EdTech for learning outcomes and impact: A comprehensive approach

A precise estimation of learners' abilities is the first and foremost step in personalized learning including EdTech solutions. Predictive analytic techniques such as Item Response Theory (IRT), Bayesian Knowledge Tracing (BKT), and Performance Factor Analysis (PFA) are established practices to achieve this purpose. However, the complexity, cost, and time involved in calibration, and the challenges in online implementation, have led to the adoption of simpler alternatives such as Elo, machine learning, and artificial intelligence. Nevertheless, the estimation of abilities is just one facet of personalized learning, and designing effective personalized learning experiences is equally essential to guide learners through their unique learning journeys and drive improvement in learning. The research body provides mixed evidence regarding the impact of EdTech on learning outcomes. Designing impactful learning experiences requires a foundation in scientific principles drawn from learning sciences and learning design and a sharp focus on learning progressions. A one-size-fits-all approach is quite unlikely to yield significant learning gains. The importance of robust implementation models cannot be underestimated, as even the best designs can falter if poorly executed. Learning science and design principles not only assist in developing effective EdTech products but also inform professional development programs to ensure the intended usage of the product and services. The objective of this paper is to understand different EdTech models and propose a coherent design and implementation framework to enhance their effectiveness and impact on learning.