using sign language: An interaction based sign language game. In Proceedings of the 2013 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), Tokyo, Japan, 7–9 November 2013. 19. Costa, S.; Soares, F.; Pereira, A.P.; Santos, C.; Hiolle, A. Building a Game Scenario to Encourage Children with Autism to Recognize and Label Emotions using a Humanoid Robot. In Proceedings of the 23rd IEEE International Symposium on Robot and Human Interactive Communication, 2014 RO-MAN, Edinburgh, UK, 25–29 August 2014. Sustainability 2020, 12, 5605 17 of 17

20. Park, S.J.; Han, J.H.; Kang, B.H.; Shin, K.C. Teaching Assistant Robot, ROBOSEM, in English Class and Practical Issues for its Diffusion. In Proceedings of the 2011 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), Half-Moon Bay, CA, USA, 2–4 October 2011. 21. Kose, H.; Yorganci, R.; Itauma, I.I. Humanoid Robot Assisted Interactive Sign Language Tutoring Game. In Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO), Karon Beach, Phuket, Thailand, 7–11 December 2011. 22. Han, J.; Kang, S.; Song, S. The Design of Monitor-based Faces for Robot-Assisted Language Learning. In Proceedings of the 2013 IEEE RO-MAN, Gyeongju, Korea, 26–29 August 2013. 23. Amal, A.; Az-Eddine, N.; Souhaib, A. Survey of Intelligent Collaborative E-Learning Systems. In Proceedings of the 2017 15th International Conference on Emerging eLearning Technologies and Applications (ICETA), La Plata, Argentina, 26–27 October 2017. 24. Tanizaki, Y.; Jimenez, F.; Kanoh, M. Learning effect of robotic encouragement-based collaborative learning. In Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 9–12 July 2017. 25. Chang, Y.-H.; Lin, P.-R.; Lu, Y.-T. Development of a Kinect-Based English Learning System Based on Integrating the ARCS Model with Situated Learning. Sustainability 2020, 12, 2037. [CrossRef] 26. Maldonado, J.J.; Pérez-Sanagustín, M.; Bermeo, J.L.; Muñoz, L.; Pacheco, G.; Espinoza, I. Flipping the classroom with MOOCs. A pilot study exploring differences between self-regulated learners. In Proceedings of the 2017 Twelfth Latin American Conference on Learning Technologies (LACLO), Stary Smokovec, Slovakia, 9–13 October 2017. 27. Tjhin, V.U.; Tjhin, V.U.; Soraya, K. Evaluating the performance of students through collaborative learning: Case study: Distance education program in Indonesia. In Proceedings of the 2017 10th International Conference on Human System Interactions (HSI), Ulsan, Korea, 17–19 July 2017. 28. Keller, J.M. Development and use of the ARCS model of instructional design. J. Instr. Dev. 1987, 10. [CrossRef] 29. Lytras, M.D.; Raghavan, V.; Damiani, E. Big data and data analytics research: From metaphors to value space for collective wisdom in human decision making and smart machines. Int. J. Semant. Web Inf. Syst. 2017, 13, 1–10. [CrossRef] 30. Lytras, M.D.; Visvizi, A. Who Uses Smart City Services and What to Make of It: Toward Interdisciplinary Smart Cities Research. Sustainability 2018, 10, 1998. [CrossRef] 31. Marechal, C.; Mikołajewski, D.; Tyburek, K.; Prokopowicz, P.; Bougueroua, L.; Ancourt, C.; W˛egrzyn-Wolska, K. Survey on AI-Based Multimodal Methods for Emotion Detection. In High-Performance Modelling and Simulation for Big Data Applications. Lecture Notes in Computer Science; Kołodziej, J., González-Vélez, H., Eds.; Springer: Cham, Switzerland, 2019; p. 11400.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).