A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDS
|
|
- Everett Harrington
- 6 years ago
- Views:
Transcription
1 KAAV INTERNATIONAL JOURNAL OF SCIENCE, ENGINEERING & TECHNOLOGY A REFEREED BLIND PEER REVIEW QUARTERLY JOURNAL KIJSET/JUL-SEP (2017)/VOL-4/ISS-3/A15 PAGE NO ISSN: IMPACT FACTOR (2017) A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDS 1 MEHTA SMRUTI HEMANT KUMAR 1 Research Scholar, Pacific Academy of higher Education and Research University, Udaipur 1 DR. NIMESH I. MODI 1 I/C H.O.D., Hemchandracharya North Gujarat University, Patan Abstract Data mining extracts the information from large amount of data. The goal of organization is to give quality education to its students. One way to achieve highest level of quality in higher education system is by predicting student s performance. This paper provides various data mining techniques. These techniques include classification, clustering, association rule, prediction etc. Keywords Data Mining, Education Data Mining, Knowledge discovery from data (KDD), Data mining techniques 1. Introduction In the real world, higher educational institutions are facing very high competitions. The aim of these institutions is to get more advantages over the other business competitors. To achieve this goal the institutions have to get highest level of quality and satisfy their customers. The way to reach the highest level of quality in higher education system is good prediction of student s success in higher learning institution. To achieve this prediction model is used using anyone of the various approaches. Students and professors are the valuable assets for these institutions. To remain competitive in educational domain, these institutions have to be knowledgeable for a better assessment, evaluation, planning and decision - making. To be a knowledgeable, knowledge is required and this knowledge can be acquired from the historical and operational data that reside in the database of educational institution. For these, data mining techniques can be used to extract knowledge from large data sets. Data mining can be applied in various areas like finance, banking, telecommunication, industry, medical, education, marketing, surveillance, fraud detection, statistical analysis, engineering, sales etc. Data Mining is a process of extracting previously unknown, valid, potentially useful and hidden patterns from large data sets (Connolly, 1999) [1]. The amount of data stored in educational databases is increasing rapidly. Efficient data mining techniques are required in order to get required benefits from such a large data and to find out hidden 84
2 relationships between variables (Han and Kamber, 2006) [4]. Data Mining, sometimes also called Knowledge Discovery in Databases (KDD). The primary goal of data mining is to uncover hidden information. Knowledge Discovery in Database refers to the overall process of extracting useful information from large data sets, where data stored in databases, data warehouses or other information storage areas. It interacts with user or knowledge base. Knowledge Discovery in Database is used for finding new knowledge from database that is used in decision making process. 1.1 Steps of KDD Knowledge discovery process is depicted in following figure. Figure 1: Steps of KDD KDD have iterative sequence of the following steps [12]: 1. Develop an understanding for the application domain and identify the goal. 2. Create a target dataset Selecting a dataset or focusing on a subset of samples or variables on which to make discoveries 3. Data cleaning and preprocessing removing of noise and outliers from collecting necessary information to model or account for noise handling of missing data accounting for time sequence information. 4. Data reduction and projection Finding useful features to represent the data relative to the goal dimensionality reduction/transformation ==> reduce number of variables identification of invariant representations. 5. Selection of appropriate data-mining task Summarization, classification, regression, clustering, etc. 6. Selection of data-mining algorithm(s) Methods to search for patterns decision of which models and parameters may be appropriate match method to goal of KDD process 7. Data-mining Searching for patterns of interest in one or more representational forms 8. Interpretation and visualization Interpretation of mined patterns visualization of extracted patterns and models visualization of the data given the extracted models 85
3 Data mining includes fitting models to or determining patterns from observed data. The fitted models play the role of brings knowledge. Deciding whether, the model reflects useful knowledge or not is a part of the overall KDD process for which subjective human judgment is usually required. Data mining consists of five major elements: 1) Extract, convert, and load transaction data into data warehouse system. 2) Storage and then management of this data in a multidimensional database system. 3) Provide access of this data to information technology professionals and business analysts. 4) Analysis of data using application software. 5) Present the data in a useful form, such as a table or graph [11]. 2. Educational data mining Baker and Yacef (2009) [5] defined educational data mining as Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in. The raw data coming from educational system are converted into useful information during EDM process which could have an ample impact on educational practice. In recent years EDM has become an active research area for researchers all over the world. EDM is a process of developing various techniques or methods like prediction, clustering, classification, association rule mining etc. for extracting the different types of data from educational database and using those methods to understand the students. The main area of Educational Data Mining is predicting student s performance, enrolment management, grouping students, predicting student s profiling, planning and scheduling, user modeling, detecting cheating in online examination. The main objective of any higher educational system is to improve the quality of education. To accomplish this goal, the data mining techniques can be used. Educational data mining have some advantages over the higher educational system such as decreasing student s drop-out rate, increasing student s promotion rate, increasing student s retention rate, increasing student s transition rate, increasing educational improvement ratio, increasing student s learning outcome, maximizing educational system efficiency and reducing the cost of system processes. To achieve these goals the data mining system will be helpful to put insights for decision makers in the higher educational system. Higher education system involves different groups of users or participants. They describe information related to education according to their own mission, vision and objectives. Higher education can be classified into different Users/Stakeholders as follows [16]: 1. Learners / Students :- To personalize e-learning, recommend activities to learners, provide learning tasks that could further improve their learning, to suggest interesting learning experiences to the students. 2. Educators / Teachers / Instructors :- To detect which students require support, to predict student performance, to classify learners into groups, to find a learner s regular as well as irregular patterns, to find the most frequently made mistakes, to analyze student s learning and behavior, to detect which students require support. 3. Course Developers / Educational Researchers :- To compare data mining techniques in order to be able to recommend the most useful one for each task, to develop specific data mining tools for educational purposes etc. 4. System Administrators / Network Administrator :- To utilize available resources more effectively, to enhance educational program offers and determine the effectiveness of the distance learning approach. 3. Data Mining Techniques Data mining techniques are used to manage large amounts of data to discover hidden patterns and relationships. These patterns are helpful in decision making. Data mining techniques includes algorithms 86
4 like classification, regression, association, prediction, clustering and time series analysis etc. These techniques are used for knowledge discovery from database. 3.1 Classification Classification is a classic data mining technique based on machine learning. Classification technique maps data into a set of predefined classes to describe a model [10]. Classification uses decision tree, neural network and classification rule (IF - Then). For example we can apply the classification rule on the past record of the student who left for university and evaluate them. 3.2 Clustering Clustering is a collection of similar data object. Dissimilar object is another cluster. It is way finding similarities between data according to their characteristic. This technique based on the unsupervised learning (i.e. desired output for a given input is not known). For example, image processing, pattern recognition, city planning [14]. 3.3 Prediction Prediction techniques discover the relationship between one or more independent variables and dependent variables [8]. In data mining independent variables are attributes already known and response variables are what we want to predict. Prediction model is based on continuous or ordered values. 3.4 Regression Regression is used to map a data item to a real valued prediction variable [2]. In other words, regression can be adapted for prediction. In the regression techniques target value are known. For example, you can predict the child behavior based on family history. 3.5 Time Series Analysis Time series analysis is the process of using statistical techniques to model and explain a time-dependent series of data points. Time series forecasting is a method of using a model to generate predictions (forecasts) for future events based on known past events. For example stock market. 3.6 Association Rule: It is a technique to identify specific relationships among data. This technique is useful to identify students failure patterns [6], parameters related to the admission process, migration, contribution of alumni, student assessment, co-relation between different group of students, to guide a search for a better fitting transfer model of student learning etc. [3] 3.7 Sequence Discovery Uncover relationships among data [2]. It is set of object each associated with its own timeline of events. For example, scientific experiment, natural disaster and analysis of DNA sequence. 4. Literature Survey Brijesh Kumar Baradwaj and Saurabh Pal [7] have designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student s performance and as there are many approaches that are used for data classification, the decision tree method is used here. Information s like Attendance, Class test, Seminar and Assignment marks were collected from the student s previous database, to predict the performance at the end of the semester. This study will also work to identify those students which needed special attention to reduce fail ration and taking appropriate action for the next semester examination. Pooja Gulati and Dr. Archana Sharma [9] have highlighted in their paper how the education quality is improved with the help of educational data mining. The educational systems currently face number of issues. Data mining provides a set of techniques, which can help the educational system to overcome these issues and enhance the quality of education. One of the significant facts in higher learning institution is the explosive growth educational data. These data are increasing rapidly without any benefit to the management. The main objective of any higher educational institution is to improve the quality of managerial decisions and to impart quality education. For predicting student s success in higher learning institution is one way to reach the highest level of quality 87
5 in higher education system. Komal S. Sahedani and Prof. B Supriya Reddy [12] have stated that the goal of institutions is to give quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course. In our data driven data mining model, knowledge is existed in data, but just not understandable for human. Educational Data Mining is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. This paper will focus exclusively on ways that data mining is used to improve student success and processes directly related to student learning. Dr. K M Alaskar, Prof. Prashant G. Tandale and Prof. A A Basade [13] have discussed that one of the biggest challenges that higher education system face today is to improve the quality of managerial decisions. One way to achieve these challenges is to provide new knowledge related to the educational processes and entities to the managerial system. This knowledge can be extracted from historical and operational data that reside in the educational organization s databases using the techniques of data mining. This paper is based on the use of data mining to analyze the student s feedback on curriculum. The result of this study indicates that Data Mining Techniques provide effective improving tools for student feedback analysis. It showed that how data mining can be useful in higher education to predict acceptance and changes of curriculum by students. We collected the data from student by using questionnaire to find the relationships between behavioral factors of student. S. Lakshmi Prabha and Dr.A.R.Mohamed Shanavas [15] have presented broad areas of applications in which educational data mining can be applied to e-learning. The application areas discussed in this paper are: User modeling, User grouping or profiling, Domain modeling and Trend analysis. The experiment is done on 6 th grade Student log collected from MathsTutor for mensuration. By identifying the knowledge level of a students and grouping them will make easier for the teacher to concentrate the areas for week students. Smita, Priti Sharma [14] defines that Data mining extracts knowledge from a large amount of data which stores in various databases. They studied the survey of various data mining techniques. These techniques include classification, association, correlation, clustering and neural network. This paper also conducts a formal review of the application of data mining such as the education sector, marketing, fraud detection, manufacturing and telecommunication. The main objective of data mining techniques is to discover the knowledge from active data. D. Fatima, Dr. Sameen Fatima and Dr.A. V. Krishna Prasad [17] have discussed in their papers to study the application of data mining to analyze data generated by various information systems supporting learning or education. They also deal with EDM applications with an actual impact on the future of learning and teaching. There are a wide variety of applications of EDM discussed in this paper i.e. Improving Student Models, Discovering or improving models of the knowledge structure of the domain, studying the pedagogical support provided by learning software, Scientific discovery about learning and learners. Hardeep Kaur [18] discusses various techniques of data mining like classification, clustering, association rule mining etc. Each technique has its own importance according to his role. There are various applications of data mining in various fields like education, scientific and engineering, healthcare, business and many more. In this paper we will discuss basics of educational data mining. In this paper we will mainly focus on the applications of data mining in the field of education. Applications of data mining in field of education sector are Analysis and Visualization of Data, Predicting Student Performance, Enrolment Management, Grouping Students, Predicting Students Profiling, Planning and Scheduling, User Modeling, Organization of Syllabus, Detecting Cheating in Online Examination. Sen and Umesh Kumar [16] have tried to put emphasize on the different learning techniques such as offline educational system/traditional educational system, web mining/e-learning and intelligent tutorial system. By adopting all these learning techniques student and institutions could attain better enhancement and enrichment to obtain the knowledge in the field of academic curriculum. To apply the educational data mining effectively we will use the various data mining tools and techniques such as: classification, association rule, clustering and decision tree etc. This paper is a review of the state of the art with respect to EDM. This study would be helpful to student, teacher and institution to enhance the performance and 88
6 productivity effectively. In this research paper different classification method is used to predict the performance of students. 5. Conclusion In this paper I have discussed data mining, educational data mining and data mining techniques. The main goal of any institution is to improve the quality of education. For this data mining techniques can be used. Data mining methods are useful to understand the student s behavior and measuring their performance. References [1] Connolly T., C. Begg and A. Strachan, (1999) Database Systems: A Practical Approach to Design, Implementation, and Management (3rd Ed.). Harlow: Addison-Wesley.687. [2] Dr. M.H.Dunham, Data Mining, Introductory and Advanced Topics, Prentice Hall, [3] Freyberger,J., Heffernan, N., Ruiz, C.(2004), Using association rules to guide a search for best fitting transfer models of student learning, Workshop on Analyzing Student-Tutor Interactions Logs to Improve Educational Outcomes at ITS Conference [4] Han, J. and Kamber, M., (2006) "Data Mining: Concepts and Techniques", 2nd edition. The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor. [5] Baker, R., & Yacef, K. (2009). The State of Educational Data mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1): 3-17 [6] Oladipupo,O.O.,Oyelade,O.J.(2009), Knowledge Discovery from Students Result Repository: Association Rule Mining Approach, International Journal of Computer Science & Security,Vol.4, No.2, pp , [7] Brijesh Kumar Baradwaj, Saurabh Pal, Mining Educational Data to Analyze Students Performance, IJACSA, Vol. 2, No. 6, [8] Aakanksha Bhatnagar, Shweta P. Jadye, Madan Mohan Nagar, Data Mining Techniques & Distinct Applications: A Literature Review International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 9, November [9] Pooja Gulati, Dr. Archana Sharma, Educational Data Mining for Improving Educational quality, IJCSITS, Vol. 2, No. 3, June [10] Rajni Jindal, Malaya Dutta Borah, A Survey on Educational Data Mining And Research Trends, IJDMS, Vol.5, No.3, June [11] Nikita Jain, Vishal Srivastava, Data mining techniques: A survey paper, IJRET: International Journal of Research in Engineering and Technology, Volume: 02 Issue: 11, Nov [12] Komal S. Sahedani, Prof. B Supriya Reddy, A Review: Mining Educational Data to Forecast Failure of Engineering Students, IJARCSSE, Volume 3, Issue 12, December [13] Dr. K M Alaskar, Prof. Prashant G. Tandale, Prof. A A Basade, Data Mining Applications in Higher Education, Proceedings of National Conference on Emerging Trends: Innovations and Challenges in IT, 19-20, April [14] Smita, Priti Sharma, Use of Data Mining in Various Field: A Survey Paper, IOSR-JCE, Volume 16, Issue 3, PP 18-21, May-Jun [15] S. Lakshmi Prabha, Dr.A.R.Mohamed Shanavas, Educational Data Mining Applications, ORAJ, Vol. 1, No. 1, August [16] Sen, Umesh Kumar, A Brief Review Status of Educational Data Mining, IJARCST, Vol. 3, Issue 1, Jan.-Mar [17] D. Fatima, Dr. Sameen Fatima, Dr. A.V.Krishna Prasad, A Survey on Research work in Educational Data Mining, IOSR-JCE, Volume 17, Issue 2, Pages 43-49, Mar Apr [18] Hardeep Kaur, A Review of Applications of data Mining in the Field of Education, IJARCCE, Vol. 4, Issue 4, April
Mining Association Rules in Student s Assessment Data
www.ijcsi.org 211 Mining Association Rules in Student s Assessment Data Dr. Varun Kumar 1, Anupama Chadha 2 1 Department of Computer Science and Engineering, MVN University Palwal, Haryana, India 2 Anupama
More informationOn-Line Data Analytics
International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] On-Line Data Analytics Yugandhar Vemulapalli #, Devarapalli Raghu *, Raja Jacob
More informationThe 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X
The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationScienceDirect. A Framework for Clustering Cardiac Patient s Records Using Unsupervised Learning Techniques
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 98 (2016 ) 368 373 The 6th International Conference on Current and Future Trends of Information and Communication Technologies
More informationTwitter Sentiment Classification on Sanders Data using Hybrid Approach
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationWord Segmentation of Off-line Handwritten Documents
Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
More informationRule Learning with Negation: Issues Regarding Effectiveness
Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX
More informationLongest Common Subsequence: A Method for Automatic Evaluation of Handwritten Essays
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. IV (Nov Dec. 2015), PP 01-07 www.iosrjournals.org Longest Common Subsequence: A Method for
More informationComparison of EM and Two-Step Cluster Method for Mixed Data: An Application
International Journal of Medical Science and Clinical Inventions 4(3): 2768-2773, 2017 DOI:10.18535/ijmsci/ v4i3.8 ICV 2015: 52.82 e-issn: 2348-991X, p-issn: 2454-9576 2017, IJMSCI Research Article Comparison
More informationAssignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
More informationLecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More informationProduct Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments
Product Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments Vijayshri Ramkrishna Ingale PG Student, Department of Computer Engineering JSPM s Imperial College of Engineering &
More informationHumboldt-Universität zu Berlin
Humboldt-Universität zu Berlin Department of Informatics Computer Science Education / Computer Science and Society Seminar Educational Data Mining Organisation Place: RUD 25, 3.101 Date: Wednesdays, 15:15
More informationHuman Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationMining Student Evolution Using Associative Classification and Clustering
Mining Student Evolution Using Associative Classification and Clustering 19 Mining Student Evolution Using Associative Classification and Clustering Kifaya S. Qaddoum, Faculty of Information, Technology
More informationApplications of data mining algorithms to analysis of medical data
Master Thesis Software Engineering Thesis no: MSE-2007:20 August 2007 Applications of data mining algorithms to analysis of medical data Dariusz Matyja School of Engineering Blekinge Institute of Technology
More informationChapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA. 1. Introduction. Alta de Waal, Jacobus Venter and Etienne Barnard
Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA Alta de Waal, Jacobus Venter and Etienne Barnard Abstract Most actionable evidence is identified during the analysis phase of digital forensic investigations.
More informationLecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
More informationAustralian Journal of Basic and Applied Sciences
AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy C-Mean
More informationUSER ADAPTATION IN E-LEARNING ENVIRONMENTS
USER ADAPTATION IN E-LEARNING ENVIRONMENTS Paraskevi Tzouveli Image, Video and Multimedia Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens tpar@image.
More informationINTRODUCTION TO DECISION ANALYSIS (Economics ) Prof. Klaus Nehring Spring Syllabus
INTRODUCTION TO DECISION ANALYSIS (Economics 190-01) Prof. Klaus Nehring Spring 2003 Syllabus Office: 1110 SSHB, 752-3379. Office Hours (tentative): T 10:00-12:00, W 4:10-5:10. Prerequisites: Math 16A,
More informationIntroduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
More informationCSL465/603 - Machine Learning
CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am
More informationSINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)
SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Hans Christian 1 ; Mikhael Pramodana Agus 2 ; Derwin Suhartono 3 1,2,3 Computer Science Department,
More informationCREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT
CREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT Rajendra G. Singh Margaret Bernard Ross Gardler rajsingh@tstt.net.tt mbernard@fsa.uwi.tt rgardler@saafe.org Department of Mathematics
More informationImplementing a tool to Support KAOS-Beta Process Model Using EPF
Implementing a tool to Support KAOS-Beta Process Model Using EPF Malihe Tabatabaie Malihe.Tabatabaie@cs.york.ac.uk Department of Computer Science The University of York United Kingdom Eclipse Process Framework
More informationEvolutive Neural Net Fuzzy Filtering: Basic Description
Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:
More informationImpact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees
Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Mariusz Łapczy ski 1 and Bartłomiej Jefma ski 2 1 The Chair of Market Analysis and Marketing Research,
More informationLearning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
More informationReducing Features to Improve Bug Prediction
Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science
More informationWelcome to. ECML/PKDD 2004 Community meeting
Welcome to ECML/PKDD 2004 Community meeting A brief report from the program chairs Jean-Francois Boulicaut, INSA-Lyon, France Floriana Esposito, University of Bari, Italy Fosca Giannotti, ISTI-CNR, Pisa,
More informationClassification Using ANN: A Review
International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 7 (2017), pp. 1811-1820 Research India Publications http://www.ripublication.com Classification Using ANN:
More information(Sub)Gradient Descent
(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include
More informationSpeech Emotion Recognition Using Support Vector Machine
Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,
More informationData Structures and Algorithms
CS 3114 Data Structures and Algorithms 1 Trinity College Library Univ. of Dublin Instructor and Course Information 2 William D McQuain Email: Office: Office Hours: wmcquain@cs.vt.edu 634 McBryde Hall see
More informationTop US Tech Talent for the Top China Tech Company
THE FALL 2017 US RECRUITING TOUR Top US Tech Talent for the Top China Tech Company INTERVIEWS IN 7 CITIES Tour Schedule CITY Boston, MA New York, NY Pittsburgh, PA Urbana-Champaign, IL Ann Arbor, MI Los
More informationOCR for Arabic using SIFT Descriptors With Online Failure Prediction
OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,
More informationTest Effort Estimation Using Neural Network
J. Software Engineering & Applications, 2010, 3: 331-340 doi:10.4236/jsea.2010.34038 Published Online April 2010 (http://www.scirp.org/journal/jsea) 331 Chintala Abhishek*, Veginati Pavan Kumar, Harish
More informationLearning From the Past with Experiment Databases
Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University
More informationTime series prediction
Chapter 13 Time series prediction Amaury Lendasse, Timo Honkela, Federico Pouzols, Antti Sorjamaa, Yoan Miche, Qi Yu, Eric Severin, Mark van Heeswijk, Erkki Oja, Francesco Corona, Elia Liitiäinen, Zhanxing
More informationCourse Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE
EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center
More informationSoftware Maintenance
1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories
More informationCONCEPT MAPS AS A DEVICE FOR LEARNING DATABASE CONCEPTS
CONCEPT MAPS AS A DEVICE FOR LEARNING DATABASE CONCEPTS Pirjo Moen Department of Computer Science P.O. Box 68 FI-00014 University of Helsinki pirjo.moen@cs.helsinki.fi http://www.cs.helsinki.fi/pirjo.moen
More informationCircuit Simulators: A Revolutionary E-Learning Platform
Circuit Simulators: A Revolutionary E-Learning Platform Mahi Itagi Padre Conceicao College of Engineering, Verna, Goa, India. itagimahi@gmail.com Akhil Deshpande Gogte Institute of Technology, Udyambag,
More informationSystem Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
More informationRyerson University Sociology SOC 483: Advanced Research and Statistics
Ryerson University Sociology SOC 483: Advanced Research and Statistics Prerequisites: SOC 481 Instructor: Paul S. Moore E-mail: psmoore@ryerson.ca Office: Sociology Department Jorgenson JOR 306 Phone:
More informationEvaluation of Usage Patterns for Web-based Educational Systems using Web Mining
Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining Dave Donnellan, School of Computer Applications Dublin City University Dublin 9 Ireland daviddonnellan@eircom.net Claus Pahl
More informationEvaluation of Usage Patterns for Web-based Educational Systems using Web Mining
Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining Dave Donnellan, School of Computer Applications Dublin City University Dublin 9 Ireland daviddonnellan@eircom.net Claus Pahl
More informationDifferent Requirements Gathering Techniques and Issues. Javaria Mushtaq
835 Different Requirements Gathering Techniques and Issues Javaria Mushtaq Abstract- Project management is now becoming a very important part of our software industries. To handle projects with success
More informationMajor Milestones, Team Activities, and Individual Deliverables
Major Milestones, Team Activities, and Individual Deliverables Milestone #1: Team Semester Proposal Your team should write a proposal that describes project objectives, existing relevant technology, engineering
More informationMachine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler
Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina
More informationLeveraging MOOCs to bring entrepreneurship and innovation to everyone on campus
Paper ID #9305 Leveraging MOOCs to bring entrepreneurship and innovation to everyone on campus Dr. James V Green, University of Maryland, College Park Dr. James V. Green leads the education activities
More informationTENNESSEE S ECONOMY: Implications for Economic Development
TENNESSEE S ECONOMY: Implications for Economic Development William F. Fox, Director Center for Business and Economic Research The University of Tennessee, Knoxville August 2005 U.S. ECONOMY W.F. Fox, CBER,
More informationCWIS 23,3. Nikolaos Avouris Human Computer Interaction Group, University of Patras, Patras, Greece
The current issue and full text archive of this journal is available at wwwemeraldinsightcom/1065-0741htm CWIS 138 Synchronous support and monitoring in web-based educational systems Christos Fidas, Vasilios
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationRule discovery in Web-based educational systems using Grammar-Based Genetic Programming
Data Mining VI 205 Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming C. Romero, S. Ventura, C. Hervás & P. González Universidad de Córdoba, Campus Universitario de
More informationDinesh K. Sharma, Ph.D. Department of Management School of Business and Economics Fayetteville State University
Department of Management School of Business and Economics Fayetteville State University EDUCATION Doctor of Philosophy, Devi Ahilya University, Indore, India (2013) Area of Specialization: Management:
More informationWE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT
WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT PRACTICAL APPLICATIONS OF RANDOM SAMPLING IN ediscovery By Matthew Verga, J.D. INTRODUCTION Anyone who spends ample time working
More informationSTUDYING ACADEMIC INDICATORS WITHIN VIRTUAL LEARNING ENVIRONMENT USING EDUCATIONAL DATA MINING
STUDYING ACADEMIC INDICATORS WITHIN VIRTUAL LEARNING ENVIRONMENT USING EDUCATIONAL DATA MINING Eng. Eid Aldikanji 1 and Dr. Khalil Ajami 2 1 Master Web Science, Syrian Virtual University, Damascus, Syria
More informationcontent First Introductory book to cover CAPM First to differentiate expected and required returns First to discuss the intrinsic value of stocks
content First Introductory book to cover CAPM First to differentiate expected and required returns First to discuss the intrinsic value of stocks presentation First timelines to explain TVM First financial
More informationAUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders
More informationBusiness Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence
Business Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence COURSE DESCRIPTION This course presents computing tools and concepts for all stages
More informationGACE Computer Science Assessment Test at a Glance
GACE Computer Science Assessment Test at a Glance Updated May 2017 See the GACE Computer Science Assessment Study Companion for practice questions and preparation resources. Assessment Name Computer Science
More informationCS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationComputerized Adaptive Psychological Testing A Personalisation Perspective
Psychology and the internet: An European Perspective Computerized Adaptive Psychological Testing A Personalisation Perspective Mykola Pechenizkiy mpechen@cc.jyu.fi Introduction Mixed Model of IRT and ES
More informationPredicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks
Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim Samsung Electronics Co., Ltd. Seoul, South Korea {dev.chaplot,eunhee.rhim,jihie.kim}@samsung.com
More informationACCOUNTING FOR MANAGERS BU-5190-OL Syllabus
MASTER IN BUSINESS ADMINISTRATION ACCOUNTING FOR MANAGERS BU-5190-OL Syllabus Fall 2011 P LYMOUTH S TATE U NIVERSITY, C OLLEGE OF B USINESS A DMINISTRATION 1 Page 2 PLYMOUTH STATE UNIVERSITY College of
More informationLinking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
More informationP. Belsis, C. Sgouropoulou, K. Sfikas, G. Pantziou, C. Skourlas, J. Varnas
Exploiting Distance Learning Methods and Multimediaenhanced instructional content to support IT Curricula in Greek Technological Educational Institutes P. Belsis, C. Sgouropoulou, K. Sfikas, G. Pantziou,
More informationAn Introduction to Simio for Beginners
An Introduction to Simio for Beginners C. Dennis Pegden, Ph.D. This white paper is intended to introduce Simio to a user new to simulation. It is intended for the manufacturing engineer, hospital quality
More informationDesigning a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses
Designing a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses Thomas F.C. Woodhall Masters Candidate in Civil Engineering Queen s University at Kingston,
More informationLen Lundstrum, Ph.D., FRM
, Ph.D., FRM Professor of Finance Department of Finance College of Business Office: 815 753-0317 Northern Illinois University Fax: 815 753-0504 Dekalb, IL 60115 llundstrum@niu.edu Education Indiana University
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationChemical Engineering Mcgill Cegep Entry
Mcgill Cegep Entry Free PDF ebook Download: Mcgill Cegep Entry Download or Read Online ebook chemical engineering mcgill cegep entry in PDF Format From The Best User Guide Database 4.1.1 BSc in & Process.
More informationCOURSE LISTING. Courses Listed. Training for Cloud with SAP SuccessFactors in Integration. 23 November 2017 (08:13 GMT) Beginner.
Training for Cloud with SAP SuccessFactors in Integration Courses Listed Beginner SAPHR - SAP ERP Human Capital Management Overview SAPHRE - SAP ERP HCM Overview Advanced HRH00E - SAP HCM/SAP SuccessFactors
More informationPp. 176{182 in Proceedings of The Second International Conference on Knowledge Discovery and Data Mining. Predictive Data Mining with Finite Mixtures
Pp. 176{182 in Proceedings of The Second International Conference on Knowledge Discovery and Data Mining (Portland, OR, August 1996). Predictive Data Mining with Finite Mixtures Petri Kontkanen Petri Myllymaki
More informationNational Survey of Student Engagement (NSSE)
2008 NSSE National Survey of Student Engagement (NSSE) Understanding SRU Student Engagement Patterns of Evidence NSSE Presentation Overview What is student engagement? What do we already know about student
More informationJONATHAN H. WRIGHT Department of Economics, Johns Hopkins University, 3400 N. Charles St., Baltimore MD (410)
JONATHAN H. WRIGHT Department of Economics, Johns Hopkins University, 3400 N. Charles St., Baltimore MD 21218. (410) 516 5728 wrightj@jhu.edu EDUCATION Harvard University 1993-1997. Ph.D., Economics (1997).
More informationOdysseyware Login Macon County
Login Macon County Free PDF ebook Download: Login Macon County Download or Read Online ebook odysseyware login macon county in PDF Format From The Best User Guide Database Judicial Circuit, Macon County,
More informationDiploma in Library and Information Science (Part-Time) - SH220
Diploma in Library and Information Science (Part-Time) - SH220 1. Objectives The Diploma in Library and Information Science programme aims to prepare students for professional work in librarianship. The
More informationEvidence for Reliability, Validity and Learning Effectiveness
PEARSON EDUCATION Evidence for Reliability, Validity and Learning Effectiveness Introduction Pearson Knowledge Technologies has conducted a large number and wide variety of reliability and validity studies
More informationAutomating the E-learning Personalization
Automating the E-learning Personalization Fathi Essalmi 1, Leila Jemni Ben Ayed 1, Mohamed Jemni 1, Kinshuk 2, and Sabine Graf 2 1 The Research Laboratory of Technologies of Information and Communication
More information2017? Are you skilled for. Market Leader. Prize Winner. Pass Insurance. Online Learning F7, F8 & F9. Classroom Learning P1-P7
Are you skilled for 2017? ACCA June 2017 Association of Chartered Certified Accountants Market Leader More than 50 years of professional accounting experience worldwide with the biggest professional accounting
More informationThe Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence Algorithms
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS The Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence
More informationSTUDENT SATISFACTION IN PROFESSIONAL EDUCATION IN GWALIOR
International Journal of Human Resource Management and Research (IJHRMR) ISSN 2249-6874 Vol. 3, Issue 2, Jun 2013, 71-76 TJPRC Pvt. Ltd. STUDENT SATISFACTION IN PROFESSIONAL EDUCATION IN GWALIOR DIVYA
More informationA Context-Driven Use Case Creation Process for Specifying Automotive Driver Assistance Systems
A Context-Driven Use Case Creation Process for Specifying Automotive Driver Assistance Systems Hannes Omasreiter, Eduard Metzker DaimlerChrysler AG Research Information and Communication Postfach 23 60
More informationExperiment Databases: Towards an Improved Experimental Methodology in Machine Learning
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning Hendrik Blockeel and Joaquin Vanschoren Computer Science Dept., K.U.Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium
More informationData Fusion Models in WSNs: Comparison and Analysis
Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Data Fusion s in WSNs: Comparison and Analysis Marwah M Almasri, and Khaled M Elleithy, Senior Member,
More informationOPAC and User Perception in Law University Libraries in the Karnataka: A Study
ISSN 2229-5984 (P) 29-5576 (e) OPAC and User Perception in Law University Libraries in the Karnataka: A Study Devendra* and Khaiser Nikam** To Cite: Devendra & Nikam, K. (20). OPAC and user perception
More informationProbability and Statistics Curriculum Pacing Guide
Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods
More informationLip reading: Japanese vowel recognition by tracking temporal changes of lip shape
Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape Koshi Odagiri 1, and Yoichi Muraoka 1 1 Graduate School of Fundamental/Computer Science and Engineering, Waseda University,
More informationA student diagnosing and evaluation system for laboratory-based academic exercises
A student diagnosing and evaluation system for laboratory-based academic exercises Maria Samarakou, Emmanouil Fylladitakis and Pantelis Prentakis Technological Educational Institute (T.E.I.) of Athens
More informationUNA PROFESSIONAL ACCOUNTING PREP PROGRAM
UNA PROFESSIONAL ACCOUNTING PREP PROGRAM Course: AC 463P Financial Statement Auditing Professor: E-mail: Keith T. Jones, PhD, CPA Professor of Accounting University of North Alabama kjones5@una.edu TEXTBOOK:
More informationANNUAL CURRICULUM REVIEW PROCESS for the 2016/2017 Academic Year
ANNUAL CURRICULUM REVIEW PROCESS for the 2016/2017 Academic Year Annual Curriculum review is a process undertaken in advance of each new academic year to renew, revise and update curriculum. Faculty members,
More informationSpecification of the Verity Learning Companion and Self-Assessment Tool
Specification of the Verity Learning Companion and Self-Assessment Tool Sergiu Dascalu* Daniela Saru** Ryan Simpson* Justin Bradley* Eva Sarwar* Joohoon Oh* * Department of Computer Science ** Dept. of
More information