Using Self-Organizing Map (SOM) for Clustering and Visualization of New Students based on Grades

Abstract

Student grouping, particularly in high school, is a necessary process to divide and classify students into classes based on their abilities and interests. Each school may have different approaches to decide the grouping, but most schools use academic grades. The activity occurs every new academic year and schools with plenty of new students registered may feel a bit overwhelmed with this grouping assignment. A decision support system which can automatically perform grouping on a list of students may be able to help the school’s staffs with this repetitive task. A self-organizing map (SOM) is an example of unsupervised learning algorithm using an artificial neural network structure to produce a low dimensional representation from a given input. However, SOM is also known as one of clustering techniques, since dimensionality reduction may also be seen as reducing (or clustering) input data to lower dimensions (or clusters). This research aims to group new enrolled students to a high school based on their academic grades using a SOM learning algorithm. The grades came from their rapport books and national examination results from their previous study. The resulting groups are three distinct clusters which represents Life Sciences, Social Sciences, and Linguistics study areas.

Analisis Cluster Data Interkomparasi Anak Timbangan dengan Algoritma Self Organizing Maps

“…Proses Clustering dilakukan dengan menentukan vector node yang dipilih untuk selanjutnya ditentukan BMU (best matching unit) nya dengan node lain. Penelitian [4] diawali dengan data yang dinormalisasi pada rentang 0 sampai dengan 1. Data yang sudah dinormalisasi kemudian dilakukan Clustering dengan menetapkan jumlah iterasi dan nilai learning rate-nya.…”

Meta-Analysis of Artificial Intelligence Works in Ubiquitous Learning Environments and Technologies

“…Most schools apply academic grades to group learners but there are other approaches that exist. As this is an annual task with new learners, both the teachers and learners feel overwhelmed with grouping [20]. A solution to this repetitive task may be the implementation of a decision support system that can automate the grouping process.…”

“…An example of an unsupervised learning algorithm is a SOM (self-organising map) which uses an artificial neural network structure to afford a reduced dimensional representation of the given input. SOM is also a clustering technique [20]. The study by Purbasari, Puspaningrum and Putra [20] used SOM to academically group 275 school learners based on their national examination results and rapport books into three distinct clusters, namely, Social Sciences, Life Sciences and Linguistics.…”

“…SOM is also a clustering technique [20]. The study by Purbasari, Puspaningrum and Putra [20] used SOM to academically group 275 school learners based on their national examination results and rapport books into three distinct clusters, namely, Social Sciences, Life Sciences and Linguistics.…”

“…The fundamental factors that have attributed to this evolution are the exponential augmentation of the algorithms, memory capacity, and processor speed of technology. Research in intelligent systems faces numerous challenges, many of which relate to representing a dynamic physical world computationally [20].…”