Analysis and Exploration on Construction of Intelligence Teaching

Environmental System

Feng Zhao

and Manling Cheng

1,2

Modern Education Technology Center, Wuhan Business University, Wuhan, China

Keywords: Wisdom, Teaching ,Laboratory.

Abstract: The construction of intelligence teaching environmental system in colleges and universities has been at an

early stage for quite a long time. To successfully complete the intelligence teaching environmental system,

the effort of innovation and development are also needed in terms of educational theory, technology

application and system construction. The author carries out an in-depth study of the definition of the

intelligence teaching environmental system, and conducts field research in colleges and universities. The

study found that there is a shortage of intelligent interaction in current intelligence teaching environmental

system. Therefore, based on this study, the author put forward suggestion of constructing intelligence

teaching environmental system.

1. INTRODUCTION

In recent years, with the rapid development of

education informatization, both colleges and

universities are actively exploring the establishment

of the "intelligence teaching environment”. However,

some of the construction models only improved the

teaching facilities without setting up a truly

"intelligence teaching environment". How is

"intelligence teaching environmental system"

formed? What is its connotation? This article

analyzes the concept and status separately of

intelligence teaching environmental system, and

provides valuable suggestions for the future.

2. DEFINITION OF

INTELLIGENCE TEACHING

ENVIRONMENTAL SYSTEM

CONCEPT

At present, there are only few international

researches on intelligence teaching environment,

including the research "Active Learning Classroom"

in University of Minnesota and "iRoom" in Stanford

University. After searching international

authoritative periodicals, it has been found that

theories as "the development of learning modes that

new technologies enter the classroom", "providing

adaptive learning content" and "the impact of

learning environment on learning performance" are

mainly focused by researchers.

The 2015 China White Paper on Intelligence

Learning Environment pointed out intelligence

learning acts as one type of learning system, which

automatically records learning processes and

evaluates learning outcomes through modern high

technologies such as Internet of Things, big data and

artificial intelligence(Ronghuai Huang,

2015).Intelligence teaching environmental system

should utilize cutting-edge technologies to manage,

aggregate, analyze, and drive multi-dimensional data

such as teaching, infrastructure facility, campus

activities, students' behavior trajectories, and related

intelligence teaching environment; construct

holographic network environment, cloud computing

Data center, big data analysis application system and

multi-dimensional IoT perception system which

aims to realize the precise distribution and allocation

of related resources in the intelligence teaching

environmental; integrate high-quality resources and

advanced technologies to build an advanced,

efficient and practical intelligence teaching

environment; realize the organic integration of

education and communication, teaching and research,

learning activities, educational administration and

campus infrastructures; realize the intelligent

decision-making, intelligent implementation and

intelligent evaluation of teaching and learning

process so as to realize online interactive teaching

without limitation of time and space; make sure that

high-quality resources can be easily shared. Through

construction of intelligence teaching environmental

system, it has good effect on university construction,

reform and development, promotion the in-depth

integration of information education technology, and

optimization of teaching environment.

3. CURRENT CONSTRUCTION

STATUS OF INTELLIGENCE

TEACHING ENVIRONMENTAL

SYSTEM

At present, many institutions are trying to build up

intelligence teaching environmental system. The

author conducted field researches in some typical

universities and summarized the types of

intelligence teaching environmental system:

3.1 Upgrade of General Laboratory

This type refers to the renovation for completed

laboratory, with the benefits such as enhancing some

equipment parameters, advancing the multi-media

device configuration, making seat configuration

more flexible and comfortable. With high-resolution

teaching tool, hive layout and other measurements,

“intelligence classroom" is to be built.

3.2 Interactive Teaching Platform

Application

"Intelligence classroom" usually refers to multiple

interactive teaching platform. The common

scenarios are including: fixed space with LAN

interaction. non-fixed site with interactive Internet.

mobile interactive teaching.

Fixed space with LAN interaction. This

interactive system is generally installed in the

computer room, where each student is equipped with

a desktop computer. Through the LAN interactive

teaching system, it can basically have achieved to

exchange data, download and upload information

between teachers and students.

Non-fixed site with interactive Internet. This

kind of "remote classroom" is usually equipped with

high-definition video camera, live broadcast,

recording and broadcasting management system,

which can cover more subjects to be taught across

multiple regions.

Mobile interactive teaching. Through unified

identity authentication, such systems can make it

more convenient and frequent to produce and record

the data of the teaching process, and initially form a

statistical report on the teaching situation.

3.3 Teaching Resources Library

Some colleges and universities are strengthening the

utilization of cloud resources with the assistance of

network teaching resources. For example, the

construction of "sky classrooms" and "online

courses" and other learning platforms provide more

options to be taught. Such learning platforms often

require students to take the initiative to learn to

reflect actual teaching value.

3.4 Internet of Things Applications

Some universities have increased IOT control

terminals, such as the electric curtain with light

control, air conditioning with external temperature

sensor in the laboratory, so that the entire teaching

environment can automatically adjust within the pre-

set threshold according to the external environment.

This type of teaching environment basically

achieves the full coverage of the network and

combines some cutting-edge technologies to display

an outline of intelligence teaching environment.

4. SHORTAGE OF TECHNICAL

INTEGRATION ON

INTELLIGENCE TEACHING

ENVIRONMENTAL SYSTEM

Through summary of status, it can be seen that at

this stage there has not been successful case of

"intelligence teaching environmental system" which

centers on teaching subjects. Currently, cutting-edge

technologies don’t complete interoperability, big

data cannot generate higher value and there is no

closed loop of teaching ecology. Here the author will

analyze in terms of environmental perception, data

standards and personality development:

4.1 Lack of Full Awareness of the

Teaching Process

Most of the IoT technologies used in intelligence

teaching environment are only very simple

applications of the perception of external teaching

environment, without perception of teaching

process(YongheZhang,2012). To record

intellectually the teaching process in the classroom,

technologies such as IoT and artificial intelligence

have to be used to capture every aspect of the

process, identify, detect and analyze the video

captured by the embedded intelligent analysis

module. Based on the algorithms of artificial

intelligence and pattern recognition principles, data

that can truly reflect the teaching process will be

produced

4.2 Lack of Unified Data Standards

At present, colleges and universities have initially

realized the application in different teaching

platforms, such as teaching interactive platforms,

online courses, sky classrooms, etc. These teaching

platforms or systems can obtain high-frequency

knowledge data from students in different teaching

environments. However, due to lack of unified data

standard, the purpose to drive learning method

through data cannot be achieved. These high-

frequency knowledge data cannot receive effective

data management, and cannot provide effective data

support for big data analysis.

4.3 Lack of Personalized Teaching

Resources to Match

The most fundamental feature of intelligence

education is purpose to change the traditional

teaching mode with information technology (Kekang

He,2015). In the traditional teaching mode, the

relationship between teacher and student's

knowledge transmission generally belongs to the

unified knowledge pushing mode. Teachers cannot

get the feedback of pushing knowledge in time.

Students also receive all or part of knowledge from

one person to another. In the end, the reception can

only be reflected through variety of centralized

exams. This traditional teaching model cannot

maximize the potential of students. To construct

intelligence teaching environmental system, it is

necessary to change three points: (1) to change the

methods and modes of knowledge dissemination and

management so that the knowledge become

touchable and pushed to the students in time; (2)

Positioning individual role of teachers and students

to achieve accurate push of knowledge; (3) through

highly interconnected teaching facilities and

analyzing teaching needs based on habits, it can be

achieved to intellectually distribution of teaching

facilities and form eco-friendly and energy-saving

intelligence teaching environmental system.

5. CONSTRUCTION DIRECTION

OF INTELLIGENCE

TEACHING ENVIRONMENTAL

SYSTEM

The author believes that the construction direction of

intelligence teaching environmental system should

innovate and integrate in terms of system

construction, technology application and theoretical

model. Several suggestions are listed below:

5.1 Strengthen the Top Design,

Improve the System Construction

The construction of intelligence teaching

environmental system includes series of construction

directions such as the campus environment, teaching

facilities, discipline construction and system

construction. In the process of forming intelligence

teaching environmental system, it is needed to

emphasize the top-level design, improve the system

construction, attach importance from school level

and cooperate from all departments. For example,

after reaching an agreement on the association

between the public cloud and the private cloud, the

identity authentication of teachers and students can

be associated with the Internet data to realize the

hybrid application of the public cloud and the

private cloud.

5.2 Multi-technology Integration to

Achieve Ecological Information

Closure

Through new generation of information technology,

it is to achieve the reconstruction of the entire

education information system. So that teaching

resources and personalized push of data and

information will be precisely distributed, along with

intellectual adjustment of teaching process, teaching

methods, teaching environment and teaching

resources. It is expected to form a closed loop of

information ecology in intelligence teaching

environment system driven by data intelligence.

5.2.1 Collection Integration and

Management if Multi-model Big Data

IoT data that can be collected and researched

through IoT data collection includes classroom

facilities (classroom doors, curtains, air conditioners,

speakers, lights, etc.), video surveillance data

(classroom video surveillance and auto tracking,

etc.), Human Data (Infrared), and various other IoT

data content.

5.2.2 Teaching Data Collection

The new teaching data collection model must take

IoT and mobile Internet as environmental

parameters to collect students' learning data,

curriculum data and classroom teaching interactive

data based on the teaching data at the level of

schools and teaching units, the teaching data at the

curriculum level, the teaching operation data of the

network course etc. Rich teaching resources are

determined by variables and models based on

specific circumstances and needs, so that it can be

able to understand students' learning situations in

different aspects and in multiple dimensions.

5.2.3 Campus Business Data Collection

With the high technology of distributed high-speed

high-reliable data acquisition, high-speed data whole

image, it can be achieved to conduct secondary

development for the collection tool; at the same time,

for business data which cannot get from the system

interface, it can be analyzed manually and check the

data accuracy through database access.

5.2.4 Students’ Track Data Acquisition

Based on the students 'basic information, location

information and behavioral action data, combined

with IoT concept and technology, through direct

acquisition and correlation analysis of two ways

‘acquisition to analyze the student's behavior

trajectory in school to provide data foundation in

terms of analyzing students' personal behavior,

group behavior characteristics and behavior early

warning to improve the management of student

behavior and control.

5.2.5 Relevant Intelligence Teaching

Environment of Internet Data

Collection

Through the crawler engine technology such as

crawling the recruitment website and talent demand

network data, it can be researched for the

relationship between positions and capabilities and

achieved to establish individual demand capability

model, so that different talent demand competency

and industry recruitment analysis can be pushed to

students; Secondly, through keyword searching,

targeted monitoring and analysis of hot forums and

site data, data reference and early warning can be

provided for the school to control public opinion risk.

5.2.6 Governance Research of Multi-model

Big Data

The establishment of full data warehouse: Through

tapping full data and incremental data from the

application system, it can establish a full original

data warehouse by Hadoop. Through standardization

of original data and storage in standardized database,

then an application theme database is established

through modeling and analysis. Subsequently, the

theme database is synchronized to application access

library to provide data access for front-end

applications. The retrieval center supports the

management and retrieval of the overall data

warehouse.

5.2.7 Full Data Backup Management

Data warehouse contains the original full data and

incremental data, standardized data, as well as the

model theme data, that is, to achieve a full backup of

the database; if backtracking of business data is

needed, the platform realize the data backup through

the independent storage of incremental data, and

manage the different versions of the backup edition

in visual way by collecting the full amount of the

original data into the full database, customizing the

backup strategy of incremental data and lock the

data of each backup in the meantime.

5.2.8 Research on the Application Interface

with Big Data Open Standard in

Intelligence Teaching Environment

Based on the big data open standard in intelligence

teaching, referring to tri-factor system of

management, supervision and working, guaranteeing

the premise of interface access performance and data

processing performance, adopting custom shading

technology and column storage technology, studies

on application interface development under the data

storage diversity of big data platform can explore the

best application service interface architecture under

the big data architecture and establish a high

performance distributed service interface that can

access all the data storage uniformly to meet the

needs of the school application development and

data query.

5.3 Build up an Open, Interactive

Intelligence Teaching

Environmental System.

Based on the five levels of campus management,

teaching, service, employment and research, studies

on the application of various types of data in the

campus, can analyze the data variation on both time

and space dimensions. Considering the development

trend of data in the future, it can achieve to establish

the management and service model of intelligent

prediction and machine learning. Based on the

campus big data management and teaching service

model, it can also have deeper investigation of

associated analysis through data acquisition, data

storage, data cleaning, analysis modeling, business

presentation and others; Through analysis and

judgment of the teachers’ behavior, it is helpful to

recognize the students, understand the students,

discover the individual characters adequately and

achieve individualized management. It can achieve

to provide each student with services such as

personal information services, early warning, study

and employment; provide good controllable teaching

support and resource application services for

teachers' teaching as well as convenient interactive

teacher-student communication services; Provide

data report for management and decision support.

6. CONCLUSIONS

The essence of intelligence teaching is to provide

better services and more convenient teaching

support for teachers and students by making full use

of advanced technical measures to improve

education management and decision-making process.

The formulation of teaching policy is no longer a

simple empirical imitation, nor limited

understanding, hypotheses and inferences by policy-

makers without full-scale surveys, arguments and

scientific judgments. Instead, it is emphasized that

more refined capture of changes at all levels Data,

and the complex correlation and causal relationships

demonstrated by the data, could turn the crisis of

instructional and policy decisions into opportunities.

In the aspect of teaching decision-making, the

intelligence teaching environmental system will help

policy-makers to understand the status more clearly

and acquire more comprehensive and valuable

information in a timely manner. The formulation,

implementation and adjustment of data-driven

teaching policy will be made. In the construction of

intelligence teaching environmental system, we

should promote steadily and improve the

construction gradually as a long-term, ecological and

advanced systematic project.

ACKNOWLEDGEMENTS

First and foremost, I would like to thank my

workmates Manling Cheng, Feng Zhao for their help

in investigation and requirements analysis. Besides,

the campus network in Wuhan Business University

as an experimental base plays an important role in

this research. Last but not least, the research is

funded by a Natural Science Foundation Project of

Hubei province (2018CFC901) and a Teaching and

Scientific Research Project of Wuhan Business

University (2017N020).

REFERENCES

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Institute of Intelligent Learning, Beijing Normal

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2. Yonghe Zhang, Guangde Xiao, Yongbin Hu,

Ronghuai Huang.2012.Learning Situation

Recognition in Smart Learning Environment——

Learning Learning Environment to Serve Learners

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89.

3. Kekang He.2015. The transformation of smart

classrooms + classroom teaching structure——the

fundamental way to realize the grand goal of

educational informationization.Education

Research,(11):76-77.