# Evaluation and Construction of College Students’ Growth and Development Index System Based on Data Association Mining and Deep Learning Model

Authors: Yanjie Li; He Mao
Journal: Security and Communication Networks
 (2021)

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## Abstract

The rise of big data in the field of education provides an opportunity to solve college students’ growth and development. The establishment of a personalized student management mode based on big data in universities will promote the change of personalized student management from the empirical mode to the scientific mode, from passive response to active warning, from reliance on point data to holistic data, and thus improve the efficiency and quality of personalized student management. In this paper, using the latest ideas and techniques in deep learning such as self-supervised learning and multitask learning, we propose an open-source educational big data pretrained language model F-BERT based on the BERT model architecture. Based on the BERT architecture, F-BERT can effectively and automatically extract knowledge from educational big data and memorize it in the model without modifying the model structure specific to educational big data tasks so that it can be directly applied to various educational big data domain tasks downstream. The experiment demonstrates that Vanilla F-BERT outperformed the two Vanilla BERT-based models, Vanilla BERT and BERT tasks, by 0.0.6 and 0.03 percent, respectively, in terms of accuracy.


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## Body

## 1. Introduction

In recent years, big data has continued to have a profound impact on all areas of society, driving a major change in human thinking and practice. Likewise, its power is strongly impacting the entire education system and is becoming a disruptive force driving innovation and change in the education system [1, 2]. The assessment of college student development indicators is the management of respecting the unique value of individual students, discovering their potential abilities, guiding them to form independent personalities and unique personalities, and promoting the free and scientific development of individual students. Personalized development should be combined with comprehensive development [3]. The promotion of personalized management of students in colleges and universities is an important element to promote the realization of personalized education. At present, student managers basically use the combination of the empirical method, observation method, and simple data statistics to implement personalized management, which has achieved certain results, but there are also problems that are difficult to overcome, such as poor targeting and obvious lagging [4]. How to realize accurate education according to students’ abilities has indeed become a difficult problem for mass higher education at this stage. The emergence of big data in education provides an opportunity for universities to realize personalized student management [5].“Shared growth” main characteristic is that it consists of an information technology-based sharing market platform created by a third party, where individuals can exchange unused items and share their knowledge, skills, and other information for a fee [6]. With the development of the mobile Internet, the popularity of smart phones and mobile terminals, and the availability of third-party apps for smart phones to facilitate users’ participation in “sharing,” the number of commercialized sharing platforms has been growing. These phenomena are known as “knowledge-sharing.” At present, commercialized personal human creation and knowledge sharing platforms have become more and more perfect, but the research on building sharing platforms for students’ growth and development experience in universities has not been reported [7, 8]. The author believes that, driven by big data, the process of educating students in colleges and universities can introduce the concept of sharing and call it “shared growth.”The core connotation of the “shared growth” education model is that colleges and universities help students obtain the universal law and the most realistic path of growth by building a platform for recording, analyzing, and sharing the growth data of college students [9]. Firstly, the role of students is changed from the traditional role of “learning” to the role of “teaching” and “learning,” and the growth experience of individual students is transformed into an important element of education for other students. Secondly, the content of education is changed from traditional successful experience to both successful and failed educational experience so that students can learn excellent growth and success experience independently and avoid the problems of growth. Based on this, “shared growth” can effectively realize the full coverage of educational subjects, the full effect of educational resources, the full participation of the educational process, and the all-round transformation of the educational mode [10].“Shared growth” will be involved in the whole process of education. Based on big data technology, “shared growth” covers the whole process of student education. From the basic data of education and teaching of the process data of behavior recorded in real time, “shared growth” will record, analyze, and share the data of students’ growth and success throughout the whole process [11]. With the accumulation of data and the development of students, the clear and three-dimensional “portrait” of students’ growth painted by “shared growth” will accompany the whole college process. Even after students graduate, their growth “portrait” data can still be used as an educational resource, greatly expanding the time frame for education and enabling full participation in all aspects of university education. In addition, China’s rapid entry into the mobile Internet era has laid the technical foundation for the long-term practice of “shared growth” [12].


## 2. Related Work

Pretrained models were first applied in the field of machine images and computer video. In a large-scale image recognition competition by ImageNet, AlexNet [13], which achieved first place at that time, was introduced, and it adopted a convolutional neural network model architecture based on CNN [14]. Since then, AlexNet has been widely used in many machine vision tasks. The experimental results show that reusing the pretrained model can significantly increase the accuracy of the target task and also greatly reduce the training time of the new model．Google released the pretrained language model BERT [15] in the second half of 2018, which is of epoch-making significance in the natural language processing community. Next, the BERT model achieved the best model accuracy in all of the more than ten natural language understanding tasks, made significant improvements in several public natural language processing competitions, and even surpassed human accuracy in competitions such as common-sense reasoning, automatic question and answer, sentiment analysis, relationship extraction, and named entity recognition. Meanwhile, after the source code of the BERT model was released, subsequent researchers conducted various natural language processing tasks based on the open-source code and pretrained models to further improve the performance of various NLP task models significantly. For example, the top 20 models in the SQuAD [16–18] competition ranking all chose to use the BERT model, and their best scores exceeded human levels; in the CoQA [19] competition ranking, the top 12 models were all based on the BERT model, and the top model also achieved scores exceeding human levels. The top 12 models in the CoQA competition ranking are also based on the BERT model, and the top model also achieves more than the human level.


## 3. Integration of Big Data for College Student Education in Universities

Educational activities include both formal activities such as teaching, research, management, campus life, and services, as well as informal activities such as family communication and social education, and both offline on-site education and online audiovisual education. These are the environments where educational data are generated. The creators of the environment are the “subject” and the “education big data management platform,” both of which are also the sources of education big data generation [20]. The “subjects” include students, teachers, student workers, and school administrators, and the “education big data management platform” includes the basic information system, dynamic collection and processing system, teaching system, student affair system, one card system, and other kinds of education management equipment. Big data in education requires each system to break the barriers, except for the necessary confidential information, to achieve interoperability [21]. In all kinds of educational activities, the massive data generated by the “subject” and recorded with the “education big data management platform” are fully mined, connected, and integrated to integrate the education big data that can be applied to teaching management (see Figure 1).Figure 1
Integration of big data in higher education.All actors in higher education are generating dynamic data. Students leave digital fragments in their learning life, such as course selection, online social behavior, participation in party and school activities, library access, grade dynamics, and campus card usage. All these data can be fully mined, integrated, and analyzed with the help of artificial intelligence and cloud technology, transforming fragmented data into knowledgeable information. On the one hand, this enables teachers to provide students with personalized learning content and resources; on the other hand, it can also provide student workers and administrators with timely information for prediction and adjustment in the field of student management services [4]. It is evident that data-empirical student management research is gradually breaking the shackles of empirical management and developing into a real data-supported research paradigm. Jim Gray proposed the “fourth paradigm” of data-intensive research, separating big data as a separate research paradigm [22]. Thus, big data in education will certainly lead to changes in the mode and method of student management in universities.Nowadays, a tool based on big data mining and analysis to change the management mode of college students has become one of the necessary options for the development of colleges and universities now and in the future. The student management activities supported by big data in education have obvious advantages compared with traditional management, and this new management model will play a unique role in changing the personalized management practice of college students [23].Under this new management mode, student workers gradually change from managers to service providers who help students’ individual and group personalized development. The traditional campus system will be upgraded to a smart campus platform, which will continuously burn individual behavior data, store, integrate, and transcend them intelligently, accurately diagnose and evaluate the status and problems of individuals and groups based on mining and analysis tools [24], form visual analysis reports, and develop personalized management service plans after both managers and students have made corrective improvements to help students grow and become successful. According to the above analysis, this study constructs a personalized management model for college students based on educational big data (see Figure 2).Figure 2
Personalized management models of college students based on education big data.Different from traditional education data collection, the sources of education big data collection are more diversified, including internal data of service platforms, Internet data, dynamic sensing data, and IoT data [25]. On the one hand, the quantity is huge and complicated, and structural data and nonstructural data coexist. For unstructured and multisource heterogeneous data; on the other hand, it is difficult to be collected by the existing systems due to its dynamic and irregular nature, and the latest collection technologies must be adopted to collect and initially process the data. These collection technologies mainly include IoT sensing technology [26].In order to ensure the validity and usability of education data, it is necessary to keep the standard and quality gate in the process of data collection and use technical means to realize the preliminary transformation of original data points to high-quality usable information, especially to promote the transformation of unstructured data for the storage, integration, and analysis of education data. At present, the technologies that can better realize the preliminary processing of data mainly include the method of collecting high-quality original data, the method of data cleaning, the management of data for traceability, and the method of parsing multisource data.


## 4. F-BERT Model

As shown in Figure3, we propose an innovative vertical domain pretrained language model based on the BERT model architecture using self-supervised learning and multitask learning techniques in the field of deep learning such as the educational big data pretrained language model, which we name the F-BERT model [27]. The F-BERT model is also a two-stage architecture of pretraining and fine tuning. In the pretraining phase, F-BERT differs from the traditional BERT pretraining in that instead of using a small number of pretraining targets for training, multiple pretraining tasks are introduced to try to help the model learn more effectively.Figure 3
An illustration of the architecture for F-BERT.


### 4.1. Encoder

In the pretraining phase, we use the transformer encoder and adopt similar encoding methods as BERT, i.e., position embedding, segment embedding, and token embedding; but in addition, we design an additional task encoding (task embedding). For different pretraining tasks, we use task encoding to refine the modeling of different types of tasks. ForN tasks, the task IDs range from 1 to N. Each task ID is mapped to a different encoding, and the final input encoding is input encoding = location encoding + segment encoding + token encoding + task encoding. This is shown in Figure 4.Figure 4
Transformer encoder layer.Next, to compute the attention for the input codes, we use the transformer’s multiheaded self-attentive mechanism. It mainly consists of the query Query (replaced byQ), the key Key (replaced by K), and the value Value (replaced by V) as input, and then, we project Q, K, and V using linear transformations, and we can compute multiple times simultaneously, i.e., we use multiple h to represent h different linear changes. Finally, all the attention values are stitched together to complete a multiheaded attention calculation [28]. For a single Q, the output of the attention function is a weighted combination of V. To simplify the calculation, we take the same Q, K, and V values for self attention and use the scaled dot product for the attention calculation. The specific attention function is(1)attentionQ,K,V=softmaxQKTdkV,where d denotes the Q, K, and V vector dimensions.


### 4.2. Self-Supervised Multitask Learning Pretraining Task

The current pretraining stage mainly uses sentence or word co-occurrence signals to design different tasks for pretraining the language model. For example, the original BERT model constructs 2 retraining tasks (the masked language model task and the next sentence prediction task); the XLNet [29] model uses the fully aligned language model for retraining in an autoregressive manner. On the contrary, we have superimposed a large number of repeatable training targets in the F-BERT model; just like human beings taking a foreign language test, there are many different types of questions on the paper, and if the training can be integrated, it will be very helpful to improve the overall learning. Specifically, in the pretraining stage, F-BERT constructs four self-supervised learning pretraining tasks and learns different levels of knowledge from the training text corpus through multitask learning. As shown in Figure 4, the four self-supervised pretraining tasks are span substitution prediction pretraining task, capitalization prediction pretraining task, sentence disorganization pretraining task, and question-answer sentence relationship pretraining task [30].


#### 4.2.1. Span Replacement Prediction Pretraining

We adopt a word-level pretraining task to achieve span replacement prediction pretraining. This problem is further amplified for text corpus in the education big data domain, which reduces the learning effectiveness of the model [17].Regarding the span replacement prediction pretraining task, specifically, we first iteratively sample the text sequenceX=x1,x2,…,xn, i.e., for each word sequence, by defining a mask proportion threshold (e.g., 20% of the entire sequence) to construct a subset of sequence Y.In the F-BERT training task, we adopt a random selection of texts of a certain length based on a geometric distribution. Since the geometric distribution is a discrete probability distribution withk trials to get the first success in its nth Bernoulli trial, we are able to get the starting point of the word separation uniformly (randomly) and can get a shorter sequence. In order to obtain a better sampling length distribution, the geometric probability distribution in the F-BERT model has a hypernatremia P = 0.18 and a maximum length limit T = 16 (the excess is treated as discarded), and the best average sequence length we obtain through the experiment is 4.6. Next, we implement the implementation of the span replacement prediction pretraining task. In the F-BERT training process, we define the span as four words in the front and back boundaries of the span, and if these words are not in the span, we use the vectors of these four words plus the vectors of the masked words in the span to predict the original words. The specific implementation is to use a 2-layer feed-forward neural network with layer regularization, where we use ReLU [31] for the activation function. Thus, splicing the encoding vector and the position vector are as follows:(2)h=Layer NormReLUW1X,f=Layer NormReLUW2h.We also use cross entropy as a loss function, which is the loss of the span replacement prediction pretraining target for model training.


## 4.1. Encoder

In the pretraining phase, we use the transformer encoder and adopt similar encoding methods as BERT, i.e., position embedding, segment embedding, and token embedding; but in addition, we design an additional task encoding (task embedding). For different pretraining tasks, we use task encoding to refine the modeling of different types of tasks. ForN tasks, the task IDs range from 1 to N. Each task ID is mapped to a different encoding, and the final input encoding is input encoding = location encoding + segment encoding + token encoding + task encoding. This is shown in Figure 4.Figure 4
Transformer encoder layer.Next, to compute the attention for the input codes, we use the transformer’s multiheaded self-attentive mechanism. It mainly consists of the query Query (replaced byQ), the key Key (replaced by K), and the value Value (replaced by V) as input, and then, we project Q, K, and V using linear transformations, and we can compute multiple times simultaneously, i.e., we use multiple h to represent h different linear changes. Finally, all the attention values are stitched together to complete a multiheaded attention calculation [28]. For a single Q, the output of the attention function is a weighted combination of V. To simplify the calculation, we take the same Q, K, and V values for self attention and use the scaled dot product for the attention calculation. The specific attention function is(1)attentionQ,K,V=softmaxQKTdkV,where d denotes the Q, K, and V vector dimensions.


## 4.2. Self-Supervised Multitask Learning Pretraining Task

The current pretraining stage mainly uses sentence or word co-occurrence signals to design different tasks for pretraining the language model. For example, the original BERT model constructs 2 retraining tasks (the masked language model task and the next sentence prediction task); the XLNet [29] model uses the fully aligned language model for retraining in an autoregressive manner. On the contrary, we have superimposed a large number of repeatable training targets in the F-BERT model; just like human beings taking a foreign language test, there are many different types of questions on the paper, and if the training can be integrated, it will be very helpful to improve the overall learning. Specifically, in the pretraining stage, F-BERT constructs four self-supervised learning pretraining tasks and learns different levels of knowledge from the training text corpus through multitask learning. As shown in Figure 4, the four self-supervised pretraining tasks are span substitution prediction pretraining task, capitalization prediction pretraining task, sentence disorganization pretraining task, and question-answer sentence relationship pretraining task [30].


### 4.2.1. Span Replacement Prediction Pretraining

We adopt a word-level pretraining task to achieve span replacement prediction pretraining. This problem is further amplified for text corpus in the education big data domain, which reduces the learning effectiveness of the model [17].Regarding the span replacement prediction pretraining task, specifically, we first iteratively sample the text sequenceX=x1,x2,…,xn, i.e., for each word sequence, by defining a mask proportion threshold (e.g., 20% of the entire sequence) to construct a subset of sequence Y.In the F-BERT training task, we adopt a random selection of texts of a certain length based on a geometric distribution. Since the geometric distribution is a discrete probability distribution withk trials to get the first success in its nth Bernoulli trial, we are able to get the starting point of the word separation uniformly (randomly) and can get a shorter sequence. In order to obtain a better sampling length distribution, the geometric probability distribution in the F-BERT model has a hypernatremia P = 0.18 and a maximum length limit T = 16 (the excess is treated as discarded), and the best average sequence length we obtain through the experiment is 4.6. Next, we implement the implementation of the span replacement prediction pretraining task. In the F-BERT training process, we define the span as four words in the front and back boundaries of the span, and if these words are not in the span, we use the vectors of these four words plus the vectors of the masked words in the span to predict the original words. The specific implementation is to use a 2-layer feed-forward neural network with layer regularization, where we use ReLU [31] for the activation function. Thus, splicing the encoding vector and the position vector are as follows:(2)h=Layer NormReLUW1X,f=Layer NormReLUW2h.We also use cross entropy as a loss function, which is the loss of the span replacement prediction pretraining target for model training.


## 4.2.1. Span Replacement Prediction Pretraining

We adopt a word-level pretraining task to achieve span replacement prediction pretraining. This problem is further amplified for text corpus in the education big data domain, which reduces the learning effectiveness of the model [17].Regarding the span replacement prediction pretraining task, specifically, we first iteratively sample the text sequenceX=x1,x2,…,xn, i.e., for each word sequence, by defining a mask proportion threshold (e.g., 20% of the entire sequence) to construct a subset of sequence Y.In the F-BERT training task, we adopt a random selection of texts of a certain length based on a geometric distribution. Since the geometric distribution is a discrete probability distribution withk trials to get the first success in its nth Bernoulli trial, we are able to get the starting point of the word separation uniformly (randomly) and can get a shorter sequence. In order to obtain a better sampling length distribution, the geometric probability distribution in the F-BERT model has a hypernatremia P = 0.18 and a maximum length limit T = 16 (the excess is treated as discarded), and the best average sequence length we obtain through the experiment is 4.6. Next, we implement the implementation of the span replacement prediction pretraining task. In the F-BERT training process, we define the span as four words in the front and back boundaries of the span, and if these words are not in the span, we use the vectors of these four words plus the vectors of the masked words in the span to predict the original words. The specific implementation is to use a 2-layer feed-forward neural network with layer regularization, where we use ReLU [31] for the activation function. Thus, splicing the encoding vector and the position vector are as follows:(2)h=Layer NormReLUW1X,f=Layer NormReLUW2h.We also use cross entropy as a loss function, which is the loss of the span replacement prediction pretraining target for model training.


## 5. Experimental Analysis

In this section, we first give the construction of the pretraining dataset; then, we compare the performance of F-BERT with the latest pretraining model on several educational big data tasks; finally, we perform a detailed model analysis, including the impact of pretraining on model performance, pretraining analysis with a small amount of pretraining data, and a discussion of the size of the pretraining corpus.


### 5.1. Pretraining DataSet

In order to better train the F-BERT model, we mainly build a training corpus based on the general domain and the education big data domain. We also build the training dataset in the education big data domain by crawling various financial text data on the education big data website, including financial news and dialogues [32]．


### 5.2. Experimental Result

The proposed method obtained an ES (end of sentence) score of 0.93, a BS (beginning of sentence) score of 0.95, and a MEAN (mean) score of 0.938. The BS (beginning of sentence) score of 0.95 and MEAN (average) score of 0.938 are the best scores for the evaluation metrics. As seen in the experimental results, the pretrained F-BERT on the general domain corpus and the big educational data domain corpus is very effective and achieves significant model performance improvement on the big educational data sentence boundary detection task [33].From Table1, we can see the performance of the F-BERT model and other competitive methods on the financial phrase bank and education big data sentiment analysis dataset (FiQA Task1).Table 1
Experimental result of FinSBD for any dataset of education big data text boundary detection.
ModelESBSMeanRule-based0.800.860.930BiLSTM-CRF0.830.880.855Deep-Att0.830.910.875BERT-S0.880.890.885F-BERTBASE0.890.900.897F-BERTLARGE0.930.950.938The sentiment analysis dataset FiQA Task1 consists of two main types of data: education big data news headlines and education big data microbiology. FiQA Task1 specifically has two evaluation metrics [26]: mean squared error (MSE) and R-squared value (R2). In Table 2, MSE (H) and MSE (P) denote the mean square error of educational big data microbiology and educational big data news headlines, respectively, and R2 (H) and R2 (P) denote the R-squared values of educational big data microbiology and educational big data news headlines, respectively. It is clear from Table 3 that the optimal model F-BERTLARGE on FiQATask 1 obtains the values of MSE (H) = 0.30 and R2 (H) = 064, and MSE (P) = 0.34 and R2 (P) = 0.27.Table 2
Experimental results of phrase bank, a dataset for sentiment analysis of educational big data.
ModelF1 valueCUKG0.46ULMFit0.79FB-SA0.84F-BERTBASE0.86F-BERTLARGE0.89Table 3
Experimental results of FiQA Task1, a dataset for sentiment analysis of education big data.
ModelMSE (H)R2 (H)MSE (P)R2 (P)IIT-dhi0.220.160.1200.408If-UFG0.230.190.1300.156SC-V0.080.40RCNN0.060.41FB-SA0.070.55F-BERTBASE0.220.570.250.19F-BERTLARGE0.300.640.340.27The experimental results in Table3 show that F-BERT outperforms all other methods at the time of submission on both financial phrase bank and FiQA Task1. F-BERT significantly outperforms all other methods at the time of submission, which demonstrates the effectiveness of the method. The current experimental results are encouraging considering the complexity of state-of-the-art models using numerous language features, and they highlight the importance of corpus pretraining designs specific to the big educational data domain.


### 5.3. Experimental Analysis and Discussion

In order to analyze the impact of each component within the architecture on the final performance in detail, a detailed model analysis of the architecture is performed in this section, including a discussion of the impact of pretraining on model performance, a small amount of pretrained data for pretraining analysis, and the size of the pretrained corpus.


#### 5.3.1. Impact of Pretraining on Model Performance

The effect of pretraining on model performance was further measured as shown in Table4. The corresponding models are evaluated on the test dataset using the score of accuracy, precision, and recall.Table 4
Ablation experiments for text classification dataset of education big data.
ModelAccuracyAccuracy rateRecallVanilla BERT0.770.310.29BERT-task0.800.470.41Vanilla F-BERT0.830.510.47F-BERT task0.850.590.61Although the BERT task was further pretrained on the educational big data domain training set, Vanilla F-BERT outperformed the two Vanilla BERT-based models, Vanilla BERT task and BERT task, by 0.0.6 and 0.03 percent, respectively, in terms of accuracy. This indicates that F-BERT effectively utilizes domain-specific knowledge from a large amount of unlabeled educational big data texts during pretraining.


#### 5.3.2. Analysis of a Small Amount Pretraining Data

Pretrained models usually require a large training corpus for training, but it is difficult to have a large annotated training corpus in many applications in the field of education big data. Therefore, in order to further validate the advantages of F-BERT, a model experiment is conducted in this paper. The experiment uses a small corpus to pretrain BERT and F-BERT, respectively. Specifically, randomly selected 1/8 size of text data in the whole big educational data training dataset is used as the training corpus. Then, based on this small corpus constructed by our own simulation, all models are pretrained and tested on the same tasks as the experiments in Section5.2, and the experimental results are detailed in Table 5.Table 5
Ablation experiments on pretrained data with small datasets.
ModelFinancial text boundary detectionFinancial sentiment analysisFinancial intelligenceQ & AMeanAccuracyF1 valuenDGMRRBERT0.830.820.810.370.227F-BERT0.850.830.820.450.39As we can see from the experimental data in Table5, the experiment shows the same trend compared to the previous experiments, and the F-BERT model consistently outperforms BERT in all tasks. The results of this experiment further confirm that F-BERT provides a stability enhancement when the model is trained on different sizes of databases. As shown in Table 5, these experimental data also show that the F-BERT model can provide more help on specific educational big data text processing tasks, such as educational big data machine reading comprehension tasks, educational big data sentiment analysis tasks, and educational big data sentence boundary detection tasks. Overall, experiments simulate the situation of pretraining education big data text with limited data, which is a frequently encountered problem in specific domains, thus demonstrating the potential of F-BERT to handle small training dataset problems in specific domains [14].All functional departments of colleges and universities have generally established basic databases for college students, which record students’ personal information, academic life, social activities, and other basic data during their school years. The datum is not compatible and not interoperable. Therefore, to promote “shared growth,” we must first explore the integration and sharing of various databases in university education under the current conditions. On the one hand, the information construction department of the university should do a good job in top-level design, explore a comprehensive and effective technical solution for database integration, further standardize the database management methods, and promote the integration of existing basic databases of students from top to bottom. On the other hand, all departments of the university should actively cooperate, break the barriers to achieve data integration, further investigate and de-duplicate from a joint effort, establish a long-term docking mechanism, and continuously optimize and integrate the large database [19].


## 5.1. Pretraining DataSet

In order to better train the F-BERT model, we mainly build a training corpus based on the general domain and the education big data domain. We also build the training dataset in the education big data domain by crawling various financial text data on the education big data website, including financial news and dialogues [32]．


## 5.2. Experimental Result

The proposed method obtained an ES (end of sentence) score of 0.93, a BS (beginning of sentence) score of 0.95, and a MEAN (mean) score of 0.938. The BS (beginning of sentence) score of 0.95 and MEAN (average) score of 0.938 are the best scores for the evaluation metrics. As seen in the experimental results, the pretrained F-BERT on the general domain corpus and the big educational data domain corpus is very effective and achieves significant model performance improvement on the big educational data sentence boundary detection task [33].From Table1, we can see the performance of the F-BERT model and other competitive methods on the financial phrase bank and education big data sentiment analysis dataset (FiQA Task1).Table 1
Experimental result of FinSBD for any dataset of education big data text boundary detection.
ModelESBSMeanRule-based0.800.860.930BiLSTM-CRF0.830.880.855Deep-Att0.830.910.875BERT-S0.880.890.885F-BERTBASE0.890.900.897F-BERTLARGE0.930.950.938The sentiment analysis dataset FiQA Task1 consists of two main types of data: education big data news headlines and education big data microbiology. FiQA Task1 specifically has two evaluation metrics [26]: mean squared error (MSE) and R-squared value (R2). In Table 2, MSE (H) and MSE (P) denote the mean square error of educational big data microbiology and educational big data news headlines, respectively, and R2 (H) and R2 (P) denote the R-squared values of educational big data microbiology and educational big data news headlines, respectively. It is clear from Table 3 that the optimal model F-BERTLARGE on FiQATask 1 obtains the values of MSE (H) = 0.30 and R2 (H) = 064, and MSE (P) = 0.34 and R2 (P) = 0.27.Table 2
Experimental results of phrase bank, a dataset for sentiment analysis of educational big data.
ModelF1 valueCUKG0.46ULMFit0.79FB-SA0.84F-BERTBASE0.86F-BERTLARGE0.89Table 3
Experimental results of FiQA Task1, a dataset for sentiment analysis of education big data.
ModelMSE (H)R2 (H)MSE (P)R2 (P)IIT-dhi0.220.160.1200.408If-UFG0.230.190.1300.156SC-V0.080.40RCNN0.060.41FB-SA0.070.55F-BERTBASE0.220.570.250.19F-BERTLARGE0.300.640.340.27The experimental results in Table3 show that F-BERT outperforms all other methods at the time of submission on both financial phrase bank and FiQA Task1. F-BERT significantly outperforms all other methods at the time of submission, which demonstrates the effectiveness of the method. The current experimental results are encouraging considering the complexity of state-of-the-art models using numerous language features, and they highlight the importance of corpus pretraining designs specific to the big educational data domain.


## 5.3. Experimental Analysis and Discussion

In order to analyze the impact of each component within the architecture on the final performance in detail, a detailed model analysis of the architecture is performed in this section, including a discussion of the impact of pretraining on model performance, a small amount of pretrained data for pretraining analysis, and the size of the pretrained corpus.


### 5.3.1. Impact of Pretraining on Model Performance

The effect of pretraining on model performance was further measured as shown in Table4. The corresponding models are evaluated on the test dataset using the score of accuracy, precision, and recall.Table 4
Ablation experiments for text classification dataset of education big data.
ModelAccuracyAccuracy rateRecallVanilla BERT0.770.310.29BERT-task0.800.470.41Vanilla F-BERT0.830.510.47F-BERT task0.850.590.61Although the BERT task was further pretrained on the educational big data domain training set, Vanilla F-BERT outperformed the two Vanilla BERT-based models, Vanilla BERT task and BERT task, by 0.0.6 and 0.03 percent, respectively, in terms of accuracy. This indicates that F-BERT effectively utilizes domain-specific knowledge from a large amount of unlabeled educational big data texts during pretraining.


### 5.3.2. Analysis of a Small Amount Pretraining Data

Pretrained models usually require a large training corpus for training, but it is difficult to have a large annotated training corpus in many applications in the field of education big data. Therefore, in order to further validate the advantages of F-BERT, a model experiment is conducted in this paper. The experiment uses a small corpus to pretrain BERT and F-BERT, respectively. Specifically, randomly selected 1/8 size of text data in the whole big educational data training dataset is used as the training corpus. Then, based on this small corpus constructed by our own simulation, all models are pretrained and tested on the same tasks as the experiments in Section5.2, and the experimental results are detailed in Table 5.Table 5
Ablation experiments on pretrained data with small datasets.
ModelFinancial text boundary detectionFinancial sentiment analysisFinancial intelligenceQ & AMeanAccuracyF1 valuenDGMRRBERT0.830.820.810.370.227F-BERT0.850.830.820.450.39As we can see from the experimental data in Table5, the experiment shows the same trend compared to the previous experiments, and the F-BERT model consistently outperforms BERT in all tasks. The results of this experiment further confirm that F-BERT provides a stability enhancement when the model is trained on different sizes of databases. As shown in Table 5, these experimental data also show that the F-BERT model can provide more help on specific educational big data text processing tasks, such as educational big data machine reading comprehension tasks, educational big data sentiment analysis tasks, and educational big data sentence boundary detection tasks. Overall, experiments simulate the situation of pretraining education big data text with limited data, which is a frequently encountered problem in specific domains, thus demonstrating the potential of F-BERT to handle small training dataset problems in specific domains [14].All functional departments of colleges and universities have generally established basic databases for college students, which record students’ personal information, academic life, social activities, and other basic data during their school years. The datum is not compatible and not interoperable. Therefore, to promote “shared growth,” we must first explore the integration and sharing of various databases in university education under the current conditions. On the one hand, the information construction department of the university should do a good job in top-level design, explore a comprehensive and effective technical solution for database integration, further standardize the database management methods, and promote the integration of existing basic databases of students from top to bottom. On the other hand, all departments of the university should actively cooperate, break the barriers to achieve data integration, further investigate and de-duplicate from a joint effort, establish a long-term docking mechanism, and continuously optimize and integrate the large database [19].


## 5.3.1. Impact of Pretraining on Model Performance

The effect of pretraining on model performance was further measured as shown in Table4. The corresponding models are evaluated on the test dataset using the score of accuracy, precision, and recall.Table 4
Ablation experiments for text classification dataset of education big data.
ModelAccuracyAccuracy rateRecallVanilla BERT0.770.310.29BERT-task0.800.470.41Vanilla F-BERT0.830.510.47F-BERT task0.850.590.61Although the BERT task was further pretrained on the educational big data domain training set, Vanilla F-BERT outperformed the two Vanilla BERT-based models, Vanilla BERT task and BERT task, by 0.0.6 and 0.03 percent, respectively, in terms of accuracy. This indicates that F-BERT effectively utilizes domain-specific knowledge from a large amount of unlabeled educational big data texts during pretraining.


## 5.3.2. Analysis of a Small Amount Pretraining Data

Pretrained models usually require a large training corpus for training, but it is difficult to have a large annotated training corpus in many applications in the field of education big data. Therefore, in order to further validate the advantages of F-BERT, a model experiment is conducted in this paper. The experiment uses a small corpus to pretrain BERT and F-BERT, respectively. Specifically, randomly selected 1/8 size of text data in the whole big educational data training dataset is used as the training corpus. Then, based on this small corpus constructed by our own simulation, all models are pretrained and tested on the same tasks as the experiments in Section5.2, and the experimental results are detailed in Table 5.Table 5
Ablation experiments on pretrained data with small datasets.
ModelFinancial text boundary detectionFinancial sentiment analysisFinancial intelligenceQ & AMeanAccuracyF1 valuenDGMRRBERT0.830.820.810.370.227F-BERT0.850.830.820.450.39As we can see from the experimental data in Table5, the experiment shows the same trend compared to the previous experiments, and the F-BERT model consistently outperforms BERT in all tasks. The results of this experiment further confirm that F-BERT provides a stability enhancement when the model is trained on different sizes of databases. As shown in Table 5, these experimental data also show that the F-BERT model can provide more help on specific educational big data text processing tasks, such as educational big data machine reading comprehension tasks, educational big data sentiment analysis tasks, and educational big data sentence boundary detection tasks. Overall, experiments simulate the situation of pretraining education big data text with limited data, which is a frequently encountered problem in specific domains, thus demonstrating the potential of F-BERT to handle small training dataset problems in specific domains [14].All functional departments of colleges and universities have generally established basic databases for college students, which record students’ personal information, academic life, social activities, and other basic data during their school years. The datum is not compatible and not interoperable. Therefore, to promote “shared growth,” we must first explore the integration and sharing of various databases in university education under the current conditions. On the one hand, the information construction department of the university should do a good job in top-level design, explore a comprehensive and effective technical solution for database integration, further standardize the database management methods, and promote the integration of existing basic databases of students from top to bottom. On the other hand, all departments of the university should actively cooperate, break the barriers to achieve data integration, further investigate and de-duplicate from a joint effort, establish a long-term docking mechanism, and continuously optimize and integrate the large database [19].


## 6. Conclusions

In this paper, we use self-supervised learning and multitask learning techniques in the field of deep learning to propose an innovative pretraining model of growth and development indicators for college students based on the BERT model architecture F-BERT. By minimally modifying the model structure for specific educational big data tasks, F-BERT outperforms the current state-of-the-art models on several educational big data text data mining tasks. The core of the “shared growth” education model consists of a growth database, a data mining and analysis system, and a sharing platform. The growth database mainly includes students’ “basic data” and “behavioral data.” The basic data cover the traditional school data such as basic information, academic achievement, rewards, and punishments of students.



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