Planning of educational training courses by data mining: Using China Motor Corporation as an example

Huang, Chiao-Tzu; Lin, Wen-Tsann; Wang, Shen-Tsu; Wang, Wen-Shan

doi:10.1016/j.eswa.2008.09.009

Cited by 18 publications

(7 citation statements)

References 8 publications

(9 reference statements)

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“…For example, students who enroll in a course but do not actually participate, or those whose information or data are incomplete (missing data) [62]. In some extreme cases, in order to clean data and ensure their completeness, students who have all or almost all their values missed can be removed from data.…”

Section: Data Cleaningmentioning

confidence: 99%

A Survey on Pre-Processing Educational Data

Romero

Ventura

2013

Studies in Computational Intelligence

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Section: Data Cleaningmentioning

confidence: 99%

A Survey on Pre-Processing Educational Data

Romero

Ventura

2013

Studies in Computational Intelligence

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“…To the first category belongs, for example, the paper [25], where association rule mining is used to provide feedback to instructors from the multiple-choice quiz data. Studies concerning planning and scheduling can be found, for example, in [26], which enhances course planning by establishing the probability of enrollees completing their courses based on the student's preferences and profession, in [27], which discovers explicit knowledge that can be useful for decision-making processes as well as proposing analytical guidelines for higher education institutions, and in [28], which uses cluster analysis, decision tree algorithm and neural networks for planning courses.…”

Section: Novelty Of the Papermentioning

confidence: 99%

Analysing Computer Science Courses over Time

Campagni

Merlini

Verri

2022

Data

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In this paper we consider courses of a Computer Science degree in an Italian university from the year 2011 up to 2020. For each course, we know the number of exams taken by students during a given calendar year and the corresponding average grade; we also know the average normalized value of the result obtained in the entrance test and the distribution of students according to the gender. By using classification and clustering techniques, we analyze different data sets obtained by pre-processing the original data with information about students and their exams, and highlight which courses show a significant deviation from the typical progression of the courses of the same teaching year, as time changes. Finally, we give heat maps showing the order in which exams were taken by graduated students. The paper shows a reproducible methodology that can be applied to any degree course with a similar organization, to identify courses that present critical issues over time. A strength of the work is to consider courses over time as variables of interest, instead of the more frequently used personal and academic data concerning students.

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“…To measure employability, a reliable and valid instrument was presented those are based on a five‐dimensional conceptualization of employability. The success of education training is determined by Huang et al with the usage of a linkage model to identify essential key factors in education data mining. They proposed a training model that fulfills the industry demand through planning the courses and the personnel's participation in the training.…”

Section: State‐of‐the‐artmentioning

confidence: 99%

Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

Sood

Singh

2019

Comp Applic In Engineering

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In the present scenario, the state of engineering education worldwide is facing a big challenge of the employability of graduates. Cloud computing technology has changed the educational landscape by allowing educators and administrators to shift to the cloud for making actionable decisions. In this paper, a smart learning framework is proposed where universities/colleges can advocate the management of employability enhancement and start skill-set enhancement courses through e-learning. This proposed framework monitors the academic/skill data of students to classify their employability at the early stage of graduation. In addition, an algorithm is proposed for skill-set assessment to find various clusters of students who lack in required skill-set. The predicted clusters of students can be offered opportunities to improve their required skill-set through e-learning. Further, we used the design science research methodology for conducting online courses. Moreover, the prediction of resource usage of the proposed e-learning framework is calculated to improve its effectiveness. In our experiment, results show that our proposed hybrid classier achieves 96.45% accuracy of classification. The prediction of resource usage offers better opportunities for adaptive resource elasticity. The results depict that by introducing the proposed framework in engineering education; more effective decisions can be made to improve the employability and overall growth of students. K E Y W O R D S employability enhancement, fog/cloud computing, optical network, resource usage pattern prediction, skill-set assessment, smart learning 1 | INTRODUCTION Universities/colleges are under great pressure to make the youth ready for jobs since the market demands for skilled workers. The education system throughout the world requires a smart system that can be monitored, controlled, quantitatively measured, and continuously improved with the collaboration of universities/ learning centers to enhance students' employability potential during their early years of graduation. Students' employability depends on a variety of features, such as marks obtained in high school, diploma, graduation, etc., extra technical competence, or other relevant information [29]. The information collected from various resources regarding their features is to be accurate, well-timed and precise.As it may be seen in the next references along with this article, according to authors [1,3,4,10,35,37,38,40,41], today's technology is capable of improving students' skills towards employability through e-learning. It consists of multiple skill-set service providers such as learning centers, recruitment companies, institutions, and private training centers, etc. It provides a personally customized and effective educational environment that reduces the gap between students' skill-set and industry demand at multiple employability parameters, such as aptitude, reasoning, communication, technical/professional certifications, etc.The potential of cloud computing encourages education syste...

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Planning of educational training courses by data mining: Using China Motor Corporation as an example

Cited by 18 publications

References 8 publications

A Survey on Pre-Processing Educational Data

A Survey on Pre-Processing Educational Data

Analysing Computer Science Courses over Time

Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

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