Abstract

The TEAM Model smarter classroom is a teaching and learning (TAL) expert system with integrated software, hardware, and cloud services. The “TEAM Model Smarter Discussion Room” is a TEAM Model smarter classroom with a “Socrates TAL activity data analysis system” featuring intelligent technology and auto TAL activity analysis. In this paper, a “TEAM Model Smarter Discussion Room” is presented to improve the development of education and provide a means of collecting teaching and learning data in the classroom. This data can then be analyzed and the results used to enhance teachers’ professional development. The purposes of this study including: the analysis of the data obtained from the interaction between teacher and students will be able to help teachers to develop student-centered learning; the collection and digitization of the details of teaching methods employed by teachers will be able to identify means to promote teaching effectiveness; and the logging of Q & A sessions between teachers and students in the classroom will be able to improve such interaction. 

Keywords: big data, teacher professional development, TEAM Model, smarter discussion room

 Introduction

Among many innovative IT developments, eight of them are being used in the classroom to effectively change education. These include augmented reality (AR), 3D printing, cloud computing, online social networking, flexible displays, eye tracking biometrics, multi-touch LCD screens, and game-based learning. For example, the multi-touch LCD screen allows users to interact with an application, using several fingers at the same time, through a graphic user interface. Students can get better interaction by swiping the screen and dragging and dropping icons around big tablets (Hongkiat, 2017). Multi-pattern classrooms featuring these eight technologies are making an impact, not only on the types of technology used by teachers and students around the world, but also on innovation and change in the domestic classrooms.

The explosive growth of information and networking technology is attracting more attention to data gathering and analysis and the new concept of “big data” is being recognized by the general public. The “big data” approach involves the fast gathering, processing, and analysis of huge amounts of data to produce readable and useful information. This is done with newly developed information technology to facilitate learning about status, to predict trends and make decisions. Big data is used by cosmologists, atmospheric scientists, in medical care, in social networks, policing, traffic safety, and e-commerce, amongst many others. This improves operation efficiency and business development (Wikipedia, 2017a). The same is being done in education to facilitate teachers’ professional development using the big data generated by daily teaching and learning (TAL). In this paper a means of extracting key information from teaching and learning big data is presented. A “TEAM Model Smarter Discussion Room” is presented to improve the development of education and provide a means of collecting TAL data in the classroom. This data can then be analyzed and the results used to enhance teachers’ professional development. The purposes of this study including: the analysis of the data obtained from the interaction between teacher and students will be able to help teachers to develop student-centered learning; the collection and digitization of the details of teaching methods employed by teachers will be able to identify means to promote teaching effectiveness; and the logging of Q&A sessions between teachers and students in the classroom will be able to improve such interaction.

The definition and design of TEAM Model Smarter Classroom

The TEAM Model Smarter Classroom is aimed at improving the value and strength of education to change the world. It covers “instant insights” that teachers may bring to their students with an intelligence enriched curriculum and “watching, learning and seeds of hope” to shape up a perfect smarter classroom and a TEAM Model support system to refine and integrate “technology, method, and content” into “smart chain teaching”. This new method features classroom activities that can rapidly create “effective interaction” between teachers and students. In short, the “Smart Chain Teaching Method” adopted for the “TEAM Model Smarter Classroom” may create an even better contemporary classroom of “instant insight and smart chain reaction”. The TEAM Model system assists teachers to integrate teaching, assessment, diagnosis, and rectification into a TAL expert system. Students are guided into an autonomic learning cycle, leading to in-class cooperation and after-school review. TEAM has been coined from “Teaching, assEssing, diAgnosing and remediation”. Simply put, this means team work and integration (HABOOK Information Technology, 2016).

The team-based learning (TBL) smarter classroom of the TEAM Model integrates multiple TAL support systems including HiTeach of interactive TAL, IRS of instant feedback, HiGroup, and the HiTA of smart assistant to initiate the TBL model and strategy during a classroom session. Teachers may fine tune their TAL strategy in advance by making the most of autonomy-learning by individual students with IES. It is easy and efficient to collect, sum up, and classify students' discussion results and get hold of learning results of each team to take advantage of TBL and enhance performance. Students can review the course material with their smart companions and learn about their own difficulties from diagnostic reports. Student seats in the TBL smarter classroom (see Figure 1) are divided into groups equipped with a tablet as a tool for data collection, output and feedback.

Each student in the classroom has access to an instant IRS system for personal feedback, questions, investigation, and mutual evaluation. The HiTeach interactive TAL system allows teachers to present course material on an interactive whiteboard or touch control screen, sum up the results of student discussions, and review learning results of individual groups. Smart patrolling of the classroom with the HiTA smart assistant can also be done to allow checks on discussions, team practice results or even the ideas and work of an individual student. The system allows sharing with all those present to further enrich the TAL material. 

The design philosophy of TEAM Model Smarter Classroom

 The model employs a whole new technology application service model and is ideal for the implementation of TEAM (see Figure 2). Development of the model begins with “technology supported education, teaching, and learning” which is the basis of education, rather than the inadequate concept of technology only TAL. 

The TEAM Model Smarter Classroom

To address the application requirements for TAL, the TEAM Model focuses on software and hardware R&D for effective strategy and methodology. The smarter classroom approach may also improve conventional lecturing, problem-based learning (PBL), TBL of team-based learning, master learning, and one-on-one learning, among other TAL methods and strategies. The value of TAL applications based on technology may be assessed by the three ICE assessment indexes of convenience, efficiency, and intelligence (see Figure 3). In short, the core e-classroom TEAM Model can provide teachers with ICE technology assistance and classrooms featuring TAL, assessment, diagnosis, and remediation, in other words a “TEAM Model Smarter Classroom”. 
 

ICE assessment index

The design and AI of the “TEAM Model Smarter Discussion Room” environment

The design concept of the environment 

The TEAM Model smarter classroom is a TAL expert system with integrated software, hardware, and cloud services. The “TEAM Model Smarter Discussion Room” is a TEAM Model smarter classroom with a “Socrates TAL activity data analysis system” featuring intelligent technology and auto TAL activity analysis. It has three main components: a larger room (Figure 4), a simple open room (Figure 5), and a discussion room with a one-way mirror partition (Figure 6). Teaching in the TEAM Model smarter discussion room is done using the HiTeach interactive TAL system, sessions are recorded with the ezStation smart recording system while experts observe the TAL sessions, or simply watch through the one-way mirror, see Figures 4, 5 and 6. The experts each have a handheld Socrates observation interface to make input to the “Socrates TAL activity data analysis system” for processing.
 

Education data and AI 

Artificial intelligence (AI) is intelligence, frequently displayed as decision making, by man-made machines, usually computers (Wikipedia, 2017b). AI can facilitate “individual specific teaching” which is becoming a direction explored for use in AI education and which mandates data collection and analysis. Data is generated at every stage of the TAL process and the very first step to be taken in the application of AI in education is the gathering of this data (The Internet Education Center, 2017). The TEAM Model Smarter Discussion Room can collect vast amounts of TAL activity data, both automatically and semi-automatically. The “Socrates Analysis System” makes the most of AI machine learning technology by data gathering, analysis, modeling and behavior prediction. 

The “Socrates Analysis System” analyzes TAL activity data characteristics and generates reports to assist the observing experts and teachers in the scientific study of course activities. In a TEAM Model smarter discussion room the TAL activity data are recorded by the HiTeach interactive TAL system, and automatically uploaded to the IES cloud TAL platform. The gathered data is immediately available to the experts and teachers for discussions and study, a diagram of the TAL activity data collection process is shown in Figure 7.   

The TEAM Model smarter discussion room collects TAL activity automatically and analyses data as follows: 
1. Active trend data will reveal whether the teachers direct the interaction between themselves and the students or if the students initiate the discussions. 
2. Effective interaction data gives information about the frequency of effective interactive during a teaching period. This data allows the determination and tallying of exchanges between groups, individuals, or the class as a whole and the teachers about particular interactive points. 
3. Tool usage data reveals usage of the smarter classroom tools in each course and the statistics include frequency, time ratio, and the interaction intensity of individual tools.
4. Method application data provides information about characteristics and trends in the application of methods used in each course from an analysis of the TAL activity data from each segment. 
5. Question raising marker data gathered by expert observers using their Socrates interfaces can be used to study recognized question raising markers. This information can then be used to supplement or correct the AI system. 
6. Response tag data gathered during course periods by the experts will reveal the effectiveness of response markers. This information can then be used to supplement or correct the AI system.  

 

 Development Status and Application Cases 

The “Wisteria Cloud” smarter discussion room  

The Yindu Wisteria Elementary School, part of the Chengdu Normal College in the Chengdu High-tech Zone, Sichuan, China has built an innovative TEAM Model smarter discussion room. The “Wisteria Cloud” smarter discussion room is being used to train smarter teachers and to improve professional development for use in the school-wide smarter classroom. This smarter discussion room has a complete TBL smarter classroom environment, and teachers conduct classroom TAL (see Figure 8 top) with mixed groups of students and use HiTA, IRS and tablets to carry out multiple interactions with the students. Seats along walls of the classroom allow other teachers or guests to attend and watch the interaction between the teachers and students. There is also an observation room with a large one-way mirror where experts can observe teaching sessions without intrusion, see Figure 8 below. In addition to the one-way mirror, there are screens in the room that show real-time TAL activities relayed from cameras in the classroom using the Socrates observation program.

 

The “Wisteria Cloud” smarter classroom and smarter discussion room

Data gathering and application analysis

After the open courses, the teachers and observers, guests, and experts hold discussions and study the “TAL activity data analysis report” collected by the “Socrates System” as well as videos and smart markers from the ezStation smart recording system. This includes the TAL activity characteristics data and expert’s comments based on observations as well as the Socrates TAL activity videos and AI analysis. This makes it possible to review a 40 minute teaching session in one minute and helps teachers refine their approach in a scientific way. 

The key to developing smarter education is the smarter classroom and the smarter teacher. The nurturing of smarter teachers is greatly enhanced by the excellent training facilitated by using the TEAM Model smarter discussion room. The large amounts of TAL data collected allow fast and efficient guidance and realization of an excellent teaching model. First rate methods evolve which are not affected by the subjective factors suffered by traditional teacher assessment. This instant feedback ratchets up the efficiency of professional instruction by objective data analysis and TAL professional knowledge. Teachers may align the TAL activity analysis report (see Figure 9) with their course design to advance teaching content and evaluate the quality of their teaching in an autonomy-controlled model. 
A look at the smarter classroom presented in a team race by the cross-strait smarter classroom program showed that teaching periods were carefully designed after dozens of refinements using TAL activity data analysis. This valuable source of information can be used for teacher’s professional growth, and the TAL activity analysis report shown in Figure 10 (Teacher Xu Da-wei) shows at least 10 interactions that suggest rather diversified technology. This “Technology-time distribution chart” shows that Teacher Xu deployed 5 whole class interactive Q&A sessions followed by a short interval of student selection or discussion. Teacher Xu used the statistics diagram after Q&A sessions which suggest that each question raised was dealt with carefully. Two were intentional secondary replies which show that Xu's decisions were made after careful thought. The second part of the session involved learning by page push, receipt, and work alignment, followed by the presentation and exploration of the key concept with discussion and weight snatch by students in timed presentations. It is interesting to note that using a score card led to longer times spent on the interactive whiteboard. A score card could also be provided for group scoring to enable fast tallying. 



Figure 10 TAL activity data analysis report (Teacher Xu Da-wei).


Based on the TAL activity data analysis report shown in Figure 10, the first part of the course given by Teacher Xu was designed along Q&A lines with interaction being required as well as digital decision making. Score cards were used for all the course sessions as a tool for team work enhancement. Tool use was comprehensive and appropriate which showed a high degree of familiarity with technology. Enough time for exploration and expansion was left in the second part and the combination of two parts demonstrated a clear tempo and definitive strategy.

A TAL activity analysis report by Teacher Wang Yu, the R&D Director of the Affiliated Experimental Elementary School of the University of Taipei, Taiwan is shown in Figure 11. Teacher Wang is one of the few smarter classroom system users in Taiwan who has more than 10 years’ experience of the system. She has received several awards in smarter classroom research and has been invited to give lectures and training programs in the subject on both sides of the Taiwan Strait. Her diagrams show TAL activity data analysis, including statistics of tool use frequency and time, teacher-student trend analysis, effective group interaction analysis, and method application analysis that explore smarter classroom structure in a most scientific way. The goal of this language course is to teach students to anticipate the thoughts of characters described in text. Teacher Wang set up the course with three complete structured segments, two Q&A sessions, and a team work course with added teacher-student Q&A, as well as lectures by the teacher, group discussions, and student presentations between each section. These parts together form a complete language course. 
From the autonomy analysis results shown in Figure 12 it can be seen that the whiteboard marking use frequency score in “Tool use frequency” was ahead of the others. This is the case with most other TAL situations and suggests that the teacher has encouraged positive behavior. “Double split compare” accounts for the highest score in use time indicating that a lot of time was spent in discussions and the review of student work. The “Lead trend analysis” suggests the course is teacher lead while some time is open to student initiated group discussion. “Method application analysis” suggests the course features higher interaction throughout. The “Socrates Analysis System” keeps a complete log of Teacher Wang's TAL activities for analysis and diagram generation. 


Figure 12. Autonomous model analysis 
 

Conclusions 

The TEAM Model smarter discussion room makes it possible for both teachers and experts to observe the course sessions and follow their growth. It facilitates the growth of the teacher teams and the use of education administration units and bureaus can also advance the professional development in smarter schools, as well as integrating big data, analysis review, the awareness of differences and targeted professional assistance. The model can also help with the integration of district education resources and the growth of teacher specialty. The TEAM Model discussion room is a combination of the smarter classroom with a “diagnosis room” where experts can assess progress using big data output from TAL activity. This not only enables education experts to ramp up their professional skills in scientific ways, but also provides a new direction for professional improvement in teaching.

 References

• Hongkiat (2017). 8 Technologies that will shape future classrooms. Retrieved from https://www.hongkiat.com/blog/future-classroom-technologies/  
  The Internet Education Center (2017). Education AI shall have even more application 
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  Wikipedia (2017a): Big data. Retrieved from https://zh.wikipedia.org/wiki/%E5%A4%A7%E6%95%B8%E6%93%9A
  Wikipedia (2017b). Artificial intelligence. Retrieved from https://en.wikipedia.org/wiki/Artificial_intelligence