Logging for Success: Advancing the Use of WWW Logs to Improve Computer Mediated Distance Learning

Peled, Alon; Rashty, David

doi:10.2190/nlr6-k355-laqy-u01d

Cited by 40 publications

(19 citation statements)

References 14 publications

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“…Studies that looked at learning differences found a large variability in students' use of e-learning components in terms of, for example, participation in discussion groups, running simulations, reading information, watching presentations, and voting in polls (Leiba & Nachmias, 2006, Nachmias & Segev, 2003Peled & Rashty, 1999). It was also found that individual differences significantly affect students' online learning.…”

Section: Individual Differences In Computer Related Behaviormentioning

confidence: 99%

“…It was also found that individual differences significantly affect students' online learning. Variables such as gender (McSporran & Young, 2001;Peled & Rashty, 1999), age (Cavanaugh et al, 2004), culture (McGee, 2002), and learning styles (Sabry & Baldwin, 2003) seem to indicate different patterns in learning preferences, usage, and interactions in online learning environments. Our study focuses on two such variables: age and gender.…”

Section: Individual Differences In Computer Related Behaviormentioning

confidence: 99%

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Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Ben-Zadok¹,

Leiba

Nachmias

2010

IJELL

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The research objectives of this study are: (a) to compare learning behaviors in an online science learning environment -in school vs. at home; and (b) to explore the existence of some behavioral differences, in school and at home, in terms of age and gender. The actions of 1,179 elementary school students in an online science learning environment were documented in a log file and statistically analyzed. Results suggest that students who learn at home tend to spend more time learning; they learn at a slower pace and score higher on a test than students who learn in school. However, no significant differences were found between home and school in terms of the amount of completed activities, the rate of simulation use, and the sequence of learning. Comparison between age groups indicates that younger students tend to learn for more time, at a slower pace, and complete fewer activities than older students. Comparison between genders, on the other hand, reflects similar learning behaviors for boys and girls. The results also suggest that neither age nor gender affect the differences found between school and home behaviors. Our conclusions indicate that extending teaching time to the home by means of online learning environments is possible and worthwhile, regardless of age or gender. Nevertheless, it should be noticed that in some cases more learning time is necessary for young students in online assignments.

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Section: Individual Differences In Computer Related Behaviormentioning

confidence: 99%

Section: Individual Differences In Computer Related Behaviormentioning

confidence: 99%

Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Ben-Zadok¹,

Leiba

Nachmias

2010

IJELL

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“…Sheard, Albrecht, and Butbul (2005) analyzed Web log files and found that knowing when students access various resources helps instructors understand the students' preferred learning patterns. While analyzing log data to investigate learning effectiveness, Peled and Rashty (1999) found that the most popular online activities were, in general, passive activities, such as retrieving information, rather than contributing activities. Dringus and Ellis (2005) reported on how to analyze asynchronous discussion form usage data to evaluate the progress of a threaded discussion.…”

Section: Behavioral Interactionsmentioning

confidence: 99%

Analysis of Time-on-Task, Behavior Experiences, and Performance in Two Online Courses with Different Authentic Learning Tasks

Park

2017

IRRODL

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This paper reports the findings of a comparative analysis of online learner behavioral interactions, timeon-task, attendance, and performance at different points throughout a semester (beginning, during, and end) based on two online courses: one course offering authentic discussion-based learning activities and the other course offering authentic design/development-based learning activities. Web log data were collected to determine the number of learner behavioral interactions with the Moodle learning management system (LMS), the number of behavioral interactions with peers, the time-on-task for weekly tasks, and the recorded attendance. Student performance on weekly tasks was also collected from the course data.Behavioral interactions with the Moodle LMS included resource viewing activities and uploading/downloading file activities. Behavioral interactions with peers included discussion postings, discussion responses, and discussion viewing activities. A series of Mann-Whitney tests were conducted to compare the two types of behavioral interactions between the two courses. Additionally, each student's behavioral interactions were visually presented to show the pattern of their interactions. The results indicated that, at the beginning of the semester, students who were involved in authentic design/development-based learning activities showed a significantly higher number of behavioral interactions with the Moodle LMS than did students involved in authentic discussion-based learning activities. However, in the middle of the semester, students engaged in authentic discussion-based learning activities showed a significantly higher number of behavioral interactions with peers than did students involved in authentic design/development-based learning activities. Additionally, students who were given authentic design/development-based learning activities received higher performance scores both during the semester and at the end of the semester and they showed overall higher performance scores than Analysis of Time-on-Task, Behavior Experiences, and Performance in Two Online Courses with Different Authentic Learning Tasks Park 214 students who were given authentic discussion-based learning activities. No differences were found between the two groups with respect to time-on-task or attendance.

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“…Attesting to this growth, a 2005 report shows that more than 60% of US colleges that offered traditional classroom-based programs also offered online programs, and student enrolments in online programs jumped 19% from the previous year (Sloan, 2005). Some researchers have even touted e-learning as a teaching and learning revolution (Peled & Rashty, 2000;Garrison & Anderson, 2003).…”

Section: Conceptual Developmentmentioning

confidence: 99%

The Roles of Extrinsic Factors in a Community of Inquiry Model of E-Learning

Lee

Faulkner

2011

E-Learning and Digital Media

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Despite its proliferation in higher learning institutions, e-learning has been criticised as being nothing more than trivial online conversations, or a mere means of delivering class materials electronically. If e-learning is to address such criticisms and become an effective pedagogical platform, educators need to identify elements critical to the success of e-learning. A well-known framework for studying e-learning as a pedagogical platform is the community of inquiry (COI) model. In this study, the authors develop a conceptual COI model to propose that while satisfactory e-learning experience stems from the interactions among three presences -social, cognitive, and teaching -the relative influences of the presences on e-learning experience are moderated by five extrinsic factors: content richness, perceived ease of use of e-learning platform, type of curriculum, teaching orientation, and participant age. They argue that this extended and more comprehensive COI framework would help educators better understand e-learning's use as an effective pedagogical platform.

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Logging for Success: Advancing the Use of WWW Logs to Improve Computer Mediated Distance Learning

Cited by 40 publications

References 14 publications

Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Analysis of Time-on-Task, Behavior Experiences, and Performance in Two Online Courses with Different Authentic Learning Tasks

The Roles of Extrinsic Factors in a Community of Inquiry Model of E-Learning

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