Deciphering the attributes of student retention in massive open online courses using data mining techniques

Gupta, Shivangi; Sabitha, A. Sai

doi:10.1007/s10639-018-9829-9

Cited by 30 publications

(23 citation statements)

References 25 publications

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“…It helps in removing the anomalies from the dataset and make it ready for the analysis phase. In particular, Data cleaning refers to the technique of cleaning data by removing outliers, replacing missing values, smoothing noisy data, and correcting inconsistent data (Gupta & Sabitha, 2019). It is a significant step as the missing, incorrect, and erroneous data can pose a significant problem to the reliability and validity of study outcomes (Salkind, 2010).…”

Section: Data Preprocessingmentioning

confidence: 99%

Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system

et al. 2021

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Recently, the whole world has faced the deadliest and dangerous consequences due to the transmission of infectious novel coronavirus (nCov). With the outbreak of COVID-19, the education learners, practitioners, and other stakeholders were at the sake of a loss, as it causes the suspension of physical classes and physical interaction of the learners. In these circumstances, Electronic learning (E-learning), Online learning, and the use of Information and Communications Technology (ICT) tools came in handy. It helped the learners in the dissemination of ideas, conducting online classes, making online discussion forums, and taking online examinations. Like the government of each country, the Indian government was also caught off-guard but the existing E-learning infrastructure was able to leverage on while devising plans to tailor them to new situations and launching new ones. The initiatives at the forefront of this noble battle launched by the Department of School Education and Literacy, Ministry of Human Resources Development (MHRD) includes Diksha, Swayam Prabha Channel, Shiksha Van, E-Pathshala, and National Repository of Open Educational Resources (NROER). It worth noting that apart from the Indian central government efforts, each state has various online education initiatives that are tailored to their needs. This research evaluated each of these initiatives commenced by central and state governments and present a detailed analysis of most of the relevant initiatives. Additionally, a survey is conducted to get insights of learners in concern to online learning. Despite the issues raised in this learning, the outcomes come to be satisfactorily favoring online learning.

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Section: Data Preprocessingmentioning

confidence: 99%

Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system

et al. 2021

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“…Fifteen of the analysed studies were performed with online classes. These are the ones referred to in the following studies addressed in [71][72][73][74][75][76][77][78][79][80][81][82][83][84]. Additionally, the studies referred to in [85,86] include both traditional and online classes.…”

Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

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et al. 2021

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Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

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“…Recently, a large number of research studies have been undertaken to analyze clickstream data generated from online learning platforms (MOOCs, LMS, VLE) to measure students' online engagements. While most of the studies try to explore the relationship between clickstream data and students' online engagements, limited studies have taken a step further to facilitate instructors in when and how to intervene students at the optimal time e.g., [35]- [38] Shivangi Gupta and A. Sai Sabitha in their research study attempted to decipher those variables that are responsible for student retention in e-learning [39]. Decision Tree (DT) algorithm was used to determine the significant features to help MOOC learners and designers in improving course content, course design, and delivery.…”

Section: Background and Related Work A Educational Data Mining (mentioning

confidence: 99%

Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models

et al. 2021

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Deciphering the attributes of student retention in massive open online courses using data mining techniques

Cited by 30 publications

References 25 publications

Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system

Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models

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