Moon-Heum Cho

Self-regulated learning: the role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course

Enrollment in online remedial mathematics courses has increased in popularity in institutions of higher learning; however, students unskilled in self-regulated learning (SRL) find online remedial mathematics courses particularly challenging. We investigated the role of SRL, specifically motivation, emotion, and learning strategies, in students’ learning experiences in a remedial online mathematics course. With an online survey of 229 college students, we found that student motivation explained a small portion of variance in achievement; whereas student motivation and emotion explained a significant portion of variance in satisfaction. In addition, significant differences in motivation and emotion were found in passing and nonpassing students; however, learning strategies did not influence student achievement and satisfaction. Implications for teaching and learning in self-paced online remedial mathematics courses are discussed.

Use of a game-like application on a mobile device to improve accuracy in conjugating Spanish verbs

ABSTRACT Interest in using mobile applications to enhance students’ learning in Spanish classrooms runs high; however, little empirical research about their effects has been conducted. Using intentionally designed classroom activities to promote meaningful learning with a mobile application, we investigated the extent to which students of Spanish as a second language (L2) could improve accuracy in conjugating verbs. Pre- and posttest results show that these activities helped students improve not only accuracy but also confidence in conjugating Spanish verbs. The 80 students who participated in this study for four consecutive semesters reported an overall positive experience with the verb-conjugating activities but noted some limitations. Results of this empirical study enhance the learning of Spanish with a mobile application in the classroom.

Exploring online students’ self-regulated learning with self-reported surveys and log files: a data mining approach

ABSTRACT Many researchers who are interested in studying students’ online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students’ SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students’ online SRL patterns with the use of data mining techniques. We examined both self-reported self-regulation surveys and log files to predict online students’ achievements and found using log files was more powerful in predicting students’ achievements in an online course than self-reported survey data. Discussions to enhance teaching and learning practices with the use of data mining are provided.

Self-regulation in three types of online interaction: a scale development

Abstract The purpose of this study was to develop a scale with which to examine students’ self-regulation (SR) in three types of online interaction. Using scale development steps, we constructed the online self-regulation questionnaire (OSRQ), a self-report survey. A total of 799 online students participated in the study. Data from 400 randomly selected participants were used for exploratory factor analysis (EFA), and data of the remaining 399 participants were used for confirmatory factor analysis (CFA). The EFA yielded three factors as hypothesized: SR in interaction between student and content, SR in interaction between student and student and SR in interaction between student and teacher. The CFA demonstrated that the factor structures appearing in the EFA were also observed with different participants. In addition, we found convergent validity in the OSRQ. The study contributes to understanding SR in online learning settings.