Preliminary study on educational recommender system

Adam, Noor Latiffah; Zulkafli, Muhammad Alif; Soh, Shaharuddin Cik; Kamal, Nor Ashikin Mohamad

doi:10.1109/ic3e.2017.8409245

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…As mentioned earlier, previous study [18,19,20,21] in recommender system indicate despite the fact that several BG recommender algorithms have been proposed, only a small number of projects are committed to the development of complete recommender system infrastructures and deal with chronic diseases such as GDM. In our study, the total of relevant result is 25 studies but we only obtained 20 studies that satisfied our inclusion criteria.…”

Section: Methods or Techniques In Bg Recommender System (Rq1)mentioning

confidence: 99%

“…As we reviewed the literature, we found several existing techniques for recommender system [18][19][20][21][22][23][24][25][26], but we do not know which one is the most appropriate for BG recommender system, particularly for patients with GDM. Therefore, in this study, we review research studies about BG recommender system that used several techniques to incorporate the BG recommender system and BG models for GDM.…”

Section: Introductionmentioning

confidence: 99%

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A mapping study on blood glucose recommender system for patients with gestational diabetes mellitus

Rosli¹,

Rosli²,

Nordin³

2019

Bulletin EEI

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Blood glucose (BG) prediction system can help gestational diabetes mellitus (GDM) patient to improve the BG control with managing their dietary intake based on healthy food. Many techniques have been developed to deal with blood glucose prediction, especially those for recommender system. In this study, we conduct a systematic mapping study to investigate recent research about BG prediction in recommender systems. This study describes an overview of research (2014-2018) about BG prediction techniques that has been used for BG recommender system. As results, 25 studies concerning BG prediction in recommender system were selected. We observed that although there is numerous studies published, only a few studies took serious discussion about techniques used to incorporate the BG algorithms. Our result highlighted that only one study discusses hybrid filtering technique in BG recommender system for GDM even though it has an ability to learn from experience and to improve prediction performance. We hope that this study will encourage researchers to consider not only machine learning and artificial intelligent techniques but also hybrid filtering technique for BG recommender system in the future research.

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Section: Methods or Techniques In Bg Recommender System (Rq1)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A mapping study on blood glucose recommender system for patients with gestational diabetes mellitus

Rosli¹,

Rosli²,

Nordin³

2019

Bulletin EEI

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“…The research framework for this project has been discussed in (11). User acceptance test has been carried out and reported in the discussion section.…”

Section: Methodsmentioning

confidence: 99%

“…The system suggested is a recommender system that focuses on suggesting any suitable and relevant websites based on the basic calculus topics. The preliminary study of this project can be found in (11).…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommender System for Calculus using Content-Based Filtering Approach

Adam¹,

Zulkafli

Soh³

et al. 2018

IJET

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In this millennial age, Internet is becoming essential to human kind. Along with the growth of Internet users, information is also becoming huge and starting to cause difficulties to find the relevant contents. Thus, the recommender system was introduced. It helps the user to suggest the items based on the user’s preferences. This system could help the students as Calculus is one of the tough subjects feared by most students. Credits given to the technology as many sources on the web can provide tutorials, working examples and solutions on the subjects. However, there are too many of them. Students had to make a few selections, which one can fulfil their needs of specific calculus topics. The personalized recommender system developed was a content-based filtering recommender system with its own scraping engine to collect the sources from the Internet which focuses on the basic Calculus topics. The system and engine were constructed by using Flask framework together with its relevant libraries.

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“…The total score will have a value between 0 and 16, classifying them as deficient (0-2), sufficient (3)(4)(5), good (6-10), very good (11)(12)(13), and excellent (14-16).…”

Section: Metrics Value Weightmentioning

confidence: 99%

Recommendation Systems for Education: Systematic Review

2021

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Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

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Preliminary study on educational recommender system

Cited by 10 publications

References 18 publications

A mapping study on blood glucose recommender system for patients with gestational diabetes mellitus

A mapping study on blood glucose recommender system for patients with gestational diabetes mellitus

Personalized Recommender System for Calculus using Content-Based Filtering Approach

Recommendation Systems for Education: Systematic Review

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