Selecting Directors Using Machine Learning

Erel, Isil; Stern, Lea Henny; Tan, Chenhao; Weisbach, Michael S.

doi:10.2139/ssrn.3144080

Cited by 25 publications

(42 citation statements)

References 11 publications

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“…• Support Vector Machine (SVM) (Naghibi, Ahmadi, & Daneshi, 2017) • K Nearest Neighbors (KNN) (Martinez-Espana et al, 2018) • Decision Tree (DT) (Erel, Stern, Tan, & Weisbach, 2018) • Naive Bayes (NB) (Gao, Cheng, He, Susilo, & Li, 2018) • Logistic Regression (LR) (Os, Ramos, Hilbert, & Leeuwen, 2018) Our study was not limited to classical machine learning algorithms but also included the combinatorial method. According to the literature, the work proposed to predict a learner's class was based on the use of a single algorithm among the classical machine learning algorithms.…”

Section: Machine Learnig Algorithmsmentioning

confidence: 99%

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

Mourdi¹,

Sadgal²,

Fathi³

et al. 2020

Turkish Online Journal of Distance Education

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At the beginning of the 2010 decade, the world of education and more specifically e-learning was revolutionized by the emergence of Massive Open Online Courses, better known by their acronym MOOC. Proposed more and more by universities and training centers around the world, MOOCs have become an undeniable asset for any student or person seeking to complete their initial training with free distance courses open to all areas. Despite the remarkable number of course enrollees, MOOCs have a huge dropout rate of up to 90%. This rate significantly affects the efforts made by the moderators for the success of this pedagogical model and negatively influences the learners' experience and their supervision. To address this problem and help instructors streamline their interventions, we present a solution to classify MOOC learners into three distinct classes. The approach proposed in this paper is based on the filters methods to select the most relevant attributes and ensembling methods of machine learning algorithms. This approach has been validated by four MOOC courses from Stanford University. In order to prove the performance of the model (92.2%), a comparative study between the proposed model and other algorithms was made on several performance measures.

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Section: Machine Learnig Algorithmsmentioning

confidence: 99%

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

Mourdi¹,

Sadgal²,

Fathi³

et al. 2020

Turkish Online Journal of Distance Education

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“…One of the exciting developments that can be expected in the future is the usage of AI and machine learning. One of the observed exciting ideas is to use machine learning algorithms to help select directors (Erel et al 2018).…”

Section: Limits Of the Research And Possible Next Stepsmentioning

confidence: 99%

The Role of Independent Directors in Corporate Governance in IT Companies in Serbia

Jevtović¹,

Stefanović²

2020

Proceedings of the 7th International Scientific Conference - FINIZ 2020

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“…According to Erel et al (2018), DTs are used to visually represent decisions and show or inform decision-making. When working with machine learning and data mining, DTs are used as a predictive model.…”

Section: Predictive Model and Comparisonmentioning

confidence: 99%

“…According to Naghibi et al (2017) and Erel et al (2018), RF is a supervised learning algorithm. As the name suggests, it creates a forest and makes it random.…”

Section: Predictive Model and Comparisonmentioning

confidence: 99%

A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs

Mourdi

Sadgal

Kabtane

et al. 2019

IJWIS

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Purpose Even if MOOCs (massive open online courses) are becoming a trend in distance learning, they suffer from a very high rate of learners’ dropout, and as a result, on average, only 10 per cent of enrolled learners manage to obtain their certificates of achievement. This paper aims to give tutors a clearer vision for an effective and personalized intervention as a solution to “retain” each type of learner at risk of dropping out. Design/methodology/approach This paper presents a methodology to provide predictions on learners’ behaviors. This work, which uses a Stanford data set, was divided into several phases, namely, a data extraction, an exploratory study and then a multivariate analysis to reduce dimensionality and to extract the most relevant features. The second step was the comparison between five machine learning algorithms. Finally, the authors used the principle of association rules to extract similarities between the behaviors of learners who dropped out from the MOOC. Findings The results of this work have given that deep learning ensures the best predictions in terms of accuracy, which is an average of 95.8 per cent, and is comparable to other measures such as precision, AUC, Recall and F1 score. Originality/value Many research studies have tried to tackle the MOOC dropout problem by proposing different dropout predictive models. In the same context, comes the present proposal with which the authors have tried to predict not only learners at a risk of dropping out of the MOOCs but also those who will succeed or fail.

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Selecting Directors Using Machine Learning

Cited by 25 publications

References 11 publications

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

The Role of Independent Directors in Corporate Governance in IT Companies in Serbia

A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs

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