learning analytics: todaytomorrow · using learning analytics to improve engagement, learning, and...
TRANSCRIPT
Learning Analytics: Today....Tomorrow
“Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding andoptimising learning and the environments in which it occurs.”-1st International Conference of Learning Analytics & Knowledge Banff, Alberta 2011
Using data and modeling to understand, predict and/or impact learning.
What is Learning Analytics?1.The collection and analysis of data regarding learning performance and activity in order to understand the learning process and help individual students succeed. Learner Engagement in Blended Learning2.The collection, analysis, and visualization of data associated with a learning process. Integrating Assessment, Feedback, and Learning Analytics in Educational Games: Literature Review and Design of an Assessment Engine3.The collection and analysis of data regarding learning performance and activity in order to understand the learning process and help individual students succeed. Current Status and Future Directions of Blended Learning Models4.Combined automatic data collection and analysis that tracks participant activity and provide statistics on how participants approach course content and activities. Massive Open Online Courses (MOOCs) and the Technologies That Support Learning with Them5.The analysis and application of historical and dynamic student data to improve learning outcomes and academic support. Competency-Based Education in Higher Education6.Measuring, collecting, analyzing and reporting of data about learners to gain insights in to understand and optimize learning. Teaching Using the Cloud: The Author's Personal Cloudy Reflections!7.Learning analytics is the measurement, collection, analysis, and reporting of data on learners and their contexts for the purposes of understanding and optimizing learning and the environments in which learning occurs. Challenges and Opportunities for Active and Hybrid Learning related to UNESCO Post 20158.Learning analytics is the collection, measurement, analysis and reporting of data about learners and their learning activities, for purposes of understanding and optimizing learning outcomes and processes. Learning Management Systems in a Changing Environment9.An embedded or external program that collects, measures, analyzes, and reports of data about learners and their contexts, for the purpose of understanding and optimizing learning. Using Video Gameplay to Measure Achievement for Students with Disabilities: A New Perspective to Grading and Achievement Reporting
https://www.igi-global.com/dictionary/learner-engagement-in-blended-learning/41044
10.Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. Mission HydroSci: Distance Learning through Game-Based 3D Virtual Learning Environments11.A procedure that use big data for measurement, analysis and reporting of information on individual learners for optimizing learning. Concept Science Evidence-Based MERLO Learning Analytics12.The analysis and application of historical and dynamic student data to improve learning outcomes and academic support. Academic
Technology for Competency-Based Education in Higher Education 13.Advanced data analysis for understanding and optimizing learning and teaching. It is an emerging field of technology-enhanced education. Learning Analytics14.Combined automatic data collection and analysis that tracks participant activity and provide statistics on how participants approach course content and activities. Developments in MOOC Technologies and Participation Since 201215.Understanding and optimizing learning and learning environments through the measurement, collection, analysis and reporting of data about learners and learner contexts. Scalable Data Warehouse Architecture: A Higher Education Case Study16.Measurement, collection, analysis and application of data about learners, including their learning and contexts, used to help to optimise learning experiences. Disruptive Democratisers?: The Complexities and Incongruities of Scale, Diversity and Personalisation in MOOCs17. The collection and analysis of data about learning and learning context. Indicators for Cooperative, Online-Based Learning and Their Role in Quality Management of Online Learning18. Is defined by The University of Adelaide as the practice of developing actionable insights through the collection, analysis and reporting of data about participants and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Using Learning Analytics to Improve Engagement, Learning, and Design of Massive Open Online Courses19. The work of scanning through large datasets to discover actionable insights in support of student learning; products arising from such work; related to the fields of data science, educational data mining, and/or business intelligence. “Solve the Big Problems”: Leading Through Strategic Innovation in Blended Teaching and Learning
https://www.igi-global.com/dictionary/learner-engagement-in-blended-learning/41044
Gartner Hype Cycle
Google Trends | Term: “Learning Analytics”, Time range: 2004-2018
Analytics is often broken down into three categories:
● Descriptive Analytics: Uses data aggregation to provide insight based on past performance.
● Predictive Analytics: Uses statistics, modeling, machine learning, and AI to make predictions
● Prescriptive Analytics: Uses optimization and simulation algorithms to define actions that will affect outcomes.
A few other dependent variables:
CostTransparencyReliabilityLock-inComplexity
Gartner Analytics Maturity Model
Descriptive:
Predictive:
Prescriptive:
● Inform students and teachers when a student’s or class performance falters
● Adjust curriculum design based on student performance relative to objectives
● Identify correlations between societal or institutional conditions & student success
The value proposition...
● Increase retention ● Improve student outcomes ● Get students to change
behavior ● Identify students at risk ● Judge effectiveness of
interventions ● Track student learning and
progress
● Monitor institutional performance
● Track student support● Track faculty performance● Track course perf’m’nce● Personalize learning● Track engagement● Improve instr. design● Reduce costs● Track resource use
1546 – 1601 Uranienborg ~1577-1597
Mistaken assumption(s)
“Data scientists all too often lose sight of the folks at the receiving end of the transaction” - Cathy O’Neill Weapons of Math Destruction
Fredrick W. Tayor“Father of Scientific Management”
Neglecting predictable human responses
Faulty models - too simple or complex
“Understanding that fans have grown weary of games that run longer and longer, the Commissioner's office has been recently instituting subtle changes to speed up gameplay...But the ever-increasing use of data and analytics, with its impact on pitching and other aspects of the game, is causing the trend to go in the other direction.”
1950s -1970s: 2.5 hrs → 2017: 3 hrs. 8 min.
The i4cp Productivity Bloghttps://www.i4cp.com/productivity-blog/avoiding-unintended-consequences-when-leveraging-people-data-and-analytics
Unintended consequences
Comparing the retention rates of the 2007 and 2009 cohorts, Caulfield suggested much of what Purdue described as data analysis just measured how many courses students took. As Signals in 2008 left its pilot and more students across campus enrolled in at least one such course, Caulfield found the retention effect “disappeared completely.”
Skewed or incomplete data
“Metric fixation leads to a diversion of resources away from frontline producers toward managers, administrators, and those who gather and manipulate data.” - Jerry Z. Muller, The Tyranny of Metrics
Systemic or institutional bias
I. Newton: Philosophiæ Naturalis Principia Mathematica, 1687
Tycho @ Uranienborg ~1577-1597
Perfect accuracy in Learning analytics is unlikely to be possible in a wide variety of authentic learning scenarios, nor is it always desirable — embracing imperfection opens up new possibilities for teachable moments. Imperfection is sometimes a feature, not a bug!-Embracing Imperfection in Learning Analytics, Kitto, Shum, and Gibson
“Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom.” - Cliff Stoll
“The world cannot be understood without numbers. But the world cannot be understood with numbers alone.” - Hans Rosling
“I know of no time in human history where ignorance was better than knowledge.”- Neil DeGrasse Tyson
“Intuition becomes increasingly valuable in the new information society precisely because there is so much data.” - John Naisbitt
“Knowledge is power. Information is liberating. Education is the premise of progress, in every society, in every family.” - Kofi Annan
“We've got to use every piece of data and piece of information, and hopefully that will help us be accurate with our player evaluation.” - Billy Bean
Calvin: IT’S TRUE HOBBES. IGNORANCE IS BLISS. ONCE YOU KNOW THINGS YOU START SEEING PROBLEMS EVERYWHERE. BUT IF YOU DON’T KNOW ANY BETTER YOU CAN KEEP DOING WHATEVER YOU LIKE.
Hobbes: WE’RE HEADING FOR THAT CLIFF!Calvin: I DON’T WANT TO KNOW ABOUT IT.Both: WAAAUGGHHH!Hobbes: I’M NOT SURE I CAN STAND SO MUCH
BLISS.Calvin: CAREFUL! WE DON’T WANT TO LEARN
ANYTHING FROM THIS.