Developing Personalized Education: A Dynamic Framework

Abstract

Personalized education—the systematic adaptation of instruction to individual learners—has been a long-striven goal. We review research on personalized education that has been conducted in the laboratory, in the classroom, and in digital learning environments. Across all learning environments, we find that personalization is most successful when relevant learner characteristics are measured repeatedly during the learning process and when these data are used to adapt instruction in a systematic way. Building on these observations, we propose a novel, dynamic framework of personalization that conceptualizes learners as dynamic entities that change during and in interaction with the instructional process. As these dynamics manifest on different timescales, so do the opportunities for instructional adaptations—ranging from setting appropriate learning goals at the macroscale to reacting to affective-motivational fluctuations at the microscale. We argue that instructional design needs to take these dynamics into account in order to adapt to a specific learner at a specific point in time. Finally, we provide some examples of successful, dynamic adaptations and discuss future directions that arise from a dynamic conceptualization of personalization.

Understanding Cognitive Load in Digital and Online Learning: a New Perspective on Extraneous Cognitive Load

“…The temporal development of cognitive load during digital learning is an important aspect that should be investigated in relation to potential increases in germane processing. While it is plausible that some forms of perceptually rich learning can induce a higher initial cognitive load (for instance, during the time interactive controls need to be learned) that decreases over time, it is also possible that an initially high engagement can wear off (for a recent analysis concerning the dynamics of learning processes, see Tetzlaff et al, 2020).…”

Designing personalised instruction: A research and design framework

“…(2018); Shemshack and Spector (2020) and Tetzlaff et al. (2020). Our aim was to integrate insights that provide an indication of likely perspectives of practitioners working with personalised learning and that indicate priority areas for a framework that can be usefully applied both in research and design of personalised instruction.…”

“…Their emerging framework specified the dimensions of personalisation in technology-enhanced learning, including what is being personalised, what type of learning occurs when personalisation is used, which personal data are used to carry out various types of personalisation and who the agent, recipient and beneficiary of personalisation are. Adopting a psychology perspective, Tetzlaff et al (2020) proposed a student model of personalised education, which focuses on the educational progress from student assessment to mastery of learning. Tetzlaff et al's (2020) cognitive learning framework specifies the instructional adaptations connected to different learner dynamics over different timescales.…”

“…(2020) proposed a student model of personalised education, which focuses on the educational progress from student assessment to mastery of learning. Tetzlaff et al.’s (2020) cognitive learning framework specifies the instructional adaptations connected to different learner dynamics over different timescales. We propose a Framework of Personalised Instruction that is broader in scope and can serve as a basis for identifying the best practice in both research and design.…”

“…Adopting a psychology perspective, Tetzlaff et al. (2020) proposed a student model of personalised education, which focuses on the educational progress from student assessment to mastery of learning. Tetzlaff et al.’s (2020) cognitive learning framework specifies the instructional adaptations connected to different learner dynamics over different timescales.…”