Graph-based Massive Open Online Course (MOOC) Dropout Prediction using Clickstream Data in Virtual Learning Environment

Author(s):  
Izumi Nitta ◽  
Ryo Ishizaki ◽  
Masafumi Shingu ◽  
Satoshi Nakashima ◽  
Koji Maruhashi ◽  
...  
Author(s):  
Shweta Pandey ◽  
Satyam Prakash Tripathi

As the Massive Open Online Course (MOOC) concept is adding a new dimension to online learning and presenting a deeper impact in different disciplines including the library and information science area, library and information science professionals are producing scholarly literature on MOOC-related issues. Through this chapter, the authors gave the overview of the genesis of MOOCs in a new learning environment. This article gives the outlook of MOOCs, which are one of the latest trends in education. This chapter also explores various literature reviews on the conceptual framework and discusses the online courses in general and specifically for LIS domain.


Author(s):  
Ahmed Al-Azawei ◽  
Miami Abdul Aziz Al-Masoudy

This study aims at predicting undergraduate students' performance in the Virtual Learning Environment (VLE) based on four time periods of the examined online course. This is to provide an early and continuous prediction of students' academic achievement. This research depends on data from one of the scientific courses at the Open University (OU) in Britain, which offers its lectures using VLE. The data investigated consists of 1938 students in which the influence of demographic and behavioral variables was explored first. Then, three features were generated to improve the prediction accuracy as well as examining the effect of learners' engagement on their academic performance. Accordingly, a comparison was made between the prediction accuracy of integrating the proposed features with the behavioral and demographic features and the use of the original features only. The findings suggest that some of the demographic variables and all behavioral features had a significant impact on students' performance. However, the accuracy was highly improved after using the new generated features. It was found that the level of the financial and service instability, level of participation in the course, assessment grades, the total number of clicks, the interaction with different course activities, and students' engagement were significant predictors of academic achievement.


2009 ◽  
Vol 2 (1) ◽  
Author(s):  
Victoria Lynn Walker

This article will present the process and the curricular and pedagogical lessons learned from adding a 3D virtual learning environment to an online course in a distance and hybrid education master’s degree program. Based on student surveys, course evaluations, and faculty interviews, the author will summarize attitudes and expectations from the varied stakeholders and offer practical recommendations on the design and delivery of an effective virtual world learning environment in an online course. The author is involved in developing 3D virtual learning environments and integrating their use in graduate level counseling courses in traditional, blended, and online master’s programs. In the fall of 2007, the author began the process of incorporating the virtual world Second Life into an online counseling skills and techniques course in the Human Services Counseling Program at Regent University. The course objectives consisted of teaching graduate level students expertise such as clinical counseling skills and techniques. One of the critical competencies, the student’s ability to demonstrate the basic counseling skills needed to be an effective counselor including attending, listening, empathy, warmth, and respect, was very difficult to teach and evaluate from a distance. In the past, program instructors have taught online and blended courses with the asynchronous learning environment Blackboard and the synchronous technologies, Skype and Instant Messenger. With the use of new learning environments, such as 3D virtual learning environments, the author hoped to provide the instructors and students with an environment more conducive to developing effective counseling skills. The author implemented the virtual learning environment – a simulated counseling facility and tested the virtual counseling facility’s use as an innovative learning environment for simulation of student counseling sessions. This article will discuss the author’s personal experiences as well as the empirical research collected during this case study. Given the potential for significant growth in the use of virtual learning objects, this article should provide useful information for instructors and administrators considering virtual environments as a means of teaching practical skills at a distance in online programs.


2019 ◽  
Vol 11 (24) ◽  
pp. 7238 ◽  
Author(s):  
Naif Radi Aljohani ◽  
Ayman Fayoumi ◽  
Saeed-Ul Hassan

In higher education, predicting the academic performance of students is associated with formulating optimal educational policies that vehemently impact economic and financial development. In online educational platforms, the captured clickstream information of students can be exploited in ascertaining their performance. In the current study, the time-series sequential classification problem of students’ performance prediction is explored by deploying a deep long short-term memory (LSTM) model using the freely accessible Open University Learning Analytics dataset. In the pass/fail classification job, the deployed LSTM model outperformed the state-of-the-art approaches with 93.46% precision and 75.79% recall. Encouragingly, our model superseded the baseline logistic regression and artificial neural networks by 18.48% and 12.31%, respectively, with 95.23% learning accuracy. We demonstrated that the clickstream data generated due to the students’ interaction with the online learning platforms can be evaluated at a week-wise granularity to improve the early prediction of at-risk students. Interestingly, our model can predict pass/fail class with around 90% accuracy within the first 10 weeks of student interaction in a virtual learning environment (VLE). A contribution of our research is an informed approach to advanced higher education decision-making towards sustainable education. It is a bold effort for student-centric policies, promoting the trust and the loyalty of students in courses and programs.


Author(s):  
Inge De Waard ◽  
Sean Abajian ◽  
Michael Sean Gallagher ◽  
Rebecca Hogue ◽  
Nilgün Keskin ◽  
...  

<p>In this paper, we look at how the massive open online course (MOOC) format developed by connectivist researchers and enthusiasts can help analyze the complexity, emergence, and chaos at work in the field of education today. We do this through the prism of a MobiMOOC, a six-week course focusing on mLearning that ran from April to May 2011. MobiMOOC embraced the core MOOC components of self-organization, connectedness, openness, complexity, and the resulting chaos, and, as such, serves as an interesting paradigm for new educational orders that are currently emerging in the field. We discuss the nature of participation in MobiMOOC, the use of mobile technology and social media, and how these factors contributed to a chaotic learning environment with emerging phenomena. These emerging phenomena resulted in a transformative educational paradigm. <br /><br /></p>


10.2196/12152 ◽  
2018 ◽  
Vol 4 (2) ◽  
pp. e12152 ◽  
Author(s):  
Jennie C De Gagne ◽  
Kim Manturuk ◽  
Hyeyoung K Park ◽  
Jamie L Conklin ◽  
Noelle Wyman Roth ◽  
...  

Author(s):  
Rubí Estela Morales-Salas ◽  
Daniel Montes-Ponce

A virtual learning environment is conceived as an interaction space that ease the realization of mediated activities by technology, in this case the internet; besides using multimedia materials, learning objects, social networks, among others; which have changed imminently the traditional education. In this article an instrument is proposed in a checklist format, to evaluate any platform that has interaction spaces such as a Virtual Learning Environment, in this case responding to four spaces or general indicators: information Space, Mediation / Interaction Space, Instructional Design Space and Exhibition Space. Criteria are used according to the interactions and activities carried out by the consultant and virtual student. These, in turn, come up from the analysis and interaction of the advisers achieved in the discussion forums and portfolio activities through collaborative work. It was situated as a qualitative research, with a descriptive nature since it is not limited to data collection only, but also it refers and analyzes the interaction of the advisers achieved in the discussion forums and portfolio activities through the collaborative work of the workshop course "Virtual Learning Environments" developed in a virtual learning environment.


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