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EDUCATIONAL DATAMINING(EDM)
Dr. Dheeraj Mehrotra
(N ational A wardee)
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PREFACE The world of computing has generated the interest of learning much more than it was ever before. Thanks to the World Wide Web based scenario which makes it more as WWW- What Ever, When Ever,Where Ever, to the surprise of many.
I am sure with this little introduction of EDM, the Educational Data Mining which incorporateslearners resource to extract when in need apart from the Head Top to an arena of substance, inparticular. I t is a research based observatory skill to enhance the teaching learning process as a deliveryin ultima.
The e-book is a step towards understand a niche of the above. Hope you enjoy learning !
Cheers.
Dr. Dheeraj Mehrotra
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WHAT I S EDM?
Educational Data Mining is an emerging discipline,
concerned with developing methods for exploring theunique types of data that come from educational settings,
and using those methods to better understand students,
and the settings which they learn in.
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AN EDUCATI ONAL AN ALYSI S
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ABOUT AN ALY SI S
The data is taken from students' use of interactive
learning environments, computer-supportedcollaborative learning, or administrative data from
schools and universities, to perceive a new definition
of understanding and feasibility towards explorage.
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ABOUT:
With distractions on the rise, within the classrooms, Large amounts
of data are generated while students interact with computer based
learning systems. These data can be analysed through data mining
techniques to find patterns or train models that can help tutoring
systems or teachers to provide better support towards gaining
excellence in the classrooms of today at large.
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ABOUT:
Commonly, teachers try to identify the groups that work effectively to
leave them work more independently and be able to devote time to
groups needing their attention in particular. A new age learner hence
produces learning via a framework tested by his students and works
on their demands and actions.
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TY PES OF DATA MI NI NG
Data Mining Applies at length to improving Student Learning
Processes. Data mining has its roots in machine learning, artificial
intelligence, computer science. There are a variety of different data
mining techniques and approaches, such as clustering, classification,
and association rule mining.
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ACADEMI C FRONT
Educational data mining is a field of study that analyzes and applies
data mining to solve educationally-related problems. Applying this
further helps the researchers and practitioners to discover new ways
to uncover patterns and trends within large amounts of data. It
applicatesbetter understanding the students and the settings which
they learn in. It also provides and give way to institutional
effectiveness and student retention issues.
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OUR OBJECTIVE-Ultimate
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Empowered by ICT
Learning 221Learning in the SecondDecade of the 21 stCentury.
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The new age learning scenario
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EDM must explore : Rockstar Teachers ! ! !
A Rockstar teacher uses 21st century ICT tools tocreate self-directed learners acting as agents of change,
who will transform the world in severaldifferent spheres of activities. Their students are achievers,creators, thinkers and innovators, driven by passion and
commitment, to make a difference.
BE A ROCK STAR TEACHER !
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Mantra through EDM..
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THE TEACHINGPROFESSION IS IN
TRANSITION
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STEPS TOBE T T E R ST UDE NT
ENGAGEMENTSBY TE ACHE RS I N TH E CLA SSROOMSOF TODAY !
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OUR CHI LDREN MUSTKNOW.
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Create an EmotionallySafe Classroom
Students who have been shamed or belittled by the teacher or anotherstudent will not effectively engage in challenging tasks.
STEP #1
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Create an
IntellectuallySafe Classroom Begin every activity with a task that 95 percent of the class can do without your help.
Get your students used to the fact that when you say, "Please begin," they should pickup a pencil and start working successfully.
STEP #2
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Create Appropriate Intermediate Steps create intermediate steps -- such as
brainstorming,searching
online for phone numbers, crafting high-quality interview questions, and role-playing the interview -- that train all students for success.
Rote learning an evil in education system, national survey reveals
STEP #4
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Communicate with Students
Encourage and focus their writing with a
prompt.
STEP #5
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Create a Culture of
ExplanationInstead of a Culture of the Right Answer
Challenge them to operate at a higher level of thinking than when they can shareonly the "correct" answer.
STEP #6
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Teach Self-AwarenessAbout Knowledge Many students let too much of their knowledge float in a sea of confusion and
develop a habit of guessing, sometimes without even knowing that they areguessing.
STEP #7
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Use Questioning Strategies That Make All Students
Think and Answer
Every day, include some questions you require every student to answer. Find a question you knoweveryone can answer simply, and have the class respond all at once.
STEP #8
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APPLICATIONS
As a part of exploring the usage, in order to encapsulate
improvement of student success and processes which directly
relate to students learning, the Educational Data Mining
examines various course management systems which can be
mined to provide new patters of student behavior.
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TRE NDS I N EDM
A growing field for supporting learning.
Must involve teachers and parents as stake holders
Promote Educational Climate at schools
Review the data.
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PDCA I MPLE MENTATI ON
The module relates to implementation of PDCA Plan Do Check Act
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EXPLORAGE REQUI RED
Exploring is required via the students.
Understanding the nature or learning
Evaluation of Desire and Demand
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Data Mining Gets Traction in Education: Researchers Sift Data ExhaustFor Clues to Improve Learning
by Sarah D. Sparks / Education Week / 11 January 2011 The new and rapidly growing field of educational data mining is using thechaff from data collected through normal school activities to explorelearning in more detail than ever, and researchers say the day wheneducators can make use of Amazon.com-like feedback on studentlearning behaviorsmay be closer than most people think.
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EDM- A REVI EW
Educational data mining uses some of the typical data included in
state longitudinal databases, such as test scores and attendance, but
researchers often spend more time analyzingancillary data, such as
student interactions in a chat log or the length of responses to
homework assignmentsinformation that researchers call data
exhaust. by Sarah D. Sparks
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A BI G A I D TO TH E CLA SSROOM
To explore the DISTRACTIONS IN THE CLASSROOMS.
A big support to gain confidence in learning scenario among the
students.
A great change and modulation in desire by the Management.
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At present, educational datamining tends to focus on developingnew tools for
discovering patterns in data. These patterns are generally about themicroconcepts
involved in learning: one-digit multiplication, subtraction with carries, and so on.
Learninganalyticsat least as it is currently contrasted with dataminingfocuses
on applyingtools and techniques at larger scales, such as in courses and at schools
and postsecondary institutions. But both disciplines work with patterns and
prediction: If we can discern the pattern in the data and make sense of what is
happening, wecan predictwhat should comenextandtaketheappropriateaction. Source: Classroom-aid.com
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QUOTES
U.S. D epartment of E ducation, O ffice of E ducational
Technology, E nhancing Teaching and L earning T hrough
E ducational D ata M ining and L earning Analytics: An I ssue
Brief , Washington, D .C ., 2012.
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Data Use in Adaptive Learning Systems
Educational Data Mining and Learning Analytics Applications User Knowledge Modeling User Behavior Modeling User Experience Modeling User Profiling Domain Modeling Learning System Components and Instructional Principle Analysis Trend Analysis Adaptation and Personalization
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This report is available on the Departments Web site at
http:/ / www.ed.gov/ technology . And, its in public domain. Its
content covers the following topics:Personalized Learning Scenarios
Data Mining and Analytics: The Research Base Educational Data Mining Learning Analytics Visual Data Analytics
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Implementation Challenges and Considerations Technical Challenges Limitations in Institutional Capacity Privacy and Ethics Issues
Recommendations Educators Researchers and Developers
Collaborations Across Sectors
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E X P L O R I N G Q UA L I T Y L E A RN I N G F O R A L LV I A SI X SI G M A I N E D U C AT I O N
Queries and Comments:
www.sixsigmaineducation.com
Dr. Dheeraj Mehrotra(National Awardee)