Beyond marks: new tools to visualise student engagement via social networks

Badge, Joanne Louise; Saunders, Neil F. W.; Cann, Alan J.

doi:10.3402/rlt.v20i0/16283

Cited by 9 publications

(13 citation statements)

References 22 publications

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“…Geleneksel öğrenme ortamlarında ve çevrimiçi öğrenme ortamlarında çevresel düzenlemelerin, çevresel uyaranların, öğretmen ve öğrenci rollerinin farklılaşması öğrencilerin öğrenme yollarında da farklara yol açabilir (Badge, Saunders, & Cann, 2012;Maki & Maki, 2007). Beadles II ve Lowery (2007), e-öğrenme ortamını tercih eden öğrenciler ile geleneksel öğrenme ortamını tercih eden bireylerin öğrenme stillerinin farklı olduğundan söz etmektedirler.…”

Section: Introductionunclassified

E-öğrenme Ortamlarında Öğrenme Stilleri ve Akademik Başarı Arasındaki İlişkinin İncelenmesi

Ergün

Kurnaz

2019

Kuramsal Eğitimbilim

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In this study, the effects of students learning styles on academic achievement was investigated in elearning environments and the answer for the question "are e-learning style, participation in online course and watching the lecture videos offline predictors of academic achievement?" is tried to examined. E-Learning Style Scale (Gülbahar and Alper, 2014) and Achievement Test were applied 501 (350 female and 151 male) students who received pedagogical formation education in e-learning environment during the 2016-2017 academic year. The research was carried out online with learning management systems in the measurement and assessment course for 14 weeks. The certificate of pedagogical formation was given to students at the end of the course. The Achievement Test developed by researchers. This test was tested in another group similar to the study group and found reliable and valid results. The scores obtained from this test were accepted as a measure of academic achievement. Data were analyzed by stepwise multiple regression analyses. According to the results, participation in online course, watching video recordings, active learning style and independent learning style were found as predictors of students' academic achievement. These four variables account for 7% of the total variance in academic achievement.

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Section: Introductionunclassified

E-öğrenme Ortamlarında Öğrenme Stilleri ve Akademik Başarı Arasındaki İlişkinin İncelenmesi

Ergün

Kurnaz

2019

Kuramsal Eğitimbilim

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“…Integrating the design patters from such social networking sites into learning and content management system could be beneficial to students. The inclusion of the design and social approaches of the freely available social networking sites and web 2.0 technologies to science education have positive impact on learning [22,30]. Ensuring the guidelines derived from this research should provide the students with a flexible, personalized and hence satisfactory learning experience which is critical for success in fully online computing courses.…”

Section: Discussionmentioning

confidence: 99%

What Factors Impact Student – Content Interaction in Fully Online Courses

Nandi¹,

Hamilton²,

Harland³

2015

IJMECS

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Abstract-The rapid advancement of fully online courses has made education accessible from -anywhere‖ and -anytime‖. One of the major success factors of online courses is effective student -content interaction -which defines how students interact with the content in the fully online learning environment. However, there appears to have been little research published about the relationship of content to course design and consequent outcomes for students. In this article, we report on our research on the investigation of factors that affect student -content interaction in fully online computing courses. We have conducted our research through surveys and used a grounded theoretic approach for data analysis. Our results identify the factors that are perceived by students as critical in ensuring effective student -content interaction in fully online computing courses.

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“…Two studies that utilised social network tools to visually represent student LMS engagement data are of relevance to the current paper. The first one, by Badge et al (2012), used what it calls student academic contributions to a public social network as a proxy for student engagement. It took place at one university in the United Kingdom, and involved a cohort of undergraduate students in the School of Biological Sciences in the 2009/2010 academic year.…”

Section: Related Literaturementioning

confidence: 99%

Online module login data as a proxy measure of student engagement: the case of myUnisa, MoyaMA, Flipgrid, and Gephi at an ODeL institution in South Africa

Chaka

Nkhobo

2019

Int J Educ Technol High Educ

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The current study employed online module login data harvested from three tools, myUnisa, Moya MA and Flipgrid to determine how such data served as a proxy measure of student engagement. The first tool is a legacy learning management system (LMS) utilised for online learning at the University of South Africa (UNISA), while the other two tools are a mobile messaging application and an educational video discussion platform, respectively. In this regard, the study set out to investigate the manner in which module login data of undergraduate students (n = 3475 & n = 2954) and a cohort of Mathew Goniwe students (n = 27) enrolled for a second-level module, ENG2601, as extracted from myUnisa, Moya MA , and Flipgrid served as a proxy measure of student engagement. Collectively, these students were registered for this second-level module at UNISA at the time the study was conducted. The online login data comprised myUnisa module login file access frequencies. In addition, the online login data consisted of the frequencies of instant messages (IMs) posted on Moya MA by both the facilitator and Mathew Goniwe students, and video clips posted on and video clip view frequencies captured by Flipgrid in respect of the afore-cited module. One finding of this study is that student engagement as measured by login file access frequencies was disproportionally skewed toward one module file relative to other module files. The other finding of this study is that the overall module file access metrics of the Mathew Goniwe group were disproportionally concentrated in a sub-cohort of highly active users (HAU).

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Beyond marks: new tools to visualise student engagement via social networks

Cited by 9 publications

References 22 publications

E-öğrenme Ortamlarında Öğrenme Stilleri ve Akademik Başarı Arasındaki İlişkinin İncelenmesi

E-öğrenme Ortamlarında Öğrenme Stilleri ve Akademik Başarı Arasındaki İlişkinin İncelenmesi

What Factors Impact Student – Content Interaction in Fully Online Courses

Online module login data as a proxy measure of student engagement: the case of myUnisa, MoyaMA, Flipgrid, and Gephi at an ODeL institution in South Africa

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