Develop skills for systematically analyzing learning, or other types of systems, that need to be improved. Develop data collection instruments (e.g. surveys, observation protocols, interviews); analyze secondary data; analyze tasks or activities in the system, and interpret data to make recommendations for system improvement. Skills will be learned by doing and applying to real systems that need improvement.
This course gave me a solid foundation in front-end analysis and taught me the importance of using structured, data-driven methods to assess learning and performance needs. In a day in time where training and instructional design are often influenced by fast-paced trends and new technologies, this class brought me back to the fundamentals. Proven methods for conducting needs assessments, analyzing tasks, and making informed decisions about training solutions. I appreciated the weekly explanations and real-world applications, which helped break down complex concepts into practical steps. Learning how to develop surveys, conduct performance analyses, and interpret data gave me confidence in my ability to diagnose learning gaps and recommend effective interventions. The older texts on needs assessment were especially valuable because they gave me timeless strategies that still apply today, teaching me that a well-conducted front-end analysis is the key to designing impactful training.
One of the biggest takeaways from this course was understanding how to select and apply the right needs assessment tools based on the situation. Whether it was survey design, observation guides, task analysis, or instructional objectives, each technique had a specific role in shaping the overall analysis process. I also appreciated the focus on diversity, equity, and inclusion (DEI) in needs assessments, which made me more aware of how systemic factors impact learning and performance outcomes. This course strengthened my ability to think critically about training solutions and avoid jumping straight to design without first identifying the root cause of a problem. Moving forward, I feel much more prepared to conduct thorough front-end analyses, ensuring that the learning programs I develop are based on real needs rather than assumptions.