Micro-Course Model Based on Precision Teaching Theory

The course of Sports Scientific Research Methods is mainly designed to help students acquire knowledge on sports scientific research methods in order to lay a good foundation for their engaging in sports scientific research in future. Considering the current teaching system which is not timeefficient, “Internet+”-based micro-course was developed to focus on Precision Teaching model of the Sports Scientific Research Methods. To conduct this study, students’ overall learning process data, classroom teaching process data and teaching affair administration data were collected firstly. Individualoriented Precision Teaching model was designed by integrating a series of new educational ideas and analysis methods such as Precision Teaching, big data analysis and adaptive learning technology under the context of digital learning. The teaching model was set with Precision Teaching function modules based on “Internet+” technology in the form of micro-course for teachers and students respectively. Lastly, student learning attribute analysis system was built to focus on student attributes for evaluating student learning ability, learning state and task independence. Teaching practice proves that the model is helpful to promote students’ learning interest largely and has enriched the teaching model of the Sports Scientific Research Methods. Keywords—Precision Teaching; micro-course; scientific research methods; Internet+


Introduction
The Sports Scientific Research Methods involves physical education, exercise training and sports management under the guidance of mathematic statistics theories and methods [1]. As a research tool of modern sports science, it aims to help students mastering relevant sports scientific research methods. It will train students to acquire good scientific thinking and research ability by designing teaching content and collecting and analyzing statistical materials with scientific research methods, so that they can possess the capacity required for sports scientific research in future [2]. Usually, traditional teaching mode is adopted for this course. Specifically, the teaching activities of this course mainly include classroom teaching, written assignment and final examination. This mode is useless for students who don't have a comprehensive understanding and a good command of effective scientific research methods and students usually lack practical ability [3]. In view of this, educators are supposed to continuously reform the teaching mode and teaching methods of this course. The reform goal of the teaching mode and teaching methods of this course can be realized only by differentiated the exact teaching in accordance with students' aptitude. It helps students comprehend the connotation of sports scientific research methods & theories and improve the practical ability of dealing with real problems.
This study aims to explore how the teaching mode and teaching methods of various courses should be reformed in the context of information technology by case study of the Sports Scientific Research Methods. Based on this, a micro-course-based scientific research method course and teaching management system was set and a web-based open course management teaching mode was provided to mainly focus on the learning mode of online course users. Meanwhile, a lot of application scenarios for micro-course mode were developed. The teaching objectives were specified clearly and simply by means of WeChat micro-course, convenient sound or video recording. It was adopted to enable students deeply understand the applications of the course content of the Sports Scientific Research Methods through repeated feedback and exercises. Besides, a new teaching mode for the Sports Scientific Research Methods was formulated under the theoretical guidance of Precision Teaching in accordance with students' learning and cognitive characteristics based on precise student position with a view to provide reference to the educators in teaching of scientific research methods.

State of the Art
Currently, the overseas and domestic researches on teaching mode are developing rapidly with the development of information technology and network technology. "Precision Teaching" was proposed based on Skinner's behaviorist learning theory. Precision Teaching refers to a teaching method of meeting the learning needs of individuals and the groups with learning difficulties by planning teaching programs [4]. Strømgren et al. [5] applied Precision Teaching in the teaching of mathematics for 20 minutes a week and 8 weeks a school year and conducted tests before and after the course. The experimental findings showed that the group receiving Precision Teaching had made greater progress than the group receiving conventional mathematics teaching did. Lu et al. [6] tried to apply Precision Teaching in the course of computer programming. To conduct the study, the methods of group design and case analysis were adopted and 10 students were taken as research objects. The result showed that the experimental group comprehended programming problems more accurately and had higher learning efficiency. Kubina Jr et al. [7] found that Precision Teaching is made for improving the educational effect on autistic students and considered Precision Teaching as a method of frequently evaluating student performance, promoting curriculum decision-making and training student fluency. This is how the teaching methods could be enriched based on the learning characteristics of autistic students. The booming of big data provides opportunities for the development of Precision Teaching in China. For example, Mi et al. [8] discussed individualized teaching in the context of big data and pointed out that information technology was helpful for grasping students' individual needs more accurately. Jiang [9] made analysis by integrating teacher with students developed a harmonious teaching process and improved students' learning interactivity. Yang et al. [10] optimized lesson mode and explored the features and popularization mode of Precision Teaching and individualized education in the context of big data. These literatures all reflect the directions and methods of education in the "Internet+" data era. Fu & Tan [11] constructed a Precision Teaching mode based on big data from the perspectives of establishment of teaching objectives, design of teaching process framework and teaching evaluation and prediction which provides reference for the optimization of the teaching mode referred to herein. Nevertheless, it is also pointed by some literatures that the existing Precision Teaching designs cannot be applied in current teaching design directly with the popularization of digital learning, development of new educational ideas and the gradual maturation of artificial intelligence technology. In addition, there are practical difficulties in respect to data, theory, and analytical technology. Micro-course [12][13] is a teaching mode of developing and improving teachers' teaching skills using modern audiovisual technologies and equipment. In this mode, the teaching process of the "teacher" can be recorded with recording and broadcasting system and the teaching time of 10-15 minutes can help simplifying micro-course. This study aims to explore the performance of the reformed teaching mode, based on big data by focusing on Precision Teaching, supplemented by micro-course technology and innovatively applying it in the teaching of the Sports Scientific Research Methods.

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Precision Teaching method in scientific research method teaching 3.1 Efficient teaching method based on "Internet+" Precision Teaching paradigm based on "Internet+": Precision Teaching is mainly applied to individuals and children having difficulties in acquiring some professional skills via programmed instruction. For application in basic education, the mode usually needs to be furnished with clear teaching objectives, courses that can be modularized and monitoring function for evaluating teaching efficiency.
With the penetration of "Internet+" into the educational field, students' learning environment has turned from multimedia environment into digital learning environment and the learning time and space continuously extend and develop. The education units and institutions have built an information environment rapidly and introduce it in elementary and secondary schools via learning platform, mobile APP, digital terminal and wearable devices. The teaching mode based on "Internet+" is based on big data has improved the scientific teaching mode and the intelligence level of teaching technology in teaching research process, built a data-driven teaching mode in the context of big data and drastically reformed the conventional teaching mode as shown in Fig. 1.
In the experience-imitation teaching paradigm, teacher shows the dominance in the overall teaching structure. Most of the students are passive receivers, teaching content mainly covers booklore, experience and skills teaching media includes paper, pen, book, blackboard, chalk and other conventional teaching aids. Contrastively, in the era where the "Internet+" big data applications become increasingly popular and teaching aids start to evolve into more scientific and personal tools as shown in Fig. 2. It is realizable to build education big data by making full use of big data on educationrelating fields combining with learner needs and learning progress. Also combining with gathering data on learner status and warning information, make scientific data mining and analysis, and establish individual learning sources of smart teaching preparation to provide necessary conditions for the implementation of Precision Teaching.  iJET -Vol. 15, No. 14, 2020 Precision Teaching method based on "Internet+": The Precision Teaching mode based on "Internet+" is mainly implemented on the basis of students' overall learning process data, classroom teaching process data and teaching affair administration data. For example, teacher designs a mode of teaching students in accordance with their aptitude by turning from experience-orientation into exact pattern and analyzing students' preliminary study performance based on big data analysis. The teaching mode also has been turned from group teaching into customized differentiated and individualized teaching. Promoting individualized learning is good for students to realize multi-dimensional intellectual development and form key competence.
In this teaching mode, the original closed learning environment has turned into a learner-centered open learning environment and is continuously evolving into a learning environment with obvious digitization features supported by data analysis and centered on learners. The learning data throughout the learning process is recorded and the in-depth explorations of artificial intelligence. User modeling and educational data mining technology provide more accurate analysis results for measurement, inference and implementation of learner data. In the context of digital learning, we integrated a range of new educational ideas and analysis methods such as Precision Teaching, big data analysis, adaptive learning technology and designed an individual-oriented Precision Teaching mode as shown in Fig. 3. The mode, taking learner model general and data updating as design core and starting with analysis of student's personal characteristics and differentiated information. It integrates pre-class learning plan design, learning effect analysis, differentiated in-class teaching interaction guidance, after-class individualized independent study in accordance with the idea of teaching students and in accordance with their aptitude. In its implementation, the main task before class is to design differentiated activities to help students mastering basic knowledge at their cognitive level and the task in class to design differentiated learning objectives based on the completion status of learning content, weak point in knowledge and engagement in activities. In class, it is supposed to select a teaching mode suitable for the course content and design, implement and guide teaching based on the results of analysis of student individual difference and learning evaluation. After class, it is suggested to ask students to conduct independent exercise via adaptive learning platform, so as to further internalize and grasp knowledge via the individualized learning resource pushing and learning path on the platform. Fig. 3 shows a schematic diagram of typical Precision Teaching mode based on "Internet+". It shows that students' individual behaviors in learning process are taken as basic data to get detailed information on students' learning state and included in the teaching mode system as analysis factors. A student model is built on this basis, and evaluation is conducted on student learning ability to further develop Precision Teaching. In the implementation of teaching process, it is also supposed to make full use of detailed learning information of students and teach students in accordance with their individual differences. In this process, relevant information on "Internet+" should be made full use of.  Fig. 3. Precision Teaching method based on "Internet+"

Precision Teaching curriculum design of the sports scientific research methods integrated with micro-course
The main purpose of Precision Teaching is to evaluate and track learners with the technology of big data analysis, customize learning plan and guidance, intervene the learning process via key learning points. Moreover, to form a complete closed Precision Teaching learning feedback system that can guide teacher promptly. In the context of big data, how effective information technology can be made full use of to facilitate the optimization of teaching mode is one of the main research directions of teaching reform.
Precision Teaching function module integrated with micro-course: At the time while mobile network is universally popularized; the conventional means of education have been abandoned and various new-style teaching aids play an important role in the education field. For example, micro-course, also called "small teaching" refers to a method of train student or developing teacher's skills using modern teaching techniques and methods. In this mode, real-time teaching recording and automatic courseware generation are realized by the aid of video and network. Moreover, interclass live video communication is realized via the functions of various modern media facilitating teachers in teaching experience exchange [10]. The Precision Teaching mode integrated with tools that make teaching function modules more vivid, convenient and visual, which is good for getting a better effect. In this study, Precision Teaching function modules integrated with micro-course technology have been developed by combining micro-course means with Precision Teaching mode. The framework diagram is as shown in Fig. 4. In this mode, specialized analysis is made on the recorded teaching process reflecting micro-course means via "Internet+" teaching cloud platform and data analysis platform. The analysis covers before-class watching of teaching video demonstration, drafted teaching lesson plans, in-class demonstration videos, after-class teaching evaluation and analysis in Precision Teaching process. Micro-course can realize real-time recording of this process and precisely position of individual students, so that the teacher can have a full understanding of the implementation of classroom teaching, conduct individualized teaching better, and achieve the goal of Precision Teaching.

Fig. 4. Precision Teaching function module integrated with micro-course based on "Internet+" technology
Precision Teaching design integrated with micro-course technology: Fig. 5 shows the framework diagram of Precision Teaching mode integrated with microcourse based on the foregoing contents which reflects the information of both teacher and students. Teacher makes full use of micro-course technology in teaching content arrangement, definition of starting point of teaching and teaching design as well as teacher-student interaction and online instruction. Students make full use of microcourse videos and courseware to realize online learning preparation, interactive discussion, advance preparation, in-class internalization, online review and online testing for consolidation as shown in Table 1

Precision Teaching mode management system of the sports scientific research methods based on "Internet+"
To conduct the study, a teaching management system was established in accordance with RBF network theory of which the basic profile is to map the input of low-dimensional space to higher-dimensional space with hidden unit RBF and fit curve. RBF network is a type of forward network. Its input layer is constituted by signal source nodes, the hidden layer corresponding to it is the second layer and the third layer corresponds to linear output layer. The number of hidden units contained in the hidden layer depends on specific circumstance. Hidden units correspond to RBF radial basis transformation function. The topological structure is as shown in Fig. 6. In this study, a student learning attribute analysis system was established based on student attributes for evaluating students' learning ability, learning state and task independence as shown in Table 1. Students' participation <0. 1 5 Student individualized learning characteristics statistical data was established by means of quantitative analysis for building a student learning state analysis model. In this study, a teaching management system was built based on the data analysis model of attention that students should obtain. The specific practice is to take mass student data as sample, extract its characteristic value, take the characteristic value data as the learning data of neural network and automatically output judgement standard on student learning concentration after neural network training to help establishing a rational teaching management system. The mathematical model of attention that students should obtain meets [A, R, I] indicators. A refers to student activeness, R refers to feedback on learning, and I refers to learning independence. The proportion relation of the indicators is: The ARI values are shown in Table 2. The relation between the attained ARI values and the corresponding attention that students should obtain is shown in Table  3.  Gaussian radial basis function is used to build the neural network analysis function based on this data and the mathematical model was built to facilitate database storage of student individual learning needs, personalized performance and capability of independent learning.
The proper data should be normalized before being input into RBF network. Normalization means normalizing the range of indicator input value into the interval of [0，1] by operation. To conduct this study, the maximum/minimum method is selected. Both input data and output data were normalized into the interval of [0，1] with the following formula: Wherein i X is input data, min X is the minimum of input data and max X is the maximum of input data.
The below is further analysis of topological structure of fixed network. There are 25 nodes at the input layer of RBF: 5 radial basis function nodes at the hidden layer of RBF and each corresponds to one type; there is one node at the input layer via which evaluation result is output. Before L-M iteration, K-mean clustering algorithm was employed to determine the weight of output layer and set initial value and the center and variance of radial basis function nodes of hidden layer. The basic idea of K-mean clustering algorithm is to minimize total intraclass distance (sum of distances from sample to center in all classes).
The processing procedure is as below: 1. Initialization: number of clusters k, number of iterations N; minimum deviation of subsequent iteration  ; current generation number 0 = t ; select k centers at random (usually the first k centers) j m .
The variance is determined with the empirical equation is as below.
Wherein, max d is the maximum Euclidean distance among the centers, and h is the number of centers. The weight of the output layer is determined to be random number uniformly distributed in the interval of (-1,1). As in parameters of L-M algorithm, 0.1 1 =  , the number of iterations is 5000, and the error precision is -2 10 .

Effect evaluation and analysis
The collected 528 sample data relating to the course of Sports Scientific Research Methods was submitted into RBF network for training. Formula 5.6 was applied to make the built model consistent with the questionnaire survey result on graduates for this study to ensure the correctness of the model.
In this study, MATLAB functions were used to establish RBF network for analyzing student Precision Teaching analysis model and the Precision Teaching mode model of the course of Sports Scientific Research Methods was obtained finally.

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Teaching Example and Effect

Teaching example
The process of the course of sports teaching method implemented herein is provided below. Specially adapted teaching of the course of sports research methods has been conducted in combination with the steps and contents of Precision Teaching method and based on the Precision Teaching analysis model which consists of teaching time allocation and teaching process as shown in Fig. 8. Fig. 9

Teaching effect
The teaching results were evaluated based on the theory test scores and usual performance. The online and classroom participation rate of the observation group is 100% on an average. The test scores of the observation group is higher than that of the control group and the difference is of statistical significance (P<0.05). Students' satisfaction with curriculum provision also was surveyed. The survey result shows that the students of the observation group are satisfied with the Precision Teaching mode of the Sports Scientific Research Methods based on Internet+ in this study as shown in Table 4. Up to 81% students show favor of this teaching mode, 89% students consider the Precision Teaching mode has improved students' independent learning ability largely and 93% students believe it can stimulate learning enthusiasm. In micro-course mode, students can make full use of electronic information products and students are encouraged to contact and use information via the internet to broaden their intellectual field. Students' participation has been improved significantly. 79% students like interactive question and answer session and 81% students think that excellent resources can be obtained via the Internet+ technology. before, in and after class, so that students can seek guidance from teacher when having doubts, communicate with classmates and submit assignments for summarization. In this way, students can integrate theory with practice well which is good for improving learning effect. 3. Meanwhile, the audio-visual micro-course process can reflect students' knowledge mastery level in time. Teachers can make use of audio-visual technology to present the method design of scientific research methods and operations of statistically software to students visually, so that students can consolidate knowledge via the pictures, videos and audios shared by teachers repeatedly to enhance the teaching efficiency. Besides, students can make discussions via WeChat group through which teachers can make review and guidance to give feedback information to students. Moreover, this eases the teaching burden of teachers to a certain extent and raises teachers' work efficiency.