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A framework for using Learning Analytics to inform online doctoral program assessment and improvement
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Framework for using Learning Analytics for
Doctoral Program Assessment & Improvement
L. Roxanne Russell | All Rights Reserved
Doctoral Level Program Evaluation
Acknowledge that students are advanced level professionals- typical approaches to measuring student success or performance may not be appropriate Students are expected to succeed Pass/Fail program with substantive faculty feedback
Recognize the assessment value of student expectations and perceptions for their learning experiences in doctoral level graduate programs Credible judges Institutional impact (reputation, alumni, word of mouth)
Fulfilling Student Expectations
Caliber of faculty Originality of perspective- scholarship Personal exposure Charisma/personality
Prestige of institution Quality of environment (facilities/interface/tools) Quality of service & support Caliber of and access to peer network
Challenge of curriculum Relevance and level of content Worthwhile course activities Scaffolded project
Student Success
How are students likely to measure their success?
Self-actualization/Reflective practitioners Career competency/advancement Ability to serve their constituency
Student Attribution
What will students attribute their success in the program to? (perceptions of learning experience) Faculty interactions Peers Curriculum Content Manageability
Design features
Faculty Interacti
on{
Synchronous sessions
Asynchronous interactions
Office Hours
Campus Visits
Project Consultation
Design features
Peers {Group assignments
Breakout groups in synchronous sessions
Synchronous discussions
Asynchronous discussions
Design features
Curriculum
{Sequencing
Track
Advanced
Design features
Content {Multimedia
Reading methods
Lectures
Design features
Manageability
{Weekly structure
Calendar
Navigation
Workload
Project scaffolds
Student Behaviors
Run reports of design feature related behaviors with corresponding learning analytics
Student Perceptions
Student ranks level of perceived success in the program (personal success/learning)
Student ranks fulfillment of expectations for the program
Student ranks how these rankings could be attributed to design feature elements: Ranks level of satisfaction with element Ranks level of importance of element Ranks ease of use of primary tools Open-ended question space to elaborate on experiences with each
element
Analysis
Compare survey and questionnaire responses to actual behaviors Individual student data comparison E.g. student claims time management was a challenge/
student does not use time management design features like the planner/calendar
Conduct mid-term/mid-program learner analysis to offer chance for student reflection and change in behaviors; modification of design
Next
Long-term measures of student success
Faculty perceptions of student success
Framework for evaluating faculty experience/notions of success Preparation & development Course design, scaffolds, tools Facilitation & feedback Learning experience for students Project process