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