“For learning to transfer, instruction must move beyond recall to application in realistic contexts.” ~Quinn
The discussion highlights how aligning educational technology with cognitive principles can enhance both learner engagement and real-world performance.
Quinn’s (2021) perspective reinforces the idea that lasting learning depends on more than memorization; it requires experiences that promote real-world application. This week’s readings highlight how both cognitive theory and educational technology contribute to designing instruction that supports transfer. Quinn emphasizes the value of active involvement, layered support, and meaningful feedback in helping learners develop and apply understanding. Complementing this, Pan et al. (2023) explore how learning analytics within learning management systems (LMS) can offer targeted, data-driven assistance that adapts to learner needs. Together, these authors show how aligning instructional strategies with both research and technology can promote deeper engagement and real-world competence.
Pan et al. (2023) outline key features of effective LMS platforms, such as interactive dashboards, automated prompts, and early-alert systems that allow educators to respond proactively to student progress. These tools help instructors deliver feedback that is timely and specific, supporting Quinn’s (2021) view that reflection and ongoing guidance are essential for growth. Their research also explores adaptive technology that adjusts support as learners improve, gradually reducing scaffolding to foster independence, an idea Quinn refers to as “fading guidance.” Importantly, Pan et al. stress that learning analytics should be tied to specific instructional goals to ensure data leads to purposeful intervention, not just passive monitoring. This alignment enables educators to use technology not just to track progress, but to meaningfully enhance the learning process.
In the context of real estate, a dashboard system can be leveraged for dual purposes, enhancing professional development and improving client interaction. Similar to how learning analytics are used to monitor academic progress and pinpoint areas for improvement, client dashboards can track user engagement with property listings to uncover trends in buyer preferences regarding home features. This insight enables agents to tailor their property suggestions in real time, fostering a more customized experience. Such a system parallels the adaptive feedback mechanisms described by Quinn (2021), providing dynamic, need-based support that empowers both learners and clients to make informed decisions.
Ultimately, meaningful learning occurs when knowledge is transformed into purposeful action. Drawing on the insights of Quinn (2021) and Pan et al. (2023), I see how the purposeful integration of design, responsive data systems, and active engagement can lead to lasting, meaningful outcomes. Learning analytics offer instructors a way to personalize support, while learning science ensures those tools are applied effectively. As I reflect on these ideas, it becomes clear that combining human insight with technological resources not only strengthens instruction but also supports long-term growth in fast-paced, evolving fields like real estate, where adaptability and continuous learning are essential.
References:
Pan, Z., Biegley, L., Taylor, A., & Zheng, H. (2023). A systematic review of learning analytics–incorporated instructional interventions on learning management systems. Journal of Learning Analytics, 11(2), 52–72. https://doi.org/10.18608/jla.2023.8093
Quinn, C. (2021). Learning science for instructional designers: From cognition to application. Association for Talent Development
