Data Mining for Detecting E-learning Courses Anomalies:  An Application of Decision Tree Algorithm

Elghibari, Fatiha; Elouahbi, Rachid; Khoukhi, Fatima El

doi:10.18517/ijaseit.8.3.2756

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…An example how data mining has been used to predict rainfall has been shown in [5,6,7]. Beside prediction task, data mining has been used to detect e-learning courses anomalies as explored in [8].…”

Section: Introductionmentioning

confidence: 99%

Classification Techniques for Predicting Graduate Employability

Othman¹,

Shan²,

Yusoff³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Unemployment is a current issue that happens globally and brings adverse impacts on worldwide. Thus, graduate employability is one of the significant elements to be highlighted in unemployment issue. There are several factors affecting graduate employability, traditionally, excellent academic performance (i.e., cumulative grade point average, CGPA) has been the most dominant element in determining an individual's employment status. However, researches have shown that not only CGPA determines the graduate employability; in fact other factors may influence the graduate achievement in getting a job. In this work data mining techniques are used to determine what are the factors that affecting the graduates. Therefore, the objective of this study is to identify factors that influence graduates employability. Seven years of data (from 2011 to 2017) are collected through the Malaysia's Ministry of Education tracer study. Total number of 43863 data instances involved in this employability class model development. Three classification algorithms, Decision Tree, Support Vector Machines and Artificial Neural Networks are used and being compared for the best models. The results show decision tree J48 produces higher accuracy compared to other techniques with classification accuracy of 66.0651% and it increased to 66.1824% after the parameter tuning. Besides, the algorithm is easily interpreted, and time to build the model is small which is 0.22 seconds. This paper identified seven factors affecting graduate employability, namely age, faculty, field of study, co-curriculum, marital status, industrial internship and English skill. Among these factors, attribute age, industrial internship and faculty contain the most information and affect the final class, i.e. employability status. Therefore, the results of this study will help higher education institutions in Malaysia to prepare their graduates with necessary skills before entering the job market.

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Section: Introductionmentioning

confidence: 99%

Classification Techniques for Predicting Graduate Employability

Othman¹,

Shan²,

Yusoff³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

“…There are many previous studies that adopted the use of Naive Bayesian Classifier algorithm for the classification texts and documents. Also, the studies' results indicated that obtained classified materials have a high accuracy and the algorithm had the ability to work in a way that surpasses the most advanced classification models such as boosted trees or random forests models (e.g., [20], [19]- [31]) classifying learning materials based on Decision Tree algorithm [32]. However, a comparison between our work and previous mentioned research is not valid as they have been used in different contexts.…”

Section: Resultsmentioning

confidence: 69%

Adaptive MOOCs Based on Intended Learning Outcomes Using Naïve Bayesian Technique

Ewais

Samara²

2020

Int. J. Emerg. Technol. Learn.

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Widespread adoption of MOOCs got researchers interest to support learners in their learning process. However, most of provided courses are teacher-centered approach rather than learner-centered approach. One of the possible solutions to enhance the learning process is to enable learner to learn a course that achieve a number of Intended Learning Outcomes (ILOs). Therefore, the main goal of this research work is to propose an approach for adapting MOOCs learning materials based on ILOs using classification algorithm namely Naïve Bayesian algorithm. Furthermore, the proposed approach considered the pedagogical aspects by generating a learning path based on the pedagogical relationship between learning concepts which are mapped to learning materials. As a result, the learner will be able to follow a course generated automatically based on selected ILOs and pedagogical relationships. To validate the proposed approach, a prototype has been developed and the effectiveness of the adopted technique has been validated using a precision-recall indicator. The results were promising as the precision-recall indicators provided interesting results in the classification process.

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“…e latter analyzes the health, social activity, relationships, and academic performance, most related to and affect the performance of students. e convenience content material for learners is also predicted by decision trees [24].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Course Plan Recommendation for Higher Education: A Framework of Decision Tree

Chen

Zheng

et al. 2020

Discrete Dynamics in Nature and Society

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The framework of outcomes-based education(OBE) has become a central issue for global university education, which is benefited to drive the education development by a series of assessments for historical teaching data, especially student course score, and employment information. The issue of how to timely update the talent training plans for computer major in a university has received considerable critical attention. It is becoming extremely difficult to ignore the requirement of fast shortened update cycle in IT area. One of the main obstacles is that the teaching inertia and the fixed awareness of a major training plan may delay the feedback of teaching problems. There is still a contradiction between the plan rationality and the real-time needs of contemporary IT enterprises. Hence, this paper puts forward a novel data-based framework to evaluate the relevance between the major courses, employment rate, and enterprise needs through the decision tree expression, thus providing reliable data support for systematic curriculum reform. On top of that, A recommendation algorithm is proposed to automatically generate the computer course group that satisfies the staff requirements of IT enterprises. Finally, teaching and employment data of Xihua University in China is applied as an example to undertake course optimization and recommendation. The consequences have an obvious positive effect on student employment and enterprise feedback.

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Data Mining for Detecting E-learning Courses Anomalies: An Application of Decision Tree Algorithm

Cited by 6 publications

References 26 publications

Classification Techniques for Predicting Graduate Employability

Classification Techniques for Predicting Graduate Employability

Adaptive MOOCs Based on Intended Learning Outcomes Using Naïve Bayesian Technique

Intelligent Course Plan Recommendation for Higher Education: A Framework of Decision Tree

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