Moodle4SPOC: A Resource-Intensive Blended Learning Course

Ziebarth, Sabrina; Hoppe, H. Ulrich

doi:10.1007/978-3-319-11200-8_27

Cited by 13 publications

(3 citation statements)

References 8 publications

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“…Such online operational behaviors, once properly interpreted, can reflect the students' online learning behaviors. Online learning behaviors are mostly calculated on the basis of frequency and time (Shang et al, 2020), including the total frequency of which a course was accessed (Asarta & Schmidt, 2013), the total frequency and total duration of which a resource was accessed (Morris et al, 2005), the total frequency or duration of which a teaching video was accessed (Lin et al, 2017;Lust et al, 2013;Ziebarth & Hoppe, 2014), the time of which a test was taken (Li & Baker, 2018), and the total number of posts created in online discussion (Sunar et al, 2018). After researchers collect these online learning behaviors, data cleaning must be performed to prevent bias caused by abnormal values (Agrawal et al, 2014), and the adequacy of the behavioral data collection must be verified (Dumais et al, 2014).…”

Section: Online Learning Behaviorsmentioning

confidence: 99%

Effects of the COVID-19 pandemic on the online learning behaviors of university students in Taiwan

2021

Educ Inf Technol

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Prior to the coronavirus disease 2019 (COVID-19) pandemic, due to the rarity of pandemics in recent centuries, suitable conditions did not exist in educational institutions for the implementation of asynchronous distance teaching. No empirical studies have been conducted on whether the considerable environmental changes caused by COVID-19 have affected students’ online learning behaviors. Therefore, this study collected information on students’ online learning behaviors during the COVID-19 pandemic and other periods to examine whether pandemic-caused environmental changes affected students’ online learning behaviors. This study focuses on the 60-day transmission after the beginning of the second semester of the 2019 academic year. The data source was from a comparative assessment between the pandemic group (331 students) and the control group (101 students). The Spearman Rank Correlation Test and the Wilcoxon signed-rank test were used as our statistical methods. This paper presents preliminary results on how COVID-19 has affected students’ online learning behaviors and proposes asynchronous online learning as a method for maintaining university students’ learning during the COVID-19 pandemic.

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Section: Online Learning Behaviorsmentioning

confidence: 99%

Effects of the COVID-19 pandemic on the online learning behaviors of university students in Taiwan

2021

Educ Inf Technol

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“…Thus, in summer semester of 2013, the exercise was "virtualized" when the presence-based activity was replaced by a combination of individual and group assignments with reporting back incorporated into the Moodle platform (Ziebarth & Hoppe, 2014). The plenary face-toface setting of the lecture was used as a forum to take up and discuss results from the virtual exercise activities.…”

Section: The Gills Blended Learning Coursementioning

confidence: 99%

“…When there is no or very limited direct communication between learners and a knowledge conveying person (such as a teacher or tutor), learners have to be provided with a set of learning resources that enable self-directed acquisition of knowledge. To achieve this, traditional reading materials and lecture videos can be accompanied by quizzes for self-assessment and by wikis or forums to support collaborative writing, peer reviewing, and discussing the material (Ziebarth & Hoppe, 2014). The combination of the aforementioned resource types and activities provides the typical setting of what we call a "resource-intensive online course."…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dynamic Resource Access Patterns in Online Courses

2014

Self Cite

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This paper presents an analysis of resource access patterns in two recently conducted online courses. One of these has been a master level university lecture taught as a blended learning course with a wide range of online learning activities and materials, including collaborative wikis, self-tests, and thematic videos. The other course has been offered in the form of a MOOC. As a specialty of this course, master level students from two different universities could participate as a regular university class and receive credits for successful completion. In both courses, online learning resources such as videos, scientific literature, and wikis played a central role. In this context, the motivation for our research was to investigate characteristic patterns of resource usage of the learners. In order to gain deeper insights into the usage of learning materials, we have adapted methods from social network analysis and applied them to dynamic bipartite student-resource networks built from event logs of the students' resource access. In particular, we describe the clustering of students and resources in such networks and propose a method to identify patterns of the cluster evolution over time.

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