fact2 learning analytics task group (latg) scoa briefing
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Enhancing Excellence in Assessment: Institutional Effectiveness and Learning
Analytics
SUNY Council on Assessment Learning Analytics Task Group
October 15, 2013
FACT2 and task groups
FACT2:
• “ a well-established venue to foster collaboration and consensus within a highly diverse university community.
• “FACT2governance includes representatives from faculty, librarians, and IT across individual campuses from all Carnegie sectors.”
http://fact.suny.edu
FACT2 and task groups
Task groups• Initiatives developed collaboratively with the SUNY
Provost and the FACT Advisory Council• 2010-2012 projects:– Learning analytics
– E-publishing
– Innovative learning space inventory
• 2013 projects:– Learning analytics
– Online Accessibility
– Experiential Education
2013 Task Group will focus on…
Identifying and sharing best practices and uses of Learning Analytics for
assessment student readiness course placement
& remedial education
Institutional Level Use
Advising Placement Learning outcomes
Course outcomes
Instructional effectiveness
Student Feedback
Intervention
Support Persistence Degree completion
LATG Survey:course success data is used to identify disciplines that have lower than expected success rates; Data are also correlated with student characteristics and other factors like date of registration, data of application.
Institutional Levela) identify students at risk of leaving the college
without a degree from the college [54%]b) identify students who are in need of developmental
courses [46%]c) place students in appropriate credit courses [54%]
d) evaluate student progress on course objectives [38%]
e) predict student performance in courses [15%]f) advise students on course selection [54%]
FACT2 Learning Analytics Task Group, CIT May 2013
Interventions & Feedback
FACT2 Learning Analytics Task Group,
Learning Analytics - Working Definition
Learning analytics can be used to… –diagnose student needs, –provide feedback to the student, faculty,
instructional developer, and advisor, – combine with data from other learning
systems to generate new insights about learning and instruction.
Learning Analytics - Working Definition
• Learning analytics uses software that collects and analyzes multiple data sets related to the process of learning to PREDICT and IMPACT student success.
• This includes data collected in– blended and online learning environments, – online portals, – enrollment data, – and other emergent resources connected to the
teaching and learning experience.
New approachmining the
“pile of big data” generated by technology
longstanding approach “asking research
questions” and gathering data
Where is the data?
DiscreteAnalytics
Tools
LMS Platform Analytics
Stand-alonePlatform Analytics
FACT2 Learning Analytics Task Group
Collected from explicit student actions-completing assignments and exams,
From tacit actions-online social interactions,extracurricular activities, posts on discussion forums, and other activities that are not directly assessed as part of the student’s educational progress.
Data in online activities….
Learning Analytics Tools
SPSS
Approaches• Institutional• Faculty and
advising• Student
assessment and feedback
Traditional analysis tools
Learning Analytics & Online Learning
Examples of uses…• Persistence and
retention (APUS)• Intelligent/adaptive
tutoring (Carnegie Mellon)
• Research on conditions that facilitate learning (CSU Chico)
FACT2 Learning Analytics Task Group
Use Learning analytics to ….
Provide automated feedback to studentsCustomize course delivery to student learning styles.
Quizzes or Learning Sequences in Blackboard or Angel
Use Learning analytics to ….
Provide individualized learning paths to students based on pre-entry conditions.
Provide adaptive learning paths to students based on performance in course.
Learning analytics enables tailoring of responses, such as through adapting
instructional content, intervening with at-risk students, and providing feedback.
Use Learning analytics to ….
Revise course content, activities, assessments and/or course structure.
In adaptive learning, the path of each student is highly personalized
ASSESSMENT AND LEARNING ANALYTICS
Typically use End of semester or mid-term data…Focus on course outcomes, but no real-time data for interventions
PLACEMENT & COURSE SELECTIONLearning analytics in
Predictive Analytics: Building Models
Placement for success and completion..– which students should be steered
toward which courses? Which programs?
Can advising process leverage data on student performance? – If so, what are the best predictors
of performance?
Predictive Analytics: Building Models
• Can we identify characteristics of a successful outcome?
• an unsuccessful outcome?
DATA SOURCES
Grade in course
Can it be predicted by other data?• Major• High school GPA• English placement exam
score• Math placement exam
score• HS Regent scores….• SAT Verbal, SAT math• SAT Writing“every student with a HS
average of 83 or less, did not successfully complete the
course…”
PILOTING APPROACHES
FACT2 Learning Analytics Task Group,
Assessment: study habits
Explore the efficacy of student study habits
Assessment: study habits
Share information about what practices lead to success with students
Automated feedback with quizzes.
What can we learn from the data?
04/11/2023 Teaching, Learning, and Analytics at Michigan
How does E2Coach work?
MTS
Detailed information
about thousands of students and their current status
Expertise of hundreds of students, dozens of instructors and behavior change experts Individually
personalized messages:
what we all agree we
would say to each
student, if only we could…
The Michigan Tailoring System: a mature open-source software system for
creating content designed specifically for an individual based on data about that
individual
Where the real effort lies
Learning Analytics Opportunities
Leverage…• institutional practices and tools in place• interest in using tools for interventions and
student feedback– More systemic and consistent approach to placing
students in appropriate courses and developmental courses.
– Especially for online learning
Explore further…FACT2 Learning Analytics Task Group,
Retention Initiatives
LATG 2013-14 GOALS1. Identify and share known best practices and exemplary uses of Learning Analytics
for assessment, and early intervention strategies. • Identify the common questions for these areas, and share best practices through web resources and
other communication channels.
2. Develop and conduct professional development activities for use of learning analytics for:
• Assessment, student feedback, and early intervention activities in a course.• Leveraging existing campus data sources to inform strategies for student readiness, course placement,
and remedial education; and to identify what data is readily available and policy guidance in the use of data.
• Identify ways where learning analytics may help to eliminate some administrative burdens while improving academic achievement.
3. Provide opportunities for SUNY faculty to explore Learning Analytics in pilot projects.
• Develop “Proof of concept” projects through IITG using learning analytics approaches/tools. • Develop a Pilot program that would recruit a small group of faculty and courses to implement
assessment strategies based on analytics, and evaluate. (though Open SUNY initiative?)
4. Use the finding from best practices research and pilot projects to identify a course of action for further expansion of Learning Analytics across SUNY.
Adaptive Systems to Pilot or Explore
SUNY Pilot of Learning Analytics Tools
SPSS
QUESTIONS?Visit SUNY Learning CommonsLearning Analytics Task Group
greg.ketcham@oswego.edu
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