mining educational databases: a focus on stem+c majors for ... · (afr ican-americans, hispanics,...
TRANSCRIPT
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Mining Educational Databases:A Focus on STEM+C Majorsfor Inclusive Development
ByAnu A. Gokhale
Professor and Coordinator, Computer Systems TechnologyIllinois State University
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What is STEM+C?
STEM+C is an acronym coined by theNational Science Foundation (NSF) in
the U.S. and it stands for:
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STEM+C is an acronym coined by theNational Science Foundation (NSF) in
the U.S. and it stands for:
Science, Technology, Engineering,Mathematics, plus Computing!
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Host Country: Interesting Facts
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• The objective is to provide students the cognitivestrategies that enable them to think critically,make decisions, and solve problems.
• Business and political leaders are increasinglyasking schools to develop skills such as problemsolving, critical thinking, communication,collaboration, and self-management — oftenreferred to as "21st century skills."Reference: Pellegrino and Hilton (2013)
Why Focus on Education?• The objective is to provide students the cognitive
strategies that enable them to think critically,make decisions, and solve problems.
• Business and political leaders are increasinglyasking schools to develop skills such as problemsolving, critical thinking, communication,collaboration, and self-management — oftenreferred to as "21st century skills."Reference: Pellegrino and Hilton (2013)
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Why Should We Care About STEM+C?
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STEM+C Degrees: Historical Perspectives
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1980 1985 1990 1995 2000 2005 2010 2015
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Workforce byGender
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How Do We Recruit and Retain Studentsin STEM+C Majors and Careers?
• Federal agencies and colleges have “data inventories”that could be used for multiple purposes:– Adaptive Learning Systems
• Extensive information about individuals or groups of learners canbe analyzed to adapt a learning resource to the learner
– Assessments Embedded in Learning Systems• Non-cognitive affective-domain features, such as interest,
persistence, that are recognized as important but that could not bemeasured reliably and efficiently in the past
– Linking various types of student data (like familydemographics) to academic outcomes
• Federal agencies and colleges have “data inventories”that could be used for multiple purposes:– Adaptive Learning Systems
• Extensive information about individuals or groups of learners canbe analyzed to adapt a learning resource to the learner
– Assessments Embedded in Learning Systems• Non-cognitive affective-domain features, such as interest,
persistence, that are recognized as important but that could not bemeasured reliably and efficiently in the past
– Linking various types of student data (like familydemographics) to academic outcomes
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How Much Data is Created Today?
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Humor about Personal Computing
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What Is Data Mining or KDD?(Knowledge Discovery from Data)
• Data mining, also referred to as KDD, is:– The process of finding anomalies, implicit
patterns, and correlations within large datasets topredict outcomes, or in other words,
– The search for relationships and global patternsthat exist in large databases but are 'hidden'among the vast amounts of data
• Data mining, also referred to as KDD, is:– The process of finding anomalies, implicit
patterns, and correlations within large datasets topredict outcomes, or in other words,
– The search for relationships and global patternsthat exist in large databases but are 'hidden'among the vast amounts of data
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Elements of Analyzing Datasets forKnowledge Discovery
Datasets AnalyticTools
Expanding DomainUnderstanding
Knowledge Discovery
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Project Goal
• This NSF-funded project is designed to:
– Use learning communities to recruit and retain a greaternumber of students in a computing-related major
• Special emphasis on minorities(African-Americans, Hispanics, and Native Americans)
– Conduct exploratory research, employing KDD techniques• The objective is to extract, analyze, and understand information
about effectiveness of learning communities from large semi-structured data
• This NSF-funded project is designed to:
– Use learning communities to recruit and retain a greaternumber of students in a computing-related major
• Special emphasis on minorities(African-Americans, Hispanics, and Native Americans)
– Conduct exploratory research, employing KDD techniques• The objective is to extract, analyze, and understand information
about effectiveness of learning communities from large semi-structured data
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• Online Learning Communities
• Face-to-face Seminars with Professionals
• Faculty Learning Communities
Methodology:Three-Prong Approach
• Online Learning Communities
• Face-to-face Seminars with Professionals
• Faculty Learning Communities
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Review of Literature:Why Online Learning Communities?
• Principles Underlying Online Learning Communities(Ref: Gartner Research; C. Rozwell & D. Morello, Oct. 2011)
– Are Self-Selecting– Are Inclusive– Are Efficient; News Travels at Lightning Speed– Make an Effective Recruiting and Retention Strategy
• Right Approach to Content:(Ref: Gartner Research; J. Lundy, Sept. 2013)
– Engaging, relevant content for all stakeholders
• Principles Underlying Online Learning Communities(Ref: Gartner Research; C. Rozwell & D. Morello, Oct. 2011)
– Are Self-Selecting– Are Inclusive– Are Efficient; News Travels at Lightning Speed– Make an Effective Recruiting and Retention Strategy
• Right Approach to Content:(Ref: Gartner Research; J. Lundy, Sept. 2013)
– Engaging, relevant content for all stakeholders
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Who are our Students?
• Most students in colleges or high schools are bornbetween 1980 and 2000
• Members of this age group are often referred toas the Millennials, Net Generation, orGeneration Y
• Online is the preferred,or rather the only modeof communication
• Most students in colleges or high schools are bornbetween 1980 and 2000
• Members of this age group are often referred toas the Millennials, Net Generation, orGeneration Y
• Online is the preferred,or rather the only modeof communication
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Some Humor…
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Benefits of Online Learning Communities:In a Nutshell
• Fits the student culture
• Combines solitary reflection and social interaction
• Can be a powerful promoter of creative andintuitive thinking
• Fits the student culture
• Combines solitary reflection and social interaction
• Can be a powerful promoter of creative andintuitive thinking
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Online Learning Communities
• Students (seniors) were selected for blogging
• Four-member blogging/tutor team each semester– Blogs posted twice/week during the semester
• Sunday and Wednesday night– Quiz based on the blog earned students extra credit
(up to 2 percentage points)– Student participants discussed the blog contents and
related peripheral issues
• Students (seniors) were selected for blogging
• Four-member blogging/tutor team each semester– Blogs posted twice/week during the semester
• Sunday and Wednesday night– Quiz based on the blog earned students extra credit
(up to 2 percentage points)– Student participants discussed the blog contents and
related peripheral issues
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Additional Online Activities• Student Organizations’ Chat Rooms
– IEEE (Institute of Electrical and Electronics Engineers)– ACS (Assoc. of Computing Machinery)– AITP (Assoc. for Information Technology Professionals)
• Classes-related Online Tutoring
• Networking and Support Structure– Mentor Network
• Student Organizations’ Chat Rooms– IEEE (Institute of Electrical and Electronics Engineers)– ACS (Assoc. of Computing Machinery)– AITP (Assoc. for Information Technology Professionals)
• Classes-related Online Tutoring
• Networking and Support Structure– Mentor Network
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Face-to-face Seminars with Professionals
• Professionals in STEM+C fields are invited to visitwith students participating in this project– Fieldtrips are also arranged to their workplaces
• These after-class informal seminars happen once aweek during the semester
• Why?– There is no reason to think that students are aware of the
work environment and job responsibilities of variousSTEM+C professionals (FBI Cyber Security, HealthInformation Systems, Systems Analyst)
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• Professionals in STEM+C fields are invited to visitwith students participating in this project– Fieldtrips are also arranged to their workplaces
• These after-class informal seminars happen once aweek during the semester
• Why?– There is no reason to think that students are aware of the
work environment and job responsibilities of variousSTEM+C professionals (FBI Cyber Security, HealthInformation Systems, Systems Analyst)
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Benefits of Seminars: In a Nutshell
• Goal is to give students a better sense of therange of opportunities in STEM+C careers, andhumanize the field
• Professionals were trained by project directorsto speak at these meetings, they were asked to:– Tell their life story– Talk about day-to-day work and the environment– Use multimedia to engage students
• Goal is to give students a better sense of therange of opportunities in STEM+C careers, andhumanize the field
• Professionals were trained by project directorsto speak at these meetings, they were asked to:– Tell their life story– Talk about day-to-day work and the environment– Use multimedia to engage students
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Faculty Learning Communities
• Teaching style adjustments– Focusing on concepts that
may not always be familiarto all student groups
• Sensitive to Diversity among Students
• Teaching style adjustments– Focusing on concepts that
may not always be familiarto all student groups
• Sensitive to Diversity among Students
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Project Activities Over Five YearsGenerate BIG Data
• Data is of various types– Numeric data
• Number of students who registered in a computing-related course, number of students who declared acomputing-related major
– Non-numeric data• Discussion in ‘words’
• Data is unstructured
• Data is of various types– Numeric data
• Number of students who registered in a computing-related course, number of students who declared acomputing-related major
– Non-numeric data• Discussion in ‘words’
• Data is unstructured
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Big Data Humor…
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Benefits & Challenges of Data Generationin Online Learning Systems
• Benefits:– One advantage of online learning systems is that they
can collect very large amounts of data (big data) frommany users quickly
– As a result, they permit the use of multivariateanalytic approaches (analyses of more than onestatistical variable at a time) early in the life cycle ofan innovation
• Challenges:– BIG Data
• Benefits:– One advantage of online learning systems is that they
can collect very large amounts of data (big data) frommany users quickly
– As a result, they permit the use of multivariateanalytic approaches (analyses of more than onestatistical variable at a time) early in the life cycle ofan innovation
• Challenges:– BIG Data
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Data Analysis• KDD framework acknowledges that decisions
require different levels of confidence and entaildifferent levels of risk
• Objective is evidence-centered design
• Analytics can uncover patterns of learnerbehavior that can be used to guide improvement
• A key challenge for the uses of evidence is toidentify the relationship between simple userbehaviors and complex behavior outcomes
• KDD framework acknowledges that decisionsrequire different levels of confidence and entaildifferent levels of risk
• Objective is evidence-centered design
• Analytics can uncover patterns of learnerbehavior that can be used to guide improvement
• A key challenge for the uses of evidence is toidentify the relationship between simple userbehaviors and complex behavior outcomes
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Preparing Data for KDD Analysis• The process involves:
– Transferring data into a data warehouse– Cleaning the data to remove errors and check for
consistency of formats– Searching the data using statistical procedures, database
queries, visualization, or machine learning methods
• The process involves:– Transferring data into a data warehouse– Cleaning the data to remove errors and check for
consistency of formats– Searching the data using statistical procedures, database
queries, visualization, or machine learning methods
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KDD Analytic Tools• The KDD architecture supports a variety of methods.
Here are some examples:• Statistical Analysis
– Used for both summarizing large datasets (i.e., average,min/max, etc.) and in defining models for prediction
– Most statistical tools prefer to compute over numericaland categorical data organized in columns
• Modeling and Visual Analytics– Generates interactive visualizations across many datasets
with the hope that users will be able to discoverinteresting relationships
• The KDD architecture supports a variety of methods.Here are some examples:
• Statistical Analysis– Used for both summarizing large datasets (i.e., average,
min/max, etc.) and in defining models for prediction– Most statistical tools prefer to compute over numerical
and categorical data organized in columns• Modeling and Visual Analytics
– Generates interactive visualizations across many datasetswith the hope that users will be able to discoverinteresting relationships
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SomeHumoraboutData
Mining…
SomeHumoraboutData
Mining…
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Results
• The Eureqa machine learning package wasused to uncover trends in data– Eureqa is a software tool for automated Machine
MAPPing™(Modeling, Analyzing, Predicting, Prescribing)
• There is strong agreement between statisticaltool (factor analysis) and machine learningtool (Eureqa)
• The Eureqa machine learning package wasused to uncover trends in data– Eureqa is a software tool for automated Machine
MAPPing™(Modeling, Analyzing, Predicting, Prescribing)
• There is strong agreement between statisticaltool (factor analysis) and machine learningtool (Eureqa)
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Scoring the Attitude toward IT Scale• Factor 1 (Interest in Learning about IT)
– Items: 4, 10, 23, 24, 25, 26, 27, 28• Factor 2 (IT and the Quality of Life)
– Items: 6, 9, 15, 16• Factor 3 (Attitudes Toward Women in IT)
– Items: 2, 14, 19, 20, 30• Factor 4 (Opportunities for Women in IT)
– Items: 1, 7, 12, 13, 21
• Items to be scored negatively:– Items: 2, 6, 9, 15, 16, and 20
Reference: Gokhale, A. and Machina, K. “Scale to Measure Attitudes Toward Information Technology.”International Journal of Information and Communication Technology Education, Volume 9 Issue 3,July 2013, Pages 13-26.
• Factor 1 (Interest in Learning about IT)– Items: 4, 10, 23, 24, 25, 26, 27, 28
• Factor 2 (IT and the Quality of Life)– Items: 6, 9, 15, 16
• Factor 3 (Attitudes Toward Women in IT)– Items: 2, 14, 19, 20, 30
• Factor 4 (Opportunities for Women in IT)– Items: 1, 7, 12, 13, 21
• Items to be scored negatively:– Items: 2, 6, 9, 15, 16, and 20
Reference: Gokhale, A. and Machina, K. “Scale to Measure Attitudes Toward Information Technology.”International Journal of Information and Communication Technology Education, Volume 9 Issue 3,July 2013, Pages 13-26.
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Reliability Coefficients for Factors
• N = 455• Factor 1: Alpha = 0.8543• Factor 2: Alpha = 0.6602• Factor 3: Alpha = 0.7404• Factor 4: Alpha = 0.7133
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• N = 455• Factor 1: Alpha = 0.8543• Factor 2: Alpha = 0.6602• Factor 3: Alpha = 0.7404• Factor 4: Alpha = 0.7133
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Pre-Post ANOVA for Each Factor• The significant difference occurred for
– Factor 1: Interest in Learning about STEM– Factor 3: Attitudes Toward Women in STEM– Factor 4: Opportunities for Women in STEM
FactorFactor MeanMeanSquareSquare
FF SignificanceSignificance
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FactorFactor MeanMeanSquareSquare
FF SignificanceSignificance
11 1.961.96 3.8343.834 0.0510.05122 0.1580.158 0.4170.417 0.5190.51933 7.9477.947 14.29714.297 0.0000.00044 1.921.92 0.3190.319 0.0530.053
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Conclusions
• Compared to the Control group, Experimental group studentsshowed:– More positive attitudes toward IT– More positive attitudes toward women & minorities working in IT
• Online activities are an effective medium to communicatewith students, especially the Net-generation
• At the same time, some in-person contact is also important– Students liked the seminars where they would ask questions to the
professionals who came to the classroom
• Compared to the Control group, Experimental group studentsshowed:– More positive attitudes toward IT– More positive attitudes toward women & minorities working in IT
• Online activities are an effective medium to communicatewith students, especially the Net-generation
• At the same time, some in-person contact is also important– Students liked the seminars where they would ask questions to the
professionals who came to the classroom
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Humor About Offering _____ as a Service
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Further Research…
• The online learning resources continue to collectlarge amounts of fine-grained data as usersinteract with them, in real time and over time
• It is critical that we employ sophisticated datamining techniques to decipher information, andverify what’s learned
• The online learning resources continue to collectlarge amounts of fine-grained data as usersinteract with them, in real time and over time
• It is critical that we employ sophisticated datamining techniques to decipher information, andverify what’s learned
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