The Influence of Virtual Learning Environments in Students' Performance.

This paper focuses mainly on the relation between the use of a virtual learning environment (VLE) and students' performance. Therefore, virtual learning environments are characterised and a study is presented emphasising the frequency of access to a VLE and its relation with the students' performance from a public higher education institution during the academic year of 2014-15. The main aim of this research work is to obtain indicators which may help understand relations between the use of VLEs and students' performance. Finding the frequency of access to the VLE and assessing the consequences of such use represent challenges to which teachers and researchers try to respond in order to know students better and consequently, develop strategies which meet their interests and needs. This study is mainly quantitative with descriptive features, involving data obtained from literature research and from experimental research using a sample of approximately 6300 undergraduates. The data was extracted from the VLE and student registration system databases using learning analytics procedures. The results show that there are relatively positive indicators regarding students' access to a virtual learning environment and the relation between such access and their performance.

[1]  Ilya Zitter,et al.  Adding a design perspective to study learning environments in higher professional education , 2011 .

[2]  Anastasios A. Economides,et al.  Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence , 2014, J. Educ. Technol. Soc..

[3]  George Siemens,et al.  Current state and future trends: a citation network analysis of the learning analytics field , 2014, LAK.

[4]  Ryan S. Baker,et al.  Educational Data Mining and Learning Analytics , 2014 .

[5]  Ji Won You,et al.  Identifying significant indicators using LMS data to predict course achievement in online learning , 2016, Internet High. Educ..

[6]  Carlos Morais,et al.  Valorização dos ambientes virtuais de aprendizagem por professores do ensino superior , 2013 .

[7]  Zdenek Zdráhal,et al.  Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment , 2013, LAK '13.

[8]  Miguel Ángel Conde González,et al.  Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning , 2014, Comput. Hum. Behav..

[9]  Dragan Gasevic,et al.  Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success , 2016, Internet High. Educ..

[10]  Stavros Valsamidis,et al.  E-Learning Activity Analysis , 2014 .

[11]  Carlos Morais,et al.  Open educational resources : higher education students’ knowledge and use , 2014 .

[12]  Dana-Kristin Mah,et al.  Learning Analytics and Digital Badges: Potential Impact on Student Retention in Higher Education , 2016, Technology, Knowledge and Learning.

[13]  Hendrik Drachsler,et al.  Translating Learning into Numbers: A Generic Framework for Learning Analytics , 2012, J. Educ. Technol. Soc..

[14]  George Siemens,et al.  Guest Editorial - Learning and Knowledge Analytics , 2012, J. Educ. Technol. Soc..

[15]  D. Christopher Brooks,et al.  The Current Ecosystem of Learning Management Systems in Higher Education: Student, Faculty, and IT Perspectives. , 2014 .

[16]  George Siemens,et al.  Let’s not forget: Learning analytics are about learning , 2015 .

[17]  A. Almeida,et al.  Complexidade e Comunicação Mediada por Computador na Aprendizagem de Conceitos Matemáticos , 2000 .