These resources highlight two strategies that focus on optimizing processes and improving outcomes. The first one focuses on analyzing performance gaps, using data and feedback, which can improve academic advising strategies; the second emphasizes task analysis in instructional design, offering methods to break down tasks and enhance learning experiences, which are directly applicable to refining academic advising and support systems.
This report provides an in-depth analysis of employee time-off requests within a company, drawing on internal audit data and focus group feedback to assess compliance with submission procedures. It identifies performance gaps, such as confusion over the new system and lack of proper training, which contribute to inefficiencies (Caldwell, 2024).
The resource demonstrates how data and stakeholder feedback can be used to identify gaps and refine processes. In academic advising, this approach can help in understanding student challenges, tailoring advising strategies, and improving overall student experience and engagement. The methods of needs analysis presented here can inform the development of more effective advising practices and interventions.
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As Jill E. Stefaniak (2024) notes in her chapter, "Using Task Analysis to Inform Instructional Design" in the eBook, Design for Learning: Principles, Processes, and Praxis, her writing emphasizes the importance of task analysis in instructional design, a key phase in the ADDIE model. It explores different task analysis methods — such as procedural, cognitive, hierarchical, and time-and-motion studies — that break down tasks step-by-step, helping to design effective training programs. The process includes identifying tasks, selecting methods, and sequencing steps, all aimed at understanding and improving task performance.
Task analysis is essential for creating structured, relevant, and effective training and learning experiences. For academic advising, understanding task breakdowns can help refine advising processes, improve student support strategies, and design more effective learning environments. This chapter is particularly useful for designing educational programs that consider both cognitive and procedural complexities.