MOOCs: A comprehensive study to highlight its strengths and weaknesses

Sachdeva, Arjit; Singh, Prashast Kumar; Sharma, Amit

doi:10.1109/mite.2015.7375346

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Moreover, these platforms help students to bridge the gap between the skills they acquire from academia and the skills demanded by the industry. MOOCs provide a better flow of information and knowledge to the students (Chen et al, 2014; Sachdeva et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Early prediction of student engagement in virtual learning environments using machine learning techniques

Renumol

2022

E-Learning and Digital Media

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Learning analytics aims at helping the students to attain their learning goals. The predictions in learning analytics are made to enhance the effectiveness of educational interferences. This study predicts student engagement at an early phase of a Virtual Learning Environment (VLE) course by analyzing data collected from consecutive years. The prediction model is developed using machine learning techniques applied to a subset of Open University Learning Analytics Dataset, provided by Open University (OU), Britain. The investigated data belongs to 7,775 students who attended social science courses for consecutive assessment years. The experiments are conducted with a reduced feature set to predict whether the students are highly or lowly engaged in the courses. The attributes indicating students' interaction with the VLE, their scores, and final results are the most contributing variables for the predictive analysis. Based on these variables, a reduced feature vector is constructed. The baseline used in the study is the linear regression model. The model’s best results showed 95% accurate, 95% precise, and 98% relevant results with the Random Forest classification algorithm. Early prediction’s relevant features are a subset of click activities, which provided a functional interface between the students and the VLE.

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Section: Introductionmentioning

confidence: 99%

Early prediction of student engagement in virtual learning environments using machine learning techniques

Renumol

2022

E-Learning and Digital Media

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“…To overcome such issues, Massive Open Online Courses (MOOCs) are introduced. MOOCs are mostly free of charge, which help learners to study different courses and attain certifications that they can use in their professional life (Sachdeva et al, 2016). This helps learners to remotely access the courses and learn from top educators which otherwise is physically impossible.…”

Section: Introductionmentioning

confidence: 99%

Facilitating Learner Centric Decision Making for Massive Open Online Courses

Kumar¹,

Bhardwaj²,

Hasteer³

et al. 2022

JEET

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: Learners opt for MOOCs as it provides multiple benefits and the most important being that these courses can be accessed anytime and from anywhere with internet access. Learners can study their desired course at their convenience of time and desired pace. But as there are multiple options for them, they may get confused about selecting a course. This study highlights the findings of implementing the Analytical Hierarchical Process (AHP), a Multiple Criteria Decision Making (MCDM) technique used to choose the best option in case of multiple alternatives. Attributes were selected from literature and a survey was administered to learners. Responses were analyzed and courses were ranked based on their scores. From the analysis, we interpret that usefulness of the course in the University Curriculum is the most preferred criteria while selecting the course. Keywords : MOOC, Multiple Criteria Decision Making, Analytical Hierarchical Process

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“…Online education has been a trend in modern higher education. Massive Open Online Courses (MOOCs) have enabled any people with Internet connections to access high quality learning materials from top universities [1]- [3]. All over the world, the fast development of Information and Communications Technology (ICT) and various online learning platforms, e.g.…”

Section: Introductionmentioning

confidence: 99%

Online Laboratory Sessions for the Education of Electrical Machines and Drives

Dong

Soeiro

Koeners

et al. 2020

2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)

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This paper presents a method to implement online laboratory sessions for education in Electrical Engineering on subjects related to electrical machines and drives. The assignments for the laboratory sessions consist of: the preparation homework, where students have to study relevant theories; the development of circuit simulations; and the participation on a practical session online. For the latter, the students have to implement remotely various models in a physical setup based on the results of their homework tasks. After taking the assignments, the students should be able to: set up circuit simulations of electrical machines and drives; to implement relevant control of motor drives in a physical setup; and finally, to interpret the differences between the simulation and experiment results. Detailed design of the physical setup, the assignments, the relevant Information and Communication Technology (ICT) facilities are presented and discussed in this paper. An example and the feedback of applying it in a master level course is presented. The experience gained so far shows that the proposed online laboratory sessions can be used as supplementary educational resource to existing online education platforms.

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MOOCs: A comprehensive study to highlight its strengths and weaknesses

Cited by 10 publications

References 3 publications

Early prediction of student engagement in virtual learning environments using machine learning techniques

Early prediction of student engagement in virtual learning environments using machine learning techniques

Facilitating Learner Centric Decision Making for Massive Open Online Courses

Online Laboratory Sessions for the Education of Electrical Machines and Drives

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