Analysis of self-directed learning ability, reading outcomes, and personalized planning behavior for self-directed extensive reading

ABSTRACT

Self-directed learning (SDL) ability, its usefulness in higher education and life-long learning have been highlighted in previous literature. However, there has been much less understanding of the effects of SDL ability in the school settings, specifically the effects on learners’ SDL behaviors and processes. To address this limitation, this study investigated the relations between SDL ability, SDL behaviors, and reading outcomes and further explored the process of planning behaviors in SDL. This study examined the context of SDL for extensive reading using a goal-oriented active learning system, GOAL. The results showed that the high SDL ability students demonstrated significantly more reading outcomes in terms of books completed and the number of days read than those with low SDL ability. The high SDL ability students engaged significantly more in planning behaviors, that were found to be significantly correlated with reading outcomes, than the low SDL ability students. Cluster analysis and transition analysis also differentiate groups of learners with different planning behaviors. These findings suggested that the learning behaviors and outcomes facilitated by the environment were affected to varying degrees by the levels of students’ SDL ability, and personalized feedback can be created using the SDL behavioral variables and patterns in the environment.

KEYWORDS:

• Self-directed learning
• extensive reading
• learning behavior
• planning behavior
• secondary school

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study is supported by JSPS KAKENHI Grant-in-Aid for Scientific Research (S) Grant Number 16H06304, NEDO Special Innovation Program on AI and Big Data JPNP18013, JSPS KAKENHI Grant-in-Aid for Early-Career Scientists 20K20131, Kyoto University SPIRITS 2020.

Notes on contributors

Huiyong Li

Dr. Huiyong Li is a post-doctoral researcher at the Academic Center for Computing and Media Studies, Kyoto University. His research focuses on learning analytics, self-directed learning, self-regulated learning, and technology-enhanced language learning.

Rwitajit Majumdar

Dr. Rwitajit Majumdar is a senior lecturer at the Academic Center for Computing and Media Studies, Kyoto University. His research focuses on learning analytics and data-informed decision making in the teaching-learning context.

Mei-Rong Alice Chen

Dr. Mei-Rong Alice Chen is a post-doctoral researcher at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan. Her research focuses on technology in language learning, computer-assisted language learning, and pedagogy and learning design.

Yuanyuan Yang

Ms. Yuanyuan Yang is a PhD candidate at the Graduate School of Informatics, Kyoto University. Her research interests include learning analytics, self-directed learning, learning performance prediction, educational data mining.

Hiroaki Ogata

Dr. Hiroaki Ogata is a professor at the Academic Center for Computing and Media Studies and the Graduate School of Informatics at Kyoto University, Japan. His research includes computer supported ubiquitous and mobile learning, personalized and adaptive learning environments, mobile and embedded learning analytics, educational data mining, and educational data science.