A Machine Learning Based Approach to Enhance Mooc Users’ Classification

Abstract

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.

Resumes Classification Using Neural Network Approaches Combined with Bert and Gensim: CVS of Moroccan Engineering Students

“…Accordingly, based on the same experience, the prediction of accomplishment is very important for this group of students, where the decision to support or reinforce courses is suitable for both students and the educational system [18]. Furthermore, the author [19] has focused his research on using massive open online course (MOOCS) data to classify students and predict their dropout problems, therefore, the objective is a predictive model where the issue of huge dropout rate reaches 90%. Based on machine learning, the accuracy of the compared models was classified as fellow Support Vector Machines (85.2%), K Nearest Neighbors (83.9%), Decision Trees (77%), Naive Bayes (85.5%), and Logistic Regressions (86.8%) with a combinatorial approach based on voting (92%) [19].…”

Harmony in Numbers: Unifying Management and Accounting For Financial Success

“…With this integration enabled through management accounting, they can put together an analysis of costs, revenue, and other performance metrics. Through their business data analytics influence, managers become capable of assessing the financial implications of their decisions, improving the weaker areas, and mitigating the risks more actively (Mourdı et al 2020). This blended skill portrayed by the partnership between management and accounting ensures tranquility to the competitive business environment since the companies is able to operate with their standards intact.…”

Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A Review

“…These datasets can be considered benchmark datasets, allowing researchers to evaluate the performance of the model compared with the others. The public datasets in this domain are the Students' Academic Performance Dataset (SAPData) [11], which has been utilized for grade prediction [12]; Open University Learning Analytics Dataset (OULAD) [13] developed by the Open University (OU) and used for at-risk [14], pass/fail [15], grade [16], dropout [15], and engagement prediction [17]; Center for Ad-vanced Research Through Online Learning (CAROL) [18] has been used for dropout [19] and fail/success prediction [20,21]; KDD Cup 2015 (KDDcup) [22] has been extensively used in the literature as a whole or subset to predict dropout, such as [23,24]; and HarvardX and MITx dataset (HMedx) [25] have been used for dropout prediction [26] and performance prediction [26,27]. Figure 4 shows the statistics of each dataset used in previous studies.…”

“…Several studies have applied correlation-based feature selection approaches such as chi-square (X 2 ), mutual information (MI), information gain, fast correlation-based filter (FCBF), relief, and Pearson's correlation coefficient [21,30,31] to select the topmost related features to the targeted values and discard irrelevant and noisy attributes. This approach has the benefit of being adaptable to any machine-learning model.…”