New Finger Biometric Method Using Near Infrared Imaging

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%

Self-Regulated Learning in Massive Open Online Courses

This three-article dissertation aims to examine self-regulated learning (SRL) in Massive Open Online Courses (MOOCs) through the conduct of a systematic literature review and two empirical studies. The first article is a systematic literature review study that investigates the current status of studies on SRL in MOOCs, SRL strategies employed by MOOC learners, and interventions and design guidelines that have been proposed to support SRL in MOOC environments. The second article is a quantitative study that examines the relationships between the use of SRL strategies, self-efficacy, and task value in MOOCs. This research notes that there is a positive relationship between the use of SRL strategies and self-efficacy as well as that between the use of SRL strategies and task value in MOOCs. The third article is a quantitative study that investigates the influence of successful MOOC learners’ SRL strategies, self-efficacy, and task value on their perceived effectiveness of one particular MOOC. The results show that successful MOOC learners’ perceived effectiveness of the MOOC is significantly predicted by their task value belief and use of SRL strategies. The findings of these three articles provide empirical evidence of the importance of SRL in MOOCs as well as a variety of practical implications for MOOC instructors and instructional designers

The Influence of Successful MOOC Learners’ Self-Regulated Learning Strategies, Self-Efficacy, and Task Value on Their Perceived Effectiveness of a Massive Open Online Course

High dropout rates have been an unsolved issue in massive open online courses (MOOCs). As perceived effectiveness predicts learner retention in MOOCs, instructional design factors that affect it have been increasingly examined. However, self-regulated learning, self-efficacy, and task value have been underestimated from the perspective of instructors even though they are important instructional design considerations for MOOCs. This study investigated the influence of self-regulated learning strategies, self-efficacy, and task value on perceived effectiveness of successful MOOC learners. Three hundred fifty-three learners who successfully completed the Mountain 101 MOOC participated in this study by completing a survey through e-mail. The results of stepwise multiple regression analysis showed that perceived effectiveness was significantly predicted by both self-regulated learning strategies and task value. In addition, the results of another stepwise multiple regression analysis showed that meta-cognitive activities after learning, environmental structuring, and time management significantly predicted perceived effectiveness

The Relationships Between Self-Efficacy, Task Value, and Self-Regulated Learning Strategies in Massive Open Online Courses

This study examines the relationships between self-efficacy, task value, and the use of self-regulated learning strategies by massive open online course (MOOC) learners from a social cognitive perspective. A total of 184 participants who enrolled in two MOOCs completed surveys. The results of Pearson’s correlation analysis show a positive correlation between self-efficacy and the use of self-regulated learning strategies, as well as a positive correlation between task value and the use of self-regulated learning strategies. The results of hierarchical multiple regression analysis show that self-efficacy and task value are significant predictors of the use of self-regulated learning strategies. There was a statistically significant difference in the use of self-regulated learning strategies between learners who possessed high self-efficacy and those who possessed low self-efficacy. In addition, learners who had high task value showed statistically significant higher average self-regulated learning scores than those who had low task value. Implications and future research directions are discussed based on the findings