Towards a personalized task selection model with shared instructional control

Abstract

Modern education emphasizes the need to flexibly personalize learning tasks to individual learners. This article discusses a personalized task-selection model with shared instructional control based on two current tendencies for the dynamic sequencing of learning tasks: (1) personalization by an instructional agent which makes sequencing decisions on the basis of learner’s expertise, and (2) personalization by the learner who is given control over –final—task selection. The model combines both trends in a model with shared instructional control. From all available learning tasks, an instructional agent selects a subset of tasks based on the learner’s performance scores and invested mental effort (i.e., system-control). Subsequently, this subset is presented to the learner who makes the final decision (i.e., learner control). A computer-assisted instructional program has been developed to put the model into practice and preliminary results are discussed. The model can be used to increase the efficiency and effectiveness of instruction and to make it more appealing by providing the learner an optimal level of control over task selection.