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Research Article
Interactive Teaching System of Basketball Action in College Sports
Based on Online to Offline Mixed Teaching Mode
Wenbo Guo and Yunjie Niu
Institute of Physical Education, Henan University, Kaifeng 475001, China
Correspondence should be addressed to Yunjie Niu; toutou2020@qztc.edu.cn
Received 8 March 2021; Revised 8 April 2021; Accepted 18 April 2021; Published 22 April 2021
Academic Editor: Muhammad Babar
Copyright ©2021 Wenbo Guo and Yunjie Niu. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
e mixed teaching mode can be used to improve students’ academic performance. In this study, an interactive teaching system of
basketball action in college sports based on online to offline mixed teaching mode is developed. e system is mainly comprised of
teacher function module, student function module, and system analysis module. In the teacher function module, online to offline
mixed teaching mode is introduced to realize the interactive teaching of basketball action in college sports in the form of organic
combination of microclass, massive open online course, and traditional classroom teaching. e student function module is
mainly used to manage the information related to students learning basketball actions. e system analysis module uses the data
mining model based on multiant colony clustering combination algorithm to obtain the learners’ behavior data and then designs a
targeted interactive teaching course for basketball action. After testing, it was concluded that the designed system can improve the
students’ mastery of basketball movement in sports and can be applied to improve student’s academic performance.
1. Introduction
In the tide of higher education reform, a series of reforms
and innovations have been carried out in ideology, theory,
teaching materials, and practice system of physical educa-
tion. e traditional teaching methods cannot meet the
needs of teaching development. With the in-depth reform of
physical education, the implementation of the new curric-
ulum system, and the scientific management of work, the
management content of physical education has greatly in-
creased [1]. is requires physical education administrators
to constantly improve their management level. is includes
the ability to understand sports management activities, se-
riously understand and master the scientific teaching
methods and management content, and actively explore new
ways of physical education management.
e teaching management system of basketball action in
college sports is composed of the main body, object, and
means of sports teaching management and has some sim-
ilarities and differences compared with the teaching man-
agement of other courses [2]. erefore, in addition to the
general rules of teaching management, the management of
basketball action in college sports teaching should also
follow the rules of human body function adaptability, action
formation, and changes of human physiological function.
Only by following these scientific rules of human activities to
manage physical education, can the management task of
basketball action teaching in college be completed more
smoothly [3]. From the perspective of work arrangement,
the teaching management of basketball action in college
sports is composed of several different and interrelated
stages, such as midterm management and final management.
Its characteristics are that there are few managers and more
management contents. It needs to work in an orderly way in
multiple stages, with a large amount of work and compli-
cated management tasks. In addition, the teaching man-
agement of basketball action in colleges and universities has
the characteristics of repetition, circulation, and indirect
coordination. It includes term circulation, academic year
circulation, and irregular circulation. e management
personnel should not only do their own job of organizing
and managing physical education, but also carry out
Hindawi
Mobile Information Systems
Volume 2021, Article ID 9994050, 10 pages
https://doi.org/10.1155/2021/9994050
teaching work to the leaders of higher authorities and
teaching management organizations at the same level. It has
a wide range of work contacts, including both vertical
contact and horizontal coordination. It can coordinate and
deal with the relationship at all levels and solve all kinds of
problems in the teaching process in time; basketball action
teaching management in college sports needs to be in line
with the overall teaching order, subject to the school’s
teaching decision-making. But because it is a relatively in-
dependent teaching mode, it has its own management object
and scope. erefore, basketball actions in college sports
teaching management and school teaching management
have both subordination and relative independence. Man-
agers of basketball action teaching in college sports keep
close contact with students and teachers in the process of
specific management, coordination, and organization of
teaching activities. is is accomplished to ensure a good
teaching environment and teaching order through this
teaching management mode, to create necessary precon-
ditions for serving the students and teachers [4].
e rapid development of modern education technology
and Internet communication technology has affected many
aspects of life. e emergence of Taobao online store has
curbed the monopoly of physical stores in the industry. Didi
taxi has broken the situation of no competition in the taxi
industry, prompting taxi drivers to improve their service
quality. Compared with the conservatism and closeness of
traditional physical education, openness and cooperation are
the most significant characteristics of the “Internet +” era.
e application of new classroom teaching modes, methods,
and resources with stronger interactivity and visibility, such
as microclass and massive open online course (MOOC), has
developed rapidly in China’s Curriculum Teaching Reform
[5]. As the medium of transforming theory into practice, the
innovation of the teaching mode of physical education plays
an important role in teaching. At present, the demand for
interdisciplinary talents is increasing with the development
of society, and the goal of school education is no longer
limited to the cultivation and investigation of students’
single professional courses and professional ability. e
development of teaching content and teaching form has
enriched a lot [6, 7]. erefore, blended teaching mode is
more and more popular among educators and students, and
O2O teaching mode is one of them [8].
O2O mode was initially used in commercial marketing,
which is expressed as online to offline (O2O) in English. It
refers to a marketing mode that drives offline operation and
consumption through online marketing purchase or res-
ervation. It is also called offline business mode. It is applied
in the field of education and teaching, which is called the
O2O teaching mode. At this time, great changes have taken
place in the meaning of O2O mode, which mainly refers to
the sharing of teaching and learning resources through
network information technology, the change of traditional
learning methods and teaching methods, and the creation of
an open, interactive, personalized, efficient, and comfortable
teaching environment [9–11]. rough a certain teaching
design, the teaching mode flexibly combines network with
traditional classroom education and effectively makes up for
the defects of single online learning and traditional class-
room learning. It is not only the mixture of autonomous
learning, collaborative learning, acceptance learning, and
discovery learning but also the mixture of real classroom
environment and virtual network environment or the
mixture of offline communication and online communi-
cation between teachers and students [12].
e main contribution of this work is as follows:
is paper designs an interactive teaching system of
basketball action in college sports based on O2O mixed
teaching mode
e system provides an interactive teaching system and
can meet the design requirements
2. TheInteractiveTeaching System of Basketball
Action in College Sports Based on O2O Mixed
Teaching Mode
2.1. Design of System Architecture. Based on the O2O mixed
teaching mode, all the functions of the interactive teaching
system for basketball action in college sports are realized
according to the user’s browser and the interaction of
basketball action’s interactive teaching server. e database
server of the system can organize store and maintain all the
data related to the system. e Internet is used to connect the
interactive teaching server of basketball action. Students,
teachers, and administrators section of the system send their
access requests to the server through the browser. e in-
teractive teaching server of basketball action is applied to the
interactive teaching course of basketball action on the da-
tabase server. Figure 1 represents the architecture of bas-
ketball action in college sports interactive teaching system
based on O2O mixed teaching mode.
is system is comprised of three parts: teacher function
module, student function module, and system analysis
module. e overall functional structure of the interactive
teaching system of basketball action in college sports based
on O2O mixed teaching mode is shown in Figure 2.
2.1.1. Physical Education (PE) Teaching Management.
e PE teaching management part of the teacher manage-
ment module consists of curriculum management, perfor-
mance management, teaching resources, teaching analysis,
question answering and discussion, class hour statistics, and
system management. In the course management, O2O
mixed teaching mode is mainly used to realize the interactive
teaching of basketball action. e architecture of the O2O
mixed teaching mode is shown in Figure 3.
Microclass, MOOC, and traditional classroom com-
plement each other. e emergence of microclass makes the
teaching method of physical education more flexible, and the
construction of MOOC broadens the coverage of traditional
knowledge dissemination [13]. Making accurate microclass
and massive MOOCS can provide indispensable online
resources for basketball action classroom teaching, thus
organically integrating with traditional classroom teaching
and fundamentally subverting the existing teaching mode.
2Mobile Information Systems
Combined with the successful experience of microclass and
MOOC in classroom teaching, this paper constructs a three-
dimensional O2O sports teaching mode based on micro-
class-MOOC-traditional classroom.
Basketball technology in sports is different from the
teaching of other disciplines. e introduction of microclass
and MOOC cannot completely replace the traditional
classroom teaching. e integration of microclass and
MOOC into traditional sports classroom teaching is not
simple addition. It should combine the characteristics and
needs of basketball teaching course to study the three-
dimensional teaching mode of the organic combination of
microclass, MOOC, and traditional classroom teaching. e
microclass embedded in the interactive teaching of bas-
ketball actions can shorten the time spent by teachers in
explaining the essentials of basketball technical actions.
MOOCS and microclasses are not limited by time and space.
Before classroom teaching, students can complete the pre-
view of new courses and review of learned courses. After
class, no matter when and where students can watch the
video of microclass, they can learn and imitate basketball
actions repeatedly to deepen their understanding and
mastery, so as to realize the organic combination of sports
basketball inside and outside class and save a lot of time for
basketball teaching in classroom of teachers’ to make dif-
ferentiated instruction.
e O2O basketball action teaching mode of “micro-
class-MOOC-traditional classroom” includes four parts:
interactive teaching course construction, classroom teach-
ing, after class learning, and evaluation feedback. e
construction of interactive teaching course is to create
valuable microclass resources in a moody class platform.
Firstly, it should conduct detailed research on basketball
action, decompose the contents of basketball action, extract
Student
Student
Student
Teacher
System administrator
Browser
Browser
Browser
Browser
Browser
Internet
Database server
Basketball action
interactive server
Figure 1: Architecture of basketball action in college sports interactive teaching system based on O2O mixed teaching mode.
e overall functional structure of the interactive teaching system of college sports
basketball action based on O2O mixed teaching mode
Teaching management
module
Student management
module
System analysis
module
Teac hi ng
management
of sports
basketball
Teacher
information
Student
learning
management
Data analysis
Course management Course management
Teaching resources
Teaching analysis
Q&A discussion
Q&A discussion
Credit statistics
Class hour statistics
System management
Personal home page
Control information
Teacher information
Sports basketball study
Score inquiry
Learning resources
Figure 2: e overall functional structure of the interactive teaching system of basketball action in college sports based on O2O mixed
teaching mode.
Mobile Information Systems 3
the essence of basketball action, prepare for the shooting of
microclass, open the moody class platform, provide high-
quality online course resources, and provide corresponding
text teaching materials, self-test summary, learning tasks,
and so on. rough the MOOC platform, students can have
targeted and repeated learning difficulties knowledge points
and can also consolidate and master basketball action
knowledge points through online Qand A, so as to build an
independent learning support system for students. Before
classroom teaching, students can preview the course through
the MOOC platform. e preview task is designed to be
interesting, so as to stimulate learning interest and solve the
knowledge learning beyond the key and difficult points of
teaching. e key and difficult points of teaching are the
important problems to be solved in basketball action
classroom teaching. Classroom teaching focuses on solving
students’ problems and improving vulnerable learning
groups. It also gives corresponding measures for students’
feedback and evaluation of online testing exercises. After
class learning, students can flexibly control the time and
place of autonomous learning, skip what they have mastered,
and focus on consolidating their own unskilled knowledge
points. Feedback evaluation is not only a link between online
and offline but also a booster to promote the continuous
development of teaching. Feedback evaluation includes two
aspects: basketball performance evaluation and students’
periodic evaluation feedback on MOOC platform and
microclass video. Performance evaluation includes evalua-
tion of old teachers, mutual evaluation of students, and
evaluation of offline activities. rough the analysis and
combined with the results of students’ feedback, teachers can
optimize and adjust the course content. Each link of this
mode influences and promotes each other, which is a kind of
teaching mode of learning initiative, integration diversifi-
cation, teaching diversification, and three-dimensional
support [14, 15].
2.1.2. Teacher Information. e teacher information section
of the teacher management module consists of the following
modules: personal home page, teacher information, and
contact information.
(a) Personal home page: teachers’ personal home page
used to publish some teachers’ personalized
information
(b) Teacher information: the age, professional title,
curriculum vitae, and other pieces of basic infor-
mation of the teacher
(c) Contact information: the contact information of
teachers
2.1.3. Student Learning Management. e student learning
management part of the student management module is
mainly composed of the following modules: curriculum
management, sports technology learning, score query,
learning resources, question answering and discussion, and
class hour statistics.
(a) Course management: students can conduct inter-
active teaching of basketball actions, course query,
course selection, and other operations
Platform construction
Platform construction Platform construction
Content decomposition
Shooting explanation
Curriculum design
Classroom teaching
Clear goals
Stimulate interest
Learning task and
guidance
Interactive teaching resources
of basketball action
Grading in class
Grading aer class
Grading aer class
Microclass watching
Communication and
interaction
Problem finding and
preclass summary
Study aer class Evaluation feedback
In-class test
Network record
Answering question
Figure 3: Architecture of O2O mixed teaching mode.
4Mobile Information Systems
(b) Sports technology learning: after students choose the
interactive teaching course of basketball action, they
can learn the project
(c) Score query: after the teacher publishes the scores,
students can query their basketball test scores online
through the login system
(d) Learning resources: some necessary learning re-
sources, such as courseware and software
(e) Q&A and discussion: in the process of learning,
students can communicate with teachers or class-
mates online when they encounter difficult problems
(f) Credit statistics: make statistics on the credits of
courses taken by students
2.1.4. System Analysis Module. e system analysis module
is mainly based on the relationship between the data in the
database, and then a data warehouse is built to analyze the
data. is system introduces data mining model based on
multiant colony clustering combination algorithm, which
can find the information hidden in learners’ behavior data
and then guide PE teaching [16].
2.2. Data Mining Model Based on Multiant Colony Clustering
Algorithm. One of the difficulties in network teaching is
how to monitor the learning process of students, so as to
realize the effective combination of teaching and learning
based on network teaching platform. In this paper, data
mining technology is applied to establish the corresponding
data mining model and to mine the association between
learner behavior data. According to the mining data, dif-
ferent teaching objectives and teaching content organization
strategies are used for different student groups to realize the
hierarchical teaching of students’ network [17, 18].
2.2.1. Model Structure. Figure 4 shows the structure of the
data mining model based on multiant colony clustering
combination algorithm. Ant colony algorithm is inspired by
the foraging behavior of ants. At the core of this behavior is
the indirect communication between the ants with the help
of chemical pheromone trails, which enables them to find
short paths between their nest and food sources.
e first layer of the proposed model is composed of
three ant colony modules with different speed types, and the
middle layer is composed of clustering module, which
combines the preliminary clustering results into a hyper-
graph. e last layer is the graph partition module, which
uses the graph partition algorithm based on ant colony
algorithm to divide the hypergraph twice to get the final
clustering results of learner behavior data.
2.2.2. Clustering Combination. e clustering results of a
known group of learners’ behavior data are expressed as a
hypergraph. Assuming that O�o1, o2,. . . , on
represents a
set of learners’ behavior data, the nlearners’ behavior data
are divided into kclasses, which can be expressed as a label
vector λ∈In. In the rcluster results, the known q-th learner
behavior data λ(q)is divided into k(q)categories, and then it
can get a binary member matrix H(q)∈I. In this matrix,
each cluster is represented as a superedge (corresponding to
the column of the matrix). By combining these member
matrices, an adjacency matrix of a hypergraph with nver-
tices and r
q�1k(q)hyperedges is obtained.
H�H(1),. . . , H(r)
.(1)
Each row of the matrix Hrepresents a vertex (learner
behavior data), and each column represents a superedge. e
value of the vertex belonging to the same superedge is 1;
otherwise, it is 0. So far, the clustering results of a group of
learner behavior data have been mapped into the critical
matrix of hypergraph.
Here is a simple example to illustrate the above concept.
Table 1 shows three cluster marker vectors with seven
learner behavior data oi(i�1,2,. . . ,7), among which
clusters 1 and 2 are logically consistent, while cluster 3 has
some disputes on the classification of objects 3 and 5. e
adjacency matrix H of the hypergraph is shown in Table 2,
where seven vertices vi(1,2,...,7)of hypergraph corre-
spond to seven objects. Each cluster is represented as a
hyperedge, and there are nine hyperedges.
Next, the adjacency matrix Hof a hypergraph with n
vertices and r
q�1k(q)hyperedges can be determined by the
following formula:
S�HHT.(2)
It is transformed into a symmetric adjacency matrix Sof
n×n, where HTis the transpose matrix of H. Each row and
column of Scorrespond to a vertex in the hypergraph, and
the value on the nondiagonal line reflects the weighted value
of the hyperedge. If two vertices belong to the same
superedge more times, the weight of the superedge is larger.
Ant colony
1 constant
Ant colony 2
random
number
Ant colony 3
decreasing
random
number
Clustering
combination
Graph division
Final
clustering
results
Hypergraph
Figure 4: Structure diagram of data mining model based on
multiant colony clustering combination algorithm.
Mobile Information Systems 5
2.3. Graph Partition Algorithm Based on Ant Colony
Algorithm. e main idea of the second partition of
hypergraph is that the operation of the learner behavior data
object oiis changed to the operation of the vertex vi, and the
distance d(oi, oj)between the learner behavior data is
changed to the distance d(vi, vj)between the vertices.
erefore, the graph partition algorithm based on ant colony
algorithm is a dynamic clustering algorithm by moving
vertices on the plane [19,20].
Suppose G(V, E)is a graph, V�vi, i �1,2,3,. . . , n
is
the set of vertices, and Eis the set of edges. e adjacency
matrix of graph A� [aij]. Where
aij ≠0 if and only if (vi, vj)∈E
aij ≠0 if and only if (vi, vj)∉E
, the distance d(vi, vj)
between any two vertices is defined as
d vi, vj
�Dρvi
,ρvj
ρvi
+ρvj
,(3)
where ρ(vi)is the set of all vertices adjacent to vertex vi,
including viitself; ρ(vj)is the set of all vertices adjacent to
vertex vj, including vjitself; Dis the symmetry difference
between the two sets.
If two vertices viand vjhave a large number of common
adjacent nodes, that is, ρ(vi)∪ρ(vj) − ρ(vi)∩ρ(vj)is a small
set, then d(vi, vj)is smaller, that is, viand vjwill eventually
come together and fall into one category. On the contrary, if
there are only a few or no adjacent edges between the two
vertices viand vj, that is, ρ(vi)∪ρ(vj) − ρ(vi)∩ρ(vj)is a
large set, then d(vi, vj)is larger. In other words, viand vjwill
eventually be far apart and belong to different classes.
3. Results
In order to test the application effect of the system, the
experimental environment is designed to ensure the normal
operation of the system. Table 3 shows the details.
3.1. e Rationality Test of the System. e rationality of the
system in this paper is tested. Taking the support and
confidence as the test index, the rationality of the basketball
action course taught by the system to students is analyzed.
e details of the interactive teaching course of basketball
action are shown in Table 4. e rationality test results of the
system are shown in Figures 5 and 6 .
rough the analysis of the above table, we can know
that, in the interactive teaching of five kinds of basketball
actions, such as passing and catching the ball + dribbling,
holding the ball breakthrough, personal defense, grabbing
the ball, grabbing the basket, and pitching, the support
degree and confidence degree of the basketball action course
displayed by the system in this paper for students are as high
as 0.99, so the rationality of the basketball action course
displayed by this system for students is very high, and it
matches the students’ learning progress very well. e reason
is that the data mining model based on multiant colony
clustering algorithm is used in this system to mine the
learning behavior data of student groups. According to the
mining model, different teaching objectives and teaching
content organization strategies are used for different student
groups to realize the hierarchical teaching of student
network.
3.2. e Application Satisfaction Test of the System.
rough the form of a questionnaire survey, this paper
analyzes the application satisfaction of users after using the
system, and the test content is shown in Table 5.
e test results are shown in Figure 7.
From the above table, it can be seen that, after the ap-
plication of the system in this paper, the user’s satisfaction
with this system is higher than 98%. erefore, this system is
deeply loved by users, and the application feedback is good.
3.3. e Teaching Effect Test of the System. In order to test the
teaching effect of the system in this paper, this paper tests the
students’ mastery of basketball action before and after using
this system and after interactive teaching of basketball action
in college sports, as well as the examination results of
basketball action in college sports. e types of basketball
action in college sports are moving, passing and catching,
dribbling, shooting, holding breakthrough, personal de-
fense, grabbing, breaking, and grabbing. e test results are
shown in Table 6. Among them, the full score is 10. More
than 6 points are qualified, and less than 6 points are
unqualified.
Table 1: Cluster marker vector.
λ(1)λ(2)λ(3)
o1121
o2121
o3122
o4232
o5233
o6313
o7313
Table 2: Adjacency matrix of hypergraph.
H 1 2 3
1100010100
2100010100
3100010010
4010001010
5010001001
6001100001
7001100001
Table 3: System experimental environment design.
Project Content
Operating system Microsoft Windows 10
Database MySQL server
Browser Internet Explorer
CPU Intel i5
Memory 8 GB
Hard disk 600 GB
6Mobile Information Systems
According to the data in Table 6, when the types of
basketball action in college sports are moving, passing and
catching, dribbling, shooting, breakthrough with the ball,
personal defense, grabbing the ball, breaking the ball,
grabbing the basket, and pitching, before using the system in
this paper, the students’ mastery of basketball action in
college sports is poor, and the test score of sports basketball
action is less than 6 points, with an average score of 5 points.
After using this system, the students have a good grasp of
basketball action in college sports, and the test score of
basketball action is more than 6 points, with an average of 9
points. In contrast, this system can effectively improve the
students’ mastery of basketball action and improve their
sports performance.
3.4. e Data Mining Effect of Students’ Group Learning
Behavior of the System. e data mining effect of the system
in this paper is tested on five students’ learning behavior, and
the interactive teaching course of two kinds of basketball
actions is taken, namely, holding the ball breakthrough and
personal defense, as an example to test the mining effect of
this system. e mining effect is mainly reflected by pre-
cision P and recall R. e test results are shown in Figures 8
and 9 .
P�Nij
Ni
,
R�Nij
Nj
,
(4)
where Nij is the number of classification iin cluster j;Njis
the number of all objects in cluster j;Niis the number of all
objects in cluster i.
As shown in Figures 8 and 9, this system has a good effect
on the data mining of five students’ learning behavior. In the
interactive teaching course of holding the ball breakthrough
and personal defense, after the data mining of five students’
learning behavior, the maximum precision rate is 0.98, and
the maximum precision rate is 0.98. e mining perfor-
mance is significant, which has a positive impact on the
setting of interactive teaching course of basketball action.
3.5. e Concurrency Test of the System. At present, in the
application performance test of all the systems, it needs to
pay attention to its concurrency resistance. e concurrency
resistance reflects whether the system can carry concurrent
user access. e number of concurrent users of this system is
set to 10, 20, 30, and 40 in turn. Taking the interactive
teaching course of three kinds of basketball actions, such as
grabbing the ball, breaking the ball, grabbing the basket, and
pitching, as an example, under the condition of a different
number of users, the system in this paper is running
smoothly. Whether the screen gets stuck in the interactive
teaching of basketball action is mainly reflected by the
utilization rate of CPU. e test results are shown in
Figure 10.
As shown in Figure 10, when the number of concurrent
users of this system is 10, 20, 30, and 40, under the condition
of a different number of users, although the CPU utilization
rate of this system is gradually increasing, the maximum
value is less than 40%, the CPU utilization rate is low, and
there is no significant interference to the interactive teaching
screen of this system. erefore, the concurrency of this
system is low. e test is passed, and the performance re-
mains the same under different concurrent users.
Table 4: Interactive teaching course of sports basketball action.
Action type Course details
1 Pass and catch + dribble
2 Breakthrough with the ball
3 Individual defense
4 Grab the ball
5 Grab the basket and throw the ball
1
Support
23 4
0.20
0.40
0.60
0.80
1.00
5
0.00
Action type
Aer application
Before application
Figure 5: Support test results.
1
Confidence level
234
0.20
0.40
0.60
0.80
1.00
5
0.00 Action type
Aer application
Before application
Figure 6: Results of confidence test.
Mobile Information Systems 7
Table 5: e content of the system application satisfaction test.
Evaluation item code Evaluation content Option
A Views on the interactive teaching course of basketball action setup in this paper Approve/disapprove
B Is the teaching of this system reasonable Reasonable/unreasonable
C is paper discusses the effect of the system on students’ practical ability Positive/negative
1
Satisfaction (%)
23
20
40
60
80
100
Evaluation item code
Aer application
Before application
Figure 7: Test results of application satisfaction of this system.
Table 6: Teaching effect test of this system.
Action type Score before use Score after use
Move 5 9
Pass and catch 5 8
Dribble 5 9
Shoot 5 9
Breakthrough with the ball 5 9
Individual defense 4 9
Grab the ball 4 8
Break the ball 4 8
Blue bowling 5 9
Average 5 9
Precision ratio
0.20
0.40
0.60
0.80
1.00
0.00 12345
Student code
Aer application
Before application
(a)
Precision ratio
0.20
0.40
0.60
0.80
1.00
0.00 1 2345
Student code
Aer application
Before application
(b)
Figure 8: Precision test results. (a) Breakthrough with the ball. (b) Individual defense.
8Mobile Information Systems
4. Conclusions
With the information technology, distance education system
based on network has made great progress. Moreover, at this
stage, multimedia technology has been fully integrated with
network communication technology, and teaching work is
more carried out in the network environment, so that the
teaching mode has been completely changed. e networked
teaching system has the characteristics of autonomy and
interaction, which makes the communication between
students and students and teachers more convenient. is
paper designs an interactive teaching system of basketball
action in college sports based on O2O mixed teaching mode
and verifies its availability in the experiment. PE is one of the
compulsory courses, which can enhance the system and
health of students. Networked teaching solves the time and
space constraints of PE, meets the requirements of inter-
action between students and teachers in the process of PE,
and promotes the development of PE. erefore, the ap-
plication of networked technology in PE has a great role in
promoting PE. However, due to the practicality of PE
teaching activities, it cannot be separated from the tradi-
tional way of PE. erefore, for networked PE teaching, it is a
beneficial supplement to the traditional PE teaching and
cannot replace the traditional PE teaching.
Data Availability
e data used to support the findings of this study are
available from the corresponding author upon request.
Conflicts of Interest
e authors declare that they have no conflicts of interest.
References
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10
Concurrent users/person
CPU utilization (%)
20 30 40
20
40
60
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Grab the ball
Break the ball
Grab the basket and throw the ball
Figure 10: Concurrency test results of the system.
Recall ratio
0.20
0.40
0.60
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1.00
0.00 1 2 3 4 5
Student code
Aer application
Before application
(a)
Recall ratio
0.20
0.40
0.60
0.80
1.00
0.00 1 2 3 4 5
Student code
Aer application
Before application
(b)
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10 Mobile Information Systems
