The Optimization of Strategies for Precision Teaching Reform of Vocal Music Education in Colleges and Universities in the Framework of Information Technology

Time has changed since the twenty-first century, with the development of Internet information technology and human beings entering the era of big data. In this era, the original constraints on the “precision teaching” of the technical conditions change, become the key to revitalize the help, some scholars bluntly, with big data analysis technology as the representative of the intelligent information technology support of the “precision teaching” has entered the 2.0 era. In this new stage of development, “precision teaching” has gained considerable development in China. An information technology-supported “precision teaching” model has been constructed, and attempts have been made to utilize information technology to effectively solve the unresolved problems in foreign research on “precision teaching”. As a result, “precision teaching” has begun its journey in the wave of research on education informatization, and with the increasing emphasis on educational data and the maturity of information technology, “precision teaching” has developed a new connotation in China. On the one hand, intelligent diagnostic systems such as “intelligent learning companions” and “electronic schoolbags” are widely used in education and teaching, and educational information such as students’ learning participation behaviors, learning attitudes and learning results can be visualized and quantified. Various IT-assisted teaching tools, such as tablet teaching and the Hivo platform, make it possible for teachers to monitor the learning process of students in real time, dynamically, and in a holistic manner. From accurately excavating students’ learning conditions to accurately developing learning resources, from accurately judging students’ learning performance to accurately giving feedback, information technology has comprehensively affected “precision teaching”, effectively solved the problem of excessive data records left over from history, and effectively brought into play the background of teaching students according to their aptitude in the meaning of “precision teaching”, and ushered in substantial development in the differentiated teaching of “quantifiable student subjects and customizable learning services”. On the other hand, theory is the forerunner of practice, and “precision teaching” in the information age is constantly absorbing humanistic theory, personalized learning theory, differentiated learning theory and other scientific theories on the basis of development and improvement, and moving towards maturity. From the “learning” training that focuses on data to the “learning” literacy that points to educating people, advocating lean culture, adhering to teaching students according to their aptitude, respecting personality differences, and emphasizing personality development, “precision teaching” has become a humanistic teaching practice in today’s era, emphasizing the release of learners’ enthusiasm and initiative, attaching importance to the realization of learners’ individual values, and aiming to cultivate high-quality talents with innovative ability in the new era.

The integration of the vocal teacher education curriculum with information technology-based precision teaching promotes the change of the vocal teacher education curriculum, which is not only reflected in the presentation and updating of the content of the vocal teacher education curriculum, but also in the transformation of the teaching method. In the traditional voice teacher education classroom teaching, the teacher is the lecturer of knowledge and the students are the passive receivers of knowledge. Vocal teacher education students have a single channel to acquire knowledge, which can only be obtained through the teacher’s words and teachings. It can be seen that teachers have a high degree of control over the classroom during teaching, and they are the masters of the classroom and the authority of knowledge. Such a classroom presents a “teacher-centered” teaching method, which lacks good and sufficient communication and interaction between teachers and students, and lacks the independent acquisition of knowledge and exploration by the voice teacher trainees. In the age of information technology, the teaching method of voice teacher education has been transformed by giving full play to the advantages of precise teaching and realizing the integration with information technology in voice teacher education courses.

As shown in Figure 1, the most important feature of the precise teaching model is to deeply explore the educational value of “big data” and design teaching activities based on “big data”. Firstly, we use big data analysis technology to process, analyze and handle educational data, so as to transform the fuzzy and perceptual teaching objectives of traditional teaching into measurable and quantifiable problems, and to determine precise teaching objectives. Secondly, the scientific theory of programmed teaching is used as a guide to design a programmed teaching process framework based on “big data”. Programming the teaching process is the core link to ensure the effective implementation of the teaching model, that is, through the establishment of a big data educational resource base as the basis and premise of the program, forming four cycles of iterative teaching process of practice, measurement, recording and intervention, so as to refine the object of the design of the teaching content from the class as a whole to the specific students, and based on the real and reliable measurement data, provide students with learning resources and development programs tailored to the needs of the individual. Based on real and reliable measurement data, we provide students with learning resources and development programs that are tailored to their individual needs, thus truly realizing personalized teaching. Finally, in the big data environment, the integration of various advanced technologies to achieve accurate and effective attention to the learning process of each student and records, the formation of an all-round, all-staff, all-process real-time evaluation, which accurately depicts the personalized growth of each student’s “portrait” from multiple perspectives and turns it into a visual evaluation report, which allows teachers to predict the growth of students in the coming period of time and give effective learning suggestions or programs in advance. Based on the report, teachers can predict the growth of students in the coming period of time, so as to provide effective learning suggestions or programs in advance.

Figure 1.

Precision teaching mode

The development of the precise teaching plan is aimed at optimizing the teaching effect, so the accurate portrayal of the teaching effect is the prerequisite for the development of the precise teaching plan. Student characteristics and behavior X is a vector of length p, i.e., X=(X1,X2,⋯,Xp)$$X = \left( {{X^1},{X^2}, \cdots ,{X^p}} \right)$$, X∈ℝP$$X \in {\mathbb{R}^P}$$, and consists of p indicators X′ that represent student characteristics and behavior, which include information on basic characteristics such as gender and age of students, as well as information on learning behaviors such as whether they wake up early or not, and the number of times they go to the library. The instructional program A utilized in the teaching process takes the value of 0 or 1, i.e., A ∈ {0, 1} for example, A = 1 means that early warning interventions are provided to students during instruction, A = 0 means that no early warning interventions are provided to students during instruction, etc. The instructional outcomes of interest Y∈ℝ$$Y \in \mathbb{R}$$ can be test scores, test rankings, or the degree of accomplishment of instructional objectives, etc.

In practice, the overall distribution P is often unknown, thus it is not possible to evaluate the precise teaching program by directly obtaining the conditional expectation E(Y|A, X) of the teaching effect, which needs to be estimated. Considering that the teaching effect is related to the teaching program as well as the corresponding students’ characteristics and behavioral performance, based on the regression model, the evaluation model of the precise teaching program is constructed as follows: (1)E(Y|A,X)=γTX˜+A⋅(βTX˜)$$E(Y|A,X) = {\gamma ^T}\tilde X + A \cdot \left( {{\beta ^T}\tilde X} \right)$$

To obtain parameter estimates for the accurate teaching program evaluation model, a least squares-like idea is used. Given the observed data of n student {Xi,Ai,Yi;i=1,2,⋯,n}$$\left\{ {{X_i},{A_i},{Y_i};i = 1,2, \cdots ,n} \right\}$$, the loss function is defined as: (2)Ln(β,γ)=1n∑i=1n[Yi−γTX˜i−βTX˜i(Ai−π(X˜i))]2$${L_n}(\beta ,\gamma ) = \frac{1}{n}\sum\limits_{i = 1}^n {{{\left[ {{Y_i} - {\gamma ^T}{{\tilde X}_i} - {\beta ^T}{{\tilde X}_i}\left( {{A_i} - \pi \left( {{{\tilde X}_i}} \right)} \right)} \right]}^2}}$$

where π(x) = P(A = 1|X = x) and is satisfied: (3)0<π(x)<1,∀x$$0 < \pi (x) < 1,\forall x$$

Denoting the parameter estimates of the precise instructional program evaluation model by (β˜,γ˜)$$(\tilde \beta ,\tilde \gamma )$$, we have: (4)(β˜T,γ˜T)T=argminβ,γLn(β,γ)$${\left( {{{\tilde \beta }^T},{{\tilde \gamma }^T}} \right)^T} = \arg {\min \nolimits_{\beta ,\gamma }}{L_n}(\beta ,\gamma )$$

Definition: (5)L(β,γ)=‖Y−X˜γ−WX˜β‖2$$L(\beta ,\gamma ) = {\left\| {Y - \tilde X\gamma - W\tilde X\beta } \right\|^2}$$

where Y=(y1,y2,⋯,yn)T$$Y = {\left( {{y_1},{y_2}, \cdots ,{y_n}} \right)^T}$$ is the observation vector, β and γ are the unknown parameters, X˜=(1,XT)T$$\tilde X = {\left( {1,{X^T}} \right)^T}$$ denotes the known design matrix, and W is a pair of corner matrices, viz: (6)W=(a1−π(x1)⋱an−π(xn))$$W=\left( \begin{array}{*{35}{l}} {{a}_{1}}-\pi \left( {{x}_{1}} \right) & {} & {} \\ {} & \ddots & {} \\ {} & {} & {{a}_{n}}-\pi \left( {{x}_{n}} \right) \\ \end{array} \right)$$

and satisfies (WX, X) for a full rank matrix.

Clearly there is: (7)(β˜T,γ˜T)T=argminβ,γLn(β,γ)=argminβ,γL(β,γ)$${\left( {{{\tilde \beta }^T},{{\tilde \gamma }^T}} \right)^T} = \arg {\min \nolimits_{\beta ,\gamma }}{L_n}(\beta ,\gamma ) = \arg {\min \nolimits_{\beta ,\gamma }}L(\beta ,\gamma )$$

L(β, γ) is obtained by taking the partial derivatives of β and γ and making them zero: (8)∂L∂β=−2X˜TWT(Y−X˜γ−WX˜β)=0∂L∂γ=−2X˜T(Y−X˜γ−WX˜β)=0$$\begin{array}{l} {\frac{{\partial L}}{{\partial \beta }} = - 2{{\tilde X}^T}{W^T}(Y - \tilde X\gamma - W\tilde X\beta ) = 0} \\ {\frac{{\partial L}}{{\partial \gamma }} = - 2{{\tilde X}^T}(Y - \tilde X\gamma - W\tilde X\beta ) = 0} \end{array}$$

From there, there is: (9)(X˜TWTX˜T)Y=((WX˜)T(WX˜)X˜TWTX˜X˜TWX˜X˜TX˜)(βγ)$$\left( \begin{array}{c} {{\tilde X}^T}{W^T} \\ {{\tilde X}^T} \\ \end{array} \right)Y = \left( {\begin{array}{*{20}{c}} {{{(W\tilde X)}^T}(W\tilde X)}&{{{\tilde X}^T}{W^T}\tilde X} \\ {{{\tilde X}^T}W\tilde X}&{{{\tilde X}^T}\tilde X} \end{array}} \right)\left( \begin{array}{c} \beta \\ \gamma \\ \end{array} \right)$$

The system of equations has a unique solution under the condition that matrix (WX, X) is full rank: (10)(β˜T,γ˜T)T=((WX˜)T(WX˜)X˜TWTX˜X˜TWX˜X˜TX˜)−1(X˜TWTX˜T)Y$${\left( {{{\tilde \beta }^T},{{\tilde \gamma }^T}} \right)^T} = {\left( {\begin{array}{*{20}{c}} {{{(W\tilde X)}^T}(W\tilde X)}&{{{\tilde X}^T}{W^T}\tilde X} \\ {{{\tilde X}^T}W\tilde X}&{{{\tilde X}^T}\tilde X} \end{array}} \right)^{ - 1}}\left( \begin{array}{c} {{\tilde X}^T}{W^T} \\ {{\tilde X}^T} \\ \end{array} \right)Y$$

The following shows that β˜$$\tilde \beta$$ and γ˜$$\tilde \gamma$$ minimize L(β, γ). For any β and γ, there are: (11)‖Y−X˜γ−WX˜β‖2=‖Y−X˜γ˜−WX˜β˜+X˜(γ˜−γ)+WX˜(β˜−β)‖2=‖Y−X˜γ˜−WX˜β˜‖2+‖X˜(γ˜−γ)+WX˜(β˜−β)‖2+2(γ˜−γ)TX˜T(Y−X˜γ˜−WX˜β˜)+2(β˜−β)T(WX˜)T(Y−X˜γ˜−WX˜β˜)$$\begin{array}{l} {\left\| {Y - \tilde X\gamma - W\tilde X\beta } \right\|^2} = {\left\| {Y - \tilde X\tilde \gamma - W\tilde X\tilde \beta + \tilde X(\tilde \gamma - \gamma ) + W\tilde X(\tilde \beta - \beta )} \right\|^2} \\ = {\left\| {Y - \tilde X\tilde \gamma - W\tilde X\tilde \beta } \right\|^2} + {\left\| {\tilde X(\tilde \gamma - \gamma ) + W\tilde X(\tilde \beta - \beta )} \right\|^2} \\ + 2{(\tilde \gamma - \gamma )^T}{{\tilde X}^T}(Y - \tilde X\tilde \gamma - W\tilde X\tilde \beta ) \\ + 2{(\tilde \beta - \beta )^T}{(W\tilde X)^T}(Y - \tilde X\tilde \gamma - W\tilde X\tilde \beta ) \\ \end{array}$$

Since γ˜$$\tilde \gamma$$ and β˜$$\tilde \beta$$ satisfy the condition of Eq. (10), the last two terms of the above equation are 0 and ‖X˜(γ˜−γ)+WX˜(β˜−β)‖2$${\left\| {\tilde X(\tilde \gamma - \gamma ) + W\tilde X(\tilde \beta - \beta )} \right\|^2}$$ is non-negative, thus there: (12)‖Y−X˜γ−WX˜β‖2≥‖Y−X˜γ˜−WX˜β˜‖$${\left\| {Y - \tilde X\gamma - W\tilde X\beta } \right\|^2} \ge \left\| {Y - \tilde X\tilde \gamma - W\tilde X\tilde \beta } \right\|$$

That is, L(β,γ)≥L(β˜,γ˜)$$L(\beta ,\gamma ) \ge L(\tilde \beta ,\tilde \gamma )$$.

To summarize, the parameters of the precise teaching program evaluation model are estimated as: (13)(β˜T,γ˜T)T=((WX˜)T(WX˜)X˜TWTX˜X˜TWX˜X˜TX˜)−1(X˜TWTX˜T)Y$${\left( {{{\tilde \beta }^T},{{\tilde \gamma }^T}} \right)^T} = {\left( {\begin{array}{*{20}{c}} {{{(W\tilde X)}^T}(W\tilde X)}&{{{\tilde X}^T}{W^T}\tilde X} \\ {{{\tilde X}^T}W\tilde X}&{{{\tilde X}^T}\tilde X} \end{array}} \right)^{ - 1}}\left( \begin{array}{c} {{\tilde X}^T}{W^T} \\ {{\tilde X}^T} \\ \end{array} \right)Y$$

This section combines the practical application of precision teaching in School Z, and then in accordance with the collection and processing of data, student status and characterization. Data standardization is the basis of students’ vocal performance, mainly through the collection, processing, cleaning and storage of basic data, including data cleaning and descriptive data aggregation, data integration and transformation, data statute, data discretization and so on. Combined with the situation of vocal music teaching in School Z, the research object in this section is proposed to be selected from 178 students in the first year of university in School Z. Among them, Classes 1 and 2 are the experimental group, Classes 3 to 6 are the control group (traditional teaching methods), and the experimental group (precision teaching) has a total of 60 students, and the final valid samples obtained are 58 copies. The control group totaled 118 students and finally got 117 valid samples.

From the indicators of students’ learning status, students’ gender, classroom attendance, post-class training, and grades were selected as variable indicators to characterize students’ learning status. The basic relationship of learning status characteristics can be viewed and analyzed through the SPSS 23 software scatter plot, which is used to visualize the relationship between the independent variable X and the dependent variable Y. The results are shown in Figure 2. By analyzing the data collected from the students’ learning status characteristics, there is a linear relationship between the independent variables (class attendance and after-school training) and the dependent variable (vocal performance) (P=0.001), in which the student’s performance is significantly correlated with the student’s class attendance and after-school training.

Figure 2.

Learning status data

In this section of the experiment, the university education management system acquired the learning characteristics data of the experimental group for the entire academic year, the data spanning one academic year, including three quizzes classroom attendance and post-class practice rate and other variables, and some of the indicators are shown in a box-and-line plot to demonstrate the dynamic changes in the learning status characteristics of the teaching-guidance as shown in Figure 3. The box-and-line plot is used to represent a continuous data distribution, which sequentially represents the upper edge, upper quartile, median, lower quartile, and lower edge. The upper quartile and lower quartile represent the 25% and 75% percentile of the data, respectively, the outliers represent the anomalies, and the median represents the middle level of a set of data and is located in the middle of the box. The length of the box represents the degree of dispersion of the data. The longer the box, the greater the gap between the upper and lower quartiles, which in turn reflects a greater degree of dispersion of the data. In contrast, a shorter box indicates a smaller gap between the upper and lower quartiles, which suggests a higher degree of data concentration.

Figure 3.

Learning feature data

Looking longitudinally at the classroom attendance boxplots, students’ boxplots in the vocal subject have narrower boxes, indicating that there is little difference in the variation of the mean time of students’ classroom attendance. From the after-class training box plot, students’ interest in and enjoyment of the subject determines the difference in the length of after-class training, resulting in a greater variation in the amount of time students spend in after-class training. From the horizontal view of the box-and-line graph, the overall trend of student performance was upward over the three exams.

After one semester of experimentation, a comparative analysis of the final mathematics grades of two classes of students in the second semester of 2022-2023 in University Z was conducted to assess the learning of the students in the two classes, in order to validate the effectiveness of the information technology-based precision teaching model in promoting the development of teachers. In this study, SPSS 21.0 was used to analyze the differences in the final grades of the students in the two classes. Firstly, the Q-Q diagram was used to test the normal distribution of students’ grades in the two classes, and the results are shown in Figure 4. From the Q-Q diagram, it can be seen that the distribution of the grades of the two classes is skewed, but the overall distribution is around a straight line, indicating that it basically conforms to the normal distribution, which is suitable for parametric testing.

Figure 4.

Q-Q

The Levenve value test results of the variance of the two samples are shown in Table 1, the variances of the two groups are unequal, and the two samples are non-chiral, so the final test result is a T-value of 2.221, and the Sig-value is 0.03 < 0.05, so the average achievement of the experimental class is higher than the control class, and the achievement is more stable is not a result of chance, and the achievement of the two classes has a significant difference, i.e., the results of the two classes based on the There is a significant difference between the two classes, i.e., the use of information technology-based precision teaching mode has a more significant effect in promoting students’ development, and it also shows that this mode has a better effect in promoting students’ learning ability and achievement compared with the traditional teaching mode. The actual situation of using information technology to teach effectively and promote students’ professional development. Interviews were conducted with the teaching teachers of the experimental group. The vocal teachers in the experimental group firstly affirmed the advantages of this teaching mode, due to the support of the data the new teaching mode has the advantages of strong relevance, high precision, wide coverage and meticulous evaluation compared with the traditional teaching mode, which can better reflect the real appearance of their own classroom and explain the teaching effect, and it is easier for them to get the real classroom feedback, so it is more efficient and profound in the teaching reflection and it is also more It is also easier for me to apply the feedback and results of my reflection to the actual teaching process, which is very helpful to the improvement of my teaching ability.

In conjunction with the application of the information technology-based precision teaching mode in School Z, based on the user feedback data obtained after the system was put on line, the feedback data from different objects were analyzed and quantified through on-site surveys and questionnaires to analyze and evaluate the application effect of the precision teaching mode in the precision management of vocal teaching in this school.

The results of the survey are shown in Table 2, and it can be seen from the data that 90% of the people completely agree that there are great advantages of using precision teaching. 48.5% basically agreed that the precise teaching of information technology has a complete system of scientific knowledge, while another 36.4% fully agreed, 51.7% basically agreed that with the help of precise teaching, the arrangement and organization of the content of the teaching can express the objectives of teaching at different levels, and 30.2% fully agreed. 50% basically agreed that utilizing precision teaching/learning is more relevant to vocal learning, and another 36.2% totally agreed. 80% basically agreed that utilizing precision teaching is much more effective than traditional teaching. 57.6% basically agree that the combination of information technology and classroom teaching is a general trend and is conducive to the realization of precision teaching, and 36.2% completely agree. By refining the data, it is found that: In terms of the effect of the use of precision teaching based on information technology, most of the students think that teaching with the aid of information technology is better than traditional teaching, and more conducive to precision teaching and personalized learning.

1)

Accurately identify students’ dynamic changes

Capturing the weak points of students’ knowledge, in the age of information technology, in order to improve the effect of accurate feedback in vocal lessons, we can use information technology to collect feedback data that was neither realistic nor possible in the past and realize accurate learning to meet the needs of individual students. By using information technology to make probabilistic predictions based on the collected feedback data, we can optimize the content of students’ learning, the method of learning, and the time of learning. This form of proactive monitoring and feedback helps voice teachers track students’ progress and problems in a less aggressive way. Because students’ mastery of knowledge changes before and after precision teaching, it is necessary to use intelligent means to map the changes in students’ mastery of knowledge points before and after teaching, which is conducive to voice teachers quickly clarifying the difficulties, pain points, doubts, and weaknesses encountered by students in their learning, and guiding them to break them down one by one in their teaching.