Big data analytics: Predicting academic course preference using hadoop inspired mapreduce

Guleria, Pratiyush; Sood, Manu

doi:10.1109/iciip.2017.8313734

Cited by 5 publications

(6 citation statements)

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“…The system has been shown to be able to correctly distinguish if the student will get either an ABC grade or a DF grade with 92% accuracy. The authors in [83] proposed an approach for Big data analytics for predicting academic course preference using Hadoop and MapReduce. In their work, they derived preferable courses for pursuing training for students based on course combinations.…”

Section: ) Courses Selectionmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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Section: ) Courses Selectionmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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“…Pratiyush et al [10] have predicted academic course preference of student using hadoop inspired MapReduce in big data. Their results shows that large volume of course combinations given in the form of input dataset after passed through the mapper function in map reduce framework the maximum of students have shown key interest towards "C", ,"C++" and java courses.…”

Section: Related Workmentioning

confidence: 99%

“…Data mining is one of the most cardinal areas in recent technologies for retrieving valid information from huge amount of unstructured and distributed data using parallel processing of data [7]. Data mining techniques are applied in various fields to find the novel information from huge data set.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Student Performance using Hybrid Classification

Kumar*¹,

Selvam²,

Palanisamy³

2019

IJRTE

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Data mining technologies allow collection, storage and processing huge amounts of data and carrying a large variety of data types and samples. Predicting academic performance of student is the most successive research in this era. Previous research work researchers are used different classification algorithm to predict the student performance. There is lot of research work to be taken in the field of educational data mining and big data in education to increase the accuracy of the classification algorithm and predict the academic performance of student. In this research work we used hybrid classification algorithm for predicting the performance of students. Two Popular classification algorithms ID3 and J48 were applied on the data set. To make hybrid classification voting technique is applied using weka machine learning tool. In this work we tested how the hybrid algorithm accurately predicts the student data set. To check the predicted result classification accuracy was computed. This hybrid classification algorithm gives accuracy with 62.67%.

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“…Pratiyush Guleria et al has discussed a connection between new emerging technologies implemented in the domain of educational system, larger unstructured data sets which are produced in result of implementation and Data Mining tools used to convert this unregulated information to structured data. Pratiyush also discussed how he used HDFS with MapReduce and results are aggregated to obtain the output [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Pragmatic Influence of MapReduce in Big Data using Hadoop A literature review

Kaleem¹,

Khuram²

2018

Seventh International Conference on Advances in Computing Electronics and Communication ACEC2018

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A term "Big Data" explain innovative technologies and techniques to store, manage, capture, analyze and distribute large size or petabyte data sets with high acceleration and dynamic structures. Big Data is categorized as semi-structured, unstructured, and structured, which results in an inability of traditional data managerial techniques. Data is produced from numerous resources and reached the system at different rates. This immense amount of data is processed in an efficient, and inexpensive manner, a technique of parallelism is practiced. Big Data parameters which include diversity, scale, and complexity required new techniques, architectures, analytics, and algorithms for the purpose of management of data and the knowledge hidden in it. Hadoop is a famous software platform to make data useful for the purpose of analytics to solve problems and structured the Big Data. Distributed processing is enabled by the help of Hadoop for huge datasets across the bunch of servers. It is specially designed to scale from one to thousands of high computing machines, with high fault tolerance degree.

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Big data analytics: Predicting academic course preference using hadoop inspired mapreduce

Cited by 5 publications

References 6 publications

Big Educational Data & Analytics: Survey, Architecture and Challenges

Big Educational Data & Analytics: Survey, Architecture and Challenges

Prediction of Student Performance using Hybrid Classification

Pragmatic Influence of MapReduce in Big Data using Hadoop A literature review

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