Analysis of Students’ Behavior in English Online Education Based on Data Mining

Student behavior data is a reflection of students' learning styles and habits in campus. Quantitative evaluation of various behavior indicators of students' college learning life can reflect students' learning status and laws, and the academic performance is the most important and basic index to evaluate a student's learning situation. Based on the data of students' behavior under the Four PIN education system of Beihang Shoue College, this paper adopts XGBoost gradient upgrade decision tree algorithm to fully mine and analyze the situation of college students' study life and participation in social work, and to study the potential behavior patterns with strong correlation between students' behavior data and students' performance. The performance prediction model is established through data mining technology and the prediction accuracy is 73%. The study finds that Pinjian program records the growth of students. Students' overall evaluation and student performance shows a positive correlation. Pinxue program promotes academic development. Academic guidance in college plays an important role in improving students' academic performance. Pinzhi program enhances comprehensive literacy. Cultural and artistic activities and serving as student cadre show a positive correlation with academic performance.

In this research we applied classification models for prediction of students’ performance, and cluster models for grouping students based on their cognitive styles in e-learning environment. Classification models described in this paper should help: teachers, students and business people, for early engaging with students who are likely to become excellent on a selected topic. Clustering students based on cognitive styles and their overall performance should enable better adaption of the learning materials with respect to their learning styles. The approach is tested using well-established data mining algorithms, and evaluated by several evaluation measures. Model building process included data preprocessing, parameter optimization and attribute selection steps, which enhanced the overall performance. Additionally we propose a Moodle module that allows automatic extraction of data needed for educational data mining analysis and deploys models developed in this study.

Traditional moral evaluation relies on artificial and subjective evaluation by teachers, and there are subjective errors or prejudices. To achieve further objective evaluation, students' classroom performance can be identified, and the effectiveness of moral education can be evaluated based on student behavior. Since student classroom behavior is random and uncertain, in order to accurately evaluate its indicators, a large amount of student classroom behavior data must be used as the basis for analysis, while certain techniques are used to filter out valuable information from it. In this paper, an improved graph convolutional network algorithm is proposed to study students' behaviors in order to further improve the accuracy of moral education evaluation in universities. The technique of video recognition is used to achieve student behavior recognition, thus helping to improve the quality of moral education evaluation in colleges and universities. First, the multi-information flow data related to nodes and skeletons are fused to improve the computing speed by reducing the number of network parameters. Second, the spatiotemporal attention module based on nonlocal operations is constructed to focus on the most action discriminative nodes and improve the recognition accuracy by reducing redundant information. Then, the spatiotemporal feature extraction module is constructed to obtain the spatiotemporal association information of the nodes of interest. Finally, the action recognition is realized by the Softmax layer. The experimental results show that the algorithm of action recognition in this paper is more accurate and can better help moral evaluation.

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics [1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

With the continuous improvement of the information construction of colleges and universities, the daily life and learning behaviors of college students are recorded and stored by major business systems, and they are accumulated, which has initially formed a large-scale and multi-type student personal big data environment. This paper mainly classifies and summarizes the students' data from the three aspects of student basic information, campus learning and campus life. It focuses on the feature extraction and index mining of students' campus consumption, curriculum and performance data, and constructs the student's personal big data behavior analysis model. In-depth analysis and mining of student consumption behavior data to explore students' dietary rules and consumption level. Through data analysis, the following rules were found: 1)The total number of students eating at school decreases year by year, and the breakfast rate decreases year by year; 2) Freshmen are one hour ahead of the peak period of breakfast meals for the whole group;3) The students' academic scores are highly correlated with the meal rate, breakfast meal rate and eating consumption level, and are less correlated with variables such as window selection stability, etc. 4) The more regular the student's diet, the more stable the level of consumption, and the higher the level of learning effort, the better the student's academic performance.

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

Data mining research deals with extracting useful information from large collections of data. Since data mining is a complex process that requires expertise, it is beneficial to provide it as a service on the web. On the other hand, such use of data mining services combined with data collection efforts by private and government organizations leads to increased privacy concerns. In this work, we address the issue of preserving privacy while providing data mining services on the web and present an architecture for privacy preserving sharing of data mining models on the web. In the proposed architecture, data providers use APPEL for specifying their privacy preferences on data mining models, while data collectors use P3P policies for specifying their data-usage practices. Both parties use PMML as the standard for specifying data mining queries, constraints and models.

This study aims to investigate the association of different risk factors including vitamin-D level with catheter-related-thrombosis in hemodialysis patients by applying data mining techniques. This study used the retrospectively approach and was done based on the CRISP-DM framework. The data of 1048 hemodialysis patients of Hasheminejad Kidney Center whose first catheterization was between 2014 and 2019 was used for analysis. In this study, patients with a previous history of deep venous thrombosis, thrombophilic condition, and undergone anticoagulant therapy were excluded. The decision tree J48 in WEKA software was used for modeling. The K-fold cross-validation method was also used to evaluate the classification performance. Finally, the model was evaluated by using Confusion Matrix and F-Measure. The approved model was accurate in 70.3% of the predictions, and it demonstrated an association between patients' vitamin-D range and catheter-related-thrombosis. The results indicated that in hemodialysis patients without the history of cancer or high blood pressure, vitamin-D had a significant impact on catheter related thrombosis.

In this paper, we propose four data mining models for the Internet of Things, which are multi-layer data mining model, distributed data mining model, Grid based data mining model and data mining model from multi-technology integration perspective. Among them, multi-layer model includes four layers: 1) data collection layer, 2) data management layer, 3) event processing layer, and 4) data mining service layer. Distributed data mining model can solve problems from depositing data at different sites. Grid based data mining model allows Grid framework to realize the functions of data mining. Data mining model from multi-technology integration perspective describes the corresponding framework for the future Internet. Several key issues in data mining of IoT are also discussed.

The advanced design of a centrifugal compressor with high efficiency and wide operating range is a challenging task due to the complex flow field arising from the three-dimensional geometry, especially for the high-speed, highly loaded centrifugal compressor stage, which typically has a relatively narrow operating range. A great effort has been undertaken recently to solve the time-costly three-dimensional design problem with the assistance of a metamodel. Some effort has been done to gain insight into the design space with the assistance of the data mining method. However, the published works lack any study that systematically performs the data mining between the performance and three-dimensional geometry data due to two unsolved issues, i.e., lack of reliable systematic data mining model and unresolved high-dimensional data problem in the centrifugal compressor community. To tackle these issues, a systematic metamodel-driven data mining (MDDM) model including six general modules (i.e., problem understanding, data understating, metamodeling, data set preparation, knowledge discovery, and deployment) has been proposed and implemented to the knowledge discovery of the well-known Radiver high-speed centrifugal compressor stage. Particular attention has been paid to develop the design principle of operating range extension for the examined high-speed stage. Four specific data mining techniques, i.e., descriptive statistics, self-organization map, k–d tree, and Sobol index, were used for the statistical, correlation, cluster, and sensitivity analysis. The results showed the performance improvement probabilities, the trade-off relationships between efficiency and pressure ratio/operating range, and the characteristic variation of the performance. Specifically, the wide operating range design subspace and the narrow operating range design subspace were split away from the whole design space. In these subspaces, the two most sensitive geometry parameters that controlled the meridional curvature made a large contribution to the stage performance, especially for the meridional curvature at the shroud side near the impeller outlet. The appropriate variation ranges of the two sensitive geometry parameters were recommended, and the flow mechanism behind them was clarified. The statistical results showed that over 90% of the design stages in the recommended variation ranges had a wide operating range. A design case was chosen randomly in the recommended range to verify the performance improvement via computational fluid dynamics (CFD) simulations. The outcomes of this work are particularly relevant for the advanced design of compressors with high efficiency and a wide operating range for flexibility.

The data mining experiments from this chapter are a component of a larger research which is to be used to make several correlations, analysis in order to integrate data mining process in the managerial system for optimal decision support. I offered an insight of how data mining processes are being applied in the large spectrum of education by presenting recent applications and studies published in the scientific literature, considered to be relevant to the development of this emerging science. I presented my work through a number of experiments conducted over questionnaires data and scholastic databases at Faculty of Economics and Business Administration Cluj-Napoca, using classification learning and data clustering methods. In the last part of the chapter I introduced the concept of Academic Intelligence Management, and illustrated the integration of data mining processes and their particular role in higher education management and decision support. The studies will continue with deeper mining of educational topics, such as performance in scientific research, correlations between the students' knowledge and the competences demanded on the labor market, academic failure, to perceive what and how much the students know, to realize learning gaps, and also improve teaching methods and educational management processes.

Colleges and universities must generate a lot of student behavior data during the informatization construction, such as card usage data, grades and grade point data, etc. These data can fully reflect the trajectory of students’ activities. Therefore, strengthening the analysis and research of student behavior data will help the school to better manage it, so as to better build a smart campus. The purpose of this paper is to design a smart campus student behavior analysis system based on data mining algorithm. The relevant theories and technologies involved in the functional design and implementation of the student behavior analysis system based on data mining are studied. The software architecture, the design of the system operating system, the physical development architecture for storing system data, and the physical and logical design of the database are introduced. The detailed design process of system modules is analyzed, including integrated vertical behavior module, student behavior early warning module, student big data analysis report module, student behavior trajectory module, early warning rule design, and interface design module. 82% of students borrowed books recommended by the association analysis book model within three months.

Data mining is the analysis of large observational data sets to find unsuspected relationships and to summarize the data. With the development of the broadcasting and television monitoring, monitoring data in database have become more and more. People hope that these data are both understandable and useful to future work. Therefore, it is very significant to discuss the specific applications of data mining techniques in the broadcasting monitoring. A data mining solution based on neural networks is presented in this paper. The solution can estimate equivalent face of radio broadcasting effect by data mining. The data mining methods for each of analysis themes and modeling process are described in detail. In the end we analyze and evaluate outcome of data mining.

This case study focused on the three-year implementation period of School-Wide Positive Behavior Intervention Support (SWPBIS), a positive approach to discipline following the Response to Intervention (RTI) model, in a rural K-8 elementary school. The purpose of the study was to explore the perceptions of teachers and support staff's perceptions on the implementation of SWPBIS and organizational change. The Burke-Litwin Causal Model of Change (1992) was the theory used for the analysis to explore how the SWPBIS implementation mediated with the transformative and transactional factors within a school organization. The focus of the literature review was to review the program effects of the systematic implementation of SWPBIS and how it related to teacher efficacy, school climate, student-teacher relationship, school leadership and student achievement. The research question of the study was: How did the implementation of the SWPBIS system at a small, rural K-8 school impact teacher efficacy, student-teacher relations, school climate, and student achievement as perceived by teachers and support staff? This descriptive case study attempted to explain the SWPBIS implementation through the eyes of school personnel during the implementation period. The research findings suggested that the SWPBIS implementation did influence the transformative and transactional factors of the school, as evidenced by improvement in student behavior, student referrals, teacher efficacy, and student achievement data.