Pub Date : 2021-07-08DOI: 10.4236/jdaip.2021.93010
Shaoyong Hong, Yan Zhang, Chun Yang
With the rapid development of big data technology, the personal credit evaluation industry has entered a new stage. Among them, the evaluation of personal credit based on mobile telecommunications data is one of the hotspots of current research. However, due to the complexity and diversity of personal credit evaluation variables, in order to reduce the complexity of the model and improve the prediction accuracy of the model, we need to reduce the dimension of the input variables. According to the data provided by a mobile telecommunications operator, this paper divides the data into a training sets and verification sets. We perform correlation analysis on each indicator of the data in the training set, and calculate the corresponding IV value based on the WOE value of the selected index, then binning data with SPSS Modeler. The selected variables were modeled using a logistic regression algorithm. In order to make the regression results more practical, we extract the scoring rules according to the results of logistic regression, convert them into the form of score cards, and finally verify the validity of the model.
{"title":"Research on Personal Credit Evaluation Based on Mobile Telecommunications Data","authors":"Shaoyong Hong, Yan Zhang, Chun Yang","doi":"10.4236/jdaip.2021.93010","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93010","url":null,"abstract":"With the rapid development of big data technology, the personal credit evaluation industry has entered a new stage. Among them, the evaluation of personal credit based on mobile telecommunications data is one of the hotspots of current research. However, due to the complexity and diversity of personal credit evaluation variables, in order to reduce the complexity of the model and improve the prediction accuracy of the model, we need to reduce the dimension of the input variables. According to the data provided by a mobile telecommunications operator, this paper divides the data into a training sets and verification sets. We perform correlation analysis on each indicator of the data in the training set, and calculate the corresponding IV value based on the WOE value of the selected index, then binning data with SPSS Modeler. The selected variables were modeled using a logistic regression algorithm. In order to make the regression results more practical, we extract the scoring rules according to the results of logistic regression, convert them into the form of score cards, and finally verify the validity of the model.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46426760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-08DOI: 10.4236/jdaip.2021.93013
S. Nanga, A. T. Bawah, Ben Acquaye, Mac-Issaka Billa, Francisco Baeta, N. Odai, Samuel Kwaku Obeng, Ampem Darko Nsiah
Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.
{"title":"Review of Dimension Reduction Methods","authors":"S. Nanga, A. T. Bawah, Ben Acquaye, Mac-Issaka Billa, Francisco Baeta, N. Odai, Samuel Kwaku Obeng, Ampem Darko Nsiah","doi":"10.4236/jdaip.2021.93013","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93013","url":null,"abstract":"Purpose: This study sought to review the characteristics, strengths, weaknesses \u0000variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The \u0000most commonly used databases employed to search for the papers were ScienceDirect, \u0000Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was \u0000used for the study where 341 papers were reviewed. Results: The linear \u0000techniques considered were Principal Component Analysis (PCA), Linear Discriminant \u0000Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis \u0000(LSA), Locality Preserving Projections (LPP), Independent Component Analysis \u0000(ICA) and Project Pursuit (PP). The non-linear techniques which were developed \u0000to work with applications that have complex non-linear structures considered were Kernel Principal Component \u0000Analysis (KPCA), Multi-dimensional \u0000Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map \u0000(SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and \u0000Projection (UMAP). DR techniques can further be categorized into supervised, \u0000unsupervised and more recently semi-supervised learning methods. The supervised \u0000versions are the LDA and LVQ. All the other techniques are unsupervised. \u0000Supervised variants of PCA, LPP, KPCA and MDS have been developed. \u0000Supervised and semi-supervised variants of PP and t-SNE have also been \u0000developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR \u0000techniques were explored. The different data types that have been applied on \u0000the various DR techniques were also explored.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48542897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-08DOI: 10.4236/jdaip.2021.93009
I. B. Iorliam, B. A. Ikyo, A. Iorliam, E. O. Okube, K. D. Kwaghtyo, Y. Shehu
The ability of machine learning techniques to make accurate predications is increasing. The aim of this work is to apply machine learning techniques such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human consumption if consumed after its shelf life. Okra parameters such as weight loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used as inputs into these machine learning techniques. Support Vector Machine, Naïve Bayes and Decision Tree each accurately predicted the shelf life of Okra with accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour achieved 88.89% and 88.33% accuracies, respectively. These results showed that machine learning techniques especially Support Vector Machine, Naïve Bayes and Decision Tree can be effectively applied for the prediction of Okra shelf life.
{"title":"Application of Machine Learning Techniques for Okra Shelf Life Prediction","authors":"I. B. Iorliam, B. A. Ikyo, A. Iorliam, E. O. Okube, K. D. Kwaghtyo, Y. Shehu","doi":"10.4236/jdaip.2021.93009","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93009","url":null,"abstract":"The ability of machine learning techniques to make \u0000accurate predications is increasing. The aim of this work is to apply machine \u0000learning techniques such as Support Vector Machine, Naïve Bayes, Decision Tree, \u0000Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf \u0000life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human \u0000consumption if consumed after its shelf life. Okra parameters such as weight \u0000loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used \u0000as inputs into these machine learning techniques. Support Vector Machine, Naïve \u0000Bayes and Decision Tree each accurately predicted the shelf life of Okra with \u0000accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour \u0000achieved 88.89% and 88.33% accuracies, respectively. These results showed that \u0000machine learning techniques especially Support Vector Machine, Naïve Bayes and \u0000Decision Tree can be effectively applied for the prediction of Okra shelf life.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48502284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-18DOI: 10.4236/JDAIP.2021.92006
Radouan Ait Mouha
the world is experiencing a strong rush towards modern technology, while specialized companies are living a terrible rush in the information technology towards the so-called Internet of things IoT or Internet of objects, which is the integration of things with the world of Internet, by adding hardware or/and software to be smart and so be able to communicate with each other and participate effectively in all aspects of daily life, so enabling new forms of communication between people and things, and between things themselves, that’s will change the traditional life into a high style of living. But it won’t be easy, because there are still many challenges and issues that need to be addressed and have to be viewed from various aspects to realize their full potential. The main objective of this review paper will provide the reader with a detailed discussion from a technological and social perspective. The various IoT challenges and issues, definition and architecture were discussed. Furthermore, a description of several sensors and actuators and their smart communication. Also, the most important application areas of IoT were presented. This work will help readers and researchers understand the IoT and its potential application in the real world.
{"title":"Internet of Things (IoT)","authors":"Radouan Ait Mouha","doi":"10.4236/JDAIP.2021.92006","DOIUrl":"https://doi.org/10.4236/JDAIP.2021.92006","url":null,"abstract":"the world is \u0000experiencing a strong rush towards modern technology, while specialized \u0000companies are living a terrible rush in the information technology towards the \u0000so-called Internet of things IoT or Internet of objects, which is the integration of things with the \u0000world of Internet, by adding hardware or/and software to be smart and so be \u0000able to communicate with each other and participate effectively in all aspects \u0000of daily life, so enabling new forms of \u0000communication between people and things, and between things themselves, that’s \u0000will change the traditional life into a high style of living. But it won’t be \u0000easy, because there are still many challenges and issues that need to be addressed and have to be \u0000viewed from various aspects to realize their full potential. The main objective of this \u0000review paper will provide the reader with a detailed discussion from a \u0000technological and social perspective. The various IoT challenges and issues, \u0000definition and architecture were discussed. Furthermore, a description of \u0000several sensors and actuators and their smart communication. Also, the most important application areas of IoT were \u0000presented. This work will help readers and researchers understand the IoT and \u0000its potential application in the real world.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44082674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-18DOI: 10.4236/JDAIP.2021.92007
Raghavendra Dwivedi, N. N. Pandey
To improve high quality and/or retain achieved high quality of an academic program, time to time evaluation for quality of each covered course is often an integrated aspect considered in reputed institutions, however, there has been little effort regarding humanities courses. This research article deals with analysis of evaluation data collected regarding humanities course from a College of Commerce & Economics, Mumbai, Maharashtra, India, on Likert type items. Appropriateness of one parametric measure and three non-parametric measures are discussed and used in this regard which could provide useful clues for educational policy planners. Keeping in view of the analytical results using these four measures, regardless of the threshold regarding satisfaction among students, overall performance of almost every subject has been un-satisfactory. There is a need to make a focused approach to take every course at the level of high performance. The inconsistency noticed under every threshold further revealed that under such poorly performing subjects globally, one needs to analyze merely at the global level item. Once the global level analysis reveals high performance of a course, then only item specific analysis may need to be focused to find out the items requiring further improvements.
{"title":"Analysis of Evaluation Data Collected on Likert Type Items: Humanities-Courses","authors":"Raghavendra Dwivedi, N. N. Pandey","doi":"10.4236/JDAIP.2021.92007","DOIUrl":"https://doi.org/10.4236/JDAIP.2021.92007","url":null,"abstract":"To improve high quality and/or retain achieved high quality of an academic program, time to time evaluation for quality of each covered course is often an integrated aspect considered in reputed institutions, however, there has been little effort regarding humanities courses. This research article deals with analysis of evaluation data collected regarding humanities course from a College of Commerce & Economics, Mumbai, Maharashtra, India, on Likert type items. Appropriateness of one parametric measure and three non-parametric measures are discussed and used in this regard which could provide useful clues for educational policy planners. Keeping in view of the analytical results using these four measures, regardless of the threshold regarding satisfaction among students, overall performance of almost every subject has been un-satisfactory. There is a need to make a focused approach to take every course at the level of high performance. The inconsistency noticed under every threshold further revealed that under such poorly performing subjects globally, one needs to analyze merely at the global level item. Once the global level analysis reveals high performance of a course, then only item specific analysis may need to be focused to find out the items requiring further improvements.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44719030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-18DOI: 10.4236/JDAIP.2021.92005
Radouan Ait Mouha
The application of deep learning to robotics over the past decade has led to a wave of research into deep artificial neural networks and to a very specific problems and questions that are not usually addressed by the computer vision and machine learning communities. Robots have always faced many unique challenges as the robotic platforms move from the lab to the real world. Minutely, the sheer amount of diversity we encounter in real-world environments is a huge challenge to deal with today’s robotic control algorithms and this necessitates the use of machine learning algorithms that are able to learn the controls of a given data. However, deep learning algorithms are general non-linear models capable of learning features directly from data making them an excellent choice for such robotic applications. Indeed, robotics and artificial intelligence (AI) are increasing and amplifying human potential, enhancing productivity and moving from simple thinking towards human-like cognitive abilities. In this paper, lots of learning, thinking and incarnation challenges of deep learning robots were discussed. The problem addressed was robotic grasping and tracking motion planning for robots which was the most fundamental and formidable challenge of designing autonomous robots. This paper hope to provide the reader an overview of DL and robotic grasping, also the problem of tracking and motion planning. The system is tested on simulated data and real experiments with success.
{"title":"Deep Learning for Robotics","authors":"Radouan Ait Mouha","doi":"10.4236/JDAIP.2021.92005","DOIUrl":"https://doi.org/10.4236/JDAIP.2021.92005","url":null,"abstract":"The application of deep learning to robotics over \u0000the past decade has led to a wave of research into deep artificial neural \u0000networks and to a very specific problems and questions that are not usually \u0000addressed by the computer vision and machine learning communities. Robots have \u0000always faced many unique challenges as the robotic platforms move from the lab \u0000to the real world. Minutely, the sheer amount of diversity we encounter in \u0000real-world environments is a huge challenge to deal with today’s robotic \u0000control algorithms and this necessitates the use of machine learning algorithms \u0000that are able to learn the controls of a given data. However, deep learning \u0000algorithms are general non-linear models capable of learning features directly \u0000from data making them an excellent choice for such robotic applications. \u0000Indeed, robotics and artificial intelligence (AI) are increasing and amplifying \u0000human potential, enhancing productivity and moving from simple thinking towards \u0000human-like cognitive abilities. In this paper, lots of learning, thinking and incarnation challenges \u0000of deep learning robots were discussed. The problem addressed was robotic \u0000grasping and tracking motion planning for robots which was the most fundamental \u0000and formidable challenge of designing autonomous robots. This paper hope to provide the reader an overview of DL and robotic grasping, also the problem of \u0000tracking and motion planning. The system is tested on simulated data and real \u0000experiments with success.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43558673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.4236/jdaip.2021.94014
Yizhi He, Tianchen Zhu, Mingxu Wang, Hanqing Lu
Applying machine learning to lemon defect recognition can improve the efficiency of lemon quality detection. This paper proposes a deep learning-based classification method with visual feature extraction and transfer learning to recognize defect lemons (i.e., green and mold defects). First, the data enhancement and brightness compensation techniques are used for data pre-possessing. The visual feature extraction is used to quantify the defects and determine the feature variables as the bandit basis for classification. Then we construct a convolutional neural network with an embedded Visual Geome-try Group 16 based (VGG16-based) network using transfer learning. The proposed model is compared with many benchmark models such as K-nearest Neighbor (KNN) and Support Vector Machine (SVM). Results show that the proposed model achieves the highest accuracy (95.44%) in the testing data set. The research provides a new solution for lemon defect recognition.
{"title":"On Lemon Defect Recognition with Visual Feature Extraction and Transfers Learning","authors":"Yizhi He, Tianchen Zhu, Mingxu Wang, Hanqing Lu","doi":"10.4236/jdaip.2021.94014","DOIUrl":"https://doi.org/10.4236/jdaip.2021.94014","url":null,"abstract":"Applying machine learning to lemon defect recognition can improve the efficiency of lemon quality detection. This paper proposes a deep learning-based classification method with visual feature extraction and transfer learning to recognize defect lemons (i.e., green and mold defects). First, the data enhancement and brightness compensation techniques are used for data pre-possessing. The visual feature extraction is used to quantify the defects and determine the feature variables as the bandit basis for classification. Then we construct a convolutional neural network with an embedded Visual Geome-try Group 16 based (VGG16-based) network using transfer learning. The proposed model is compared with many benchmark models such as K-nearest Neighbor (KNN) and Support Vector Machine (SVM). Results show that the proposed model achieves the highest accuracy (95.44%) in the testing data set. The research provides a new solution for lemon defect recognition.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.4236/jdaip.2021.94015
Abdullah Z. Alruhaymi, Charles J. Kim
{"title":"Case Study on Data Analytics and Machine Learning Accuracy","authors":"Abdullah Z. Alruhaymi, Charles J. Kim","doi":"10.4236/jdaip.2021.94015","DOIUrl":"https://doi.org/10.4236/jdaip.2021.94015","url":null,"abstract":"","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70996925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.4236/jdaip.2021.94016
Jinze Huang, Fengbiao Zan, Xin Liu, Da Chen
Aiming at the issue of the selectivity of routing protocols between UAV groups, a comprehensive weighting evaluation system based on game theory is proposed. Taking network simulation data as an example, three protocols, AODV, DSDV, and OLSR, are selected as the research objects. The results show that the DSDV protocol is suitable for the simple communication environment between UAV groups, the AODV protocol is suitable for the complex communication environment between UAV groups. In addition, the evaluation system is compared with the two evaluation systems of the Covariance Analytic Hierarchy Process (Cov-AHP) and the entropy method to calculate the relative deviation. The comparison results show that the new evaluation system is more reasonable than the other two evaluation systems.
{"title":"Realization of UAV Routing Protocol Evaluation System Based on Game Theory Comprehensive Weighting","authors":"Jinze Huang, Fengbiao Zan, Xin Liu, Da Chen","doi":"10.4236/jdaip.2021.94016","DOIUrl":"https://doi.org/10.4236/jdaip.2021.94016","url":null,"abstract":"Aiming at the issue of the selectivity of routing protocols between UAV groups, a comprehensive weighting evaluation system based on game theory is proposed. Taking network simulation data as an example, three protocols, AODV, DSDV, and OLSR, are selected as the research objects. The results show that the DSDV protocol is suitable for the simple communication environment between UAV groups, the AODV protocol is suitable for the complex communication environment between UAV groups. In addition, the evaluation system is compared with the two evaluation systems of the Covariance Analytic Hierarchy Process (Cov-AHP) and the entropy method to calculate the relative deviation. The comparison results show that the new evaluation system is more reasonable than the other two evaluation systems.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.4236/jdaip.2021.94017
Antonia Stefani, B. Vassiliadis
Digital educational content is gaining importance as an incubator of pedagogical methodologies in formal and informal online educational settings. Its educational efficiency is directly dependent on its quality, however educational content is more than information and data. This paper presents a new data quality framework for assessing digital educational content used for teaching in distance learning environments. The model relies on the ISO2500 series quality standard and beside providing the mechanisms for multi-facet quality assessment it also supports organizations that design, create, manage and use educational content with the quality tools (expressed as quality metrics and measurement methods) to provide a more efficient distance education experience. The model describes the quality characteristics of the educational material content using data and software quality characteristics.
{"title":"A Quality Assurance Reference Framework for Assessing Educational Data","authors":"Antonia Stefani, B. Vassiliadis","doi":"10.4236/jdaip.2021.94017","DOIUrl":"https://doi.org/10.4236/jdaip.2021.94017","url":null,"abstract":"Digital educational content is gaining importance as an incubator of pedagogical methodologies in formal and informal online educational settings. Its educational efficiency is directly dependent on its quality, however educational content is more than information and data. This paper presents a new data quality framework for assessing digital educational content used for teaching in distance learning environments. The model relies on the ISO2500 series quality standard and beside providing the mechanisms for multi-facet quality assessment it also supports organizations that design, create, manage and use educational content with the quality tools (expressed as quality metrics and measurement methods) to provide a more efficient distance education experience. The model describes the quality characteristics of the educational material content using data and software quality characteristics.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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