Campus Big Data Research Articles

Design of Fast Mining Algorithm for Educational Sport Course Data Based on Cluster Analysis

In this age of big data, education researchers are reconceptualizing and re-evaluating the value of education data. Therefore, we need to use educational data mining methods for data analysis to better guide teaching. The informatization level of colleges and universities is improving year by year, and the entire training data of students from enrollment to graduation is stored. These datasets are collected, stored, and kept by different departments, contain a large amount of regular and relevant information, and truly record the growth footprint of students. Traditional educational decision-making has not yet fully explored and used the valuable information hidden in data resources. Although some scholars have carried out research related to campus data mining at this stage, there are still many problems that have not yet been solved in the application of decision-making in colleges and universities. This paper is based on the idea of data-driven decision-making, combined with the data characteristics of campus big data, and establishes a model solution for student behavior analysis and behavior prediction by applying multiple machine learning algorithms. On the basis of the analysis of students’ academic behavior performance in the context of multi-category educational data, we proposed a cluster analysis framework for processing multi-type campus big data, and described the group characteristics of the clustering results. By introducing the K-prototype algorithm, we effectively solved the multi-category problem where traditional clustering algorithms (such as K-Means, etc.) cannot adapt to the attributes of educational data. The research results show that innovative educational decision-making models and methods are based on the idea of “data-prediction-decision”, which promotes the application research of big data science in the area of education.

Exploration of College Students' Mental Health Status Based on Campus Big Data in the Epidemic Situation

Exploration of College Students' Mental Health Status Based on Campus Big Data in the Epidemic Situation

An early warning method for abnormal behavior of college students based on multimodal fusion and improved decision tree

With the development of the Internet and the informatization construction of universities, the massive data accumulated by “campus big data” presents problems such as discreteness and sparseness. Students with abnormal behaviors have become an urgent problem to be solved in student behavior analysis. This paper proposes an early warning method for abnormal behaviour of college students based on multimodal fusion and an improved decision tree (EWMABCS-MFIDT). First, given the insufficient representation of student behavioral portraits and the problems of timeliness and dynamics in behavioral labels, a student behavioral portrait based on the multimodal fusion method is proposed. Second, aiming at the timeliness and backwardness of abnormal behavior prediction, based on student behavior classification prediction, this paper proposes an improved decision tree-based early warning method for abnormal student behavior. Finally, we design a student behavior analysis and early warning framework under the campus big data environment. Taking the abnormal early warning of students’ academic performance as an example, compared with other early warning algorithms, the EWMABCS-MFIDT method can improve the accuracy of early warning and make students’ educational work more targeted, personalized, and predictive.

A distributed framework of student data early warning system

With the deepening of research on big data, various intelligent technologies have been widely used in the digital construction of campus. But the current campus students early warning system can not achieve high accuracy results. It is necessary to collect big data of students and perform online analysis with advanced techniques to timely predict problems of learning performance and activity abnormality. In this paper, the campus big data early warning system is designed based on the distributed framework. It integrates the data collection, storage, mining and analysis, visualization and message notification. The system consists of six layers of data application structure and one security control platform, and uses multiple linear regression algorithm and logistic regression algorithm to predict the abnormal behavior of student, Hadoop and Spark are used to build a distributed framework to monitor student abnormalities.

Research on Cloud Storage Technology in Digital Campus Big Data Scenario

Realizing campus intelligent management and service mode by combining cloud storage technology is the focus of current research in digital campus construction work. By analyzing the IT development trend of digital campus, the defective problems of traditional storage system, from the architecture design and platform construction of cloud storage system for digital campus, we propose a cloud storage construction scheme for campus big data scenario, and discuss the application of virtualization, streamlined configuration, storage optimization and other technologies in cloud storage. The cloud storage technology is applied in the construction of digital campus, which strongly accelerates the process of digital campus informatization.

A Dimensionality Reduction Algorithm for Unstructured Campus Big Data Fusion

Data modeling and dimensionality reduction are important research points in the field of big data. At present, there is no effective model to realize the consistent representation and fusion of different types of data of students in unstructured campus big data. In addition, in the process of big data processing, the amount of data is too large and the intermediate results are too complex, which seriously affects the efficiency of big data dimension reduction. To solve the above problems, this paper proposes an incremental high order singular value decomposition dimensionality (icHOSVD) reduction algorithm for unstructured campus big data. In this algorithm, the characteristics of audio, video, image and text data in unstructured campus student data are tensioned to form a sub-tensor model, and the semi-tensor product is used to fuse the sub-tensor model into a unified model as the individual student tensor model. On the basis of individual model fusion, the campus big data fusion model was segmented, and each segmented small tensor model was dimensioned by icHOSVD reduction to obtain an approximate tensor as the symmetric tensor that could replace the original tensor, so as to solve the problem of large volume of tensor fusion model and repeated calculation of intermediate results in data processing. The experimental results show that the proposed algorithm can effectively reduce the computational complexity and improve the performance compared with traditional data dimension reduction algorithms. The research results can be applied to campus big data analysis and decision-making.

Research and Practice of Informatization Teaching Reform Based on Ubiquitous Learning Environment and Education Big Data

With the continuous accumulation of application data of various business information systems of network and informatization, a campus big data environment with distinctive characteristics of colleges and universities has been formed. Through the in-depth mining and analysis of the existing smart campus data using big data technology, based on the support of intelligent decision-making, personalized adaptive learning analysis and campus security warning of big data, and using the results to feed back the improvement of information literacy of university teachers and students in the era of big data, the construction of ubiquitous learning environment is carried out, which points out the direction for the construction of ubiquitous learning environment to explore the role of big data in improving the quality of education, promoting educational equity and optimizing educational philosophy, so as to improve the ubiquitous education environment.

Rancang Bangun Infrastruktur Big Data pada Institusi Pendidikan Tinggi Multi Kampus

Nowadays is referred to as the era of big data where every individual or organization produces large amounts of data through various digital devices used. Higher education institutions continuously produce data and only strore it in various data formats and physical files, thus creating massive raw data. Collection of data created from a long time produces big data, so it is difficult to be treated manually or processed using conventional data processing applications. Higher education is an educational institution that always produces data from time to time continuously through various affairs in each of its parts. Multi campus institutions have physical locations that are far apart from one to another with more than one physical infrastructure. Each campus branch produces its own data, this requires a though about how to build a large data infrastructure, so that stakeholders can use various data from each branch of the campus for the analysis process of various needs of tertiary institutions. Multi campus big data infrastructure is the focus of this research, through the stages of big data sampling on multiple campuses using statistical and prototyping methods, so that data flow and data unity can be standardized in all branches of the campus by minimizing infrastructure constraints that are based on socioculture and spatial aspects.

Research and Application on Spark Clustering Algorithm in Campus Big Data Analysis

Big data analysis has penetrated into all fields of society and has brought about profound changes. However, there is relatively little research on big data supporting student management regarding college and university’s big data. Taking the student card information as the research sample, using spark big data mining technology and K-Means clustering algorithm, taking scholarship evaluation as an example, the big data is analyzed. Data includes analysis of students’ daily behavior from multiple dimensions, and it can prevent the unreasonable scholarship evaluation caused by unfair factors such as plagiarism, votes of teachers and students, etc. At the same time, students’ absenteeism, physical health and psychological status in advance can be predicted, which makes student management work more active, accurate and effective.

Career Choice Prediction Based on Campus Big Data—Mining the Potential Behavior of College Students

Career choice has a pivotal role in college students’ life planning. In the past, professional career appraisers used questionnaires or diagnoses to quantify the factors potentially influencing career choices. However, due to the complexity of each person’s goals and ideas, it is difficult to properly forecast their career choices. Recent evidence suggests that we could use students’ behavioral data to predict their career choices. Based on the simple premise that the most remarkable characteristics of classes are reflected by the main samples of a category, we propose a model called the Approach Cluster Centers Based On XGBOOST (ACCBOX) model to predict students’ career choices. The experimental results of predicting students’ career choices clearly demonstrate the superiority of our method compared to the existing state-of-the-art techniques by evaluating on 13 M behavioral data of over four thousand students.

Research on the Identification of College Students’ Mental Health Problems Based on Campus Big Data

In recent years, according to the survey, college students have frequent mental health problems, such as anxiety, depression, inferiority, inter-personal sensitivity and other psychological problems, even the idea of suicide. It has a very serious negative impact on the family and society. If mental health problems of college students can be detected early, counselors can pay more attention to these high-risk students. At the same time, the high-risk students can receive therapies as soon as possible, reducing the harm. Therefore, it is crucial to find an effective method for early detection of mental health problems. This paper proposes a new method to detect mental health problems by analyzing students ’behavior on campus. The datasets include Internet access logs, records of entering and leaving dormitories, and records of consumption in canteens. We built classification models for differentiating the mental health problems of students from normal students. The experimental results show that the proposed method may be useful to improve the performance of public mental health services.

A Self-Control Analysis Method of University Students Based on Campus Big Data

Nowadays, the analysis of university students is the main concern of educational institutions. In this paper, we investigate the problem of analysing students’ self-control ability. Our model focuses on the process of feature extraction, and then classification algorithms are applied. We propose the concept of behaviour chain to model the behavior of student. On this basis, we propose a spatial periodic feature extraction method based on the longest common sub-sequence(LCS). And we extract some statistics features from consumption data, access control data and others. Our model SCA(Self-Control Analysis) performs with the precision of 0.72, the recall of 0.78 and the Fl-score of 0.74. It means that it’s possible to analyse student’ self-control ability with campus big data.

Design of smart campus management system based on internet of things technology

With the vigorous promotion of the construction of smart campus by the ministry of education, the development concept of smart campus will have broad application prospects. However, colleges and universities are still at the stage of digital campus and there are many problems left. It is difficult to complete the transition from digital campus to smart campus. The main problem is that the campus data has only been digitized but not informational. The purpose of this article is to study a smart campus management system based on the Internet of Things technology. This research uses the unified data collection source of face recognition terminal hardware products based on the Internet of Things technology, unified management in the background of the system, and calculates and analyzes the data to obtain valuable campus big data. This study designed and implemented a complete smart campus management system by analyzing the system design principles and design goals. This system is mainly divided into the face recognition terminal hardware and smart campus software system based on the Internet of Things. By analyzing the data generated by students and faculty and staff, it can provide a reference for campus managers to improve management quality, and help teachers and students to formulate more efficient learning and teaching and research plans. This article tests the practicability of the system and obtains the user’s satisfaction as 8.0.

Supporting poverty-stricken college students in smart campus

Chinese colleges have formulated supporting polices to help poverty-stricken college students to deal with the barriers in their living and learning. The difficulty in fully collecting the required information related to student’s financial status and the imbalanced-data classification problem caused by the small proportion of poverty-stricken students among total students makes it a challenging problem. This problem results in a heavy workload for the college staff to identify poverty-stricken students, determine the amount of corresponding subsidy, and execute the supporting polices in an efficient way. Therefore, this paper attempts to address the above-mentioned challenges by proposing a smart campus system, which makes use of campus big data to identify poverty-stricken students and support the decision-making on the subsidy for them. The proposed system can also alert the counselors to provide psychological support for students in trouble. The major contributions of this research are as follows. Firstly, in addition to the features of students’ amount of consumption on campus and its statistical characteristics used in existing researches, this paper proposes new features that describe diversity of consumable commodities, preference of consumption location and price, and characteristics of students’ campus activities. Secondly, in order to solve the problem of dataset imbalance, four imbalanced data processing methods (Subsampling, Resampling, Cost-sensitive learning and SMOTE) have been applied to produce four different experimental datasets, and five classification algorithms (Random Forest, J48, Naïve Bayes, SMO, Logistic regression) have been used to train the classification model on each dataset. The experimental results indicate that the model based on Resampling and Random Forest achieves the best performance in F1-measure of poverty-stricken students, among the combinations of four imbalanced processing methods and five classification algorithms. In addition, a method of quantization of subsidies, and strategies of early warning and counseling for students are also described in this paper. A system was developed based on the above-mentioned methods, which meets the needs of individualized and diversified support for poverty-stricken students. The methods and the proposed system have been put into practice, and it is serving more than 17,000 students. The system has significantly improved the efficiency and quality of student management, and reduced the workload of college staff.

Research and Practice on Campus Big Data Foundation Platform

Universities at home and abroad have reached a consensus on the importance of big data research and application. The importance of education data has been promoted to the national strategic level. The construction of education data centre, the sharing and application of regional data have become the main practice mode. Our work is on the research and exploration from the shared database to the big data foundation platform in the university information construction. In addition to the existing educational administration system, we design and develop a series of application subsystems based on big data foundation platform, which can be presented to readers as cases, hoping to serve as an inspiration to each other.

Advanced forecasting of career choices for college students based on campus big data

Career indecision is a difficult obstacle confronting adolescents. Traditional vocational assessment research measures it by means of questionnaires and diagnoses the potential sources of career indecision. Based on the diagnostic outcomes, career counselors develop treatment plans tailored to students. However, because of personal motives and the architecture of the mind, it may be difficult for students to know themselves, and the outcome of questionnaires may not fully reflect their inner states and statuses. Self-perception theory suggests that students’ behavior could be used as a clue for inference. Thus, we proposed a data-driven framework for forecasting student career choice upon graduation based on their behavior in and around the campus, thereby playing an important role in supporting career counseling and career guidance. By evaluating on 10M behavior data of over four thousand students, we show the potential of this framework for this functionality.

Algorithm Analysis for Big Data in Education Based on Depth Learning

The construction of campus network has provided an advanced comprehensive information environment for teaching, scientific research and management of colleges. In the process of digitization and intelligentization, the data produced by all kinds of application systems in college are growing, and the large data environment of campus has been formed. Big data of college contain abundant information, so we need to use new data storage and analysis tools to store and analyze huge amounts of college data and get useful information from them. In this paper, a depth learning analysis algorithm based on Map Reduce is proposed to deal with college data. Using Map Reduce parallel computing framework to achieve campus data computing, we studied the analysis and application systems of campus big data in different themes and levels and dug out valuable information hidden behind college data. The experimental results show that the high school data mining algorithm based on Map Reduce is effective. It provides new research ideas for large data mining in colleges and provides technical reference for the construction of smart campus.