# Information Feedback Model for IoT Applications Based on of Basic Ability in Educational Practice of Preschool Education Major in Higher Vocational Education

Authors: Ping Wang; Wensi Xie
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In order to solve the problem that the measurement of students’ behavior preference is inaccurate and affects the teaching effect in the information feedback model of basic ability of educational practice, an information feedback model of basic ability of educational practice of preschool education major in higher vocational education is proposed. A feedback metacognitive intervention mechanism is designed from the perspective of learners to reflect learning performance from three aspects: cognitive results, behavior performance, and learning strategies. The focus index is introduced to extract the characteristics of classroom teaching information of professional education. Design a multisource education information fusion algorithm, fuse the ability information of the three dimensions of learning performance, and measure students’ behavior preferences. Construct the information feedback model of practice basic ability, get the predicted information feedback score, and measure the teaching effect. The experimental test shows that the average score of this model is 8.97 points higher than the pretest, 5.05 points and 3.33 points higher than the model based on data mining and cluster analysis, respectively. Therefore, it can improve the teaching quality of preschool education.


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## Body

## 1. Introduction

In recent years, with the surge of social demand for highly educated and high-quality kindergarten teachers, vigorously developing preschool education and cultivating applied preschool education professionals have become an inevitable trend in the development of higher vocational preschool education [1]. Students majoring in preschool education should not only master certain preschool education theory, but also have rich practical knowledge, but also learn how to transform educational theory into future educational behavior through educational practice and improve their professional ability of education and teaching. Therefore, paying attention to the educational practice of preschool education has been regarded as an important task of preschool teacher education [2]. The cultivation of preservice kindergarten teachers’ professional ability is inseparable from the practical curriculum, and the most important of the practical curriculum is the educational practice curriculum [3]. The cultivation of higher vocational colleges has laid a good foundation for the development of preschool education in China, but at the same time, there are many practical problems, such as the gap between the talent cultivation of higher vocational colleges and the expectations of kindergartens for professional teachers, the long entry adaptation period, and the unclear advantages of graduates [4]. Preschool education graduates generally do not pay enough attention to educational internship and educational practice, resulting in insufficient preparation at the entry stage. Under the realistic problems, it is particularly important to organize and carry out standardized educational practice. Educational practice is an important way to improve the quality of teachers. It needs to urge all departments to do a good job in the educational practice of interns. Educational practice is undoubtedly an important way for higher vocational students to truly enter the field of educational practice, contact the educational environment and develop their practical ability [5]. Learners can make certain judgments about their own learning process and learning results. If the judgment is consistent with or exceeds the expected goal, the learner may end the current round of learning. On the contrary, if the judgment fails to achieve the expected goal, learners may use the existing metacognitive knowledge to reselect learning methods and strategies and implement the metacognitive monitoring process [6]. In order to improve the basic ability level of educational practice of preschool education specialty in higher vocational colleges, it is necessary to feed back the practice ability information and timely adjust the educational curriculum design according to the feedback results, so as to improve the implementation effect of preschool education curriculum.In this era of digitalization, technological education is constantly evolving as a result of the extinction of the Internet of Things (IoT). The formation of a technology culture that is founded on the advancement of technologies brought about by IoT is the end goal of this technical education. This strategy encourages the growth of students’ creative abilities by making use of active teaching and learning approaches as a foundation for the program. To achieve mastery of the technological culture developed by mankind through a process ascending from technological literacy to technological education and competence, up to the general vision of the technological world, technological education is an important factor for both high school and university students. This is because technological education has major influences on the life and health of the individual living in a modern society that is constantly changing [25].At the same time, the feedback results of practice basic ability also play a certain role in promoting the development of students’ professional ability and the cultivation of teachers’ core quality, which is conducive to the cultivation of kindergarten teachers’ professional affection, professional attitude, and professional values, so as to achieve the original intention of educational practice curriculum. Therefore, this paper designs the information feedback model of basic ability of educational practice of preschool education major in higher vocational colleges, so as to provide reference for improving the educational practice scheme and improving the quality.


## 2. Information Feedback Model of Basic Ability in Educational Practice of Preschool Education Major in Higher Vocational Education

### 2.1. Designing Feedback Metacognitive Intervention Mechanism from the Perspective of Learners

Educational practice course “is an indispensable and important part of teacher training,” an effective educational way to cultivate preschool normal students’ professional emotion and professional knowledge, and an important way to comprehensively check and improve the education and teaching level of normal universities [7]. Therefore, in order to promote the professional development of undergraduates majoring in preschool education and improve their professional ability, higher vocational colleges need to pay attention to and improve the arrangement of students’ educational practice. The specific arrangements of different higher vocational colleges are different, but they generally include three links: internship, internship, and study. These links are to make preschool education students better adapt to the work of kindergartens after they go to work [8]. However, in self-judgment, learners often lead to metacognitive bias due to overconfidence, that is, their understanding of their own learning process and learning results is better than the actual situation. They thought that their original performance was good enough and their learning methods and strategies were good enough to achieve good results. They did not recognize the gap between actual performance and goals, which was difficult to trigger the correction of learners’ metacognitive knowledge and the adjustment of metacognitive monitoring process [9]. Some learners are clear about the gap between their actual performance and their goals, but their metacognitive experience is weak and do not mobilize metacognitive knowledge and metacognitive monitoring awareness; or the level of metacognitive knowledge is low and do not know how to implement the metacognitive monitoring process. Peer evaluation is widely used as an alternative evaluation method. It is considered that this evaluation method can increase the communication and interaction between teachers and students and students and finally achieve the effect of promoting students’ learning. It is also considered that this evaluation activity can cultivate and improve learners’ ability to identify their peer works and views [10]. Feedback provides an objective basis for learners’ self-judgment and self-regulation [11]. Feedback intervention theory holds that regulation is produced by comparing feedback with goals (or standards). Peer evaluation has a positive impact on students’ cognition, metacognition, and emotional expression. In addition, learners develop personal critical thinking in the process of filtering evaluation [12]. After receiving the feedback information, learners will form new experience and cognition of themselves, learning tasks, and learning strategies through the comparison between actual learning performance and target learning performance, as well as their own learning performance and peer learning performance [13]. The actual learning performance is shown in Figure 1.Figure 1
Actual learning performance.Cognitive results, behavioral performance, and learning strategies are the main dimensions that can reflect learning performance. Cognitive results refer to the depth and breadth of knowledge acquisition, including the degree of knowledge mastery and knowledge architecture. Learning strategies are the learning rules, methods, and regulation methods adopted by learners in learning activities. Behavior performance includes video viewing, resource browsing, and homework submission during the interaction between learners and resources or tools, as well as the corresponding input time [14]. At present, it is more and more applied to the online learning practice of higher education, which has a positive impact on students’ learning. However, when it consciously adjusts its learning performance according to these experiences and cognition, its metacognitive monitoring process occurs and generates new learning performance data. Metacognitive knowledge and metacognitive experience always exist, but they may not be activated, and metacognitive monitoring does not often occur with learners’ actual learning process. The relationship of the three dimensions is shown in Figure 2.Figure 2
Relationship between metacognitive dimensions.Receiving the evaluation and description information about these three dimensions and the tips designed according to the information of these three dimensions can enable learners to form a new understanding of their own learning and trigger and drive the process of metacognition. The feedback information reflecting the current learning situation can enable learners to objectively understand themselves, clarify the gap between their actual learning performance and target learning performance, and trigger the metacognitive monitoring process to adjust learning strategies and learning behavior. For the research of peer feedback process, content analysis, discourse analysis, and other methods are generally used to analyze the types and characteristics of peer feedback comments. By comprehensively comparing their cognitive results, behavior performance, and learning strategy information in different learning units, we can find out which learning strategies and behavior styles are more suitable for themselves, can achieve better learning results, and is conducive to the strengthening and enrichment of learners’ metacognitive knowledge. Compared with target learning performance, peer learning performance changes dynamically at any time, which is beyond the control of learners, and can stimulate learners’ continuous efforts. The number of peer feedback will affect learning performance in the first to second rounds of peer feedback, but it is not obvious in the second to third rounds. For example, a learner’s original goal about the cognitive results of a unit is to achieve excellent performance, and he may gradually give up due to his poor metacognitive monitoring ability in the learning process. However, the descriptive feedback received shows that other peers with similar levels have achieved or even exceeded their goals in this unit. Compared with affective and metacognitive feedback, cognitive feedback can promote the improvement of learners’ work quality. This information makes learners find that the preset goal can be achieved through their own efforts, and according to the peer’s learning strategies and behavior performance information, they can preliminarily predict how much effort they need to make, so as to promote the growth of metacognitive knowledge and metacognitive monitoring.


### 2.2. Extracting the Characteristics of Classroom Teaching Information of Professional Education

In traditional classroom teaching, teachers observe students’ posture, expression, facial expression, and other information to judge whether students devote themselves to the classroom, so as to adjust the teaching accordingly, which belongs to real-time teaching feedback. As a traditional teaching method, classroom learning is another important part of blended learning. Sufficient visual and auditory information is its outstanding advantage. Teachers can also obtain after-school teaching feedback according to the test results. If the feedback result is positive, teachers can maintain the previous teaching strategy in the subsequent teaching. If the feedback is negative, teachers can also review the previous teaching content and adjust the subsequent teaching strategy [15]. When carrying out peer evaluation activities, make effective use of the classroom teaching part to realize the effective interaction between peer evaluation activities in online learning and classroom learning, so as to make up for the problems of shallow interaction level and unsatisfactory interaction effect caused by the lack of a large amount of visual and auditory information (facial expression, voice and intonation, etc.) in online peer evaluation. To obtain real-time teaching feedback, teachers can only consciously capture the feedback information from students through naked eye observation. Usually, teachers can understand the emotions and thoughts expressed by students by observing their sitting posture, so as to judge the state of the student [16]. In the analysis of classroom sitting posture, the number of students in each category is the most basic data. After the classification number is obtained, the data can be statistically analyzed, such as average, mode, maximum, and minimum. In addition to these commonly used indicators, in order to obtain more dimensional statistical information, this study simply defines an attribute called focus, which is used to express the seriousness of students in the current classroom. In this study, each pose classification is given a weight, and the focus score is calculated from the weight of each pose and the percentage of the number of people in the pose classification. After getting the number of people in each category, multiply the percentage of the number of people in each category by the corresponding weight, add the obtained values, and add a benchmark score of 40 to get the focus. The calculation formula is as follows:
(1)γ=∑abθ+40.In formula (1), γ represents concentration; a represents the set of all classifications; b represents the percentage of people in each category; θ represents the weight corresponding to the classification. For the statistics of concentration, each posture is divided into positive posture and negative posture. In the above classification, looking at the blackboard and reading and writing belong to positive posture, holding your cheek slightly tends to negative posture, and looking left and right, lowering your head, wandering, and lying on your stomach belong to negative posture. Positive posture can increase the value of concentration, while negative posture will reduce the value of concentration. The digital images of classroom teaching are input into CNN model to recognize classroom sitting behavior and complete the extraction of focus information features. The detection and recognition process of CNN model is shown in Figure 3.Figure 3
Detection and identification process of CNN model.In the CNN model, a class II label is defined for each anchor, which represents whether there are students or not in the anchor box. IoU defines the overlap degree of two boxes, that is, the coverage of the predicted box and the real box. Its value is equal to the intersection of the two boxes divided by the union of the two boxes. For a picture, the loss function is defined as follows:(2)fwz,hz=1n∑l1wz,w′z+φ1n∑w′zl2hz,h′z.In formula (2), f represents the loss function; z indicates the index of the anchor; wz represents the predicted possibility that the anchor contains an object; w′z is the label indicating the real box; hz represents the vector of the four coordinates of the prediction box; h′z represents the vector represented by the real box related to the forward anchor; l1 and l2 are classified loss and regression loss, respectively; n represents the number of all indexes; φ indicates the index parameter. For the regression loss, we use the robust loss to set, and its formula is:
(3)l2z=12z2,z<1,z−12,otherwise.Regression loss is activated only when the anchor is positive and is invalid when the anchor is negative. The outputs of classification layer and regression layer are composed of prediction possibility and box coordinate vector, respectively. For the area box, the following parameterization method is adopted for the four coordinates:(4)hx=x−αc1,hy=y−βc2.In formula (4), hx and hy represent coordinate parameters; x and y represent the coordinates of the center point of the box; α and β represent the box coordinate values of the anchor; c1 and c2 represent the width and height of the box, respectively. This can be regarded as the border regression from the anchor box to the nearby real box, and the border regression is a feature that acts on the RoI pool of any size, and all area sizes share the weight of the regression. After the classroom sitting behavior image is trained, the focused recognition results are obtained. Average focus, mode focus, maximum focus, and minimum focus can also be calculated through focus. Teaching managers can evaluate teachers’ teaching quality with reference to their concentration.


### 2.3. Design a Multisource Educational Information Fusion Algorithm

Teaching feedback information is one of the elements of teaching activities. Teaching can also be regarded as an interactive activity of information transmission between teachers and students. The progress of this information exchange depends on feedback. Educational evaluation is a highly technical work. Whether the evaluation can be organized scientifically has an important impact on the quality of evaluation and the reliability and dependence of results. Through teaching feedback, teachers can re-recognize and rethink their teaching practice, understand students’ mastery of teaching content, and timely adjust teaching progress, content, activities, and methods [17]. A variety of intervention measures are proposed from the three levels of information interaction, such as excellent work reporting, anonymous peer evaluation, and guidance of reflection direction. The purpose is to promote information interaction, realize concept interaction, and improve the interaction quality in peer evaluation activities [18]. In addition to test scores, teaching evaluation is also students’ after-school teaching feedback on teachers’ teaching behavior. In this paper, the learning performance data is divided into cognitive results, behavior performance, learning strategy selection, and other categories. It is designed from two aspects of feedback content and feedback form to form different feedback strategies. The design framework is shown in Table 1.Table 1
Feedback design framework.
Feedback providerLearning performanceLearning performance subdimensionFeedback contentFeedback formTeacherCognitive resultsKnowledge masteryEvaluation typeIllustrationEvaluation typeTextDescriptive typeIllustrationPrompt typeTextBehavior performanceLearning behavior controlPrompt typeIllustrationDescriptive typeFormEvaluation typeIllustrationLearning strategyLearning strategy guidelinesDescriptive typeIllustrationPrompt typeText, illustrationStudentCognitive resultsKnowledge systemDescriptive typeIllustrationKnowledge masteryEvaluation typeTextGenerally speaking, the information conveyed by evaluative feedback is simple and clear, mainly represented by diagrams and texts, which is convenient for learners to extract information quickly; descriptive feedback contains relatively complex information, mainly in the form of diagrams and tables, which is conducive to learners’ in-depth analysis and understanding; suggestive feedback emphasizes the guidance of information. The use of graphics and text is more conducive to learners to understand the intention of feedback and make appropriate thinking and adjustment. Evaluative feedback, descriptive feedback, and suggestive feedback are interrelated and complementary to each other, which is conducive to learners’ correct cognition of their learning process and learning results. For the above multisource education information, it needs to be fused before it can be used to output the final practice basic ability feedback results. Therefore, this paper designs a multisource educational information fusion algorithm to extract the feedback characteristics. There are three types for internship basic ability information, namely numerical scoring, text information, and learning trust relationship. The above data sources can extract information by means of web crawler and associated big data [19]. The common method of using scoring information is the hidden factor model based on matrix decomposition. The main goal is to learn the hidden correlation feature matrix, which describes students’ learning preferences. At the same time, learn the relevant characteristic matrix of hidden teaching content, which describes the characteristics of educational content. Feature learning solves a regular optimization problem under the least square loss through the scoring matrix of approximate observation. The calculation formula is:
(5)g1=r1−r22+δu2+v2.In formula (5), g1 represents the regular optimization function; r1 and r2 represent learning preference and content preference vectors; δ is the regular parameter, and the norm of the learned parameter is controlled to prevent over fitting; u and v represent the average scores of students and teaching contents. For learning dependency information sources, the idea of learning matrix decomposition is to learn the hidden learning related feature representation of users, so as to describe the characteristics of students’ dependence. That is, approximate learning similarity matrix is similar to scoring matrix decomposition. Learning matrix decomposition solves the following regularized least squares loss optimization problem, and the calculation formula is
(6)g2=p1−r22+δu2+v2.In formula (6), g2 represents the regular function of the learning relation matrix; p1 indicates the learning similarity between students. The similarity can be expressed by cosine similarity of scoring vector:
(7)p1=r1r2r12r22.The working principle of learning matrix decomposition has one hypothesis, that is, the student entity plays a dual identity: one is to implement the scoring behavior, that is, to learn the hidden preference representation from the scoring data; one is to establish learning behavior, that is, to learn hidden social representations from social relationships. For the comment text information source, topic modeling is used to find the hidden attributes and hidden topics of teaching content. Similar to the dual identity of the student entity, the teaching content also has a dual identity: the teaching content receives the students’ scores in the scoring data and gets the students’ comments in the comment data. Therefore, the content representation learned from one data source is actually different forms of the same entity, and there is a transformation to connect the two representations. Finally, the matrix decomposition collaborative filtering method is used to integrate the three heterogeneous data sources into a comprehensive model, so that the feedback of the basic ability of practice can be obtained by using the three heterogeneous information at the same time.


### 2.4. Constructing the Information Feedback Model of Practice Basic Ability

According to its source, the information feedback of basic ability of educational practice of preschool education major in higher vocational colleges can be divided into internal feedback and external feedback. Internal feedback refers to students’ self-monitoring in the learning process. External feedback refers to the feedback received by students from teachers and peers. Although it is a peer evaluation activity, teachers, as the design and organizer of learning activities and the promoter of students’ learning and development, are still inseparable from teachers’ participation. Internal feedback from self-monitoring can deepen the understanding of learning progress from the perspective of cognitive psychology; external feedback from teachers and peers can enable students to objectively understand their learning progress [20–23]. Teachers’ participation in peer evaluation can not only increase students’ enthusiasm, but also increase students’ recognition and acceptance of evaluation. According to the information provided by teachers or peers, prompt feedback is added on the basis of evaluation feedback and description feedback. As the promoter of students’ learning and the communicator and collaborator of students, teachers also play a great role in the process of mutual evaluation. Feedback information can be complex, and multiple dimensions need to be considered, such as feedback function, time, frequency, and content. From a practical point of view, this paper puts forward the information feedback of basic practice ability, as shown in Figure 4.Figure 4
Information feedback model of practice basic ability.The foundation of feedback loop is classroom culture. Feedback should at least include three elements: sender, feedback information, and receiver. In the “feedback loop” model, it is found that the learning objectives and success criteria of a class or continuous course are important contents of the stage learning process. The learning objectives come from the learning process. The feedback sender provides feedback on individual performance, understanding, and improvement. Students should be made aware of these contents at the beginning. The importance of success criteria also lies in guiding students’ learning, so that students have a clear and clear goal orientation in participating in learning, participating in classroom activities, and completing learning tasks. Teachers can provide individuals with learning information and correction and improvement information, including questioning, observation, teaching tasks, and problem solving. The data obtained by the above means can help teachers grasp students’ learning process and determine the feasibility of learning objectives and the achievement of objectives. Peers can provide individuals with alternative strategies; learning materials can provide information and help them clarify their ideas; network, teaching platform, and mobile learning terminal can provide support for it [20–25]. Based on the constructed practice basic ability information feedback model, combined with the three heterogeneous information sources obtained above, the average characteristics of teaching feedback are predicted, and the final prediction result is close to the average score of preschool education practice basic ability information feedback. The specific calculation formula can be expressed as
(8)F=∑p1r1,p1+u+v.In formula (8), F represents the predicted information feedback score. The above calculation method can eliminate the influence of existing scoring information in similarity estimation. This method decomposes the explicit scoring matrix and learns the implicit characteristic matrix at the same time, so that the influence of implicit scoring feedback model can be observed more clearly by using implicit feedback information. So far, the construction of information feedback model of basic ability of educational practice of preschool education specialty in higher vocational education has been completed.


## 2.1. Designing Feedback Metacognitive Intervention Mechanism from the Perspective of Learners

Educational practice course “is an indispensable and important part of teacher training,” an effective educational way to cultivate preschool normal students’ professional emotion and professional knowledge, and an important way to comprehensively check and improve the education and teaching level of normal universities [7]. Therefore, in order to promote the professional development of undergraduates majoring in preschool education and improve their professional ability, higher vocational colleges need to pay attention to and improve the arrangement of students’ educational practice. The specific arrangements of different higher vocational colleges are different, but they generally include three links: internship, internship, and study. These links are to make preschool education students better adapt to the work of kindergartens after they go to work [8]. However, in self-judgment, learners often lead to metacognitive bias due to overconfidence, that is, their understanding of their own learning process and learning results is better than the actual situation. They thought that their original performance was good enough and their learning methods and strategies were good enough to achieve good results. They did not recognize the gap between actual performance and goals, which was difficult to trigger the correction of learners’ metacognitive knowledge and the adjustment of metacognitive monitoring process [9]. Some learners are clear about the gap between their actual performance and their goals, but their metacognitive experience is weak and do not mobilize metacognitive knowledge and metacognitive monitoring awareness; or the level of metacognitive knowledge is low and do not know how to implement the metacognitive monitoring process. Peer evaluation is widely used as an alternative evaluation method. It is considered that this evaluation method can increase the communication and interaction between teachers and students and students and finally achieve the effect of promoting students’ learning. It is also considered that this evaluation activity can cultivate and improve learners’ ability to identify their peer works and views [10]. Feedback provides an objective basis for learners’ self-judgment and self-regulation [11]. Feedback intervention theory holds that regulation is produced by comparing feedback with goals (or standards). Peer evaluation has a positive impact on students’ cognition, metacognition, and emotional expression. In addition, learners develop personal critical thinking in the process of filtering evaluation [12]. After receiving the feedback information, learners will form new experience and cognition of themselves, learning tasks, and learning strategies through the comparison between actual learning performance and target learning performance, as well as their own learning performance and peer learning performance [13]. The actual learning performance is shown in Figure 1.Figure 1
Actual learning performance.Cognitive results, behavioral performance, and learning strategies are the main dimensions that can reflect learning performance. Cognitive results refer to the depth and breadth of knowledge acquisition, including the degree of knowledge mastery and knowledge architecture. Learning strategies are the learning rules, methods, and regulation methods adopted by learners in learning activities. Behavior performance includes video viewing, resource browsing, and homework submission during the interaction between learners and resources or tools, as well as the corresponding input time [14]. At present, it is more and more applied to the online learning practice of higher education, which has a positive impact on students’ learning. However, when it consciously adjusts its learning performance according to these experiences and cognition, its metacognitive monitoring process occurs and generates new learning performance data. Metacognitive knowledge and metacognitive experience always exist, but they may not be activated, and metacognitive monitoring does not often occur with learners’ actual learning process. The relationship of the three dimensions is shown in Figure 2.Figure 2
Relationship between metacognitive dimensions.Receiving the evaluation and description information about these three dimensions and the tips designed according to the information of these three dimensions can enable learners to form a new understanding of their own learning and trigger and drive the process of metacognition. The feedback information reflecting the current learning situation can enable learners to objectively understand themselves, clarify the gap between their actual learning performance and target learning performance, and trigger the metacognitive monitoring process to adjust learning strategies and learning behavior. For the research of peer feedback process, content analysis, discourse analysis, and other methods are generally used to analyze the types and characteristics of peer feedback comments. By comprehensively comparing their cognitive results, behavior performance, and learning strategy information in different learning units, we can find out which learning strategies and behavior styles are more suitable for themselves, can achieve better learning results, and is conducive to the strengthening and enrichment of learners’ metacognitive knowledge. Compared with target learning performance, peer learning performance changes dynamically at any time, which is beyond the control of learners, and can stimulate learners’ continuous efforts. The number of peer feedback will affect learning performance in the first to second rounds of peer feedback, but it is not obvious in the second to third rounds. For example, a learner’s original goal about the cognitive results of a unit is to achieve excellent performance, and he may gradually give up due to his poor metacognitive monitoring ability in the learning process. However, the descriptive feedback received shows that other peers with similar levels have achieved or even exceeded their goals in this unit. Compared with affective and metacognitive feedback, cognitive feedback can promote the improvement of learners’ work quality. This information makes learners find that the preset goal can be achieved through their own efforts, and according to the peer’s learning strategies and behavior performance information, they can preliminarily predict how much effort they need to make, so as to promote the growth of metacognitive knowledge and metacognitive monitoring.


## 2.2. Extracting the Characteristics of Classroom Teaching Information of Professional Education

In traditional classroom teaching, teachers observe students’ posture, expression, facial expression, and other information to judge whether students devote themselves to the classroom, so as to adjust the teaching accordingly, which belongs to real-time teaching feedback. As a traditional teaching method, classroom learning is another important part of blended learning. Sufficient visual and auditory information is its outstanding advantage. Teachers can also obtain after-school teaching feedback according to the test results. If the feedback result is positive, teachers can maintain the previous teaching strategy in the subsequent teaching. If the feedback is negative, teachers can also review the previous teaching content and adjust the subsequent teaching strategy [15]. When carrying out peer evaluation activities, make effective use of the classroom teaching part to realize the effective interaction between peer evaluation activities in online learning and classroom learning, so as to make up for the problems of shallow interaction level and unsatisfactory interaction effect caused by the lack of a large amount of visual and auditory information (facial expression, voice and intonation, etc.) in online peer evaluation. To obtain real-time teaching feedback, teachers can only consciously capture the feedback information from students through naked eye observation. Usually, teachers can understand the emotions and thoughts expressed by students by observing their sitting posture, so as to judge the state of the student [16]. In the analysis of classroom sitting posture, the number of students in each category is the most basic data. After the classification number is obtained, the data can be statistically analyzed, such as average, mode, maximum, and minimum. In addition to these commonly used indicators, in order to obtain more dimensional statistical information, this study simply defines an attribute called focus, which is used to express the seriousness of students in the current classroom. In this study, each pose classification is given a weight, and the focus score is calculated from the weight of each pose and the percentage of the number of people in the pose classification. After getting the number of people in each category, multiply the percentage of the number of people in each category by the corresponding weight, add the obtained values, and add a benchmark score of 40 to get the focus. The calculation formula is as follows:
(1)γ=∑abθ+40.In formula (1), γ represents concentration; a represents the set of all classifications; b represents the percentage of people in each category; θ represents the weight corresponding to the classification. For the statistics of concentration, each posture is divided into positive posture and negative posture. In the above classification, looking at the blackboard and reading and writing belong to positive posture, holding your cheek slightly tends to negative posture, and looking left and right, lowering your head, wandering, and lying on your stomach belong to negative posture. Positive posture can increase the value of concentration, while negative posture will reduce the value of concentration. The digital images of classroom teaching are input into CNN model to recognize classroom sitting behavior and complete the extraction of focus information features. The detection and recognition process of CNN model is shown in Figure 3.Figure 3
Detection and identification process of CNN model.In the CNN model, a class II label is defined for each anchor, which represents whether there are students or not in the anchor box. IoU defines the overlap degree of two boxes, that is, the coverage of the predicted box and the real box. Its value is equal to the intersection of the two boxes divided by the union of the two boxes. For a picture, the loss function is defined as follows:(2)fwz,hz=1n∑l1wz,w′z+φ1n∑w′zl2hz,h′z.In formula (2), f represents the loss function; z indicates the index of the anchor; wz represents the predicted possibility that the anchor contains an object; w′z is the label indicating the real box; hz represents the vector of the four coordinates of the prediction box; h′z represents the vector represented by the real box related to the forward anchor; l1 and l2 are classified loss and regression loss, respectively; n represents the number of all indexes; φ indicates the index parameter. For the regression loss, we use the robust loss to set, and its formula is:
(3)l2z=12z2,z<1,z−12,otherwise.Regression loss is activated only when the anchor is positive and is invalid when the anchor is negative. The outputs of classification layer and regression layer are composed of prediction possibility and box coordinate vector, respectively. For the area box, the following parameterization method is adopted for the four coordinates:(4)hx=x−αc1,hy=y−βc2.In formula (4), hx and hy represent coordinate parameters; x and y represent the coordinates of the center point of the box; α and β represent the box coordinate values of the anchor; c1 and c2 represent the width and height of the box, respectively. This can be regarded as the border regression from the anchor box to the nearby real box, and the border regression is a feature that acts on the RoI pool of any size, and all area sizes share the weight of the regression. After the classroom sitting behavior image is trained, the focused recognition results are obtained. Average focus, mode focus, maximum focus, and minimum focus can also be calculated through focus. Teaching managers can evaluate teachers’ teaching quality with reference to their concentration.


## 2.3. Design a Multisource Educational Information Fusion Algorithm

Teaching feedback information is one of the elements of teaching activities. Teaching can also be regarded as an interactive activity of information transmission between teachers and students. The progress of this information exchange depends on feedback. Educational evaluation is a highly technical work. Whether the evaluation can be organized scientifically has an important impact on the quality of evaluation and the reliability and dependence of results. Through teaching feedback, teachers can re-recognize and rethink their teaching practice, understand students’ mastery of teaching content, and timely adjust teaching progress, content, activities, and methods [17]. A variety of intervention measures are proposed from the three levels of information interaction, such as excellent work reporting, anonymous peer evaluation, and guidance of reflection direction. The purpose is to promote information interaction, realize concept interaction, and improve the interaction quality in peer evaluation activities [18]. In addition to test scores, teaching evaluation is also students’ after-school teaching feedback on teachers’ teaching behavior. In this paper, the learning performance data is divided into cognitive results, behavior performance, learning strategy selection, and other categories. It is designed from two aspects of feedback content and feedback form to form different feedback strategies. The design framework is shown in Table 1.Table 1
Feedback design framework.
Feedback providerLearning performanceLearning performance subdimensionFeedback contentFeedback formTeacherCognitive resultsKnowledge masteryEvaluation typeIllustrationEvaluation typeTextDescriptive typeIllustrationPrompt typeTextBehavior performanceLearning behavior controlPrompt typeIllustrationDescriptive typeFormEvaluation typeIllustrationLearning strategyLearning strategy guidelinesDescriptive typeIllustrationPrompt typeText, illustrationStudentCognitive resultsKnowledge systemDescriptive typeIllustrationKnowledge masteryEvaluation typeTextGenerally speaking, the information conveyed by evaluative feedback is simple and clear, mainly represented by diagrams and texts, which is convenient for learners to extract information quickly; descriptive feedback contains relatively complex information, mainly in the form of diagrams and tables, which is conducive to learners’ in-depth analysis and understanding; suggestive feedback emphasizes the guidance of information. The use of graphics and text is more conducive to learners to understand the intention of feedback and make appropriate thinking and adjustment. Evaluative feedback, descriptive feedback, and suggestive feedback are interrelated and complementary to each other, which is conducive to learners’ correct cognition of their learning process and learning results. For the above multisource education information, it needs to be fused before it can be used to output the final practice basic ability feedback results. Therefore, this paper designs a multisource educational information fusion algorithm to extract the feedback characteristics. There are three types for internship basic ability information, namely numerical scoring, text information, and learning trust relationship. The above data sources can extract information by means of web crawler and associated big data [19]. The common method of using scoring information is the hidden factor model based on matrix decomposition. The main goal is to learn the hidden correlation feature matrix, which describes students’ learning preferences. At the same time, learn the relevant characteristic matrix of hidden teaching content, which describes the characteristics of educational content. Feature learning solves a regular optimization problem under the least square loss through the scoring matrix of approximate observation. The calculation formula is:
(5)g1=r1−r22+δu2+v2.In formula (5), g1 represents the regular optimization function; r1 and r2 represent learning preference and content preference vectors; δ is the regular parameter, and the norm of the learned parameter is controlled to prevent over fitting; u and v represent the average scores of students and teaching contents. For learning dependency information sources, the idea of learning matrix decomposition is to learn the hidden learning related feature representation of users, so as to describe the characteristics of students’ dependence. That is, approximate learning similarity matrix is similar to scoring matrix decomposition. Learning matrix decomposition solves the following regularized least squares loss optimization problem, and the calculation formula is
(6)g2=p1−r22+δu2+v2.In formula (6), g2 represents the regular function of the learning relation matrix; p1 indicates the learning similarity between students. The similarity can be expressed by cosine similarity of scoring vector:
(7)p1=r1r2r12r22.The working principle of learning matrix decomposition has one hypothesis, that is, the student entity plays a dual identity: one is to implement the scoring behavior, that is, to learn the hidden preference representation from the scoring data; one is to establish learning behavior, that is, to learn hidden social representations from social relationships. For the comment text information source, topic modeling is used to find the hidden attributes and hidden topics of teaching content. Similar to the dual identity of the student entity, the teaching content also has a dual identity: the teaching content receives the students’ scores in the scoring data and gets the students’ comments in the comment data. Therefore, the content representation learned from one data source is actually different forms of the same entity, and there is a transformation to connect the two representations. Finally, the matrix decomposition collaborative filtering method is used to integrate the three heterogeneous data sources into a comprehensive model, so that the feedback of the basic ability of practice can be obtained by using the three heterogeneous information at the same time.


## 2.4. Constructing the Information Feedback Model of Practice Basic Ability

According to its source, the information feedback of basic ability of educational practice of preschool education major in higher vocational colleges can be divided into internal feedback and external feedback. Internal feedback refers to students’ self-monitoring in the learning process. External feedback refers to the feedback received by students from teachers and peers. Although it is a peer evaluation activity, teachers, as the design and organizer of learning activities and the promoter of students’ learning and development, are still inseparable from teachers’ participation. Internal feedback from self-monitoring can deepen the understanding of learning progress from the perspective of cognitive psychology; external feedback from teachers and peers can enable students to objectively understand their learning progress [20–23]. Teachers’ participation in peer evaluation can not only increase students’ enthusiasm, but also increase students’ recognition and acceptance of evaluation. According to the information provided by teachers or peers, prompt feedback is added on the basis of evaluation feedback and description feedback. As the promoter of students’ learning and the communicator and collaborator of students, teachers also play a great role in the process of mutual evaluation. Feedback information can be complex, and multiple dimensions need to be considered, such as feedback function, time, frequency, and content. From a practical point of view, this paper puts forward the information feedback of basic practice ability, as shown in Figure 4.Figure 4
Information feedback model of practice basic ability.The foundation of feedback loop is classroom culture. Feedback should at least include three elements: sender, feedback information, and receiver. In the “feedback loop” model, it is found that the learning objectives and success criteria of a class or continuous course are important contents of the stage learning process. The learning objectives come from the learning process. The feedback sender provides feedback on individual performance, understanding, and improvement. Students should be made aware of these contents at the beginning. The importance of success criteria also lies in guiding students’ learning, so that students have a clear and clear goal orientation in participating in learning, participating in classroom activities, and completing learning tasks. Teachers can provide individuals with learning information and correction and improvement information, including questioning, observation, teaching tasks, and problem solving. The data obtained by the above means can help teachers grasp students’ learning process and determine the feasibility of learning objectives and the achievement of objectives. Peers can provide individuals with alternative strategies; learning materials can provide information and help them clarify their ideas; network, teaching platform, and mobile learning terminal can provide support for it [20–25]. Based on the constructed practice basic ability information feedback model, combined with the three heterogeneous information sources obtained above, the average characteristics of teaching feedback are predicted, and the final prediction result is close to the average score of preschool education practice basic ability information feedback. The specific calculation formula can be expressed as
(8)F=∑p1r1,p1+u+v.In formula (8), F represents the predicted information feedback score. The above calculation method can eliminate the influence of existing scoring information in similarity estimation. This method decomposes the explicit scoring matrix and learns the implicit characteristic matrix at the same time, so that the influence of implicit scoring feedback model can be observed more clearly by using implicit feedback information. So far, the construction of information feedback model of basic ability of educational practice of preschool education specialty in higher vocational education has been completed.


## 3. Experimental Research

### 3.1. Experimental Design

This paper constructs an information feedback model of basic ability of educational practice of preschool education major in higher vocational education. In order to verify the application effect of the model, the following experimental test is carried out. The assessment of students’ academic performance and ability is a necessary means to evaluate students’ mastery of knowledge and skills, learning application, and transfer ability after learning relevant knowledge points. The experimental object of this paper is the students of preschool education in a higher vocational college. Three mathematics teachers with rich teaching experience and excellent results are selected to teach the experiment. There are 150 students in three classes participating in this experiment. During the implementation of the experiment, there was no change in teachers and students in the three classes. The experiment was carried out under natural normal conditions. The students did not know that they had participated in the experimental research, so as to ensure the reliability of the experimental research results to a certain extent. This study conducted paired samplet-test on the scores of students in three classes before and after the application of the basic ability information feedback model of teaching practice and analyzed the changes of students’ scores. In order to verify the feasibility of this model, the application results of the model are compared with the application structure of the basic ability information feedback model of educational practice based on data mining and cluster analysis. The three classes of the experimental subjects use the model established in this paper and two comparative models for teaching practice. Before the implementation of the model, the level and achievement of students’ mathematical thinking ability in three classes are obtained through the test questions as the pretest data. After the implementation of the experiment, the posttest is obtained by using the test questions with the same difficulty to analyze whether the students’ mathematical thinking ability has been significantly improved before and after the implementation. And excavate the classroom interactive feedback behavior to explore the implementation effect of the information feedback model of practice basic ability.


### 3.2. Experimental Results and Analysis

According to the test results before and after the implementation of the basic ability information feedback model of educational practice of preschool education specialty, this paper makes an overall analysis. The change results of test scores before and after the implementation of the three models are shown in Tables2–4.Table 2
Performance matching analysis before and after the implementation of this model.
Teaching thinking abilityMean valueStandard deviationStandard error of meanConcept understanding and mastery-8.125.230.85Specific image thinking ability-9.234.161.12Logical thinking ability-8.753.290.97Creative thinking ability-9.764.461.28Table 3
performance matching analysis before and after the implementation of the model based on data mining.
Teaching thinking abilityMean valueStandard deviationStandard error of meanConcept understanding and mastery-3.686.341.72Concept understanding and mastery-3.657.221.81Logical thinking ability-4.125.061.74Creative thinking ability-4.237.671.95Table 4
Performance matching analysis before and after the implementation of the model based on cluster analysis.
Teaching thinking abilityMean valueStandard deviationStandard error of meanConcept understanding and mastery-5.668.171.91Concept understanding and mastery-5.485.051.26Logical thinking ability-5.296.191.88Creative thinking ability-6.117.320.94According to the data statistics and analysis results of the students’ scores in the three classes before and after the above experiment, the three basic ability information feedback models of educational practice of preschool education specialty can improve the students’ scores to a certain extent. On the whole, the average score of this model is 8.97 points higher than the pretest and 5.05 points and 3.33 points higher than the models based on data mining and cluster analysis, respectively. Therefore, after the practice of the information feedback model designed in this paper, the improvement of students’ performance is more obvious. At the same time, the significance level of paired samplet-test is less than 0.05, indicating that the effective feedback teaching model has a significant impact on the mathematics learning of students in three classes, which verifies the effectiveness of the information feedback model on the teaching model of preschool education to a certain extent.


## 3.1. Experimental Design

This paper constructs an information feedback model of basic ability of educational practice of preschool education major in higher vocational education. In order to verify the application effect of the model, the following experimental test is carried out. The assessment of students’ academic performance and ability is a necessary means to evaluate students’ mastery of knowledge and skills, learning application, and transfer ability after learning relevant knowledge points. The experimental object of this paper is the students of preschool education in a higher vocational college. Three mathematics teachers with rich teaching experience and excellent results are selected to teach the experiment. There are 150 students in three classes participating in this experiment. During the implementation of the experiment, there was no change in teachers and students in the three classes. The experiment was carried out under natural normal conditions. The students did not know that they had participated in the experimental research, so as to ensure the reliability of the experimental research results to a certain extent. This study conducted paired samplet-test on the scores of students in three classes before and after the application of the basic ability information feedback model of teaching practice and analyzed the changes of students’ scores. In order to verify the feasibility of this model, the application results of the model are compared with the application structure of the basic ability information feedback model of educational practice based on data mining and cluster analysis. The three classes of the experimental subjects use the model established in this paper and two comparative models for teaching practice. Before the implementation of the model, the level and achievement of students’ mathematical thinking ability in three classes are obtained through the test questions as the pretest data. After the implementation of the experiment, the posttest is obtained by using the test questions with the same difficulty to analyze whether the students’ mathematical thinking ability has been significantly improved before and after the implementation. And excavate the classroom interactive feedback behavior to explore the implementation effect of the information feedback model of practice basic ability.


## 3.2. Experimental Results and Analysis

According to the test results before and after the implementation of the basic ability information feedback model of educational practice of preschool education specialty, this paper makes an overall analysis. The change results of test scores before and after the implementation of the three models are shown in Tables2–4.Table 2
Performance matching analysis before and after the implementation of this model.
Teaching thinking abilityMean valueStandard deviationStandard error of meanConcept understanding and mastery-8.125.230.85Specific image thinking ability-9.234.161.12Logical thinking ability-8.753.290.97Creative thinking ability-9.764.461.28Table 3
performance matching analysis before and after the implementation of the model based on data mining.
Teaching thinking abilityMean valueStandard deviationStandard error of meanConcept understanding and mastery-3.686.341.72Concept understanding and mastery-3.657.221.81Logical thinking ability-4.125.061.74Creative thinking ability-4.237.671.95Table 4
Performance matching analysis before and after the implementation of the model based on cluster analysis.
Teaching thinking abilityMean valueStandard deviationStandard error of meanConcept understanding and mastery-5.668.171.91Concept understanding and mastery-5.485.051.26Logical thinking ability-5.296.191.88Creative thinking ability-6.117.320.94According to the data statistics and analysis results of the students’ scores in the three classes before and after the above experiment, the three basic ability information feedback models of educational practice of preschool education specialty can improve the students’ scores to a certain extent. On the whole, the average score of this model is 8.97 points higher than the pretest and 5.05 points and 3.33 points higher than the models based on data mining and cluster analysis, respectively. Therefore, after the practice of the information feedback model designed in this paper, the improvement of students’ performance is more obvious. At the same time, the significance level of paired samplet-test is less than 0.05, indicating that the effective feedback teaching model has a significant impact on the mathematics learning of students in three classes, which verifies the effectiveness of the information feedback model on the teaching model of preschool education to a certain extent.


## 4. Conclusion

In order to promote the professional development of preschool teachers, the state and colleges pay more and more attention to the training of preservice preschool teachers. The information feedback model of practice basic ability is conducive to cultivating students’ basic ability, strengthening the arrangement of the proportion of educational practice and emphasizing the significance of educational practice to students’ professional development. This paper puts forward an information feedback model of basic ability in educational practice of preschool education major in higher vocational colleges. The experimental results show that the model can significantly improve students’ performance and improve the quality and effect of preschool education teaching. However, this study can only be regarded as the beginning and attempt. In the future research, the empirical research on the effective feedback teaching model needs to be further deepened, and the implementation effect of the teaching model has been verified by more practice.



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*Source: 8638894-2022-08-18.xml*