Digital Learning Transformation – A Strategic Framework Using the Analytic Hierarchy Process

We define and describe a strategic framework for digital learning transformation using a well-established multi-criteria decision-making (MCDM) technique – the Analytic Hierarchy Process (AHP). We classify the digital transformation paths along 12 different dimensions of becoming, cognifying, flowing, screening, accessing, sharing, filtering, remixing, interacting, tracking, questioning and beginning. These dimensions are organized as a hierarchy of digital functions after due-diligence of their interdependence analysis using the design structure matrix (DSM). This leads to identification of a digital learning structure amongst the 12 digital dimensions for the specific case. The hierarchy of digital learning dimensions is utilized for strategic prioritization of these dimensions towards a digital learning transformation of any traditional learning system towards an amalgamated traditional digital learning system in a seamless manner. The AHP method that converts pairwise comparisons by multiple experts and stakeholders into quantitative scores is utilized to create consensus amongst different stakeholders who typically may have different views on taking specific paths ahead. The AHP helps in not only checking the individual consistency of experts but also in visualizing the process of creating consensus amongst the group. The Digital Learning Transformation Framework (DLTF) proposed in this paper is generalized enough to adapt to any specific traditional learning systems to glean step-by-step roadmap for all key digital functions chosen through the DSM in specific context. Three key criteria of relative feasibility, learner’s value and technological enhancement are used to rank order chosen digital functions for the particular context or application. Three different scores are then utilized for creating a digital strategic transformation roadmap along the chosen dimensions for the specific case. The DLTF is generic and flexible enough to be applicable for variety of traditional learning systems and can help in seamless transformation decisions and execution