Evaluation of Online Log Variables that Estimate Learners’ Time Management in a Korean Online Learning Context

Jo, Il Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

doi:10.19173/irrodl.v17i1.2176

Cited by 26 publications

(41 citation statements)

References 33 publications

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“…Time management is defined as the ability to prioritize time in order to fulfill learning demands, complete tasks, and modify plans to accommodate any changes in time or demands (Jo, Park, Yoon, & Sung, 2016). Time management has been shown to be a significant predictor of online achievement (Jo et al, 2016;Kwon, 2009;Choi & Choi, 2012), although the association may be weak (r=.14, p=.00; Broadbent & Poon, 2015).…”

Section: Factors Related To Achievement In Online Coursesmentioning

confidence: 99%

“…Procrastination in general is thought to be negatively associated with course performance, and this might explain our finding (e.g., Tice & Baumeister, 1997). Other studies have found that time management in online courses predicts achievement (Jo et al, 2016;Kwon, 2009;Choi & Choi, 2012). Alternatively, this variable could simply reflect that students who were struggling in the course found it difficult to grade these assignments (i.e., it's hard to determine if something is right or wrong if you yourself are unsure of the right answer), and therefore took longer to submit it.…”

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confidence: 94%

“…Time management has been shown to be a significant predictor of online achievement (Jo et al, 2016;Kwon, 2009;Choi & Choi, 2012), although the association may be weak (r=.14, p=.00; Broadbent & Poon, 2015). Other studies have found direct effects of time management on achievement (Jo & Kim, 2013;Loomis, 2000).…”

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confidence: 98%

“…Evidence shows that the degree to which students engage in the learning process through effective online tool use is associated with their learning outcomes, even when accounting for differences in cognitive ability (Shute & Gluck, 1996). Thus, pinpointing the factors that underlie student engagement with learning materials is essential for understanding why students succeed or fail in an online course (Tempelaar et al, 2012).Time management is defined as the ability to prioritize time in order to fulfill learning demands, complete tasks, and modify plans to accommodate any changes in time or demands (Jo, Park, Yoon, & Sung, 2016). Time management has been shown to be a significant predictor of online achievement (Jo et al, 2016;Kwon, 2009;Choi & Choi, 2012), although the association may be weak (r=.14, p=.00; Broadbent & Poon, 2015).…”

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confidence: 99%

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Individual Differences Related to College Students’ Course Performance in Calculus II

Hart¹,

Daucourt²,

Ganley³

2017

Learning Analytics

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ABSTRACT. In this study, we explore student achievement in a semester-long flipped Calculus II course, combining various predictor measures related to student attitudes (math anxiety, math confidence, math interest, math importance) and cognitive skills (spatial skills, approximate number system), as well as student engagement with the online system (discussion forum interaction, time to submission of workshop assignments, quiz attempts), in predicting final grades. Data from 85 students enrolled in a flipped Calculus II course was used in dominance analysis to determine which predictors emerged as the most important for predicting final grades. Results indicated that feelings of math importance, approximate number system (ANS) ability, total amount of discussion forum posting, and time grading peer workshop submissions was the best combination of predictors of final grade, accounting for 17% of variance in a student's final grade. The point of this work was to determine which predictors are the most important in predicting student grade, with the end goal of building a recommendation system that could be implemented to help students in this traditionally difficult class. The methods used here could be used for any class.Keywords: Math performance, calculus, flipped classroom, math attitudes, cognitive performance, student engagement INTRODUCTIONOver the past few years, the attrition of STEM-focused undergraduates in the United States has become a critical national concern, with "a substantial number of undergraduate students initially enrolled in STEM degree programs [dropping] out in the first two years" (PCAST, 2012). In order to maintain highquality instructional practices in the face of STEM undergraduate attrition, growing demand for large (2017 130 numbers of graduates, and diminishing financial and human resources, many colleges and universities are turning to technologically aided teaching practices. This technological shift aims to integrate the traditional classroom environment with online course resources to enhance, replace, and supplement face-to-face instruction to reach more students in a cost-effective way (Garrison & Kanuka, 2004).To accommodate this shift toward technologically aided teaching methods, schools have implemented campus-wide Learning Management Systems (LMS), such as Moodle and Blackboard. These web-based management systems provide data related to the "user," with every action of the student tracked and recorded online. The underlying advantage of these LMS data is that they unobtrusively record individual student activity and interaction with course materials in real-time, providing a lens into traditionally unobservable learning-related behaviours and silently tracking individual students' learning progression (Macfadyen & Dawson, 2010; Gašević, Dawson, & Siemens, 2015). In traditional college courses, many performance measures, for example midterm or final exams (Lee, Speglia, Ha, Finch, & Nehm, 2015;Macfadyen & Dawson, 2010), are taken too late in the semester to identify st...

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Section: Factors Related To Achievement In Online Coursesmentioning

confidence: 99%

mentioning

confidence: 94%

mentioning

confidence: 98%

mentioning

confidence: 99%

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Individual Differences Related to College Students’ Course Performance in Calculus II

Hart¹,

Daucourt²,

Ganley³

2017

Learning Analytics

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“…Numerous studies investigating theoretical and applied aspects of distance and online learning have been published. However, the problem of effectively supporting successful student learning still persists (Jo et al, 2016). To gain insight into this problem, one line of research has focused on the characteristics and skills of distance and online learners and the extent to which they predict learning processes and outcomes.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Loads in a Distance Training for Trainee Teachers

Stiller

Bachmaier

2018

Front. Educ.

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One of the persistent problems of online and distance learning is how to effectively support students' successful learning outcomes. A promising approach to this issue is to investigate the extent that learning is determined by learner characteristics and skills. This study explores the effects of domain-specific prior knowledge, intrinsic motivation, computer attitude, computer anxiety, and learning management skills on learning in a distance-training course on media pedagogy for teachers. The data were collected from 127 trainee teachers who responded to various questionnaires and completed knowledge tests. We investigated whether the selected characteristics of learners could be used to model five cognitive load assessments that included ratings of content difficulty, learning ease, experienced pressure/tension, effort/importance while learning, and performance in tests. Computer anxiety and computer attitude were significant predictors of content difficulty, learning ease, and experienced pressure/tension. In addition, higher learning-strategy skills reduced ratings of content difficulty. Effort/Importance ratings were best modeled by intrinsic motivation and learning strategy usage. Performance was best predicted by prior knowledge, intrinsic motivation, and computer attitude.Keywords: distance training, teacher training, higher education, self-regulated learning, computer attitude, computer anxiety, cognitive load theory INTRODUCTIONDistance and online learning have become common and reputable educational methods in vocational training and higher education (e.g., Allen and Seaman, 2016). Numerous studies investigating theoretical and applied aspects of distance and online learning have been published. However, the problem of effectively supporting successful student learning still persists (Jo et al., 2016). To gain insight into this problem, one line of research has focused on the characteristics and skills of distance and online learners and the extent to which they predict learning processes and outcomes. To advance this research, we investigated the extent to which a selected set of learner characteristics affect cognitive load of trainee teachers while learning and performing in a script-based distance-training course about media pedagogy. As characteristics, domain-specific prior knowledge, intrinsic motivation, computer attitude, computer anxiety, and learning strategies were selected, because all of them have shown to be relevant for distance and online learning in empirical studies, and they represent cognitive, motivational, affective, and skill aspects. As theoretical background of the current study, the cognitive load theory (CLT) was chosen (Sweller, 2010). Stiller and Bachmaier Cognitive Loads in Distance TrainingThe CLT highlights the role of cognitive capacity in working memory for successful learning outcomes (Sweller, 2010). Working memory is limited in capacity. If memory items cannot be rehearsed, they decline within seconds. If the material to be learned is complex, that is, it compris...

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What Do Studies of Learning Analytics Reveal About Learning and Instruction? A Systematic Literature Review

Lee,

Recker

2023

Learning, Design, and Technology

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Evaluation of Online Log Variables that Estimate Learners’ Time Management in a Korean Online Learning Context

Cited by 26 publications

References 33 publications

Individual Differences Related to College Students’ Course Performance in Calculus II

Individual Differences Related to College Students’ Course Performance in Calculus II

Cognitive Loads in a Distance Training for Trainee Teachers

What Do Studies of Learning Analytics Reveal About Learning and Instruction? A Systematic Literature Review

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