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Article
Similar or Different?
A Comparison of
Traditional Classroom
and Smart Classroom’s
Teaching Behavior
in China
Suping Yi
1
, Ruwei Yun
1
,
Ximin Duan
1
, and Yefeng Lu
1
Abstract
In the “Internetþ” era, to understand the difference between the traditional
classroom and smart classroom, this study uses the current domestic and foreign
classroom teaching behavior research methods as a starting point and analyzes the
teaching behaviors in classrooms from six dimensions: resource sharing, teacher
lecturing, teacher–student interaction, group cooperation, autonomous learning,
and evaluation feedback. A data analysis method is used to conduct a complete
statistical study on the teaching behaviors of the 40 lessons selected in the first
smart classroom innovation teaching competition in Jiangsu Province, and the anal-
ysis results show that there are significant differences in teacher–student interaction,
group cooperation, autonomous learning, and evaluation feedback in the smart class-
room and the traditional classroom. There is no significant difference in data analysis
between resource sharing and teacher teaching, but through further video observa-
tion and analysis, the two still show the difference in the actual classroom.
Keywords
smart classroom, traditional classroom, data analysis, classroom teaching behavior
1
School of Educational Science, Nanjing Normal University, Nanjing, China
Corresponding Author:
Ruwei Yun, Nanjing Normal University, No.122, Ninghai Road, Jiangsu, Nanjing 210097, China.
Email: yunruwei@njnu.edu.cn
Journal of Educational Technology
Systems
0(0) 1–26
!The Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0047239521988999
journals.sagepub.com/home/ets
Classroom teaching research is an important field of teaching research, and
classroom teaching behavior research is a key part of classroom teaching
research. At the same time, the data obtained by analyzing classroom
teaching behavior are also an important source of information for classroom
teaching research. Entering the information society, new technologies such as
artificial intelligence and big data analysis are driving profound changes in the
classroom. Smart classroom is the most typical product in the current process of
technology and classroom integration. The core features of smart classrooms are
the use of information technology to promote classroom teaching to achieve
task-driven, multidimensional interaction, and accurate feedback. The teaching
behaviors and types of smart classrooms have undergone profound changes in
the technological era. This research uses data analysis technology to analyze
classroom teaching behaviors in smart classrooms and traditional classrooms,
trying to answer the following two questions:
1. Are there any differences in classroom teaching behaviors in different
environments?
2. In different classroom environments, how are the teaching behavior charac-
teristics expressed?
Research Foundation and Model Framework
Classroom Teaching Behavior
From a psychological point of view, behavior refers to the external reflection of
people’s internal physiology, mentality, and psychological changes. According to
the way the behavior exists, the behavior can be divided into implicit behavior and
explicit behavior. Therefore, teaching behavior can be defined as the work behav-
ior in which teachers select, combine, apply, and control various teaching ele-
ments in teaching implementation in the teaching process according to teaching
experience and internal relations; from this point of view, teaching behavior is a
kind of explicit behavior, which can be observed and recorded. The study of
classroom teaching behavior mainly takes classroom as the research site, which
refers to all the skills caused by teachers in class to maintain and promote stu-
dents’ learning. It includes not only teachers’ teaching behavior but also students’
learning behavior, as well as the interaction between teachers and students with
resources, environment, and other teaching factors. At present, many scholars
classify classroom teaching behavior; Van Den Hurk et al. (2016, pp. 444–451)
divided the classroom behavior of creating a safe and stimulating learning climate,
efficient classroom management, clear instruction, activating learning, adaptation
of teaching, and teaching learning strategies. From the perspective of speech act in
his works, Belgian scholar Landsheere, who enjoyed a high reputation in contem-
porary educational scientific research, divided classroom behavior into six
2Journal of Educational Technology Systems 0(0)
aspects: “promoting classroom organization, enforcing, promoting development,
promoting personalization, positive feedback, and using specific materials” (1982,
pp. 59–86). Berliner and Gage (1976, p. 31–35) grouped classroom behavior into
four categories: organization, questioning, inquiry, and reward. The founder of
microteaching Diwight Allen (1968, p. 181–185) divided classroom teaching
behavior into eight skill groups. No matter from what point of view to summarize
the class of classroom teaching behavior, classroom teaching behavior has the
situational, interactive, regular characteristics is beyond doubt. The research of
classroom teaching behavior is to explore the purpose, law, and significance of
classroom teaching behavior in seemingly scattered and chaotic classroom teach-
ing behavior and then improve the efficiency of classroom teaching (Beem &
Brugman, 1985). At present, the classroom is in the focus of the integration of
technology and education; the traditional classroom teaching design, teaching
methods, roles of teachers and students, classroom interaction, evaluation meth-
ods, homework guidance, and other common teaching links are changing under
the guidance of technology. Traditional classroom teaching is developing toward
intelligent, information-based, and data-oriented intelligent classroom teaching.
The analysis of classroom teaching behavior using data is of great significance for
promoting the objectivity of evaluation (Martinez et al., 2003), optimizing instruc-
tional design (Mor & Winters, 2007), and promoting classroom fairness (Rasooli
et al., 2019).
Smart Classroom
Smart classroom is the product of information technology in the field of educa-
tion. Many scholars have defined the smart classroom. Literature on smart class-
rooms tends to focus on technical systems and the technical feasibility of
deployment. The smart classroom described by Aguilar et al. (2017a, 2017b) is
the integration of sensor technology, communication technology, and artificial
intelligence into the classroom. The concept is to define smart classroom as a
classroom that integrates artificial intelligence and other technologies into the
teaching process (Aguilar et al., 2016), emphasizing the use of intelligent environ-
ments to improve the learning process. Smart classroom is a classroom that is
equipped with smart application services and other software and hardware, allow-
ing teachers to teach by using a wide variety of media (Song et al., 2014). Huang
et al. states, “a smart classroom relates to the optimization of teaching content
presentation, convenient access of learning resources, deep interactivity of teach-
ing and learning, contextual awareness and detection, classroom layout and man-
agement, etc.” It may be summarized as Showing, Manageable, Accessible, Real-
time Interactive, and Testing, which is nicknamed as “S-M-A-R-T” (Huang et al.,
2012). The definition of a smart classroom is based on the integration of technol-
ogy and education. Considering the earlier discussion, the author thinks that
smart classroom is the development space of teaching and learning process
Yi et al. 3
reconstruction based on the two-way deep integration of technology and teaching.
It is a kind of classroom that uses intelligent technology to build a rich and
intelligent classroom teaching environment to meet the precise teaching needs
of teachers and the personalized learning needs of students.
The Difference Between Traditional Teaching and Smart Classroom Teaching
In traditional classroom teaching, the division of labor between teachers and stu-
dents is clear. That is, the teachers speak and the students listen. In traditional
classrooms, the teaching method is mainly used, but this teaching method is very
single. In the case of a large class size, the teacher cannot even take care of every
student, so the interaction between students and teachers is not very strong. In
addition, in many cases, teachers can only judge students’ learning situation based
on their own subjective experience (Merisotis & Phipps, 1999), without accurate
grasp, and students cannot get feedback quickly and timely. In general, the tradi-
tional classroom teaching mode is not conducive to the development of students’
learning initiative; there is little interaction between teachers and students, and
students’ learning personality and creativity are difficult to achieve. However,
there is a big difference in the wisdom classroom model. With the strong influence
of new technologies like the Internet of Things, Cloud Computing, and Big Data,
“smart classroom” is a new form of information-based classroom. To meet teach-
ing requirements, the ultimate mission of “smart classroom” is to provide smart
application services to optimize teaching effects. Smart classroom emphasizes the
application of smart technology and provides students with smart learning services
(Song et al., 2014). Smart classrooms use cameras, sensors, smart devices, and
Internet of Things systems to collect new sources of data about student participa-
tion (e.g., raising hands and discussion), facial expressions, bodily postures, and
engagement (Kwet & Prinsloo, 2020). The information can help teachers under-
stand the meaning behind individual and group behaviors in class more accurately
and grasp the rhythm of classroom teaching more accurately. A smart classroom
integrates the different components in a traditional classroom, by using different
technologies as artificial intelligence, ubiquitous, and cloud paradigms, among
others, to improve the learning process (Aguilar et al, 2017a, 2017b).
Compared with traditional classroom, students are the main body of learn-
ing, and teachers play a guiding role. With the support of technology, teaching
methods are more diversified. Teachers can accurately grasp each student’s
learning situation through big data analysis and give timely feedback
(Dominguez et al, 2009), which greatly improves the teaching efficiency.
Research on Analysis Method of Classroom Teaching Behavior
With every technological change, education will also change; the purpose is to
promote human beings to better adapt to the changing society through
4Journal of Educational Technology Systems 0(0)
education, and classroom teaching behavior analysis methods are also continu-
ously improved as technology changes. First of all, it is reflected in the variety of
analytical methods, from traditional theoretical research to qualitative research
and quantitative research combined with empirical research (Schoech & Helton,
2002); second, it is reflected in the integration of technical dimensions in class-
room teaching analysis methods to adapt to classroom teaching under the infor-
mation environment analysis (Macfadyen & Dawson, 2010).
The research on the analysis method of classroom teaching behavior mainly
focuses on the preparation of scales, among which the most famous scales are
Flanders interactive analysis system (Flanders, 1963) and S-T analytical method
(Fu & Zhang, 2001). The Flanders interactive analysis system uses a set of
coding systems and uses teacher–student language interaction in the classroom
as the analysis element to grasp the law and essence of classroom teaching. S-T
analytical method is a kind of analysis system that records the classroom teach-
er’s behavior and student’s behavior by observing the classroom video and
makes a visual map to determine the class teaching category.
With the application of information technology, especially multimedia tech-
nology in education, it not only brings richer images to classroom teaching but
also promotes the generation and application of new methods of classroom
teaching behavior analysis. In the process of introducing technical features
into the classroom teaching analysis system, many scholars have also made
efforts to develop classroom teaching behavior analysis software to improve
analysis efficiency. For example, Jin and Gu’s (2010, p. 88) interactive analysis
system (ITIAS) based on information technology was proposed on the basis of
improving Flanders interactive analysis system. It not only refines teachers’
speech activities but also increases the categories of students’ speech behaviors
and technologies. From the two dimensions of behavior subject and behavior
activity mode, Cheng et al. (2017, p. 557) proposed an improved S-T analysis
method to provide a cloud model for the quantitative analysis of classroom
teaching behavior. Korean scholars (Jo & Lim, 2015) had applied Flanders
interactive system in smart classroom and divided classroom interaction into
four categories: teacher’s indirection, teacher’s question, students’ talk, and
classroom silence. The statistics of the aforementioned classroom teaching anal-
ysis behavior methods are shown in Table 1.
Research Design
Categories of Classroom Teaching
Classroom teaching behavior is the concentrated embodiment of teaching and
learning in classroom teaching. In the aspect of teachers, it is manifested in
spoken language and silent language, teaching methods and strategies, action
procedures and directions, and teachers’ emotions; In the aspect of students, it is
Yi et al. 5
manifested in the students’ emotions, words and actions in class. The previous
discussion on the related classroom teaching behavior research shows that no
matter from what angle the teaching behavior is studied, it involves teachers and
students (Cauley et al., 2017). Therefore, this study is based on the previous
research on the classification of classroom teaching behavior by relevant schol-
ars. According to the actual situation of this study, classroom teaching is divided
into six categories: resource sharing, teacher lecture, teacher–student interac-
tion, group cooperation, autonomous learning, and evaluation feedback. A
behavioral classification system is established to analyze traditional classroom
and smart classroom teaching behavior from the perspective of classroom
behavior.
Resource sharing (Wang & Noe, 2010) focuses on the presupposition and
generation of classroom, aiming to help teachers and students identify the key
and difficult points of classroom in advance and realize the maximization of
classroom teaching value. Teacher lecture (Bailey, 2008) is an indispensable part
of the classroom. Teachers impart key knowledge to students through telling,
explaining, and giving lectures to help students learn new knowledge and form
an efficient and scientific classroom. Teacher–student interaction (T-S interac-
tion) is an important way for teachers and students to communicate in the
classroom. Multidimensional and multimode teacher–student interaction can
effectively inspire students’ thinking, enhance students’ sense of participation,
and cultivate students’ subjective awareness in the classroom (Buyse et al.,
2008). Group cooperation, as the main form of communication between stu-
dents in class, aims to help students play a role in improving their abilities in
language expression and collaborative exploration through cooperative explo-
ration and discussion within the group (Baek & Touati, 2019). Autonomous
learning is an important way to test students’ learning results in short classes.
First, it can help students to quickly consolidate and deepen the knowledge they
receive; second, it can cultivate their ability to learn independently and think
Table 1. Classroom Teaching Behavior Analysis Method Coding Category Statistics.
Method Coding categories
FLAS Teacher language, student language, silence
S-T analytical method Teacher behavior, student behavior
ITIAS Teacher speech, student speech, silence, technology
Cloud model Teacher’s speech behavior , teacher’s activity behavior,
student’s speech behavior, student’s activity behavior
Flanders in smart
classroom
Teacher’s indirection, teacher’s question, students’ talk,
classroom silence
Note. FLAS ¼Flanders’ Interaction Analysis System; S-T ¼student–teacher ; ITIAS ¼interactive analysis
system.
6Journal of Educational Technology Systems 0(0)
independently within a limited period of time (Black & Deci, 2000). As a key
link of self-regulating classroom teaching, evaluation feedback can not only help
teachers evaluate students’ classroom learning situation, master classroom
teaching progress, and adjust teaching strategies but also enable students to
evaluate each other in class and promote the development of students’ critical
and innovative thinking ability (Black & Wiliam, 1998). The specific definitions
in the classroom teaching behavior classification table are shown in Table 2.
Research Implementation
Sample Selection. In this study, 40 videos of the first Smart Classroom Innovation
Teaching Competition in Jiangsu Province in 2019 are selected as the research
objects. These 40 classes are all new lectures, with an average duration of
40 minutes per class. Among them, 20 classes are traditional classroom teaching
videos using a single electronic whiteboard as the medium technology. The other
20 sections are smart classroom teaching videos created by modern teaching
equipment such as artificial intelligence and big data analysis. We take the tra-
ditional classroom as the control group and the smart classroom as the exper-
imental group. A total of 10 primary school teaching videos and 10 junior high
school teaching videos were selected in both the control group and the experi-
mental group. From the perspective of process, it is a classroom designed by the
participating teachers’ school team in a certain period of time, and it is also a
class example selected by several experts in the field of education informatiza-
tion based on the classroom effect of information technology and classroom
integration, which is scientific and rigorous to a certain extent. From a technical
Table 2. Operational Definition of Classroom Teaching Behavior Classification.
Behavior Operational definition
Resource sharing Teachers present, send, and share teaching resources (such as
videos, lesson plans, and materials) to students in certain forms
Teacher lecture Teachers impart knowledge to students by telling, explaining,
reading
T-S interaction The communication, dialogue, and question and answer between
teachers and students in class
Group cooperation Students work in groups to carry out classroom activities such as
communication, discussion, and collaborative inquiry
Autonomous learning Students take individuals as unit and independently complete the
learning tasks or requirements required by teachers in class
Evaluation feedback The process of evaluating the results of intragroup cooperation
and autonomous learning between teachers and students and
obtaining corresponding feedback accordingly
Note.T-S¼teacher–student.
Yi et al. 7
point of view, 40 lessons are the new lectures produced under the background of
the new curriculum reform and education informatization 2.0 and represent the
latest appearance of the new curriculum reform concept and information tech-
nology in the classroom. The characteristics of the five links of teacher lecture,
teacher–student interaction, group cooperation, autonomous learning, and eval-
uation feedback are newly displayed in the smart classroom of technological
empowerment.
Research Methods. Temporal sampling technology is one of the observation strat-
egies. Temporal sampling technology observation method is to observe the
predetermined behaviors in a certain period of time within a unified defined
time or to classify the behaviors according to the predefined good behavior
classification system. To conduct observation research using temporal sampling
technology, the observation time should be determined first, and the observa-
tion should be conducted at certain intervals according to a certain selected
period of time. Temporal sampling technology is adopted for observation,
which is mainly used to know whether a certain behavior or event occurs, the
frequency of occurrence of the behavior or event, and the duration of each
occurrence. The basic principle of the temporal sampling technology is that
the observation in a short period of time is tried out as a representative
sample of the normal behavior, that is, the sample is taken from the time dimen-
sion of a certain behavior. The key of this method is to select representative
samples in time.
Based on the previous research on the classification of classroom teaching
behavior by relevant scholars, according to the actual situation of this study,
classroom teaching is divided into six categories: resource sharing, teacher lec-
ture, teacher–student interaction, group cooperation, autonomous learning, and
evaluation feedback. This study uses temporal sampling technology (Liu &
Kender, 2007) to analyze the video, count the six types of behavioral data in
traditional classrooms and smart classrooms, and analyze the significance of the
two groups of control groups with the help of SPSS25 analysis tools (Field,
2013). In the collection of classroom behavior data, time sampling is mainly
based on the class coding system of classroom teaching behavior, which records
the sample data of classroom teaching behavior, that is, from the beginning of a
classroom teaching behavior as the starting recording time point, recording until
the end of the behavior in the teaching process.
Analysis of Research Results. Through the statistical study of 40 sample video
lessons, the analysis of the classroom teaching behavior analysis data of the
traditional classroom and the smart classroom is obtained, which is shown in
Table 3. This part intends to analyze and interpret the research results from the
three dimensions of data, video observation, and course demonstration.
8Journal of Educational Technology Systems 0(0)
In this study, the independent sample Ttest tool in SPSS data analysis soft-
ware is used to analyze the significance of the difference between the smart
classroom and the traditional classroom and the Tvalue data in Table 3 are
obtained. According to the requirement of significance level, when Tvalue is less
than or equal to 0.05, there is a significant difference between them (Fornell &
Larcker, 1981). Through the analysis of table data, there are significant differ-
ences in classroom behaviors of teacher–student interaction, group cooperation,
independent learning, and evaluation feedback.
Teacher–Student Interaction Dimension. The teacher–student interaction
dimension mainly has the following differences. From the point of view of
behavior frequency, the number of interactions in the intelligent classroom envi-
ronment is 116, with an average of 5.8 per class. The number of interactions in
the traditional classroom environment is 168, with an average of 8.4 per class.
From the perspective of behavior time, the teacher–student interaction in the
smart classroom environment is shorter (A ¼80.56, SD ¼56.03), accounting for
about 19% of its total duration; the teacher–student interaction in the tradi-
tional classroom environment is longer (B¼110.81, SD ¼82.72), accounting for
about 39% of its total duration. To make a significant test of classroom teacher–
student interaction in two different environments, the Tvalue is 0.0003, indi-
cating that there is a significant difference between the teacher–student interac-
tion in the smart classroom environment and the traditional classroom
environment. Combined with the research and analysis of teaching videos,
teacher’s question and student’s answer is the mainstream form of teacher–
Table 3. Statistical Summary of Classroom Teaching Behavior.
Behavior category
Environment
type N
Max
value
Min
value
Mean
value
Standard
deviation Proportion Tvalue
Resource sharing A 9 180 50 93.78 46.75 2% 0.7305
B 8 239 38 103.13 62.76 2%
Teacher lecture A 104 238 11 77.89 54.10 17% 0.3053
B 138 414 18 86.22 68.06 25%
T-S interaction A 116 338 10 80.56 56.03 19% 0.0003
B 168 605 10 110.81 82.72 39%
Group cooperation A 28 609 30 191.68 137.68 12% 0.0088
B 38 532 18 108.24 112.87 9%
Autonomous learning A 77 529 13 160.45 110.89 25% 0.00003
B 68 268 25 97.69 57.83 14%
Evaluation feedback A 86 415 21 141.86 92.92 25% 0.0306
B 49 479 24 107.27 80.02 11%
Note. The units of N, maximum, minimum, mean, and standard deviation in the table are in seconds. A:
smart classroom; B: traditional classroom. Proportion: total time of each type of behavior/total time of
class in this environment. Tvalue 0.05 is significant. T-S ¼teacher–student.
Yi et al. 9
student interaction in the current classroom (Gall, 1984); teachers and students
communicate with each other mainly through oral questions and answers and
develop the three series of initiation-response-feedback (IRF) teacher–student
interaction mode, that is, questions or instructions proposed are first raised by
the teachers, then students respond accordingly, and finally teachers summarize
(Waring, 2009). To form a good atmosphere of teacher–student interaction in
the traditional classroom, the content of question and answer between teachers
and students is relatively simple and most of them answer collectively. At the
same time, due to the limited media for classroom teaching, it is difficult for all
or even most of the students in the classroom to participate in the entire class-
room communication process, which makes it more difficult for students to
integrate into the entire teacher–student interaction process and requires more
thinking time in the communication, thus forming the situation of more time
and long interaction time in the traditional classroom. In the smart classroom,
teachers can discuss relevant issues with students by using intelligent mobile
terminal, 3-D projection technology, big data analysis, and other technologies,
which effectively enriches the ways of teacher–student interaction in the class-
room (Zhang & Wang, 2018), makes the problems studied by teacher–student
interaction in the classroom more in-depth, and reflects the generation of
knowledge.
Taking the small hole imaging experiment in the physics lesson “Reflection of
light” in junior high school as an example, Table 4 summarizes the interaction
between teachers and students in traditional classrooms and smart classrooms.
In the traditional classrooms, physics teachers randomly invited students in the
classroom to perform experimental operations, and more students observed the
experimenter’s operations in their seats to form an understanding of the prin-
ciple of small hole imaging. In the smart class, students are the main body of the
classroom, and each student carried out a group imaging experiment with 3-D
technology on a flat plate. Everyone is a participant, and everyone is an exper-
imenter. The teacher can see the progress and completion of each student’s
experiment through the equipment. On the basis of equal experimental oppor-
tunities for each student, the teacher invited the students to make interpretation
while demonstrating on the touch whiteboard so as to form an understanding of
the principle of the small hole imaging through multiple constructions.
Group Cooperation Dimension. There are following differences in the dimen-
sions of group cooperation. From the point of view of behavior frequency, the
number of group cooperation in the smart classroom environment is 28, with an
average of 1.4 per lesson; the number of group cooperation in the traditional
classroom environment is 38, with an average of 1.9 per lesson. From the per-
spective of behavior time, the group cooperation in the smart classroom is
longer (A ¼191.68, SD ¼137.68), accounting for about 12% of its total dura-
tion; the group cooperation in the traditional classroom is shorter (B ¼108.24,
10 Journal of Educational Technology Systems 0(0)
SD¼112.87), accounting for about 9% of its total duration. Furthermore, a
significant test is made on the cooperation behavior of the classroom student
groups in two different environments. The Tvalue is 0.0088, indicating that
there is a significant difference between the student classroom cooperation in
the smart classroom environment and the traditional classroom environment.
Combined with the research and analysis of teaching videos, this study believes
that the cooperation of student groups in actual classrooms in different environ-
ments is mainly manifested as the following differences. As can be seen from the
table, the minimum time of group cooperation in the traditional classroom is
18 seconds. In most cases, some students have not entered the state of
Table 4. Summary of Teacher–Student Interaction in Different Situations.
Traditional classroom Smart classroom
Teacher: After reading the book and
studying the basic principles of small
hole imaging, do you understand the two
methods of small hole imaging?
All students: We get it.
Teacher：Then we now use the teacher’s
multimedia equipment to further
understand through experiments. What
is the first method?
All students: Hold the candle position on
the base and move the light screen back.
(The teacher is demonstrating on the
stage.)
Teacher：Then we invite classmate A to
come on stage to demonstrate.
(After classmate A finished the experi-
ment.) Did you see the first one?
All students: We got it.
Teacher: Then let’s demonstrate the
second method together, what is the
second method? We invite classmate B
to demonstrate on stage.
Classmate B: We first keep the position
of the candle stationary, then move the
small hole in the middle forward and
close to the candle. (Classmate B is
demonstrating on the stage.)
Teacher: Can you see the second dem-
onstration clearly?
All students: We got it.
Teacher: After we have studied the basic
principles of small hole imaging, how do
you feel about learning?
All students: It’s a good study.
Teacher: Okay, now everyone is trying
out the small hole imaging experiment
on the tablet by yourself. I will call some
students on stage to demonstrate later.
Teacher: Now, who would like to come
on stage and show you two ways to
enlarge the small hole image?
Classmate A: We can move the light
screen backward while keeping the
position of the candles stationary (the
student is using 3-D technology to
demonstrate on the whiteboard).
Teacher: Who can demonstrate another
method?
Classmate B: On the basis of keeping the
position of the candle immobile, we
move the small hole in the middle for-
ward and close to the candle (the stu-
dents use 3-D technology to
demonstrate on the whiteboard).
Teacher: The two classmates demon-
strated very well. Do you all understand
the principle of enlarged small hole
imaging now?
All students: We get it.
Yi et al. 11
discussion, but the group cooperation has ended. Second, in group cooperation,
the group leader and members with excellent academic performance occupy the
initiative in the discussion (Holland & Muilenburg, 2011), while other students
have difficulty in expressing their opinions in the classroom, and it is difficult for
them to truly integrate into group cooperation. Finally, in traditional classroom
group cooperation, more attention is paid to the final discussion results. The
teacher plays the role of “evaluator” and judges the results of students’ group
discussion after the end of the discussion. In this situation, students find it hard
to form exploratory and innovative thinking. In group cooperation in the
technology-enabled smart classroom, teachers use the teaching platform to
send the learning resources in real time; on this basis, teachers can get timely
feedback on the discussion content of each student in different groups, and it
helps teachers to grasp the whole process of group discussion from the perspec-
tive of process, instead of forming the cognition of discussion based on the final
result, and then teachers use the electronic whiteboard to show the final result of
the group student discussion (Szewkis et al., 2011). Students use smart mobile
terminals to form online interactive communication between themselves and the
surrounding environment (Beldarrain, 2006; Dakka, 2015). Group cooperation
breaks through the space limitation, and students do not need to change their
seats, giving students more time to communicate and explore the knowledge
(Wang & Liao, 2017).
Taking the junior middle school mathematics class “From problems to equa-
tions” and the middle school Chinese class “How to distinguish true and false
news in the information age” as example. In the traditional classroom, prespace
grouping was the premise. Students carried out group cooperation according to
the mathematics discussion content proposed by the teacher. During the pro-
cess, the teacher continuously patrolled the various groups to grasp the group
situation, which formed the group cooperation process of “task assignment-
student discussion-teacher patrol-representative speech-teacher summary”
(Figure 1) in traditional class. The smart classroom used smart media as a
tool. Students followed the predesigned group cooperation mode on the smart
terminal to communicate and upload voices and pictures on the cooperation
platform, and then they praised the excellent works in the group (Figure 2).
Teacher can also know the participation level of each member of group in the
teaching platform at any time. The representative of the speech was the author
of the work of interest to the students selected from the group, which formed the
group cooperation process of “task assignment-online communication-teacher
follow-up-online submission-student praise-student speech” in smart classroom.
Autonomous Learning Dimension. There are the following differences in the
dimension of autonomous learning. From the point of view of behavior fre-
quency, the number of autonomous learning in the smart classroom environ-
ment is 77, with an average of 3.85 per class; the number of autonomous
12 Journal of Educational Technology Systems 0(0)
learning in the traditional classroom environment is 68, with an average of 3.4
per lesson. From the perspective of behavior time, the length of autonomous
learning in smart classrooms is longer (A ¼160.45, SD ¼110.89), accounting for
about 25% of its total duration; the length of autonomous learning in tradi-
tional classrooms is shorter (B ¼97.69, SD ¼57.83), accounting for about 11%
of its total duration. Further test the significance of students’ autonomous learn-
ing behavior in the classroom under two different environments, and the Tvalue
Figure 2. Group Cooperation in Smart Classroom.
Figure 1. Group Cooperation in Traditional Classroom.
Yi et al. 13
is 0.00003, indicating that there is a significant difference between the intelligent
classroom environment and the traditional classroom environment. Based on
the research and analysis of teaching videos, the traditional independent learn-
ing behavior in class is manifested as the teacher gives the learning task orally,
and then students complete the learning tasks according to the teacher’s require-
ments in the book or exercise book. In this process, the teacher keeps making
rounds in class to grasp the student’s practice progress and quality. From the
perspective of the learning process, the main difference between autonomous
learning behaviors in traditional classrooms and smart classrooms is the high
efficiency highlighted in the process. In the traditional classroom, it is not easy
for teachers to quickly grasp the completion of the practice of all students.
Teachers often determine the length of students’ practice through their prede-
termined experience and the information collected by teachers’ limited class-
room inspections, rather than based on the students’ real situation. In smart
classrooms, on one hand, efficiency refers to the fact that students can complete
the learning tasks assigned by teachers under the supervision of background
data quickly (Davidsen & Vanderlinde, 2016); on the other hand, teachers use
data analysis technology to accurately understand the progress of student work
completion and scientifically grasp the student classroom practice time, and then
teachers guide students to check after practice, let students fully use the minutes
and seconds in the process, and improve the efficiency of classroom practice
(Kessler & Bikowski, 2010).
Taking the junior middle school Chinese lesson “A drop of water through
Lijiang” and the junior middle school Chinese lesson “Beautiful color” as exam-
ple, Figures 3 and 4 reveal the situation of autonomous learning in traditional
classrooms and smart classrooms. In the traditional classroom, students quickly
browsed the text according to the teacher’s requirements and marked the where-
abouts of “a drop of water” in the text; teachers were constantly patrolling in
the classroom to grasp the progress of students. In the smart classroom, students
completed the detailed description of Mrs. Curie’s appearance and uploaded it
with a tablet photo after completion. Teachers can check the condition of each
student’s work and completion progress in the statistics column.
Evaluation Feedback Dimension. There are mainly the following differences in
the evaluation feedback dimension. From the perspective of behavior frequency,
the evaluation feedback frequency in the intelligent classroom environment is
86, averaging 4.3 per lesson. In the traditional classroom environment, the
number of group cooperation is 49, with an average of 2.45 per lesson. From
the perspective of behavior time, the evaluation feedback in the smart classroom
is longer (A ¼141.86, SD ¼92.92), accounting for about 25% of its total dura-
tion; the evaluation feedback in the traditional classroom is shorter (B ¼107.27,
SD¼80.02), accounting for about 11% of its total duration. Further test the
significance of students’ autonomous learning behavior in two different
14 Journal of Educational Technology Systems 0(0)
environments, and the Tvalue is 0.0306, indicating that there is a significant
difference in student evaluation feedback between the smart classroom environ-
ment and the traditional classroom environment. The current evaluation feed-
back in the classroom mainly includes teacher–student evaluation, peer
Figure 4. Autonomous Learning in Smart Classroom.
Figure 3. Autonomous Learning in Traditional Classroom.
Yi et al. 15
evaluation, and student self-assessment (Dochy et al., 1999). Combined with the
research and analysis of teaching videos, in the traditional classroom, teacher–
student evaluation is the teacher’s evaluation of students’ completion of exer-
cises and cooperation; peer evaluation is the mutual evaluation formed by stu-
dents exchanging learning outcomes with their desk mates; student self-
evaluation is the evaluation that students check by themselves after completing
their own learning tasks. Stufflebeam (2001), a famous American commentator,
stressed that “the most important purpose of evaluation is not to prove, but to
improve” (p. 7) that is, evaluation feedback is not limited to impart students’
knowledge but should also help teachers improve classroom teaching and stu-
dents’ ability development. The characteristics of pertinence, flexibility, and
fairness highlighted by the evaluation feedback behavior in the smart classroom
are its significant differences from the evaluation feedback in the traditional
classroom. In smart classrooms, teacher–student evaluation is that teachers
can provide objective data and graphic analysis with the help of mass storage
and fast computing power of information technology, give accurate assessment
of students’ learning results, and use intelligent teaching platforms to share all
the students’ learning in the classroom; teachers also give students targeted
timely feedback and problem-solving suggestions accordingly and adjust the
classroom teaching rhythm in time (Straub, 1997). Peer evaluation means that
each student checks the learning outcomes of other students in the class with the
help of intelligent mobile terminals, forming an evaluation situation with full
coverage and participation of student evaluation in the class (Seery et al., 2012).
Student self-assessment means that after completing the learning task, students
use the learning terminal equipment to evaluate the learning results of others
and conduct self-examination so as to help students form phased cognition of
themselves (Sitzmann et al., 2010). Therefore, from the perspective of evaluation
feedback of technical support, technology not only gives play to the advantages
of information technology in supporting information collection and data anal-
ysis but also realizes the advantages brought by technology in the evaluation
feedback link of information.
Taking the elementary school mathematics class “Multiplication of decimals”
as an example, Table 5 reveals the evaluation feedback situation in traditional
classrooms and smart classrooms. In the traditional classroom, after the teacher
invited the students to do exercises on the stage, the whole students evaluated
the vertical format and content on the blackboard, then the teacher focused on
the correction and explanation on the blackboard, and the students conducted
self-examination; it formed the evaluation process of “students do exercises –
centralized comments – teachers summarize” in traditional class. In the smart
classroom, after the students completed their homework on the tablet, each
student in the class was a “diagnostic expert.” The students viewed and evalu-
ated the work of other students in the class on the tablet and finally took the
outstanding works recommended by the students as the standard. Students
16 Journal of Educational Technology Systems 0(0)
checked their own homework again. In this process, the teacher always played
the role of “leader”; it formed the evaluation process of “students do questions-
student mutual assessment-student self-assessment” in smart classroom.
In addition to the significant differences in data analysis of the aforemen-
tioned four types of behaviors, although resource sharing and teacher lecture
behaviors do not show significant differences in statistical significance, they still
show differences in actual classroom teaching.
Statistical Significance Dimension. In the traditional classroom, resource shar-
ing is manifested in that teachers send and share relevant learning materials to
students through electronic whiteboards and paper materials based on their
subjective teaching experience. In the smart classroom, the application of
resource sharing is more scientific and reasonable (Hou et al., 2009; Yang
et al., 2011). On one hand, teachers record microlessons before class to give
students preview so as to help students get familiar with the key points and
difficulties in class. On the other hand, teachers send the learning materials
Table 5. Summary of Evaluation Feedback in Different Situations.
Traditional classroom Smart classroom
Teacher: Is classmate A’s work right?
Classmate B: No, he put the decimal in
the wrong place.
Classmate C: His vertical form is also
wrong.
Teacher: What should be the correct
format and answer?
All students: The end of the decimal
multiplication needs to be aligned. As
many decimals as there are in the mul-
tiplier, the decimal point should be
counted from the right of the product.
Teacher: Students, according to the
teacher’s blackboard, do you understand
the method of this question now?
Everyone uses the exercises on the
blackboard as a standard to check
whether they are doing correctly.
Teacher: I found that everyone has fin-
ished through the background statistics,
so now you can use your tablet to check
which students’ answers are wrong?
Where is the error?
Classmate A: I found the mistake of
classmate B. His decimal point was mis-
placed and should be marked three
places from left to right.
Classmate A: I found that classmate D
wrote very neatly, but his format was
wrong, mainly because the last digit of
the multiplier was not aligned.
Teacher: Did you find who wrote it cor-
rectly and neatly?
All students: Classmate E wrote the best.
He wrote neatly, clearly and without
mistakes.
Teacher: Let’s take classmate E’s home-
work as the standard. Everyone will
conduct a self-check to see if what you
did is correct and upload your home-
work again after completion.
Yi et al. 17
determined by the preclass students’ learning situation analysis based on the
data analysis technology to students through the cloud, making the teaching
content more relevant to the actual situation of students.
Teacher Teaching Dimension. Compared with the long-term teacher teaching
in traditional classrooms that rely on electronic whiteboards and other equip-
ment, teachers in smart classrooms mainly use big data collection, artificial
intelligence, mobile terminals, and other modern equipment to teach new knowl-
edge; in the process of teaching, it integrates a variety of information teaching
equipment to attract students’ interest, improve students’ concentration in class,
and guide students to study and think (Buabengandoh, 2012).
Based on the previous comparison of traditional classroom and intelligent
classroom teaching behavior characteristics, the contents in Table 6 are
summarized.
Through comparative analysis, this study summed up three key character-
istics of the smart classroom.
First, from knowledge transfer to knowledge elaboration, smart classroom
emphasizes accurate breakthroughs in key points. From a systemic perspective,
if a teacher wants to accurately understand the student’s academic situation, he
must first input information to the student and then analyze the feedback infor-
mation to determine the difficulty of teaching. The smart classroom applies the
process of information input and output in teaching reasonably and assists
teachers in learning situation judgment through intuitive data. It breaks the
traditional model of determining teaching priorities and difficulties and design-
ing teaching contents according to textbooks in class and respects students’
classroom subjectivity. Before the class, the teacher will send the learning con-
tent to the students in advance through the guidance knowledge to understand
the students’ knowledge mastery. Teachers use this as a basis to design class-
room links and teaching content. During the class, the teacher combined with
the feedback data obtained during the student’s practice to adjust the classroom
rhythm in time to achieve accurate classroom teaching.
Second, from one-way silence to interactive inquiry, the smart classroom
focuses on the collective generation of knowledge. Affected by traditional cul-
tures such as “respecting teachers and respecting the seniors” and “politeness
and modesty,” the silence of most students and the “solo” of a few students have
become a common phenomenon in the classroom. In fact, many students like to
express their opinions in WeChat, Facebook, and other online social places after
class, so it is not that students like silence but that traditional classrooms lack
the environment suitable for students to express and display under the influence
of teaching time and teaching progress. In the smart classroom, a cloud platform
is used to build a variety of interactive environments for students. In the process
of knowledge sharing, teachers encourage students to upload their works togeth-
er and guide students to improve their own works by viewing other students’
18 Journal of Educational Technology Systems 0(0)
Table 6. Comparison of Classroom Teaching Behavior.
Traditional classroom Smart classroom
Form Feature Form Feature
Resource sharing Play video, paper
materials
Blurred goals, group
classroom
Record microlesson,
online assignments
Online help, personal-
ized classroom
Teacher teaching Empirical design, linear
explanation
Single form, listening
classroom
Highlight the difficul-
ties, point-like
explanation
Various forms, partici-
patory classroom
T-S interaction Simple content, verbal
Q&A
Point interaction,
focus classroom
Knowledge generation,
technical Q&A
Group interaction, fair
classroom
Group cooperation Deskmate interaction,
established
conclusion
Restricted positioning,
control classroom
Online communica-
tion, diverging
conclusion
Free positioning, inqui-
ry classroom
Autonomous learning Oral transmission,
teachers patrol
Uncontrollable situa-
tion, preset
classroom
Cloud distribution,
data analysis
Accurate control, effi-
cient classroom
Evaluation feedback Blackboard comment,
exchange reviews
Subjective assessment,
experience
classroom
Whiteboard evalua-
tion, Recommend
evaluation
Technology evaluation,
accurate classroom
Note.T-S¼teacher–student; Q&A ¼question and answer.
19
works in class. In the evaluation link, teachers make personalized comments on
students’ works according to their self-recommendation. In the group coopera-
tion, teachers create online groups on the learning platform, and students com-
municate and discuss in the group without space restriction.
Third, from a uniform learning schedule to personalized learning develop-
ment, the smart classroom highlights a fair vision. Every student has his own
personality characteristics, and it is difficult for students to get the “cake” they
like, with a class system that emphasizes consistent teaching progress. From the
perspective of classroom fairness, we should not only divide the “cake” equally
but also make everyone share the “cake” of their own taste, which points to the
satisfaction of students’ learning needs. Smart classroom provides the possibility
of classroom fairness and individual development of each student. Before the
class, teachers intelligently push learning resources, and students can set their
own pace and choose appropriate learning tasks according to their actual situ-
ation. In class, teachers focus on the problems that cannot be solved by students’
independent learning and group discussion. After the class, teachers use the
homework platform to personally send learning materials according to the stu-
dents’ academic conditions. The students perform targeted compensation exer-
cises. The students can also do the homework they need to do. There is no need
to do additional problems that do not match their abilities. At the same time as
the learning burden, it also increases the enthusiasm of students to learn,
which solves the problem of uniformly teaching and arranging homework in
the traditional classroom according to the recent development zone of the
middle group.
Discussion and Conclusions
Based on the actual classroom teaching process, this study proposes a set of
methods to analyze classroom teaching behavior, with data analysis as the main
method, supplemented by video observation. This study makes a systematic
analysis of 40 participating videos selected from the first Intelligent
Classroom Innovation Teaching Competition in Jiangsu Province. It can objec-
tively and accurately reflect the differences and facts of teaching activities
between traditional classroom and wisdom classroom, which is conducive to
teachers’ continuous reflection and improvement of teaching in combination
with their own actual conditions, thus contributing to their own professional
development.
In addition to the differences, there are also similarities in classroom teaching
behaviors in the two different environments. Taking the example of teacher–
student interaction, whether in what kind of teaching environment, compared
with referential questions (questions that teachers cannot know the answer),
display questions (questions that teachers know the answer) are used more fre-
quently (Faruji, 2011), because reference questions will cause students to think
20 Journal of Educational Technology Systems 0(0)
longer, and it may form more realistic results and more complex target language
output (Brock, 1986), Due to the limitations of language ability and real situ-
ation, teachers are hard to use more reference questions (Farahian & Rezaee,
2012). Taking teacher lectures as an example, whiteboards are indispensable
teaching equipment in classrooms. Whether in traditional classrooms or in
smart classrooms, the main purpose of teachers using whiteboards is to intro-
duce the main course content and provide activity guidance and so on.
Judging from the overall proportion, compared with the teaching activity
behavior in the traditional classroom environment, the duration of the six
types of activity behavior in the smart classroom environment is more balanced.
The significance is that the application of new media technology in the class-
room puts the active right of knowledge acquisition in the students’ hands
instead of teachers’ and education managers’ (Collins & Halverson, 2009).
The classroom is returned to the students, which highlights the subjectivity of
students in the classroom. Each student participates in every link of the class-
room through the guidance of technology. The classroom is no longer a class-
room for teachers and a few students, but a classroom for the whole students to
participate in and generate collectively.
At present, information technology has promoted changes in many levels in
the classroom, but when using information technology, it is still necessary to pay
attention to avoid the problem of technical blind obedience. Information tech-
nology is only an auxiliary tool in classroom teaching. The key to effective
teaching is how to give play to teachers’ technical rationality and promote the
integration and innovation of classroom and technology. By analyzing
the video, we take the feedback behavior as an example; some teachers in the
smart classroom environment only use the information-based teaching platform
as a single data statistics tool, which is simply used to count the submission and
accuracy of student exercises; teachers still use the conventional method to
impart knowledge to students. This ignores the use of technology to achieve
accurate comments, self-recommendation and mutual evaluation, and other
innovative teaching links; it is difficult to show the deep application significance
of technology in promoting students’ critical thinking ability in evaluation.
Teachers and students are the two main subjects in the classroom, and they
should focus on improving their ability to apply information technology and
then help build a high-quality classroom in the information age. From the per-
spective of teachers, teachers need to constantly improve their technical literacy,
enhance teachers’ information teaching ability, and promote teaching innova-
tion in the technical environment. From the perspective of students, students
need to complete the transformation of their role, from “passive knowledge
receiver” to “active learner.” Before class, students complete autonomous pre-
view tasks on the learning platform; in class, students actively participate in
classroom collaboration and communication; after class, students form the
Yi et al. 21
habit of self-reflection and self-study to cultivate their self-learning ability and
adapt to the new classroom environment.
Classroom teaching is complex and rigorous. Smart classroom is an impor-
tant path for the transformation of traditional classrooms in the information
age. It is of great practical significance to provide a reference model for smart
classroom teaching by analyzing the characteristics and displaying examples of
classic classroom lessons. This research describes the differences in classrooms in
different environments from a macroperspective but lacks a detailed analysis of
each level in the classroom. Next, the team will study it from a microperspective
to promote classroom development.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, author-
ship, and/or publication of this article: This work was supported by2019 “Provincial and
Ministerial Co-construction of Lide Shuren Collaborative Innovation Center” project
(grant number 71).
ORCID iD
Suping Yi https://orcid.org/0000-0002-3921-1548
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Author Biographies
Suping Yi is a master’s degree candidate of Nanjing Normal University School
of Education Science in Vocational and Technical Education, and her research
interests include education informatization theory and practice, teacher’s infor-
mation teaching ability Evaluation. In 2019, she helped organize the first smart
classroom innovation teaching competition in Jiangsu Province and participated
in the formulation of smart classroom evaluation standards.
Ruwei Yun received PhD in educational technology in 2009, he has been a pro-
fessor at Nanjing Normal University and the Dean of the Institute of Smart
Education of Nanjing Normal University. He has published more than 40 aca-
demic papers, including more than 20 published in SCI, EI, ISTP. His research
interests include the practical study of classroom teaching reform based on
smart education, the construction of smart campuses in primary and secondary
Yi et al. 25
schools, the informationization of vocational education and the construction of
digital education resources in primary and secondary schools.
Ximin Duan is a master’s degree candidate of Nanjing Normal University School
of Education Science in Vocational and Technical Education. Her research
interests include smart laboratory.
Yefeng Lu is a master’s degree candidate of Nanjing Normal University School
of Education Science in Vocational and Technical Education. His research
interests include smart education theory and practice.
26 Journal of Educational Technology Systems 0(0)
