educational data mining: a know how !

7/28/2019 Educational Data Mining: A know how !

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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

[email protected]


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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:

[email protected]

www.sixsigmaineducation.com

Dr. Dheeraj Mehrotra(National Awardee)

educational data mining: a know how !

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