Personalization With Digital Technology: A Deep Cognitive Processing Perspective

Personalization With Digital Technology: A Deep Cognitive Processing Perspective

Robert Z. Zheng (The University of Utah, USA)
DOI: 10.4018/978-1-5225-3940-7.ch001


How to personalize learners' learning with digital technology so that learners derive optimal experiences in learning is a key question facing learning scientists, cognitive psychologists, teachers, and professional instructional designers. One of the challenges surrounding personalization and digital technology is how to promote learners' cognitive processes at a deeper level so that they become optimally engaged in critical and creative thinking, making inferences in learning, transferring knowledge to new learning situations, and constructing new knowledge during innovative learning process. This chapter examines the literature relating to deep cognitive processes and the idiosyncratic features of digital technology that support learners' deep cognitive processes in learning. Guidelines pertaining to personalization with digital technology in regard to deep cognitive processing are proposed, followed by the discussions on future research with a focus on verifying the theoretical constructs proposed in the guidelines.
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Digital technology has increasingly played a critical role in learners’ learning in terms of their cognitive, metacognitive and affective processes. With its unique features that support a wide range of learning experiences including interactive engagement (e.g., multimedia), immersive learning (e.g., virtual reality), ubiquitous information access (e.g., mobile learning), data driven individualized learning (e.g., learning analytics), digital technology is poised to redefine the educational landscape with tremendous opportunities for personalized learning and development of skills and abilities necessary to meet the challenges in 21st century (Arnab et al., 2012; Echeverri & Sadler, 2011). Digital technology has shown promises in multiple areas in education including assessment (Beggrow, Ha, & Nehm, 2014; Gierl, Bulut, & Zhang, 2018, Chapter 5; Nehm, Ha, & Mayfield, 2012), intelligent tutoring and natural language processing (Kerr, Mousavi, & Iseli, 2013; Nakamura, Murphy, & Christel, 2016), expertise acquisition (LaVoie, Streeter, & Lochbaum, 2010), educational gamification (Conrad, Clarke-Midura, & Klopfer, 2014; Gibson & Clarke-Midura, 2013), learner interaction and participation (Hamilton & Owens, 2018, Chapter 9; Sural & Yazici, 2018, Chapter 3; Schneider & Blikstein, 2015), and curricular integration and online learning (Georgiopoulos, DeMara, & Gonzalez, 2009; Oliveira & Pombo, 2018, Chapter 10; Svenningsen, Bottomley, & Pear, 2018 Chapter 8). The use of digital technology in education has led to the investigation of what and how digital technology may influence and change learners’ behavior in personalized learning. Some researchers (e.g., Hacker, 2017; Zhou & Winne, 2009) examine the relationship between self-regulated learning and digital technology in support of self-paced, goal-oriented, reflective learning. Others (e.g., Lee & Liu, 2017; Liu, Toprac, & Yuen, 2009) focus on motivational aspects by examining the impact of digital technology on learners’ motivation in learning such as intrinsic and extrinsic motivation and the locus of control. Still others are interested in understanding the phenomena of digital technology from the perspective of social learning by focusing on the influence of digital technology on learners’ social behavior such as collaboration, participation, and collective knowledge sharing and creation (Agosto, Copeland, & Zach, 2013; Vickers, Field, & Melakoski, 2015).

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