CDE InFocus Conference (London): Big data in education - theory and practiceMike Moore
Big Data in Education: Theory and Practice
Presented at the CDE InFocus Conference - London
December 10, 2013
Presented by Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System
Presentation from 'InFocus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by George Mitchell (Chief Operations Officer, CCKF Ltd, Dublin).
Audio of the session and more details can be found at www.cde.london.ac.uk.
Analytics: as if learning mattered
Presentation from 'In Focus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by Adam Cooper (Co-Director, Cetis)
Audio of the session and more details can be found at www.cde.london.ac.uk.
Online Educa Berlin conference: Big Data in Education - theory and practiceMike Moore
Online Educa Berlin Conference Presentation
Big Data in Education - Theory and Practice
Presented December 6, 2013 by
Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
CDE InFocus Conference (London): Big data in education - theory and practiceMike Moore
Big Data in Education: Theory and Practice
Presented at the CDE InFocus Conference - London
December 10, 2013
Presented by Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System
Presentation from 'InFocus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by George Mitchell (Chief Operations Officer, CCKF Ltd, Dublin).
Audio of the session and more details can be found at www.cde.london.ac.uk.
Analytics: as if learning mattered
Presentation from 'In Focus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by Adam Cooper (Co-Director, Cetis)
Audio of the session and more details can be found at www.cde.london.ac.uk.
Online Educa Berlin conference: Big Data in Education - theory and practiceMike Moore
Online Educa Berlin Conference Presentation
Big Data in Education - Theory and Practice
Presented December 6, 2013 by
Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
This presentation was provided by Corey Harper of Elsevier Labs during the NISO webinar, Using Analytics to Extract Value from the Library's Data, held on September 12, 2018.
Creating Wraparound Supports for Students through Internal PartnershipsJeremy Anderson
Presentation delivered to the Quality Matters East Regional Conference in 2020. Covered is a basic framework for developing analytics projects by combining stakeholders, IR, and IT.
A Pulse of Predictive Analytics In Higher Education │ Civitas LearningCivitas Learning
Civitas Learning presents the findings of our survey conducted during the September 2014 Civitas Learning Summit, where more than 100 leaders representing 40 Pioneer Partner institutions gathered to share more on their work. The survey, distributed to all participants, resulted in 74 responses highlighting how this cross-section of higher education institutions are using advanced analytics to power student success initiatives.
Ellen Wagner, Executive Director, WCET.
Putting Data to Work
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presentation at the 15th annual SLN SOLsummit February 27, 2014
http://slnsolsummit2014.edublogs.org/
How any institution can get started on learning analyticsJeremy Anderson
Two case studies from Bay Path University in developing predictive retention analytics at the course level and across the four-year college experience. Walks through the CRISP-DM framework and how it guided each project. Also shares resources for carrying out similar projects in Excel. Presented at NERCOMP 2021
ACM ICTIR 2019 Slides - Santa Clara, USAIadh Ounis
Talk entitled "Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks" presented at the ACM ICTIR 2019, Santa Clara. 2019.
Reference:
Jadidinejad, A. , Macdonald, C. and Ounis, I. (2019) Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks. In: 5th ACM SIGIR International Conference on the Theory of Information Retrieval, Santa Clara, CA, USA, 02-05 Oct 2019
URL: https://dl.acm.org/citation.cfm?id=3344225
2019 Midwest Scholarship of Teaching & Learning (SOTL) conference presentation. The goal of this presentation is to share our data-informed approach to re-engineer the exam design, delivery, grading, and item analysis process in order to construct better exams that maximize all students potential to flourish. Can we make the use of exam analytics so easy and time efficient that faculty clearly see the benefit? For more info see our blog at https://kaneb.nd.edu/real/
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at TribalChris Ballard
Speaking engagement at LACE SoLAR Flare hosted by the Open University. Turning Learning Analytics Research into Practice at Tribal. A video of my talk can be found at http://stadium.open.ac.uk/stadia/preview.php?whichevent=2606&s=1&schedule=3411&option=&record=0#
This presentation was provided by Corey Harper of Elsevier Labs during the NISO webinar, Using Analytics to Extract Value from the Library's Data, held on September 12, 2018.
Creating Wraparound Supports for Students through Internal PartnershipsJeremy Anderson
Presentation delivered to the Quality Matters East Regional Conference in 2020. Covered is a basic framework for developing analytics projects by combining stakeholders, IR, and IT.
A Pulse of Predictive Analytics In Higher Education │ Civitas LearningCivitas Learning
Civitas Learning presents the findings of our survey conducted during the September 2014 Civitas Learning Summit, where more than 100 leaders representing 40 Pioneer Partner institutions gathered to share more on their work. The survey, distributed to all participants, resulted in 74 responses highlighting how this cross-section of higher education institutions are using advanced analytics to power student success initiatives.
Ellen Wagner, Executive Director, WCET.
Putting Data to Work
This session explores changing data sensibilities at US post-secondary institutions with particular attention paid to how predictive analytics are changing expectations for institutional accountability and student success. Results from the Predictive Analytics Reporting Framework show that predictive modeling can identify students at risk and that linking behavioral predictions of risk with interventions to mitigate those risks at the point of need is a powerful strategy for increasing rates of student retention, academic progress and completion.
presentation at the 15th annual SLN SOLsummit February 27, 2014
http://slnsolsummit2014.edublogs.org/
How any institution can get started on learning analyticsJeremy Anderson
Two case studies from Bay Path University in developing predictive retention analytics at the course level and across the four-year college experience. Walks through the CRISP-DM framework and how it guided each project. Also shares resources for carrying out similar projects in Excel. Presented at NERCOMP 2021
ACM ICTIR 2019 Slides - Santa Clara, USAIadh Ounis
Talk entitled "Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks" presented at the ACM ICTIR 2019, Santa Clara. 2019.
Reference:
Jadidinejad, A. , Macdonald, C. and Ounis, I. (2019) Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks. In: 5th ACM SIGIR International Conference on the Theory of Information Retrieval, Santa Clara, CA, USA, 02-05 Oct 2019
URL: https://dl.acm.org/citation.cfm?id=3344225
2019 Midwest Scholarship of Teaching & Learning (SOTL) conference presentation. The goal of this presentation is to share our data-informed approach to re-engineer the exam design, delivery, grading, and item analysis process in order to construct better exams that maximize all students potential to flourish. Can we make the use of exam analytics so easy and time efficient that faculty clearly see the benefit? For more info see our blog at https://kaneb.nd.edu/real/
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at TribalChris Ballard
Speaking engagement at LACE SoLAR Flare hosted by the Open University. Turning Learning Analytics Research into Practice at Tribal. A video of my talk can be found at http://stadium.open.ac.uk/stadia/preview.php?whichevent=2606&s=1&schedule=3411&option=&record=0#
Research in to Practice: Building and implementing learning analytics at TribalLACE Project
Keynote by Chris Ballard, Data Scientist, Tribal, given at the LACE SoLAR Flare event held at The Open University, Milton Keynes, UK on 9 October 2015. #LACEflare
Educators Pave the Way for Next Generation of LearnersCognizant
As educational assessments shift to outcome-based learning, providers must adopt new forms of test delivery to increase their global reach and provide ubiquitous services to a new student population.
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Leveraging demographic data along with campus organizational structures in the student information system alongside student and faculty activities in Blackboard presents opportunities never before possible.
An assessment workshop on the six critical areas that need to be addressed in developing online assessment at scale. Led by the Centre for Online and Distance Education with a delegation of VCs and senior leaders from Nigerian Universities, and senior representatives from the National Universities Commission of Nigeria. Held on 24th March 2023.
A Deep Dive into Pune's Premier Data Science Training Institutes.docxshivanikaale214
In Pune, the convergence of technological innovation and academic excellence has fostered a vibrant ecosystem for data science education. As the demand for skilled professionals in the field continues to surge, a multitude of data science training institutes have emerged to cater to this burgeoning need.
In this comprehensive analysis, we explore the distinguishing features, program offerings, faculty expertise, industry collaborations, and placement opportunities of Pune's premier data science training institutes. By meticulously examining each institute, we aim to provide aspiring data scientists with invaluable insights to navigate their educational journey and chart a path towards success in the dynamic field of data science.
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Through this deep dive into Pune's data science training institutes and fundamental skills, we empower individuals to make informed decisions and embark on a transformative learning journey tailored to their aspirations and career goals. As Pune continues to emerge as a hub of technological innovation, these institutes serve as catalysts for driving progress, innovation, and excellence in the field of data science.
[Merit trac webinar] - it is assessments that cause improvementMeritTracSvc
The concept of having an outcomes-based approach and having a strong theory of alignment all the way down to individual learning activities helps facilitate the use of assessment data.
Presentation by Russ Little. Provides an overview of Integrated Planning and Advising Systems (IPAS). Demonstrates how the Student Success Plan software and My Academic Plan (MAP) function, and evidence of their effectiveness.
The design of data systems within education can be challenging due to a lack of easily accessible information and a large variety of stakeholders with differing needs. Architecting Academic Intelligence is the process of centralizing and making accessible the student administrative information to the every member of the administration, faculty and staff of the City Colleges of Chicago so as to more efficiently promote student success.
The Role of Non-Cognitive Indicators in Predictive and Proactive Analytics: T...SmarterServices Owen
We have all heard of IQ—but what about the importance of SQ and EQ? Join SmarterServices and Nuro Retention to learn more about how your students’ social and emotional non-cognitive data directly impacts student success and educational outcomes. Nuro Retention will share how to make BIG data actionable by combining the power of SmarterMeasure Learning Readiness Indicator's non-cognitive data along with its retention software platform and predictive analytics models.
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Don’t miss out on your chance to learn the latest strategies on the power of predictive, proactive, and prescriptive data!
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2. What is Analytics?
• Analytics is the use of data, statistical and
quantitative methods, and explanatory and
predictive models to allow organizations and
individuals to gain insights into and act on
complex issues.
• In colleges and universities, analytics is used to
improve operational efficiency and student
success.
Source: Educause, Oblinger: Let’s Talk Analytics
http://www.educause.edu/ero/article/lets-talk-analytics
3. What is Analytics?
• The term big data is often used interchangeably
with analytics, but the scientific community
uses big data to describe research that uses
massive amounts of data.
• The use of analytics to improve administrative
functions is often called business
intelligence; similarly, academic analytics is
used to help run the business of the higher
education institution.
Source: Educause, Oblinger: Let’s Talk Analytics
http://www.educause.edu/ero/article/lets-talk-analytics
4. What is Analytics?
• Finally, learning analytics focuses specifically on
students and their learning behaviors, gathering
data from course management and student
information systems in order to improve student
success.
• Although the labels can be confusing, overall the
term analytics refers to an approach that can be
used to explore a broad range of questions.
Source: Educause, Oblinger: Let’s Talk Analytics
http://www.educause.edu/ero/article/lets-talk-analytics
7. Analytics: Big Data (R2)
Multiple Levels of
Reporting
with Drill-Down
Filters
Extensive
Data Domains
Aggregates and
Trends Over Time
8. Big Data: Data Sets
• Enrollments. The enrollment data mart tracks user enrollments and withdrawals
across one or more organizations.
• Competencies. The competencies data mart tracks competencies, learning objectives,
activities and rubrics by user, department, program, institution, and system.
• User Logins. The user access data mart tracks the number of user logins/distinct
sessions over a period of time. It is a very simple way of tracking student patterns of
accessing the system.
• Content and Tool Access. The module data mart tracks content access & tool usage.
• Web Analytics. The web analytics data marts track internet statistics such as
bandwidth usage, geographical location, and browser types.
• Test and Quizzes. The quizzing data mart tracks quiz, test, and survey results,
including measuring of quiz effectiveness.
• Grades. The grades data mart tracks grades at student, course, department or school
level, including filtering by grade ranges or date ranges.
9. Tech Data
• IIS Web Analytics
• Client Access
(OS/Browser)
• SMTP
• Global/Local
Traffic Manager
Logs
26. Application Workflow
Understand the Problem
Interrogate Raw Data
Reach a Diagnosis
Intervene, Make a Referral
Track the Success
27. Limitations of Current Approach
• Interpretation
• Not enough information for intervention
• Interactivity
• Unable to interrogate and make sense of the particulars
• Generalizability
• Same model is used for every course at every
institution
30. Student Success System (S3)
SSS is an Early Intervention System. It empower institutions with predictive
analytics tools for improving student success, retention, completion, and
graduation rates.
Highlights
– Course-specific predictions of student success and risk levels
– Success index that enables comparison of key success indicators
– Innovative data visualizations
– Case history and intervention management
Availability
General Availability in 2013. (Pilot project starting Oct. 2012)
32. Powerful Reporting and Analysis
Personalized Detailed analysis lets you drill
assessment down to individual classes
Intervention }
}
management
In-depth
Success
indicators
} reporting
Innovative data visualizations
33. Challenges and Remedies
Challenges for Institutions Student Success System Remedy
Inability to predict, and consequently improve student Predictive modeling identifies at-risk students based
success, retention, graduation, and completion rates on engagement, performance, and profile data
Limited resources to create personalized intervention Visualizations and statistical indicators provide
plans diagnostic insights to help design individualized
interventions
Lack of data correlating engagement with success Analyze student engagement patterns and effects on
academic success
Inability to identify isolated students Visualize social network patterns based on discussion
data, to improve social learning
34. Value to Institutions and Students
• Predictive analytics provides early identification of at-risk
students enabling instructors to identify and understand
where issues are and create appropriate resolution plans
to address the problem
• Graduation and retention rates are increased when at-risk
students are identified early on the process and supported
throughout the term with informed counter-tactics
35. Summary – Student Success System at a Glance
Institution Challenges Description of SSS
• Improving Student Success • Early Intervention System driven by
• Identifying academically at-risk, dis- advanced predictive analysis and data
engaged or isolated students visualization to identify at-risk students
• Increasing retention, completion, and and intervene to improve their retention,
graduation Rates completion, graduation and success rates.
Student Success System Value Ideal Customer Profile
• Easily identify at-risk students, and
• Institutions looking to empower
understand where the issues lie
• Design and implement individualized instructors with predictive analytics
intervention programs to improve student success.
• Improve institutional effectiveness
• Increase student success
Market demand for predictive analytics is growing very rapidly, especially in higher education Trends indicated in EduCause reportsD2L Quarterly Market Update Q1/2012Predictive models have been developed at Capella UniversityRio Salado College University of Phoenix