Recommender system for big data in education

Dwivedi, Surabhi; Roshni, V. S. Kumari

doi:10.1109/eleltech.2017.8074993

Cited by 73 publications

(28 citation statements)

References 10 publications

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“…Mining relevant data is key to building a sound recommendation system (Dwivedi & Roshni, 2017). Current research is based on data, mined from the MES repository from 2015 till 2020 (Table 1).…”

Section: Methodsmentioning

confidence: 99%

A Recommendation System For Building School Teachers’ Multidisciplinary Skills

Roman¹,

Ekaterina²,

Yevgeny

et al. 2020

European Proceedings of Social and Behavioural Sciences

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Section: Methodsmentioning

confidence: 99%

A Recommendation System For Building School Teachers’ Multidisciplinary Skills

Roman¹,

Ekaterina²,

Yevgeny

et al. 2020

European Proceedings of Social and Behavioural Sciences

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“…Current recommender systems typically combine one or more approaches into a hybrid recommendation system to improve the recommendation accuracy. Examples of recommendation systems for educational data can be found in [112]- [119].…”

Section: Recommendation Systemsmentioning

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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“…This large information gave rise to massive data in instructional sectors. Currently, massive information analytics techniques are being employed to investigate these instructional data and generate completely different predictions and suggestions for college kids, teachers, and colleges [11]. Recommendation systems are already terribly useful in ecommerce, industry and social networking sites.…”

Section: Content Based Technique Analysismentioning

confidence: 99%

“…During this work, we have a tendency to are employing a recommendation system for large information in education This work uses collaborative-filtering based mostly suggestion techniques to recommend offered courses to students, relying upon their grade points obtained in alternative subjects. We have a tendency to are mistreatment item-based recommendation of driver Machine learning library [11] on high of Hadoop to get set of recommendations. Similarity Log-likelihood is employed to find patterns among grades and subjects.…”

Section: Content Based Technique Analysismentioning

confidence: 99%

Accuracy Invoked Course Recommender System using Collaborative Filtering

Mariappan*,

Surriyan,

Shobana

2019

IJRTE

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Abstract: It is always important that the student should choose the right course. The decision of taking the right course is very important as the student's future depends on the course they opt to study. Most of the students are not much aware of the courses that are available in their own field of study. Selecting wrong courses might be due to the mismatch between the student's aptitude, training and mental ability. Thus, the idea is to develop a system for helping the students to choose a course which would be best suited for him/her based on features like previous student selection, interest, languages known etc. Existing research has explored recommender system using content-based filtering but it can only do limited content analysis and the recommendation will not be precise at the end. An attempt is made to improve the performance of this system using Collaborative based filtering techniques which will recommend a ranked list of courses. UnderCollaborative filtering techniques, user based collaborative filtering and item based collaborative filtering is used. Sample student datasets from Kaggle, has been used to test the performance of our system.

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Recommender system for big data in education

Cited by 73 publications

References 10 publications

A Recommendation System For Building School Teachers’ Multidisciplinary Skills

A Recommendation System For Building School Teachers’ Multidisciplinary Skills

Big Educational Data & Analytics: Survey, Architecture and Challenges

Accuracy Invoked Course Recommender System using Collaborative Filtering

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