Future Work

Many studies have indicated that instruction can be adaptive based on learning styles, achievement, attitude, and interests to make learning more likely to occur.

Through an understanding of learning materials and personal traits, educators can provide learners with an adaptive approach to learning, with personalized instruction that is available to every individual in a given class. In future studies, we aim to implement the goal of the educational Internet of Everything (IoE) [72][73][74][75] in which everything (e.g., learners’ biology signals and mobile devices) can be connected and communicated everywhere, anytime. The educational IoE not only brings together learners, teachers, processes, data, and sensors to make networked connections more relevant and valuable but also turns information into actions that create personalized capabilities (see Figure 5-1). Neuroscience studies have provided new insights into the intricacies of the neural processes underlying learning. Developing teaching methods to fit the diversity of individual preferences is a major challenge for the field of educational technology in the future. Deep learning, artificial intelligence, and data mining applied in the educational IoE are the key to developing personalized learning.

Future studies should employ a machine-learning technique to discover important personal traits that contribute to personalizing learning.

Figure 5-1 The educational IoE.

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