Student grouping by neural network based on affective factors in learning English

Bachtiar, Fitra A.; Eric, C.; Kamei, Katsuhiko

doi:10.1109/iceeem.2011.6137792

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…From the previous studies [30]- [35] , the appropriate technique used to streaming students based on their performance is clustering. Clustering technique is used to group data of the same features and a popular technique for data mining [7]- [13]. Clustering technique discovers natural groupings of data sets or objects that have same features.…”

Section: Suitable Model For Data Mining Techniquementioning

confidence: 99%

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Student Academic Streaming Using Clustering Technique

Salwana

Hamid

Yasin

2017

MJCS

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The balance of human capital supply and industry demands are crucial to sustain a competitive advantage in order to ensure stability of economic growth. Unfortunately, companies often find it hard to recruit the right people. Many ideas obviously relate to connect human capital to education because human capital is created through education. In the education sector, there is a critical need to develop an effective planning mechanism to distribute the students into the most suitable area in the industry. In order to achieve this, the student's pathway needs to be planned systematically in school by identifying the student's streaming based on their academic performance. In this case, students who have the same performance will be grouped in the same cluster using a data mining technique. However, the problem is that, it is difficult to identify real potential for the students, because their performance is not well monitored, and current assessment systems do not support the student's academic planning activities. Besides, there is no specific technique used to group students into clusters based on their performance. This study aims to overcome the problem by grouping the students according to their performance using clustering techniques and to propose a suitable model. This study aims to overcome the problem by identifying a suitable clustering model that can be used to analysis an educational data. The data involves is student performance data. Based on the data, two clusters of students are created which is science and arts. A novelty in the method of study is the use of three clustering models and a comparison among them in order to find a suitable clustering model to be used with student academic performance data. The study was conducted in five schools in Malaysia to support students' grouping in two different academic streams, which are science and art. The result demonstrated the best model of clustering technique that is suitable for mining the educational data. Moreover, suitable streaming based on the students' performance and education policy was created from the results. It can be used to assist schools and students in determining the appropriate streaming for the students, and support for the human capital needs by the country in the future.

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Section: Suitable Model For Data Mining Techniquementioning

confidence: 99%

“…In this case, there is a critical need to identify students' ability, so we can distribute students into groups according to their performance [7]. For the grouping process, data mining (DM) is used, as the data mining is a popular technique for clustering [7]- [13].…”

Section: Introductionmentioning

confidence: 99%

Student Academic Streaming Using Clustering Technique

Salwana

Hamid

Yasin

2017

MJCS

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Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

Jovanović¹,

Vukičević²,

Milovanović³

et al. 2012

IJCIS

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In this research we applied classification models for prediction of students' performance, and cluster models for grouping students based on their cognitive styles in e-learning environment. Classification models described in this paper should help: teachers, students and business people, for early engaging with students who are likely to become excellent on a selected topic. Clustering students based on cognitive styles and their overall performance should enable better adaption of the learning materials with respect to their learning styles. The approach is tested using well-established data mining algorithms, and evaluated by several evaluation measures. Model building process included data preprocessing, parameter optimization and attribute selection steps, which enhanced the overall performance. Additionally we propose a Moodle module that allows automatic extraction of data needed for educational data mining analysis and deploys models developed in this study.

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Student grouping by neural network based on affective factors in learning English

Cited by 2 publications

References 8 publications

Student Academic Streaming Using Clustering Technique

Student Academic Streaming Using Clustering Technique

Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

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