Badminton Training Auxiliary Teaching System Performance Analysis Based on Artificial Intelligence

Headnote

Abstract: With the widespread use of computer technology in the field of education as well as the rapid rise of the artificial intelligence, intelligent computer auxiliary teaching system has come into being. The badminton training mode for Chinese athlete shifts from traditional on-site teaching to the teaching combined artificial intelligence with computer-aided instruction. ICAI system sets education science, psychological science and cognitive science etc. as the theoretical basis, adopts the artificial intelligence and many other computer-related technologies and has strong comprehensiveness. Targeted on the demand for badminton athletes' training mode change, the practical ICAI student model has been established based on the badminton basic skills training principles and setting the computeraided instruction system as the theory platform. This mode has a high intelligence, including the general characteristics of CAI, having the natural language ability, being able to identify different student models and provide relevant teaching environment for them. It has been known from the experimental analysis that the ICAI system established in this article has a good effect for the special training and physical fitness of the badminton athletes.

Keywords: Artificial intelligence; computer aided instruction; badminton; special training; fitness training.

(ProQuest: ... denotes formulae omitted.)

1. Introduction

With the popularization and development of China's badminton, more and more colleges and clubs open the badminton courses and badminton training institutions. Most of the badminton courts are indoor and they won't be influenced by the weather changes. Its feature of entertaining and having a wide participating group makes the number of the people who participates in badminton increase day by day. Tracy Morgan (Handel Liao Y C., 2007) thinks that the core is in the central region of the body. This area includes abdominal muscles, hip muscles, and back muscles. These muscles play a very important role in the human body's movement. They can not only provide the needed energy for the body sports to keep the body in balance under special circumstances, but also enhance the coordination of the body to complete the technical action. Travis Brown (Pilkington R M., Hartley J R., Hintze D., et al., 1992) thinks that the body's core muscles are the rectus abdominis, external oblique, internal oblique, transversus abdominis around the abdomen and the erector spinae around the spine, as well as the muscle groups connected with it. (Paul Li K F., Takano K., Johnson M G., 2011) thinks that the muscles of the core region are composed of 29 muscles, mainly including the rectus abdominis, external oblique, internal oblique, gluteus medius, gluteus maximus, tensor fascia lata, Iliocostalis, biceps femoris, psoas iliac, and erector spinae. Generally summarizing, the core muscles refer to the generic terms for the muscles to effectively control the body's center of gravity and support the body's balance. Its beginning and ending range is from the middle of the thigh to the center of the chest. In the early 1970s, SCHOLAR system of JRCorbonell (Li K F., Takano K., Johnson M G., 2011) to teach South America came out, creating this new direction of ICAI. Subsequently, the WHY system of Stevents (Papastergiou M., Gerodimos V., 2013) .etc. to teach rainfall factors analysis , the WEST systems of Burton (Hsu H H., Lee C N., Hung J C., et al., 2013) to teach arithmetic operations, and BIP system of Barr (Mokhtar I A., 2005) to teach BASIC language and other ICAI systems have come out successively. The early research of ICAI focused on the representation of the professional knowledge. In the late 1970s, AI techniques are used to construct the student model which represents the students' status and the teacher model to reflect the teachers' teaching methods and styles. In the 1980s, people began to continue to further study ICAI design principles from the perspective of cognitive psychology, and at the same time, the application of AI technology is more specific in-depth to make the education system have a higher level of response sensitivity and overall concept. This paper introduces computeraided teaching system and its characteristics, and establishes the intelligent computer aided teaching system combining the basic theory of Badminton training, and analyzes the structure and characteristics of ICAI system, to provide a more convenient system of badminton training.

2. Relevant Theoretical Basis Introduction

2.1. Principles for Badminton Basic Skills Training

The basic technique should be correctly mastered and being familiar with on the move. The badminton technology has the characteristics to complete the action on the move. In actual combat, the techniques and footwork are complementary and closely linked. The actual combat requirement will be deviated if the techniques are exercised without exercising the footwork. As a coach, during the teaching training of technique basic skills, the factor "hitting the ball" should be considered and appropriate training should be designed and organized so that the athletes can correctly master and be familiar with the action technique on the move. Continuously creating difficulty and mastering the basic skills training familiarly in the confronting situation is an adaptation to the new stimulus itself and the technical training is no exception. Badminton technology is complex and changeful, having very high demand for the human body's nervous system. Increasing the training difficulty continuously, especially in the case of confrontation, can not only improve the coordination of central nervous system and the flexibility of the body regulating function, but also make the technical movements to gradually achieve coordination and correctness. So during the technical training, the coach should pay more efforts in terms of the difficulty of suiting the remedy to the case and arrange in the appropriate time to enable the athletes to master the technology in the case of confrontation (Runa, A. I. D. N. F., & Miranda, G. L., 2015).

On the basis of developing the comprehensive technology, for the currently training to outstand advantage technique or the main technique, the world badminton technology is developing fast and comprehensively. The so-called "comprehensiveness" means that each attack and defend tactic should be mastered. Such as the high, lob, smash and chop for overhead technique, the receiving smashing, receiving lob, lift, drive of underarm technique and chop, hook, push and save on the net position and other technologies should all be mastered comprehensively, and strive to have no apparent weakness, which must be owned by a high-level athlete. So during the process the coach trains the athletes, they should pay attention to the athletes' comprehensive technical shape , especially the juvenile athletes from the amateur sports schools. They should pay more attention on technical training. The training method for arrested development only for one-sided pursuit of achievement should be forbidden. However, as a high-level athlete, it's not enough to only have the comprehensive technology alone, they must also possess certain advantage technology or unique skill. It means that in the crucial moment in the game, the skill can give the opponent a serious threat, or even the fatal blow. It's particularly important for the top class athletes.

Mastering and developing of the advantage technology must work closely with the personal play and tactics. Mastering and developing advantage technique must be in accordance with the personal play characteristics and tactics. The formation of the athlete personal play characteristics is affected by many factors, including physical conditions, such as height, shape, quality and so on. That technical conditions mean the mastering and applying degree for various techniques, mental condition and the guidance from the coach in training and competition and the gained experience by himself and so on. It needs a development process itself. Therefore, mastering and developing the advantage technology must cooperate closely with this process. For example, in regards to the formation of rapid attack play, in addition to having the relevant strength and speed, the athlete should also have the advantage technique to assault from backfield, and that means the consistency of high, lob, smash, chop technical movements should be strong and the smash should be fierce and sharp while the front court should master the unique skill of chopping, pushing and hooking. Then, in the process of forming rapid attack, the exercise for these advantage techniques should be strengthened. They should develop the advantage technique per personal play characteristics and tactics.

2.2. Definition and Status of Computer-aided Instruction System

Computer Assisted Instruction (CAI) means to make use of computer to teach instead of teachers. The teaching content can't be compiled into various "courseware". The learners can choose different content to learn according to their level so that the teaching content can be diversified and visualized, which is easy to teach the students in accordance with their aptitude, such as a variety of educational software, test database and expert systems etc. CAI has played an important role whether in the general education, higher education or in continuing education. Overseas, CAI courseware has been widely used in schools and at home and has received good results. In China, although CAI research started late, but it developed rapidly. Since the 1980s, there have been a number of powerful institutions of higher education listing the development of CAI as the key research subject. The realization of CAI requires to apply AI technology and compile complex procedures, such as natural language understanding, knowledge representation and the inference methods etc. Some special application results of AI techniques, such as the algebraic description, sign synthesis, medical diagnostics and theoretical proof etc. were all applied into the CAI system to improve its intelligence and practicality. A majority of the early CAI courseware mostly use decision theory and random learning model, which greatly simplifies the expression of the learning process, such as the early geological teaching system (SCHOCAR) and the like. Later, with the development of the artificial intelligence technology, the students' learning behavior and training strategy have been added to the CAI systems, and at the same time, AI technology is used to establish learning consultant module (storage the issues and skills for the taught courses). This method can control the training strategy and give the learning content suitable for the students. Currently, to obtain the flexibility and modularity of representation and control of curriculum knowledge, some CAI systems also use AI techniques to express training programs and strategies, for example, many programming languages CAI all belongs to this case. So far, the whole education information of the vast majority of the traditional CAI being used is preset in the way of programming. Once this kind of CAI courseware is finished, any big teaching change will bring great inconvenience for the maintaining work . Therefore, the existing CAI system faces many challenges, which mainly exist in the following areas:

1. lack of openness: lack of openness is the biggest shortcoming of the current CAI courseware. Users can not make any modifications to the courseware and they can only use the existing resources for teaching according to the setting route. Its shortcoming is that: the limitations of the fixed content make the applications of the courseware so narrow; the setting running route makes teaching lack of autonomy; the pertinence of the teaching isn't strong; the secondary development can't be made on a higher starting point by using the emerging resources.

2. lack of human-computer interaction capability: most of the existing CAI sets light disc as a carrier of information and show up the content of textbooks to in the form of multimedia. The teaching information is provided to the learner according to the preset teaching process mechanically. The learners who use CAI courseware are entirely passive. In the classroom teaching, it can only be operated per the preset courseware by teachers. Both students and teachers can't be well involved in the teaching and learning process, so the human-computer interaction can't be realized well.

3. ignoring the characteristics of the course itself: there are different requirements for each course in teaching, but the existing courseware totally ignores these different requirements. For example, many programs will all involve a large number of curves or surfaces. For some courses, it will be enough to give a simple show for these curves or surfaces, while for some courses, this show can't meet requirements for teaching purposes. For example: when teaching the generating algorithm of various curve or surface in the computer graphics, if the generating process of these graphs can be displayed directly and dynamically in the courseware and the advantages of computer-assisted instruction have been brought into full play, and the teaching for computer graphics will undoubtedly be more attractive, thus to greatly improve the teaching efficiency.

4. lack of interaction between teacher and student : as for the existing CAI courseware in students' self-study and operating to use, how to study is their own business. Teachers can't fully understand the situation of learners. The students can't ask for help from teachers when they encounter problems. The students and teachers are closed to each other , not to mention the interaction between the teacher and the student, and therefore the effect playing by the courseware is greatly reduced. At the same time, due to lack of network support, the vast majority of existing CAI courseware is running in stand-alone environment and they can't take advantage of the network to make knowledge updated quickly, of course, they can't provide a convenient space for study and discussion, teachers and students exchanging ways anywhere, anytime and the realizing conditions for distance teaching.

5. lack of teaching strategies: in the development process of courseware, the teaching strategy design can't be departed actually, but the producer of the courseware often do not realize this. For example: the vast majority of existing courseware is the single type broadcasting. Such courseware is made "finely," but it is not reversible and can't interact. Actually it's only a means and not a purpose to apply the courseware to teach. It should be under the guidance of teaching design theory, the courseware effectiveness should be stressed, and the emphasis should be put on helping students to learn new knowledge, acquire new technology, and train various capabilities rather than the "fine" surface production.

6. lack of intelligence: the existing CAI courseware system can't give the targeted education to different levels of students. Students' learning are passive, and the system can't provide the help to learn information for the students automatically to make the students learn selectively. For the teachers, their teaching can't be actively involved and they're unable to prepare the most suitable learning content based on the information provided by the system in accordance with students' cognitive model and can't give teaching mode and method in different ways. So it does not have the intelligence . In summary, there are many problems for the existing CAI. With the continual emergence of new technologies, these problems will make CAI increasingly unable to meet the new requirements. Therefore, the new computer-aided teaching system represented by intelligent CAI will become the development direction to continue to explore on education technology and strive to achieve (Tenenbaum G, Bar-Eli M., 1993).

2.3. The Definition of ICAI

Computer Assisted Instruction (Computer Assisted Instruction, CAI), is a form of computer in education. It's a modern teaching system to make the computer as a teaching media to complete the teaching process and process and delivery for teaching information, and the organic integrity is formed by teacher - computer - students. The application for computer aided instruction mainly lies in two aspects: aid classroom instruction and support individual learning. People often call the demonstrating software to aid classroom instruction the CAI courseware, while they call the software which is suitable for students' individual learning the CAI learningware or system. The CAI can be classified as : CAI courseware which aids the classroom and CAI system based on individualized learning. The CAI mentioned in this paper refers to the CAI system. The ICAI system discussed in the paper is the further development of the CAI system.

Since the early 1980s, the educational researchers around the world and computer education experts have done a lot of research for CAI impact on students' learning process . Under the influence and push of cognitivism, CAI has gradually began to enhance its adaptability to different learners, namely, to strengthen its intelligence orientation. The pursuit of the perfect artificial intelligence CAI has become a hot topic for CAI research. ICAI (Intelligent Computer Assisted Instruction)sets the cognitive science as the theoretical basis and brought the artificial intelligence (Artificial Intelligence, AI) technology into the CAI system and it's intelligent CAI. It is by studying the characteristics of human learning and thinking process, seeking to learn the cognitive model, it is an educational expert system based on knowledge. It is given to mankind advanced machine intelligence system is intelligent CAI. Because the most important feature of ICAI is tutorial, it's often be called ITS (Intelligent Tutoring System)abroad, ie, intelligent tutoring system. The study for ICAI system in this paper not only includes theoretical support but also includes technological realization of ICAI system components module, and put emphasis on the discussing of the student model (Goodyear V A., Casey A., Kirk D., 2014).

3. System Model Construction

3.1. Practical ICAI Student Model

Student model can be seen as an approach for the knowledge level of certain students to the above knowledge structure in learning progress. Overall, it is similar with the cover model, by tracking and monitoring students' learn and test conditions on certain node in AND / OR diagram and mark the node with the fuzzy measure.It's equivalent to model student's mastery degree of knowledge on single concept and the performance measures or deviation model can be used. With the interaction activity carrying on between the system and the students, the fuzzy measures on node spread between the joints on the ND / OR graphs, and this can evaluate the students' mastering degree of the knowledge system of a teaching target. This evaluation for students' cognitive level on the AND / OR graph is realized by the fuzzy measure of chain forward (from the current knowledge to preliminary knowledge) and the chain reverse (from preliminary knowledge to current knowledge). Therefore, in addition to conventional information recording, each node i needs addon domains:

sif : For any chain ij issued by i in AND/OR graphs, because the negative deviation of chain source node i of chain ij , then there is doubt for mastering degree for chain host node j. The realization of propagation algorithm in both directions all adapts delay computing technology. Only one node i is evaluated, the contribution of the chain lodge knowledge j of all chains ij connected with it to it will be considered. When the node i has the negative bias, it then deliver this negative bias to the chain lodge j along each chain ij . This pass can only happen between the adjacent layers at any moment and it will not affect the entire network and the knowledge points having nothing to do with i.

The behavior for student model on knowledge systems: the mastering degree of a knowledge point will impact to the knowledge point learning associated with it. As described above, the correlation between knowledge points can be represented with AND / OR diagram clearly. This section will use the spread of calculating fuzzy measure on AND / OR diagram to describe this correlation between the knowledge points.

From the point of view of knowledge understanding, there exists two semantic association between knowledge points:

Mastering preliminary knowledge will bring help to the understanding of the subsequent knowledge. It shows that in the AND / OR figures of student model, the mastering of chain lodge node will be helpful to understand the chain resource knowledge.

The showing difference for the mastering of the same knowledge point, especially the difference the student shows comparing with the historical record to some extent reflects the defects in their understanding on the preliminary knowledge. For example, students has forgotten the past.

In order to show the student's cognitive status completely and correctly, the effect the above-mentioned two correlations causes should be portrayed in the model.

The relationship between Some knowledge point i in AND / OR figure and the knowledge point related with it can be shown in Figure 1. It has a series of preliminary knowledge nodes (lower layer) and several subsequent node (upper layer) which sets i as the preliminary knowledge. We can depict the impact of the preliminary knowledge on the subsequent knowledge from two aspects:

1. The influence for the mastering condition of lower layer node to the understanding of node i;

2. The influence for node i to upper layer node;

According to the delay computing technology, only when knowledge point i is considered, the influence for its preliminary knowledge then will be considered. So, (2) can be reduced to the calculation of si , and only (1) is considered.

In the preliminary knowledge of node i, some are connected with AND chain(such asα1,^,αn ), some are connected with OR chain(such as on+1,^,om). They have different contributions to understand i. The entire αj ( j= 1,^,n) has influence to i and any understanding of ok(k= n+ 1,^,m) will be helpful to i. S when discussing the contribution, the two types of nodes should be handled differently. According to the disjunctive normal form in the mathematical logic, use one transformation to classify the preliminary knowledge point in the lower layer and get a series of (such as p) node cluster (Feng Y., Lapata M., 2013). They construct OR chain relationships each other and inside the cluster, there is AND chain relationship. The transform intuitive schematic diagram is shown in Figure 2.

In the preliminary knowledge of node i, some are connected with AND chain(such asα1,^,αn ), some are connected with OR chain(such as on+1,^,om). They have different contributions to understand i. The entire αj ( j= 1,^,n) has influence to i and any understanding of ok(k= n+ 1,^,m) will be helpful to i. S when discussing the contribution, the two types of nodes should be handled differently. According to the disjunctive normal form in the mathematical logic, use one transformation to classify the preliminary knowledge point in the lower layer and get a series of (such as p ) node cluster (Feng Y., Lapata M., 2013). They construct OR chain relationships each other and inside the cluster, there is AND chain relationship. The transform intuitive schematic diagram is shown in Figure 2.

O in the figure is the virtual node and so is the virtual OR chain.

For certain cluster of K, k∈(1,^, p) , if there are n nodes totally and the effect is:

...(1)

For p clusters, the combined effect is:

...(2)

The contribution for the preliminary knowledge to understand the current knowledge point can be represented by a monotonous non decreasing function f: R[arrow right][0,1], it can be:

...(3)

And order:

...(4)

Algorithm 2: Reverse delay calculation for Fuzzy measure.

Input: node i.

Output: σ.

Working variables: c, ck, l.

steps:

1. ...

2. for all the chain ij circulation issued by node i, set the control variable to j

...

IF chain ij is chain AND, THEN

Circulate for this cluster AND chain, set the control variable to l

...

3. ...

In the learning process, the phenomenon to recognize that there is defect for the past recognition and then need to restudy often happens. Forgetting of the Knowledge is an example. The forgetting of the preliminary knowledge will be shown in the test of its subsequent knowledge point. The student model should be able to track the defect of the preliminary knowledge from the poor performance of the subsequent knowledge. This track performs that the non-zero elements Sif(Sif< 0) of the node i opposite transmit this defect according to the AND/OR chain of their lower layer preliminary knowledge point. It manifests as the following:

The responsibility that the cluster K acted as the preliminary knowledge of i should take to cause S if < 0 is:

...(5)

...(6)

Where: The integer parameter L should be determined by the domain model, and all the weight w on AND / OR chain must be taken from;

W represents the average contribution intensity for chain lodge node l (l= 1,^,n) of n AND chain ij within a cluster k to the node i. If the chain ik is OR chain, W= wk,n= 1.

4. The responsibility that the node j should share for n AND node within cluster k is that:

...(7)

For any chain im ,

...(8)

...

3.2. The Features of ICAIM

In addition to have general CAI features, ICAI system also has the natural language ability and can identify different student models and provide the relevant teaching environment for them and has the function to diagnose students' mistake and can give the diagnosis prescription. It should understand the teaching materials, organize them reasonably and inference according to a certain way. Its biggest feature is it has certain intelligence and can guide the students individualized. It has changed the traditional teaching model and it can more bring the enthusiasm of students, and is helpful for students' intelligence development and ability training and it's the new method to realize the modernization of the teaching method. In order to achieve this, the computer must accomplish three W, that is to know or understand the course content, to understand the object of education and to know the teaching methods ( ie, WHAT, WHO, HOW). Therefore, the general ICAI system should have the following features:

It can automatically generate into a variety of questions and exercises;

It can select and adjust learning content and progress according to students' learning level and learning condition;

It can automatically solve the problem and generate the answers based on the understanding of teaching content;

It has the natural language generation and understanding ability in order to realize the relatively free teaching quiz systems and improve the initiative for human-computer interaction;

It has the explanation and advisory ability for teaching content;

It can diagnose the Students' mistakes, analyze the causes and then take corrective action;

It can assess students' learning behavior;

It can continue to improve teaching strategies in teaching.

As far as the existing scientific and technological level, the ICAI system to have all of the above functions can't be achieved in short term. It's generally believed that we can call it ICAI system if it has one or several CAI characteristics.

The guiding ideology for ICAI system is to make the education process based on computer scientific and personal. The scientific means that the system should incorporate the understanding of modern people on the education and should incorporate scientific research fruits of the modern education. Personalization means that the system should better meet the different needs of different students and can identify the learning status (the mastering status of knowledge and skills)the students currently have so that it can decide what information should be provided to the students in what way. Therefore, ICAI system is a dynamic system and the students are no longer limited to the preset procedure. In ICAI system, the machine solving the problem is not carried out in predetermined step, but under the guidance of the control strategy, it looks for the answers by exploring and reasoning. it's achieved by knowledge-based exploration and reasoning and using student module to dynamically generate content and strategies suitable for individual teaching. It can judge students' level of knowledge, diagnose their mistakes, judge the reason causing the mistake and then generate relevant correction strategy to make ICAI always be able to meet the needs of different students thus to achieve a high level education (Feng Y., Lapata M., 2013).

3.3. Classification of Cognitive Learning Theory

Cognitive learning theory includes various learning theories setting cognitive processes as the main research object, such as Gestalt learning theory, Tolman's sign learning theory, Bruner's cognitive structure learning theory, Ausubel's recognition structures assimilation learning theory, Gagne's information processing cognitive learning theory and constructivist learning theory and so on. Depending on the difference of person's internal information processing hypothesis, cognitive learning theory can be divided into two schools: information processing theory and constructivist theory.

Information processing theory of learning: since the modern learning theory has the impact of information processing theory, more and more people accept the idea of the computer simulation and they analogy the learning process to the computer's information processing. The basic concepts of learning information processing theory is that by means of information science and computer science, combine the human cognitive process and the information processing by the computer to study human's study. The theory states that the computer's information processing and the treating process have similarities with human's cognition process. The human brain can be explained by the computer processing information thus to get the model for the people to process the information. Cognitive learning theory suggests that external stimuli acts on the student's senses to make the nervous system to produce the corresponding activities. According to the multi-store model for memory raised by Atkinson (JWAtkinson) and Shifrin in 1968,the memory is divided into three interrelated systems, namely the sensory memory, short-term memory and long-term memory. Sensory memory effects as sensory selection passing one or two felt information to short-term memory. Shortterm memory capacity, stored information items and the time that one item can be saved are limited. After many times use of the information stored in short-term memory, through semantic encoding conversion, the information can be transferred from shortterm memory to long-term memory. In the human brain, the information is stored based on the significance of the information. When asking students to produce behavior, we must search for long-term memory, and extract the searched information and skills from long-term memory to short-term memory, and combine new incoming information to form the new learning ability, or through the reactor to convert the information into action. In 1974, Gagne (RMGagne) based on modern information processing theory put forward the basic model of the learning process. This model shows the information flow (Khot T., Natarajan S., Kersting K., et al, 2013; Walklate B M., O'Brien B J., Paton C D., et al, 2009; Majumdar P., Khanna G L., Malik V., et al, 1997) in the learning process as shown below:

3.4. ICAI System Components

The initial motivation for ICAI was to create "intelligent tutor" which can be comparable with the excellent teachers. So it must give the machine advanced intelligent - namely to understand the learning ability, learning situation and the current level of knowledge of different students and it should be able to select the best teaching strategies for the students according to different characteristics and allow man-machine conversation to use natural language to convey communication information. Thus, although the forms of ICAI system vary, but it mainly consists of four parts: Expert Knowledge, Student Model, Tutorial Model and Interface. This system architecture has become the mainstream of the international ICAI.

"Student model," records students' level of knowledge and historical knowledge; "teacher model" makes intelligent teaching strategies; "natural language interface" communicates the message passing between learners and teaching system; "Expert Knowledge" Stores the course professional knowledge to teach the students and is able to generate questions and provide the correct answer to the question and the problem solving process. ICAI system model is shown in Figure 4:

Expert Knowledge is constituted by the field knowledge, which includes two aspects of knowledge: one is teaching materials, question information, teaching materials emphasis, difficult and evaluation and other related courses; the second is about the application of this knowledge to generate questions and solve the questions. Its mission is to compose of teaching materials, generate questions and evaluate the correctness of students' answers to questions. This part is equivalent to the expert part - based on facts to obtain answers by deductive reasoning. Expert Knowledge does not only contain the knowledge itself but also include the ability to use that knowledge. In other words, the expert knowledge can understand the students' knowledge level in this area based on the obtained students' information, The Objective of student model unit is to be able to dynamically represent the students' knowledge and skills being formed. To achieve this purpose, in the student model unit, it must have the diagnostic capabilities to infer students' level of knowledge based on students' performance in the teaching activities. The objective of teachers model unit is to design teaching ways, arrange teaching content and manage students' learning reaction. In order to "intelligently" distinguish individual differences between students, student model unit and teacher model unit are inseparable. Teachers model unit determines the teaching activities to be used according to the students' level of knowledge, the system teaching objectives, individual student learning variable and other factors and whether to give tips, advice, interpretation, new teaching materials and different exercises activities,and can use tests to confirm or revise the student model. People-machine interface unit is the part to be used to render the teaching content, and communicate with the students face to face and it's ICAI appearance and also the only part that the students can see with their naked eyes (LIU Y., LI X., 2012).

4. Experimental Results and Analysis

In order to understand the physical quality of the experimental group and the comparison group before and after the test, the paired sample T-test has been done for the physical quality before and after the test of the experimental group and the comparison group. Please see table 1 and table 2 for the test results.

We can see from the test data of table 1 that after a semester of badminton special training of the experimental group, each data of their physical quality has very significant difference. The improved of shot, throwing solid ball backwards, standing jump scores means that the muscle explosive power of upper and lower extremities has been enhanced. The improvement of 30 meters and 100 meters means that the speed quality has been enhanced. The improvement of 1000 meters then indicates that the badminton special training has a very positive impact on the improvement of endurance. It can be seen from this that the muscle power, speed and endurance qualities of the students in the experimental group after quality has been significantly improved after the badminton special training.

As it can be seen from Table 2 that after one semester of badminton study, except for the significant differences of the data for the side pushing shot in place and throwing solid balls backwards, other testing data of the physical quality all has no significant differences. The reason why there is a significant difference in side pushing shot in place is that the strength of upper extremity, wrist and finger and the action speed have been enhanced during the process of playing badminton. The score of throwing solid ball backwards has also been improved significantly. One reason is that when hitting the badminton on top of the head and smash the badminton, the waist and back muscles get exercised and the strength has been enhanced. Another reason is that when test before the experiment, there are some students throwing the solid ball for the first time and their consistency of technical action is very poor. While in the second test, they have mastered the throwing ball skills, and their technical movements are more reasonable (Xing D.,2013).

5. Conclusion

With the rapid development of modern science and technology, the multimedia computer has been widely used in the education field and has exerted a profound influence to education and the teaching process. The use of computer-aided teaching system can provide the ideal teaching environment, can easily stimulate the learners' enthusiasm and initiative to study, thus to significantly improve the teaching effectiveness. The article firstly introduces the basic theory and the status of computer-aided teaching system, builds a practical ICAI student model by combining badminton technique basis in accordance with ICAI's characteristics, and uses two groups of students as the experimental group and the comparison group to carry out the physical quality experiments. It's concluded by analyzing the test data that the test data of the experimental students who have gone through the badminton special training has been improved significantly and their body quality has been strengthened significantly and especially their muscle fast explosive power, speed and endurance qualities are enhanced. The ICAI system targeted at the badminton training has very good using effect.

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