Research on the Model of Mobile Classroom Teaching under the Information Environment

Abstract: Informatization and big data have changed the way humans acquire knowledge. In the basic education sector, the organic combination of “Mu Class” and “Flip Class” brings great challenges to the class teaching system. How to solve the problem of personalized learning, teaching students in accordance with the aptitude and improving learning efficiency in the classroom teaching system based on the big data concept in the information environment is a very real problem. In this regard, the teaching model appeals under the support of informatization and big data are studied. The model construction of the mobile autonomous school is expounded. The classroom teaching support platform is designed according to different roles, and the application situation is briefly discussed.

Keywords: big data mobile autonomous school teaching mode flip classroom

Informatization and big data have changed the way people acquire knowledge. How to make full use of the technological advantages of informatization and big data to meet the individualized learning needs of students and improve the level of school education and management has become an inevitable trend of education reform. In the basic education sector, the revolution with the “flip classroom” is rapidly coming. The organic combination of “Mu Class” and “Flip Class” brings great challenges to the class teaching system. [1] How to solve the problem of personalized learning, teaching students in accordance with the aptitude and improving learning efficiency in the wireless network environment based on the big data concept is a very real problem. In view of the above problems, the article studies from the classroom teaching mode, classroom support platform requirements and design.

I. Teaching mode appeals supported by informatization and big data

The wireless network provides us with the infrastructure of mobile learning. Mobile learning can solve the problem of traditional teaching time and space constraints. It can realize teaching and learning anytime and anywhere, and carry out the 4A learning mode of “Anyone”, “Anytime”, “Anywhere” and “Anystyle”. Big data points out the direction for objective evaluation of learning effects and teaching quality. "Mu class" and "flip classroom" have become the hotspots of "teaching" and "learning" models in the information environment. But how to build a classroom teaching support platform based on wireless network and big data, absorb the advantages of "Mu class" and "flip classroom", and combine the basic teaching system of China's basic education class? To this end, we designed and built the "mobile independent school" "[2] Teaching mode.

(1) Design of the teaching mode of mobile independent school

The mobile autonomous school contains students, teachers, and administrators who can interact with the server via the Web or iPad (or other tablet) to achieve the desired functions, such as questions, volumes, assignments, exams, and Questions, corrections, etc. The teaching mode is shown in Figure 1.

Figure 1 Teaching mode

The web browser mode mainly provides administrators and teachers with a graphical user interface to facilitate the use of the computer for system management, including system parameter setting, user management, question bank management, test paper management, exam management and teaching quality analysis. .

The tablet mode can serve all roles: the administrator can understand the specified teachers and class conditions; the teacher can realize real-time questions, roll out, arrange assignments, correct assignments, change volumes, and query student learning; students can realize real-time learning, Tests, exercises and other functions.

Taking “Mobile Independent School” as the core, we also designed a “four-class” progressive self-learning method, as shown in Figure 2. The basic mode is: first learn, concentrate, post-test, and re-learn, that is, the teacher sends a resource package to each student through the student learning support service system in advance, including the guide, courseware, test questions and related learning resources (including micro Video, etc. → Student reference resource package, self-study according to the textbook, and record questions or questions → students can display feedback learning results through tablet or other media, or pre-test through the student learning support service system, and demonstrate the learning results through testing or Question → The content of the difficult and difficult points is dialed by the students or teachers, and the summary is summarized on the basis of fully questioning the communication (teacher and student interaction) → Finally, the practice evaluation class is conducted through the learning platform, and the system automatically counts the test scores and analyzes them. Students, teachers or systems conduct comments and evaluations.

Figure 2 "Four lessons" progressive learning


(2) Breakthrough in classroom reform of mobile teaching

The mobile autonomous school is a classroom teaching support platform built on the wireless network, which fully absorbs the advantages of wireless interconnection. Teachers can use resources to support the preparation of lessons, classes and other teaching links according to teaching objectives, teaching content, teaching methods, etc., and establish knowledge points. Intrinsic connection. The classroom teaching supported by this classroom teaching support platform can meet the following requirements.

First, meet the requirements of classroom teaching. MOOC and flip classroom can not support all aspects of classroom teaching, and mobile independent school supports all aspects of classroom teaching, including preparation, class, questioning, classroom exercises, unit tests, exams, student evaluation, etc., and has operability and Convenience.

Second, classroom teaching can be organized anytime and anywhere. The classroom teaching style has limitations. The flipping classroom can't carry out classroom teaching in real time. The mobile autonomous school can organize classroom teaching without any time and place under the support of wireless network.

Third, support various forms of teaching models, including the MOOC mode and the flip classroom mode. The MOOC is a typical mode of teaching. The flipping class is the first mode.

Fourth, support teaching students in accordance with their aptitude. Based on big data, data such as teaching behaviors and learning behaviors are automatically or manually acquired, and evaluation systems and data mining models are established to objectively evaluate learning effects, teaching effects, and student analysis. Based on these data and evaluation information, we will teach students in accordance with their aptitude.

Fifth, support the opening and sharing of teaching resources. In principle, the mobile autonomous school supports various forms of teaching and learning.

Second, the model construction of mobile independent school

(1) Teachers and students enter the role of mobile autonomous learning

1. "Student" role

After entering the mobile autonomous school, students will see their unfinished tasks, including the exams, homework and learning resources issued by the teachers; their own learning tasks, such as viewing learning resources and wrong exercises; the system is appropriate according to the learning curve algorithm. The time is arranged for the student's corresponding learning tasks. If the student does not review and practice a certain knowledge point for a long time, the system will push the corresponding learning resources and exercises to the students for review and practice. Students can view their recent learning records and keep abreast of their learning. The learning record includes information on which resources have been recently learned, the time spent on each resource, and feedback on the test situation, including the number of test questions and the correct rate of each knowledge point. Usually, exams and homework will produce wrong questions. Using these wrong questions can effectively improve learning efficiency. Students can use the "wrong book" function of the mobile self-learning class to query the most recent wrong questions according to the chronological order (reverse order), the number of test errors (reverse order), knowledge point classification and randomness. Each wrong question can be Perform real-time exercises, each session is automatically saved into the system, and the weight of the wrong question is adjusted according to the right or wrong of the result. At the same time, the system can automatically push knowledge points and learning resources related to a wrong question to facilitate students to conduct targeted learning (teaching in accordance with their aptitude). The mobile self-directed classroom test and homework function can automatically eliminate the test questions that the students have firmly grasped according to the student's learning record, thereby shortening the study time and improving the efficiency. Students can independently sample questions in the question bank (pre-screened by the system according to the algorithm) or specify screening conditions, and push questions related to students' bad knowledge points for students to practice according to the characteristics of students ( Shorten the study time). At the same time, according to the learning records of high-score students, the system pushes the students' learning resources and exercises for the students who are currently logged in to practice, and adjusts the push parameters according to the test conditions of the exercises to explore the learning mode that is most suitable for the students. The system effectively classifies learning resources for each student's different learning characteristics. The system establishes a network structure of knowledge points and learning resources, and establishes a hierarchical structure (mass resource classification) according to the difficulty specified by the teacher and the difficulty data formed during the actual testing process. Students can select the learning resources of the knowledge points, and the system automatically records the time taken by students to learn each resource, denoted by t. Each learning resource is automatically set by the system according to the resource content at the time of storage, and is represented by t0. When t>t0×1.5, t takes 1.5 times t0, which means that if a student learns a resource for too long, it can be considered that only 1.5 times of the standard time is learned. This can eliminate some artificial operations and avoid the consequences of affecting statistical analysis. For each learning resource, students can practice immediately after learning the resources and fight hot.

2. "Teacher" role

Teachers can use the iPad or other methods to specify questions, and specify the attributes of the questions, such as associated knowledge points, abilities and difficulty factors. For the difficulty coefficient of the test questions, the system can calculate according to the student's answer, automatically push the questions with higher error rate to the teacher and give suggestions, such as the topic is too difficult, the explanation is not enough, so as to optimize the question bank. In order to improve teaching efficiency and resource utilization, the system can count the usage of each resource, including the number of times and time of learning, and push notifications for resources that are used too frequently or too little. At the same time, the system also monitors the students' learning of designated resources, including what resources have been learned recently, how much time is invested, how the results of the questions related to these resources are, etc., so as to more accurately understand the learning situation of students and improve the efficiency of classroom teaching. Teachers can publish classroom exercises through the examination system, and timely check the students' mastery of learning so that they can solve the problems in the students' learning in this class. According to the historical data, the test system pre-screens the test questions in the test question bank, eliminates the test questions with very high correct rate and high frequency in the near future, and at the same time displays the test questions that are too high in error rate and rarely appear in the near future. More suggestions to improve the quality of the questions and to teach them in accordance with their aptitude. In the aspect of personalized teaching, the student's learning situation inquiry function in the system can make the teacher understand the overall situation of the students, including the knowledge points and problems with high error rate. At the same time, the queried data is corresponding to the time input of the corresponding student learning resources to assist the teacher in analyzing the reasons for the student's loss of points. You can also learn about your recent study files and exams, practice situations, including their weak knowledge points, blind spots in resource learning, etc., in order to give individualized learning suggestions to individuals.

(2) Creating a learning space for teachers, students and students

1. Teacher-student, student-student interaction

The mobile autonomous school adopts the teaching mode of learning, introductory, post-testing, re-learning, and having teachers to participate. In the mobile autonomous school, teachers can adopt flexible and diverse teaching methods according to the type of subject, characteristics of knowledge, characteristics of students, teaching objectives and teaching content, and the system can automatically record student behavior and teacher behavior data. According to the data provided by the system, the teacher can understand the learning situation of each student. The students can also learn the mood and learning effect of a certain knowledge point by means of “likes” or “disapproval”, “smiley face” or “cry face”. The teacher responded by responding to other situations. Students can compete for learning in a certain knowledge point. Teachers and students can initiate topic discussion for a certain knowledge point, and realize teacher-student interaction and student interaction in classroom teaching. More importantly, this allows for the acquisition of real data for student analysis and management.

2. Personalized learning

In the classroom teaching, although the students are orderly under the teacher's arrangement, the class time is mainly concentrated on the teacher's answer to the difficult question or the teaching content. Students who have not learned or missed classes in class can log in to the “Mobile Independent School” outside the classroom to learn the same content in classroom teaching. Outside the classroom, the system makes personalized recommendations for each student's learning path and recent learning situation, focusing on the difficult points in the teaching process and the error points of each student. Teachers can also personalize guidance based on data from student error questions recorded by the system.

3. Learning track and growth record

The mobile autonomous school can record the student's learning process and study habits in detail, and with the guidance of the teacher, the role of these data can be fully utilized.

(3) Changing traditional teaching through mobile independent schools

Since 2012, the Mobile Autonomous School has been applied in 10 innovative classes in 3 grades of Zhengzhou No. 2 Middle School High School, and has achieved remarkable results in 3 years. The new teaching mode frees high school students from the model of the college entrance examination system in terms of growth space, time, and freedom of thought. Students' learning becomes easier and their performance is significantly improved. The 2014 college entrance examination results showed that the online rate reached 27.45%, doubled from the previous year. Many media have reported on this application. For example, on March 20th, 2014, China Education News published a long-form report entitled “Mobile Academy”. The application of the mobile autonomous school also attracted the attention of the Ministry of Education. The leaders of the Ministry of Education had personally visited the classroom and gave high marks.

The mobile autonomous school supports the MOOC and the flipping classroom mode, which can realize “one-on-one” digital learning, change the monotonous teaching mode in the past, and make the classroom glow with innovation and vitality to achieve effective teaching and learning. [3] In addition to statistical analysis and results presentation, the mobile autonomous school has also established several data mining models based on the big data concept, such as student preference mining model, integral incentive model, and evaluation model. Among them, it is particularly worth mentioning that the knowledge point relevance model.

Practice has shown that the analysis of the degree of relevance between different knowledge points in the same discipline and feedback to teachers and students can help effective learning. From the database of the second grade organic chemistry, the author selects the five names of "names of organic compounds", "alkanes and their properties", "olefins, alkynes and their properties", "benzenes and homologues and their properties", "esters and their properties". , respectively, represented by A, B, C, D, E, and analyzed the degree of relevance, some of the analysis data are shown in Table 1.

Table 1 Knowledge point test student association table


Among them, the A knowledge point is 5 points, the B knowledge point is 4 points, the C knowledge point is 10 points, the D knowledge point is 10 points, and the E knowledge point is 10 points. In order to facilitate statistics, the knowledge point corresponds to a full score of 1 and the wrong answer is 0.

According to the data in Table 1, and using the C4.5 algorithm (decision tree constitutes algorithm 1), the nodes are strongly correlated to construct an improved C4.5 decision tree, as shown in Figure 3. According to the decision tree, based on the improved C4.5 algorithm, if D (benzene and homologues and their properties) are correct, then A (name of organic compound) is also correct, the probability is 96.29%; if D (benzene and homologue And its nature) and C (olefins, alkynes and their properties) are also wrong, then the probability of A (name of organic compound) is 85.7%; if D (benzene and homologues and their properties) is wrong C (olefins, alkynes and their properties) are also wrong, then the probability of A (name of organic compound) is also 83.3%. It can be seen that the mastery of "benzene and homologues and their properties" affects the learning of "naming of organic compounds", and teachers and students can rationally arrange teaching and learning plans based on this result.

Figure 3 Knowledge point relevance decision tree


Third, the conclusion

On the basis of absorbing the nutrition of "Mu Class" and "Flip Classroom" mode, this paper discusses the issues of mobile autonomous school design and teaching mode, and designs and develops a support platform for classroom teaching and wireless network-based mobile autonomous school. Above the school, teachers can use tablet computers to prepare lessons, class, arrange homework, test, exams, evaluations, etc. Students can use the tablet to learn, test, test, complete homework, ask questions, and learn independently. From the aspects of teaching mode and learning mode, the mobile autonomous school is the application of the “Mu Class” and “Flip Class” modes in the classroom teaching of class teaching in our country, which highlights the classroom teaching and takes into account extracurricular learning. After three years of application practice in the high school innovation class, significant results have been achieved, and students' innovation ability and teacher professional level have improved significantly. The next step is to improve the evaluation system and to set up different data mining models for different requirements, and to enrich the analysis model library, in order to support classroom teaching more effectively.

Note:

1 Decision tree algorithm is a method to approximate discrete function values. It is a typical classification method, which first processes the data, uses the inductive algorithm to generate readable rules and decision trees, and then uses it to analyze the new data. Typical algorithms for decision trees are ID3, C4.5, CART, and so on.

references:

[1] Chen Yuxi, Tian Aili. Introduction to MOOC and Flip Classroom [M]. Shanghai: East China Normal University Press, 2014: 56-72.

[2]Wang Rui, Li Yongbo, Wang Xiaodong, et al. Mobile Independent School and Its Application[J]. Journal of Henan Normal University: Natural Science Edition, 2014(6): 168-172.

[3] Liu Hongyu. Zhengzhou No. 2 “one-on-one” digital “mobile school” to achieve effective teaching and learning [EB/OL].(2014-06-27)[2015-08-27]. http://

(The author is the second middle school principal of Zhengzhou City / Master's tutor, doctoral student, senior teacher of Henan Normal University)

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