Challenging Authentic Digital Scenarios

Abstract

Components of authentic learning are globally investigated in the light of digital technologies for their potential to transform learning and teaching. Authenticity is an important criterion for observing and analyzing a digital performance, because the validity of observable evidence of knowledge is provided by actions situated in a particular context and culture (Brown et al. 1989; Rosen 2015). Components of authenticity include real world problems, inquiry learning activities, discourse in a community of learners, and student autonomy (Gibson and Ifenthaler 2016). Within these digital scenarios, data inputs are collected by an interactive computational application that either come directly from the learner or secondarily from aggregations of those inputs (Ifenthaler 2015). A mouse click, tracked eye movement, or keyboard press are examples of direct event-level interactions and a group of such actions, such as forming a word with keyboard presses or organizing screen resources into a priority order by dragging and dropping them onto an image, are examples of aggregated sets of actions (Ifenthaler and Widanapathirana 2014; Nasraoui 2006). When the components of authentic learning are enabled by technology and the event-level interactions of learners are recorded as a historical stream of items, a voluminous and varied data record of the performance in the scenario rapidly accumulates into a transcript (Berland et al. 2014; Gibson and de Freitas 2016; Romero and Ventura 2015). Currently, such large records of data about the context of authentic digital scenarios and the actual performance of individuals or teams are collected. However, real-time analysis and feedback are still to be implemented. They require intelligent adaptive algorithms in order to enable meaningful analysis as well as personalized and adaptive feedback to the learner (Ifenthaler and Erlandson 2016). The articles in this special issue stem from an