The Analysis of Student Performance Using Data Mining

Abstract

The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia.The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink ** More information about this series at

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

“…The satisfactory accuracy of the algorithm was confirmed, despite the speed of calculation and low consumption of resources. In five cases it was found that Naive Bayes had the highest accuracy (Bergin et al, 2015;Zhou et al 2015;Kamal and Ahuja 2019;Kumar et al 2019;Santoso and Yulia 2019), while the average accuracy of the algorithm reached 0.7560. It should be noted that the required hypothesis of independence between variables cannot be safely assumed or easily manufactured in educational data.…”

“…Tang et al: (2015), Klusener and Fortenbacher (2015), Brinton and Chiang (2015), Shrivas and T iwari b. Articles: Al Shehri et al: (2017), Amaya et al: (2015), Widyaningsih et al: (2019) c. Articles: Okubo et al: (2017), Singh and Kaur (2018), Santoso and Yulia (2019), Sumitha et al: (2016), Al Barrak and Al Razgan d. Articles: Amrieh et al: (2015), Ruby and David (2015), Almasri et al: (2019), Sivakumar et al: (2016), Kumar et al: (2019), Chanlekha and Niramitranon (2018) e. Articles: Livieris et al: (2018), Angiani et al: (2019), Jishan et al: (2015), Livieris et al: (2019c), Al Saleem et al: (2015, f. Articles: Kostopoulos et al: (2015), Athani et al: (2017), Sara et al: (2015), Kasthuriarachchi and Liyanage (2019), Namomsa andSharma (2018) g. Articles: Bergin et al: (2015), Navamani and Kannammal (2015), Ketui et al (2019), Bhegade and Shinde (2016), Pristyanto et al, (2018) h. Articles: Lopez Guarin et al: (2015, Pradeep et al: (2015), Yehuala (2015), Kaur and Singh (2016), Mahboob et al: (2017), Pereira et al, (2018)…”

“…(2018),Moscoso- Zea et al. (2019),Namomsa and Sharma (2018),Pandey and Taruna (2016),Santoso and Yulia (2019),Saravanan and Jyothi (2019),Satyanarayana and Nuckowski (2016),Shrivas and Tiwari (2017), Singh and Kaur (2017), Strecht et al. (2015), Yehuala (2015), Zhou et al.…”

Early Warning System for Academic using Data Mining

“…Relationship Mining is the identification of relationships between variables in large data sets [16]. This technique can identify the effective factors on responsible for a student to retake a course [17,18].…”

Genetic Programming Approach for Classification Problem using GPU

“…In today's information society, where the amount of data stored is multiplied almost exponentially every day, data mining is a fundamental tool to analyse and exploit it effectively. Data mining techniques provide insight from data relationships and provide researchers and users with rules for classification, association [5]- [7], and prediction. The use of genetic programming in solving classification problems is relatively frequent [8]- [11].…”