HANDBOOK OF EDUCATIONAL DATA MINING PDF

adminComment(0)
    Contents:

Handbook of Educational Data Mining,bestthing.info bestthing.info://bestthing.info Baker. Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the. Chapman & Hall/CRC. Data Mining and Knowledge Discovery Series. Handbook of. Educational Data Mining. Edited by. Cristobal Romero, Sebastian Ventura.


Handbook Of Educational Data Mining Pdf

Author:RENETTA RIPPEE
Language:English, Arabic, German
Country:Jordan
Genre:Health & Fitness
Pages:544
Published (Last):20.02.2015
ISBN:356-3-40854-961-9
ePub File Size:24.37 MB
PDF File Size:12.57 MB
Distribution:Free* [*Sign up for free]
Downloads:37824
Uploaded by: LATASHIA

An emerging field of educational data mining (EDM) is building on and The data that makes educational data mining possible is produced from Accessible to Non-Experts, Invited chapter in the Handbook of Automated Essay Grading. All the editors and authors of this handbook were inspired by his analytics and educational data mining, as well as a reference that could serve to increase tics. bestthing.info Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine .

Nesbit, and Philip H. Tang and Gordon G. Cooper, Winslow Burleson, and Beverly P. Pardos, Neil T. Heffernan, Brigham S. Anderson, and Cristina L. Prieto, Alfredo Zapata, and Victor H. His research interests include the application of artificial intelligence and data mining techniques to education and e-learning systems. His research interests encompass machine learning, data mining, and their applications as well as the application of KDD techniques to e-learning.

Pechenizkiy has been involved in the organization of workshops, special tracks, and conferences on applications of data mining in medicine, industry, and education.

The educators are using a number of online and offline tools to create quality and easily understandable content to learners. LMSs are proving to have limitations in their monitoring capabilities. Therefore, the second decade of this century is witnessing the emergence of distributed heterogeneous tools used by all the stakeholders of the learning process [12].

These systems are embedding data mining techniques to collect the required data, analyze them and suggest the appropriate actions. The big data can help in tracking the time taken by the students to learn a particular concept.

This will be an indicator of the level of difficulty of the concept provided in the study material, or it can help to determine the learning ability of the students. For example, Researcher Paulo Blikstein [45] examined a sample of college students in a computer programming class to see how they solve a modeling assignment.

He used NetLogo software to maintain logs of all user actions from button clicks and keystrokes to code changes, error messages, and use of different variables. Moridis and Economides [46] proposed a formula-based method and a neural network-based method to automatically collect the affect-state of the student during learning. A typical large size class, especially in e-learning environments, consists of students from different knowledge backgrounds whose educational requirements are quite different.

Offering the same learning path of content to all of them can negatively affect their overall performance. Educational data mining techniques can be used to create a customized learning environment in which students can be provided personalized learning paths for optimizing their performance [49].

Some data mining techniques such clustering and associate rules [50], feature selection [51] have been applied in developing personalized learning systems and increasing individual learning performance.

Due to their inherent strength such as displaying results in user-understandable formats, ability to analyze both continuous and discontinuous variables efficiently, and flexibility with type and scale of databases, decision trees have been popular in designing personalized learning contents [52][53]. Designing the personalized learning content path requires accurate estimation the learning abilities of the students at various stages.

Researchers have considered this issue also and used statistical techniques such as Gaussian approximation method to estimate the learning ability of the students in a typical web- based learning environment [54]. However, the use of EDM in the assessment of learning can result in faster progress as EDM can provide a real-time and continuous assessment [55]. Instead of conducting a periodic exam with a fixed set of questions for all students, big data can be used to create dynamic test according to the knowledge of the student.

This can enable the instructor to find out the precise weakness of each student and the instructor can prepare study plan tailored to the needs of the individual students. Romero et al. They used different objective and subjective rule evaluation measures to select the most interesting and useful rules.

Based on the selected rules, the proposed system provides feedback to the instructors to improve quizzes and courses. Data mining methods such as clustering, classification, and association analysis have been used to study how well the questions in the test and the corresponding elaborated feedback were designed or tailored towards the individual needs of the students [57].

Teaching and Research: Big data techniques can be useful to identify the academic resources to increase the awareness of the instructors. The analysis of textual and video data can provide many insights for instructors. An in-depth analysis of demography of students enrolled in MOOCs can provide researchers with heterogeneous samples of people from traditionally under-represented demographic and sociocultural groups in more narrowly obtained educational datasets.

The researcher can leverage this data to conduct large-scale field experiments and evaluate multiple theory at minimal cost [59]. Racial discrimination is a big issue in educational establishments. Big data can help in identifying racial discrimination. To achieve this objective, Baker et al. They found evidence of discrimination in the behavior of instructors and students.

For example, instructors wrote more replies for white male names than for white female, Indian, and Chinese names. Peer pressure is another critical issue in any educational environment. They concluded that fair and transparent peer grading procedure can promote resilience in trust of learners who received a lower than expected grade.

However, the downside of peer pressure was studied by Rogers and Feller [62] who found that exposure to exemplary peer performance causes attrition, due to the upward social comparison that undermines motivation and expected success. One such list of data mining tools can be found at sourceforge3. However, not all of these data mining tools are designed to meet the requirements of educational data mining.

Some of the tools used in educational data mining are described in this section. Education Prediction Rules EPRules : EPRules [63] is a java-based graphical tool used to solve the prediction rule discovery in adaptive systems in a web-based learning environment.

This tool can be used even by course developers or teachers who are not expert in data mining. The Data input component of this tool allows to open an existing database or create a new one using course usage file. The Prediction rule discovery component shown in figure 1 allows selecting one of the several rule discovery algorithms, to choose the specific execution parameters for the chosen algorithm, to select the subjective restriction such as the number of chapters or number of students , and to choose the objective evaluation function.

The last component Knowledge view displays the discovered rules, conditions of the rules and evaluation parameters. EPRules [64]. GISMO, in tandem with Learning Management System Moodle, can provide comprehensive visualizations that give an overview of the whole class, not only a specific student or a particular resource [27]. TADA-Ed Tool for Advanced Data Analysis in Education : It is a data mining platform that helps teachers to mine and visualize students on-line exercise work such as students' interactions and answers, mistakes, teachers' comments and so on [65].

TADA-Ed contains pre-processing facilities so that users can transform the existing database tables to a format that, when used with a particular data mining algorithm, can generate meaningful results for the teacher. It keeps track of user operations and allows to analyze the usage log files in a graphical form. COLAT is an environment for effective analysis of interrelated multiple data that may be collected during technology-supported learning activities.

LOCO-Analyst: LOCO-Analyst is an educational tool that provides teachers with feedback on the relevant aspects of the learning process taking place in a web-based learning environment. It provides feedback on student activities during the learning process, usage and comprehensibility of the learning content provided by the teacher, and contextualized social interaction among students [69].

It can store many types of data from interactive learning environments such as intelligent tutoring systems, virtual labs, simulations, and games. Datashop provides only exploratory statistical analysis of learning data. However, it allows users to export the data in the formats suitable to other statistical analysis tools. The amount of data in DataShop is constantly growing.

Researchers have utilized DataShop to explore learning issues in a variety of educational domains. These include, but are not limited to, collaborative problem-solving in Algebra [73], self-explanation in Physics [74], the effectiveness of worked examples in a Stoichiometry tutor [75] and the optimization of knowledge component learning in Chinese [76].

This tool is based on client-server architecture. The server subsystem enables to score and share the discovered rules by other teachers of the similar course. It allows instructors to view classification of students according to the activity level which can be helpful in identifying at-risk students. PDinamet: This is a web-based adaptive learning system that consists of several types of learning resources. Each resource is presented by a set of characteristics such as difficulty level and learning objectives [79].

PDinamet contains personal and academic information such as performance in the previous test of students and recommends learning resources for students. Meerkat-ED: Meerkat-ED is a tailored version of Meerkat social network analysis tool6 allowing instructors to evaluate student activities in asynchronous discussion forums of online courses.

Meerkat-ED analyzes the structure of these interactions using social network analysis techniques including community mining. Moreover, it analyzes the contents of the exchanged messages in this discussions by building an information network of terms and using community mining techniques to identify the topics discussed.

Meerkat-ED creates a hierarchical summarization of these discussed topics in the forums, which gives the instructor a quick view of what is under discussion in these forums.

It further illustrates how much each student has participated in these topics, by showing their centrality in the discussions on that topic, the number of posts, replies, and the portion of terms used by that student in the discussions [24].

It helps collaborative learning researchers to visualize network structures of discourse based on a bipartite graph of words vs discourse units.

This can be used to compare coefficients across different phases of collaborative learning between groups. KBDeX supports stepwise analysis to calculate each individual's contribution. It supports several tasks such as selection, data pre-processing, and data mining from Moodle courses [83]. This tool can be useful in providing instructors with feedback about how students learn within Moodle courses.

Data pre- processing component of the tool allows the instructor to load raw excel data, edit, anonymize, discretize, and split the data. Data selection component enables specific data summary, Logs, forum discussions, grades etc. The data mining component runs knowledge discovery algorithms for clustering and classification of data. MDM tool block in an example course [83].

However, a regular user does not know how to access these behavioral data from the learning management system. AAT is an interface based tool that allows users to ask questions related to user behavior or study materials in natural language.

AAT generates graphical representations of the answers of the user questions that can be easily understood and used by regular users such as course instructors. Analytics Graphs: Analytics Graphs is a Moodle learning analytics plug-in9 that collects existing student activity data from Moodle and displays it in visual form.

It supports grade chart, content access chart, assignment submission chart, quiz submission chart, and hits distribution chart. By using these charts, instructors can easily notice the things which could have gone unnoticed otherwise.

This system uses multiple data sets and analytics techniques in a single interface for presenting data to learners and educators. It is integrated into Moodle as a module and provides social network analysis and classification algorithms for predicting assignment submission. It helps instructors to discover and analyze students' behavior in distance learning programs by analyzing navigational and demographic data. It is a web- service that provides visualization graphs, clustering, and association algorithms.

By providing the learner's status summary such as success and failure rates, assignment due status etc. For example, log file analyses can help in better understanding whether the courses provide a sound learning environment availability and use of discussion forums, etc. It allows analyzing the use of the contents in the online-courses from a didactical point of view, thus going deeper than simply counting and visualizing the numbers of posts and clicks.

SmartKlass: SmartKlass11 is a learning analytics plug-in that can be used by any virtual learning system to measure and analyze the learning process at any time. It is an Open source and multi-platform learning analytics dashboard plug-in.

It allows teachers to see a global view of the performance of the students, check the evolution of any course, and control and check an alarm system to send messages to the students. Similarly, it enables students viewing their performances, see the evolution of the course and receive or send alert messages.

It displays timeline and number of the posts, chart of all users, their posts and how other users have replied to those posts among other things. Initially started with two courses, this project was later extended to other 10 courses at different level.

Learning analytics gives them the opportunity to do so in real-time, without the delay usually associated with student feedback and outcomes.

A preliminary evaluation of this pilot has shown retention rates increased by 2. In this initiative a student dashboard designed using learning analytics methods was rolled out throughout the institute.

The dashboard calculates student engagement score from Virtual learning environment access, library usage, card swipes and assignment submissions. The Dashboard was initially tested with four courses, forty tutors and over first- year students. After the success of the pilot project, the project was implemented throughout the University. Tutors are prompted to contact students when their engagement drops off.

University found that 27 percent of students with access to their own dashboard changed their behaviour — for example, by increasing their attendance — while one third of tutors contacted students as a direct result. They using R programming environment to implement the AOL analyzer. The document management system assists in managing various documents related to quality and education.

The data management system processes the data related to these documents. Plan Ceibal currently offers a set of educational software platforms for teaching, learning, training, hosting, exchanging and creating information. Virtual learning environments VLE at Plan Ceibal allow real-time interaction between students and their teachers and peers through a variety of resources and exercises, discussions or instant messaging.

These VLEs generate massive amounts of data on the progress and style of students learning. In order to analyze the massive data, a Big Data Centre for Learning Analytics is being planned [93]. Applications are being developed to exploit the learning analytics and big data to support the education system. One of such system is user profile [94] that is intended to build a comprehensive user online profile.

Though the system is in design stage, it aims to use advanced EDM and learning analytics techniques to support the work of educators while they plan their teaching, and to provide relevant data for the learners regarding their performance. The analysis showed a strong correlation between the involvement in the Cafe activity and the grade.

They also observed that the problems and subjects that most of the students consider challenging could be identified by analyzing the hits of the popular web pages. Also, the learning style of individual students could be determined based on the changes in the number of members over time, postings, page views, and paths leading to the Cafe. We described some common tools which can be useful to researchers, instructors, administrators, and eventually students through analyzing the behaviors and performance of the students.

We also presented several case studies of applications of learning analytics and data mining techniques in various educational institutions across the globe. The existing learning management systems and supporting tools are supporting educational institutions in analyzing performances of the students.

But, we cannot say the same about the analysis of satisfaction of the students with course curriculum, faculty performance, and LMS tools. Most of the educational institutions have already established feedback system e. But usually results from these feedback systems are biased. In order to solve this issue, feedback should be taken not only in a completely anonymous manner but also at the flexible timing. Students should be given chance to express their opinions at the time and place of their choice [96].

Social media provides an excellent platform to express opinions in anonymous and flexible manner. Users may freely express their feeling about education systems, course curriculum, faculties and learning management systems.

Analysis of these almost true feedbacks will give insight into what students really think about our education systems. Researchers need to focus on developing tools to analyze these huge feedbacks scattered over social media to gain more insights about the performance of specific lectures and professors, the usefulness of learning management systems and institutions. Some social media analysis tool, for example, sentiment viz13 , are available but they have not been designed to meet the requirements of educational institutions.

Some recent government-supported researches are underlining the importance of Big Data in higher education and research. An OECD report suggested that Big Data may be the foundation on which higher education can reinvent both its business model and bring together the evidence to help make decisions about educational outcomes [97].

Handbook of Educational Data Mining

Based on these types of researches, governments are planning future steps to improve education by using big data. Recently conducted workshop on data-intensive research in education [98] suggested the following steps to improve educational levels: 1 mobilize communities around opportunities based on new forms of evidence, 2 infuse evidence-based decision-making throughout a system, 3 develop new forms of educational assessment, 4 re-conceptualize data generation, collection, storage, and representation processes, 5 develop new types of analytic methods, 6 build human capacity to do data science and to use its products, and 7 develop advances in privacy, security, and ethics.

These initiatives by the government-funded agencies will accelerate the much-needed reform process in the education sector. References 1. Erevelles, S.

Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69, Massimiliano Giacalone and Sergio Scippacercola. Big data: issues and an overview: In some strategic sectors. Zhou, R. Creative Education, 7, Dawson, S. Current state and future trends: a citation network analysis of the learning analytics field.

Besbes R, Besbes S. Cognitive Dashboard for Teachers Professional Development. Thille, C. The future of data-enriched assessment. Wellings and M. Data Mining in Education. Romero, C. Educational data mining: a review of the state of the art. Huang, S.

Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Romero-Zaldivar, V-A. Monitoring student progress using virtual appliances: a case study. Parry, M. Kop, R. Valencia, Spain, Paper H4 32 Learning analytics and educational data mining: towards communication and collaboration. Vancouver, British Columbia, Canada; , 1—3 Eindhoven, The Netherlands; , — Web usage mining for predicting marks of students that use Moodle courses.

Computer Applications in Engineering Education, vol 21 1 , pp. Clustering Educational Data. Handbook of Educational Data Mining. Amershi, S. Anaya, A. Barnes, M.

Desmarais, C. Romero, and S. Ventura, — Ueno M. Online outlier detection system for learning time data in e-learning and its evaluation. Beijiing, China; , — Merceron, A. Romero, S. Ventura, M. Pechenizkiy, and R. Analyzing participation of students in online courses using social network analysis techniques. Eindhoven, The Netherlands; , 21— Process mining from educational data. Semantic resource management for the web: an e-learning application.

New York; , 1—10 Mazza R, Milani C. GISMO: a graphical interactive student monitoring tool for course management systems. Milan, Italy; , 1—8. The state of educational data mining in a review and future visions. J Edu Data Min , 3— Enhancing teaching and learning through educational data mining and learning analytics: an issue brief.

Washington, D. Department of Education; , 1— Johnson et al. Papamitsiou, Z. Abdous, M. Using data mining for predicting relationships between online question theme and final grade. Dropout prediction in e-learning courses through the combination of machine learning techniques.

Definition

Dekker, G. Predicting students drop out: A case study. Kizilcec, R. Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. Suthers, K. Verbert, E. Ochoa Eds. Giesbers, B.

Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing.

Computers in Human Behavior, 29 1 , — He, W. Computers in Human Behavior, 29 1 , 90— Dejaeger, K.

Educational data mining

Gaining insight into student satisfaction using comprehensible data mining techniques. European Journal of Operational Research, 2 , — Xing, W.Tracking concept drift at feature selection stage in Spam Hunting: an anti-spam instance- based reasoning system.

Kay, K. Without the opportunity to interact with learners in a face-to-face setting, it is therefore harder for instructors as well to recognize negative affect or disengagement among students. As with other areas of education, prediction modeling increasingly plays an important role in distance education. Ferguson, R. The server subsystem enables to score and share the discovered rules by other teachers of the similar course.

HEIKE from Saint Louis
Please check my other posts. I am highly influenced by kenjutsu. I do relish studying docunments arrogantly .
>