Automated Time Manager: Effectiveness of Self-Regulation on Time Management through a Smartphone Application

Bogoan Kim, Seok-Won Lee, Hwajung Hong, Kyungsik Han
2019 IEEE Access  
We investigated the effectiveness of a self-regulation strategy on time management leveraged by smartphone capabilities using a theoretical framework of self-regulation that consists of four elements: 1) goal setting; 2) task strategy utilization; 3) self-monitoring and reflection; and 4) self-efficacy and intrinsic motivation. We determined the goals and strategies adopted during college life by surveying 295 college students and identified time management as a fundamental element for
more » ... such goals and strategies. To improve students' time management, we developed a smartphone application, automated time manager (ATM), designed to provide users with visualizations of their physical activities and phone usage reports and also to acquire smartphone sensor and usage data. From a field study of 46 college students, we highlighted three primary user experiences-awareness of unawareness, preferred feedback, and contextual but obvious use-and an overall positive time management outcome with ATM. We present an empirical study that transforms self-regulation, a well-known approach in social sciences, into computing and discusses the salient design implications for supporting time management in a more effective manner with a smartphone application. INDEX TERMS Mobile application, positive computing, self-regulation, smartphone use, time management. VOLUME 7, 2019 2169-3536 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information. During the first wave of research on self-regulated learning (SRL), three representative evaluation methodologies were used in educational contexts (e.g., classes): the learning and study strategies inventory (LASSI) [28] , the motivated strategies for learning questionnaire (MSLQ) [29] , and the self-regulated learning interview scale (SRLIS) [30] , [31] . The SRLIS relates to prospective answers to hypothetical learning contexts (e.g., interviews), whereas the LASSI and the MSLQ are both retrospective reports (e.g., emotion, anxiety, depression). According to [32] , LASSI, MSLQ, 90892 VOLUME 7, 2019 HWAJUNG HONG received the B.
doi:10.1109/access.2019.2926743 fatcat:p7kewf2tf5bf3gahick4royoha