The important elements of LMS design that affect user engagement with e-learning tools within LMSs in the higher education sector

Zanjani, Nastaran; Edwards, Sylvia L.; Nykvist, Shaun; Geva, Shlomo

doi:10.14742/ajet.2938

Cited by 61 publications

(66 citation statements)

References 39 publications

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“…In online courses, as in any academic work, students' active engagement affects their learning outcomes, cognitive development, and educational quality (Soffer & Nachmias, 2018;Hew, 2016;Smith, Sheppard, Johnson, & Johnson, 2005;). Previous studies discuss various aspects of engagement, for example, the effect of LMS design factors on user engagement (Zanjani, Edwards, Nykvist, & Geva, 2017); the influence of personal aspects on student engagement (Jung & Lee, 2018;Pellas, 2014); and instructor scaffolding for interaction (Cho & Cho, 2014). Yet hardly any have looked directly at the impact of the course's pedagogical model on engagement (Khan, Egbue, Palkie, & Madden, 2017).…”

Section: Introductionmentioning

confidence: 99%

Students' engagement characteristics predict success and completion of online courses

Soffer

Cohen

2019

Computer Assisted Learning

139

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This study examined students' engagement characteristics in online courses and their impact on academic achievements, trying to distinguish between course completers and noncompleters. Moreover, this research is intended to differentiate between those who pass the final exam and those who do not. Four online courses were examined with a similar pedagogical model (Nstudents = 646) using learning analytics methods. The results revealed significant differences between students who completed the courses and students who did not, in all 13 variables. Completers' learning activities were more than twice as high, except for writing in the forums. Course subject and ongoing task and assignment submissions predicted course completion, whereas, in addition to these variables, engagement with course materials and reading the forums predicted final exam success, as well. Thus, the prediction of success in final exam emphasized the significant importance of engagement in various activities in the online course.

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Section: Introductionmentioning

confidence: 99%

Students' engagement characteristics predict success and completion of online courses

Soffer

Cohen

2019

Computer Assisted Learning

139

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“…For example, Bocevska et al (2018) defined a personalized learning method as a part of LMS improvement that refers to a variety of learning experiences, approaches and strategies for addressing various learning needs, aspirations, interest and backgrounds of individual learners, through the use of data generated by the learners' activities. Whereas Zanjani et al (2017) investigated the LMS design determinants that impact user engagement with LMS tools in higher education. Valsamidis et al (2012) also investigated the design of LMS for improving provisions through data mining techniques and therefore proposed specific metrics for the assessment of the courses.…”

Section: Aim and Contributionsmentioning

confidence: 99%

Editorial note: Learning management systems and big data technologies for higher education

Miah

Shen

2020

Educ Inf Technol

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We are living in a "data-driven era" where new data is continuously being generated by people, businesses, organisations, communities and society each and every moment. This rapidly growing and huge amount of data must be managed through effective way for producing useful information and human decision support for current and future problems solving, planning and practices improvements as well as to generate new knowledge for future generation. As a result, organizations globally are making significant investments to explore how to better utilise the huge data and its diversification to create value and actionable insights (for example, Pardos 2017; Miah et al. 2017, 2019a, b). Big data has been a popular research problem across different academic disciplines. Although this problem has been treated mainly for advancing and innovating technological development (Wang et al. 2017), organisations and business communities are continuously exploring different aspects, perspectives and contextual specifics to find or explore benefits and value adding for improving practices. A lot of existing studies have defined the big data considering large volumes of broadly varied and complexity of datasets that are continuously being generated. The consideration for defining this goes to velocity, volume, value, variety, and veracity, so-called the "five V's of Big Data" (Gandomi and Haider 2015). Organizations such as education institutes have started to treat the issues of big data for reinforcing traditional electronic learning and teaching methods and other relevant products, and services (McAfee et al. 2012). For doing this, opportunity of adopting latest analytics, predictive algorithms and other disruptive technologies are rapidly developed for advancing the traditional e-learning approaches, for example in improving learning management systems (LMS).

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“…LMS and the internet provides a selection of technologies to enhance learning like collaboration tools, discussion groups, wikis, videos, slides, simulations, online assessments, email, live chat etc. Incorporating too many tools into a course can cause a "technology overload" meaning that the course becomes overwhelming for a user, resulting in them avoiding many tasks altogether [17]. Selecting the right tool for the task involves many factors.…”

Section: E-learning Designsmentioning

confidence: 99%

“…[18] maintains that short tasks that have clearly defined outcomes and require the use of easy-to-use technology is a more favored approach for users. [17] adds that perceived ease of use encourages students to continue using the technology but cautions instructors against the technology overload that results from too many or too complex technologically enhanced tasks. Technology overload is subjective depending on the student's E-skills level capability.…”

Section: E-learning Designsmentioning

confidence: 99%

“…The results of a quantitative study of university students conducted by [19] also posits that students experienced greater satisfaction in their blended learning courses as they progressed to the next year of study. Evidence was not conclusive that the second and third year courses were better structured and easier to use, therefore the students' experience could possibly have been influenced by their increasing familiarity with the LMS in each progressive year [17] [20].…”

Section: E-learning Designsmentioning

confidence: 99%

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Exploring Elements of Effective E-learning design for the Development of Student E-skills

Soobramoney¹,

Heukelman²

2019

IJDS

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The merits of E-learning technologies are undeniable consequently; E-learning has been widely adopted by universities across the world. This widespread adoption in South Africa coexists with well documented diversities in student levels of Eskills, caused by socioeconomic challenges, causing undue pressure on students who do not have the required skills to harness the benefits of technology use for learning. Technology enhanced learning is invaluable, but careful consideration must be given to the design of technology enhanced learning tasks to prevent undue pressure on previously disadvantaged students. E-learning tasks may further be used as a tool to build student capacity to use E-learning to develop their E-skill and prepare them for a technology enhanced world. Qualitative data relating to E-skills development of sixteen undergraduate students enrolled in a blended E-learning classroom were recorded and possible relationships or effects that the use of E-learning had on students and their Eskill development were investigated. Straussiangrounded theory methods of analysis ensured thorough and consistent analysis of data. The underlying relationships and emerging theory suggest that task complexity, task duration and availability of ICT resources were important factors to be considered when selecting technology enhanced tasks. Carefully designed incrementally complex tasks coupled with the instructor's level of support were found to be crucial for students with lower levels of eskills to benefit from the learning task and develop their e-skills in the process. Selection of appropriate E-learning tools to balance the cognitive load, relating to both content and technology, assisted students in managing the task and developing their skill.

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The important elements of LMS design that affect user engagement with e-learning tools within LMSs in the higher education sector

Cited by 61 publications

References 39 publications

Students' engagement characteristics predict success and completion of online courses

Students' engagement characteristics predict success and completion of online courses

Editorial note: Learning management systems and big data technologies for higher education

Exploring Elements of Effective E-learning design for the Development of Student E-skills

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