Human Factors and Personalized Digital Learning: An Editorial

1. Introduction

The development of advanced information technologies has opened up new opportunities in the area of digital learning environments (Brandi & Iannone, 2015). A key aspect of this work lies within the fact that learners can access instructional materials at any time and any place (Wang, Chang, & Chen, 2018). As a result of such convenience, a wide range of people have begun using digital learning environments for the delivery of instructional materials. Thus, it is important to ensure that the digital learning environments can accommodate diverse users’ needs.

To address this issue, it is necessary to incorporate personalization into the development of digital learning environments. Personalization, which tailors to the needs and preferences of each individual (Adomavicius & Tuzhilin, 2005), is acknowledged as a useful approach to develop added value services in digital learning environments. On the other hand, there is a need to understand different preferences of each learner so that effective personalized digital environments can be developed (Chen & Liu, 2011). In other words, human factors play an important role. This is the reason why this special article addresses the relationships between human factors and personalized digital learning.

2. The theme of the special issue

In this special issue, we are interested in exploring issues related to human factors in the delivery of personalized digital learning. This special issue includes 13 articles, which are assigned into two issues. The first issue contains six papers and begins with three papers, which investigate reading comprehension from different aspects. Lin and Hsieh use eye tracking techniques to investigate differences between EFL Beginners and Intermediate Level Readers. On the other hand, Yin et al. and Chang, Huang and Liu emphasize on the context of electronic books, which belong to mobile devices. The other type of mobile devices is smart phones, which are addressed by Hwang, Hsu and Hsieh. More specifically, Hwang, Hsu and Hsieh examine the impacts of learning styles on listening performance and perceptions in the smartphone context. The next study authored by Wang, Mendori and Hoela also addresses learning styles, with an emphasis on difference between verbal learners and visual learners. In addition to the aforementioned empirical studies, the first issue also includes a systematic review authored by Wong et al., who present the state of the art of online self-regulated learning.

The second issue includes six papers, of which the first two papers were concerned with game-based learning but they pay attention to different human factors. Yang, Quadir and Chen emphasize on emotion intelligence while Chen focuses on cognitive styles and game experience. Cognitive styles are also addressed by Chen, Liou and Chen, who also investigate gender differences in their work. Cognitive styles are related to learning styles, which are investigated in the study by Sun and Yu, especially in the context of a village museum. Subsequently, the other essential human factor (i.e., prior knowledge) is investigated by Li, who is interested in the context of video lectures. Unlike the aforesaid ordinary human factors, Kamel, Liu, Li and Sheng finally examine a distinct human factor (i.e., human poses), in the context of Chinese Tai Chi.

3. Concluding remarks

The articles presented in this special issue demonstrate the effects of human factors in the delivery of personalized digital learning. Designers cannot assume that all learners can appreciate functionality and interface features provided by their digital learning environments. Therefore, there is a need to understand the influences of human factors so that digital learning environments can be tailored to the needs and preferences of each learner. This special issue is a small step in attempting to investigate how human factors affect digital learning and how personalization can be incorporated into digital learning. In addition to human factors examined in this issue, further research is needed to investigate other human factors (e.g., physical, cognitive, motivational and social variables). Moreover, it is necessary to develop personalized digital learning techniques with advanced machine learning or data mining techniques in the future. Finally, more comparisons of personalized digital learning and traditional digital learning systems are necessary to evaluate the suitability of personalization in different contexts.

Acknowledgments

We would like to thank Professors Gavriel Salvendy and Constantine Stephanidis, the editors of International Journal of Human–Computer Interaction, for their valuable help and support. Special thanks to all the authors for contributing to the special issue, and to our reviewers for providing high-quality, constructive and detailed reviews.

Additional information

Notes on contributors

Sherry Y. Chen

Sherry Y. Chen is currently a Chair Professor at Graduate Institute of Network Learning Technology, National Central University, Taiwan and a Visiting Professor in the Department of Information Systems and Computing at Brunel University, UK. She was granted an Outstanding Scholar Award from the Foundation for the Advancement of Outstanding Scholarship (FAOS) and the Ministry of Science and Technology in 2010 and 2017, respectively.

Jen-Hang Wang

Jen-Hang Wang was a post-doctor at Graduate Institute of Network Learning Technology, National Central University, Taiwan. His main research interest lies within Computer Support Collaborative Learning. He obtained his PhD from the Department of Computer Science and Information Engineering, National Central University in Taiwan.