4.1 Reliability and Validity of TPACK Dimensions for Normal College Students
Factor analysis was performed on items before reliability and validity tests, and the resulting KMO (Kaiser-Meyer-Olkin) and Bartlett test results (KMO = 0.952, χ2 = 9 097.552, P < 0.000 1) indicated that the data met the conditions for factor analysis. Exploratory factor analysis of the scale was performed using SPSS 28.0, and factors were extracted using principal component analysis. The factor load matrix is calculated by orthogonal rotation using the "maximum variance method" (Varimax). The selection standard of the item is that the factor load is not less than 0.5, and the factor extraction standard is that the factor eigenvalue is greater than 1. After deleting the two factors with factor load less than 0.5, the results of exploratory factor analysis showed that the eigenvalues of 7 factors were greater than 1, and the cumulative explained variance was 70.18%, indicating that 7 constructions can be extracted.
Cronbach's alpha coefficient and combined reliability (CR) were used to test the reliability of the questionnaire. The internal consistency reliability of the questionnaire was determined by calculating Cronbach's α coefficient with SPSS 28.0. The larger the α value, the higher the reliability of the data. The α values of CK, PK, PCK, TCK, and TPK were all above 0.9, indicating that the scale has good reliability; The α values of TPK and TPCK exceeded 0.8, indicating good reliability. The CR values were all greater than 0.7, and the combined reliability passed the test. Factor load and mean variance extraction (AVE) were used to test the validity of the questionnaire. Factor load was greater than 0.5 and variance extraction value (AVE) was greater than 0.5, indicating that the scale has good validity. The results of reliability and validity measures are shown in Table 1.
Table 1
Measure and index results of TPACK variables of normal students in colleges and universities
Variable | Child variable | Item | Factor load | α | CR | AVE | |
1 | 2 | 3 | 4 | 5 | 6 | 7 | | | |
PCK | PCK5 | I can deal with the mistakes of students in the subjects I teach. | 0.76 | | | | | | | 0.91 | 0.90 | 0.62 |
PCK3 | I know how to let students consolidate their teaching knowledge by assigning exercises. | 0.75 | | | | | | |
PCK4 | I know how to assess students' learning. | 0.71 | | | | | | |
PCK6 | I can deal with the difficult and important problems of the students in the subjects I teach. | 0.71 | | | | | | |
PCK1 | I know how to choose effective teaching. methods to guide students to learn and think. | 0.66 | | | | | | |
PCK2 | I know how to promote deep thinking in students by developing appropriate tasks. | 0.65 | | | | | | |
PK | PK4 | I was able to evaluate my students' learning in a number of ways. | | 0.77 | | | | | | 0.92 | 0.92 | 0.67 |
PK2 | I was able to tailor my teaching style to the type of student. | | 0.75 | | | | | |
PK6 | I am able to guide students to effectively discuss problems in group activities. | | 0.72 | | | | | |
PK1 | I can adjust the learning method according to the students' mastery of the learning content. | | 0.71 | | | | | |
PK5 | I can plan some group learning activities for students. | | 0.68 | | | | | |
PK3 | I am able to adopt a variety of teaching methods in classroom teaching. | | 0.64 | | | | | |
TCK | TCK5 | I understand how to apply technology to promote understanding of my subject area. | | | 0.77 | | | | | 0.91 | 0.89 | 0.58 |
TCK4 | I understand how to use technology to participate in scientific discussions in my subject area. | | | 0.76 | | | | |
TCK2 | I understand that in my subject area of research Which techniques were used. | | | 0.74 | | | | |
TCK1 | I understand how technological developments have changed my institute in the subject area of. | | | 0.72 | | | | |
TCK6 | I am able to use professional software to Subject areas are explored. | | | 0.63 | | | | |
TCK3 | I understand that my field of study is currently what new technologies are being developed. | | | 0.60 | | | | |
TK | TK2 | I have frequent access to multimedia technology. | | | | 0.76 | | | | 0.89 | 0.89 | 0.62 |
TK4 | I have the ability to use multimedia technology. | | | | 0.73 | | | |
TK5 | When using multimedia, I know how to solve and resolve the multimedia problems they face. | | | | 0.71 | | | |
TK1 | I understand the development of multimedia technology. | | | | 0.66 | | | |
TK3 | I know a variety of multimedia technologies. | | | | 0.64 | | | |
TPK | TPK4 | I can choose the right multimedia technology enhance students' learning ability. | | | | | 0.78 | | | 0.89 | 0.91 | 0.67 |
TPK1 | I can choose the right multimedia technology improve teaching methods. | | | | | 0.73 | | |
TPK2 | I was able to use the multimedia technology I learned in different teaching activities. | | | | | 0.71 | | |
TPK5 | I can instruct students to use multimedia and learn to cooperate. | | | | | 0.64 | | |
TPK3 | I was able to reflect on how to use multiple media technology. | | | | | 0.62 | | |
TPCK | TPCK1 | I understand that teaching content, technology, and strategies for combining teaching methods. | | | | | | 0.73 | | 0.88 | 0.89 | 0.62 |
TPCK2 | I can choose the right technology to improve the teaching tolerance and teaching methods. | | | | | | 0.70 | |
TPCK5 | I am able to design inquiry activities with appropriate multi-media physical technology guides students to understand subject knowledge. | | | | | | 0.69 | |
TPCK4 | I was able to plan activities based on subject content, helping. Students use appropriate multimedia technology to construct the same way of expressing subject knowledge. | | | | | | 0.68 | |
TPCK3 | I am able to teach the content of the course, multimedia technology. Curriculum that is integrated with teaching methods. | | | | | | 0.65 | |
CK | CK4 | For the subjects (to be) taught, I understand the importance of the history and development of the theory. | | | | | | | 0.73 | 0.91 | 0.90 | 0.65 |
CK3 | For the subjects (to be) taught, I master the basics theory and content. | | | | | | | 0.72 |
CK2 | For a subject (to be) taught, I have a specific The way of thinking of the subject. | | | | | | | 0.63 |
CK1 | For the subjects (to be) taught, I have enough Knowledge reserve. | | | | | | | 0.63 |
CK5 | I have enough knowledge of the subject I have mastered confidence. | | | | | | | 0.62 |
Confirmatory factor analysis (CFA) was performed with AMOS. CFA evaluates the consistency between factors and corresponding measures and evaluates the degree of fit between the preset factor model and the actual data. The model path map and standardized estimates are presented in Fig. 2.
Figure 2 TPACK model path diagram and standardized estimated value of college normal students
The test of the overall fit index of the model shows that the fit of the model is good, which once again confirms the validity of the scale, as shown in Table 2.
Table 2
Test of the overall fit degree of TPACK model for normal students in colleges and universities in Shaanxi Province
Fitting index | Test result | Adaptation standard | Judge |
χ2/df | 2.027 | < 3.00 | Yes |
GFI | 0.774 | > 0.80 | No |
NFI | 0.846 | > 0.80 | Yes |
TLI | 0.907 | > 0.80 | Yes |
SRMR | 0.026 | < 0.10 | Yes |
RMSEA | 0.066 | > 0.08 | Yes |
4.2 Descriptive Statistics and Correlation Analysis of TPACK Dimensions of Normal College Students
The overall average value of the TPACK scale of normal students in colleges and universities in Shaanxi Province is 3.696 to 3.895, which belongs to the upper-middle level. The order of the average value of each dimension from low to high is TCK < PK < PCK < TPACK < TK < TPK < CK (see Table 3).
Table 3
TPACK scores of normal students in colleges and universities
Dimension | Quantity | Mean value | Standard deviation |
PK | 239 | 3.733 | 0.674 |
CK | 239 | 3.895 | 0.675 |
TK | 239 | 3.883 | 0.640 |
PCK | 239 | 3.757 | 0.668 |
TPK | 239 | 3.888 | 0.672 |
TCK | 239 | 3.696 | 0.641 |
TPACK | 239 | 3.814 | 0.599 |
In this study, the correlation values between TPACK and PK, CK, TK, PCK, TPK, TCK were 0.875, 0.916, 0.919, 0.896, 0.944, 0.871, respectively. It shows that there is a high correlation between TPACK and PK, PCK, and TCK, and a very high correlation with CK, TK, and TPK (see Table 4). According to the order from low to high, TCK < PK < PCK < CK < TK < TPK is the order of the correlation values among the dimensions of TPACK of normal college students in Shaanxi Province.
Table 4
Correlation analysis of TPACK of normal college students in Shaanxi Province
Dimension | PK | CK | TK | PCK | TPK | TCK | TPACK |
PK | 1.000 | | | | | | |
CK | 0.803** | 1.000 | | | | | |
TK | 0.793** | 0.868** | 1.000 | | | | |
PCK | 0.724** | 0.746** | 0.790** | 1.000 | | | |
TPK | 0.768** | 0.843** | 0.844** | 0.849** | 1.000 | | |
TCK | 0.691** | 0.726** | 0.723** | 0.776** | 0.800** | 1.000 | |
TPACK | 0.875** | 0.916** | 0.919** | 0.896** | 0.944** | 0.871** | 1.000 |
Note: * * denotes P < 0.01, * * denotes P < 0.05. |
In order to further explore the impact of TPACK elements on the overall development of college normal students in Shaanxi Province. With TPACK as the dependent variable and PK, CK, TK, PCK, TPK, TCK as the independent variables, a regression model was constructed, and the regression analysis results are shown in Table 5. The variance inflation factor (VIF) was used to test the multicollinearity of the model. Among all regression models, the maximum VIF value was 6.335 < 10, indicating that the potential multicollinearity would not affect the regression results. The regression model was statistically significant (F = 62.1, P < 0.01), and 99.7% of the variation in the dependent variable TPACK could be explained by the independent variables PK, CK, TK, PCK, TPK, TCK (R2 = 0.997). PK, CK, TK, PCK, TPK, TCK all entered the regression equation, and the model formula was TPACK = 0.150 + 0.153 × PK + 0.158 × CK + 0.158 × TK + 0.148 × PCK + 0.202 × TPK + 0.178 × TCK. The normalized coefficient values (β values) for the respective variables showed that TPK levels had the most significant effect on the development of TPACK, followed by TCK, CK, TK, PK and PCK.
Table 5
Multiple linear regression analysis of the impact of each element of TPACK on overall development
Model | Unnormalized coefficient | Normalization factor | t | P | Collinearity statistics | Adjustment R2 | F |
B | Standard error | Tolerance | VIF |
Constant | 0.015 | 0.015 | | 1.025 | | | | | |
PK | 0.015 | 0.006 | 0.173 | 24.986 | < 0.001 | 0.300 | 3.337 | 0.997 | 61.2 |
CK | 0.153 | 0.008 | 0.178 | 20.246 | < 0.001 | 0.185 | 5.416 |
TK | 0.158 | 0.008 | 0.169 | 19.143 | < 0.001 | 0.184 | 5.433 |
PCK | 0.148 | 0.007 | 0.165 | 21.220 | < 0.001 | 0.236 | 4.229 |
TPK | 0.202 | 0.008 | 0.226 | 23.774 | < 0.001 | 0.158 | 6.335 |
TCK | 0.178 | 0.006 | 0.191 | 28.490 | < 0.001 | 0.318 | 3.147 |
4.3 Strategies for Improving the Informatization Ability of Normal College Students
In recent years, the teaching level of integrated technology for normal students in colleges and universities in China has been improved as a whole, but there is still a long way to go with the goal of "education modernization". In order to improve the informatization ability of normal students, based on the research results, the following two improvement strategies are proposed: take TPK as a breakthrough to promote the improvement of TPACK level; Pay attention to the cultivation of comprehensive knowledge and promote deep integration.
The correlation analysis found that TPK (knowledge of integrated technology) and TPACK have the highest degree of correlation, and the results of multiple regression analysis also show that TPK has the highest degree of influence on TPACK level. Therefore, it is suggested that improving the TPACK level of normal students can start from the TPK aspect and promote the integration of information technology knowledge and teaching method knowledge of normal students, thereby improving the information ability of normal students. On the one hand, we should pay special attention to the teaching of pedagogical knowledge of normal students, but it is not suitable to promote the learning and understanding of pedagogical knowledge of normal students in isolation, and we should pay attention to the combination of information technology as a variable to carry out cohesive teaching. Based on imparting the knowledge of teaching methods, we can guide the normal students to choose, apply and improve information technology, try to realize the "technology" of teaching, and then promote the continuous generation of TPACK of normal students. On the other hand, encourage attempts to use emerging intelligent training platforms or methods to promote online drills, evaluations, and improvements in subject teaching for normal students. The development of information technology provides more possibilities and space for the informatization cultivation of subject teaching method knowledge of normal students. Using network learning space and online teaching and training platform will help normal students choose the required technical methods according to the characteristics, requirements, and goals of subject teaching, and then help normal students to improve their TPACK level.
It is found that three comprehensive knowledge, namely subject teaching knowledge (PCK), subject content knowledge (TCK) and teaching methodology knowledge (TPK), play an important role in the development of TPACK. The development of TPACK is inseparable from the common development of the three comprehensive knowledge. Therefore, we should not simply emphasize the improvement of technology, teaching, or subject knowledge, but should pay more attention to the integration of these factors, comprehensively improve the information literacy of normal students, and promote it expanding from technology application to ability and quality so as to meet the requirements of the development of the information society. First of all, it is necessary to increase the development of cross-integration courses, which vigorously promote the integration of PK, CK and TK on the basis of existing school teaching, form a curriculum system that is conducive to the cultivation of compound knowledge for normal students, and cultivate students to use technology to express teaching content and implement innovative teaching methods ability. Secondly, it is suggested to reform the traditional teaching method, promote research-based teaching, and increase practical opportunities in the classroom. At the same time, relying on the on-campus training base and off-campus practice base, organizing practice flexibly and appropriately, creating an environment that is conducive to the improvement of normal students' educational ability, and makes full use of educational practice to improve the TPACK compound knowledge level of normal students. The practice process is an excellent opportunity to observe the teaching scene, complete the teaching design, and experience the teaching process, and it is also an important way to form compound knowledge. Therefore, colleges and universities should provide more high-quality practice opportunities for normal students. On the one hand, choose high-level practice zones to allow students to have the opportunity to observe the classroom activities of teaching experts. On the other hand, formulate practical training standards that take into account teaching knowledge, subject knowledge and technical knowledge, and give clear practical guidance to help normal students improve their information-based teaching capabilities. The situation dependence, recessiveness and practicality of TPACK determine its characteristics of generating in practice, developing in reflection, and adding value in sharing. It is of great significance for the development of TPACK of normal students to attach importance to accumulating comprehensive knowledge and promote the deep integration of comprehensive knowledge in practice.
4.4 Construction and Application of Teaching System of TPACK Integration Model
Based on the above model, we propose several applications of the TPACK integration model in the reform of the education system:
(1) Push resources and learn independently. Based on the psychological needs of students in the humanistic learning theory for self-realization, electronic whiteboards are used to push preview resources to students before class, such as MOOC, micro-class, learning courseware, preview tests and other learning task lists. Driven by psychological needs, students learn independently with the help of smartphones or PC terminals and other devices, preview and submit before class, so that students have a sense of expectation for learning and mobilize their enthusiasm. Through data feedback, teachers analyze and study students' preview, so as to choose and design effective teaching methods and educational technologies.
(2) Analyze the academic situation and clarify the goals. The pre-class stage of smart teaching is a complete learning process. First of all, teachers check students' historical achievements and knowledge points through electronic whiteboards and online classrooms and determine teaching goals. Secondly, students independently complete the preview test questions pushed by teachers and submit them to the platform to form big data for teachers to analyze. Students can also discuss the problems encountered in the preview process based on the class learning exchange group. Thirdly, according to the feedback of target presupposition, preview and test, the teacher conducts a comprehensive analysis of the students' learning situation, accurately understands the learning information, draws up an appropriate teaching design plan, and realizes teaching based on learning.
(3) Situation introduction to stimulate interest. In the actual teaching, we should make full use of the playing function of the electronic whiteboard for images and videos and the game design function of classroom activities to set up teaching situations, flexibly choose suitable methods, and mobilize the classroom atmosphere. The classroom atmosphere is an important psychological condition that affects teaching. How to stimulate students' interest in learning and desire for knowledge at the beginning of class, situation introduction is particularly important (Wilson B G & Myers K M., 2000). According to the teaching objectives, teachers create situations that conform to students' characteristics, set up suspense, arouse students' strong curiosity, and can stimulate students' learning interest and initiative. Because situation introduction is a direct stimulation to the senses, it is deeply loved by students. The cheerful and novel situation can quickly attract students' attention to teaching, which is a teaching link worthy of use and praise. Contextualization should be tailored to the material, and teachers need to carefully design. The common situational design is as follows: First, the situation of film and television animation. We should make full use of multimedia and show students the situation of the problem in the form of animation. The second is the problem situation. The core of smart teaching is to analyze and solve problems. As long as teachers carefully design problems before class, problems from daily life can generally arouse their interest and make students truly feel that knowledge and life are closely related. Although situation introduction is only a small part of it, it can enliven the learning atmosphere of the whole classroom and promote students to complete teaching tasks with passion.
(4) Task-driven, cooperative inquiry. The flipped classroom model is to give students the initiative in the classroom, establish a correct view of teaching and teachers and students, emphasize students' active learning, cooperative inquiry, and interaction between groups, so as to promote students to acquire knowledge independently. Teachers should abandon the idea of eager for success and quick success, pay attention to students' learning status from time to time, slow down the rhythm of the classroom, create problem situations that help students explore independently, and let students actively participate in learning. Teachers distribute learning tasks in the form of questions to students' terminal tablets through electronic whiteboards, so that students can try to explore. Driven by the goals and tasks, students discuss and explore problems through the Internet. Teachers should create an atmosphere of inquiry as much as possible and introduce learning resources in a targeted manner. Through the understanding of students' learning conditions, teachers create conditions and provide assistance for students' attempts to explore according to specific learning tasks and students' problem-solving needs. Intelligent technology guides students to solve problems and helps students easily grasp knowledge difficulties.