A Recommender System Assisting Instructor in Building Learning Path for Personalized Learning System

Jyothi, N.; Bhan, Kaveri; Mothukuri, Uday Kumar; Jain, Sandesh; Jain, Dipika

doi:10.1109/t4e.2012.51

Cited by 19 publications

(7 citation statements)

References 1 publication

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“…However, one of the major problems that researchers encounter in finding interesting patterns from educational data set is the relatively small size of the dataset [48].…”

Section: K-means Clustering Methods Has Been Usedmentioning

confidence: 99%

Clustering Algorithms Applied in Educational Data Mining

M.A

2015

IJIEE

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Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.

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“…However, one of the major problems that researchers encounter in finding interesting patterns from educational data set is the relatively small size of the dataset [48].…”

Section: K-means Clustering Methods Has Been Usedmentioning

confidence: 99%

Clustering Algorithms Applied in Educational Data Mining

M.A

2015

IJIEE

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“…Although some studies of artificial intelligence have focused on online measurement and assessment applications (Dolenc & Aberšek, 2015;Gavriushenko, Khriyenko, & Tuhkala, 2017;Jyothi, Bhan, Mothukuri, Jain & Jain, 2012;Lo, Chan, & Yeh, 2012), none of them has investigated how to support teachers' practical experiences of formative assessment. Hence, this unexplored issue calls for the current study.…”

Section: Extended Abstractmentioning

confidence: 99%

“…Şekil 1. Uzman sistemin genel yapısı (Karaosmanoğlu, 2007;Nabiyev, 2005) Her ne kadar yapay zekayla öğrencilerin çevrimiçi (online) ölçme-değerlendirme uygulamaları gerçekleştirmeyi amaçlayan çalışmalar alan yazında mevcut olsa da (Dolenc & Aberšek, 2015;Gavriushenko, Khriyenko & Tuhkala, 2017;Jyothi, Bhan, Mothukuri, Jain & Jain, 2012;Lo, Chan & Yeh, 2012), biçimlendirici değerlendirme konusunda öğretmenlerin sınıf içerisinde e-ISSN: 2536-4758 http://www.efdergi.hacettepe.edu.tr/ yürüttükleri çalışmaları desteklemek amacıyla yapay zekâyı kullanan bir ölçme-değerlendirme çalışmasına rastlanmamıştır. Bu durumda alan yazında doldurulması gereken önemli bir eksikliğe işaret etmektedir.…”

Section: Introductionunclassified

Akıllı Biçimlendirici Değerlendirme Sisteminin Akademik Başarı ve Ders Sürecine Etkisi

Bayrak¹,

Çalık²,

Doğan³

2019

HUJE

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Akıllı biçimlendirici değerlendirme sistemi, öğretmenlere ders sürecinde yaşadıkları problemlere destek olmak için hazırlanmıştır. Bu sistem, yapay zekâ yöntemlerinden biri olan uzman sistemler yöntemine göre öğrencilerin ön bilgileri, ders sonrası kazandıkları bilgileri, öğrenme stili ve çoklu zekâ bilgileri değerlendirerek öğretmene ders süreci hakkında öneriler sunmaktadır. Bu çalışmanın amacı, akıllı biçimlendirici değerlendirme sisteminin öğrencilerin akademik başarısına ve ders sürecine etkisini incelemektir. Karma araştırma desenlerinden açıklayıcı ardışık desenin kullanıldığı çalışmaya, 2 biyoloji öğretmeni ve 122 onuncu sınıf öğrencisi katılmıştır. Çalışmada, akıllı biçimlendirici değerlendirme sisteminin öğrencilerin akademik başarısını artırdığı tespit edilmiştir. Ayrıca, akıllı biçimlendirici değerlendirme sisteminin öğretmenleri ders işleme süreçlerinde bireyselleştirilmiş yöntemleri kullanmaya yöneltmekle kalmayıp biçimlendirici değerlendirmenin uygulanmasını da kolaylaştırdığı ortaya çıkmıştır. Çalışma sonucunda akıllı biçimlendirici değerlendirme sistemlerinin değişik konu, derslerin öğretimi konusunda denenmesi ve biçimlendirici değerlendirme konusunda hazırlanabilecek akıllı değerlendirme sistemleri hakkında önerilerde bulunulmuştur.

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“…The proposed system improved learning effectiveness and especially learners' satisfactory according to the questionnaire. Also, in a paper (Jyothi et al, 2012) was presented a recommender system which assists the instructor in building learning path, more specifically in identifying the groups of learners who have similar learning styles and then providing recommendations to these learners. Authors used FelderSilverman learning style model.…”

Section: Personalized Learning Systemsmentioning

confidence: 99%

An Intelligent Learning Support System

Gavriushenko

Khriyenko

Tuhkala

2017

Proceedings of the 9th International Conference on Computer Supported Education

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Abstract:Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere else, for example, in massive open online courses. A person, who is learning some particular content from outside of the university, could have some knowledge gaps which should be recognized. We argue that it is possible to respond to these challenges with adaptive, intelligent, and personalized learning systems that utilize data analytics, machine learning, and Semantic Web technologies.In this paper, we propose a model for an Intelligent Learning Support System that guides learner during the whole lifecycle using semantic annotation methodology. Semantic annotation of learning materials is done not only on the course level but also at the content level to perform semantic reasoning about the possible learning gaps. Based on this reasoning, the system can recommend extensive learning material.

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A Recommender System Assisting Instructor in Building Learning Path for Personalized Learning System

Cited by 19 publications

References 1 publication

Clustering Algorithms Applied in Educational Data Mining

Clustering Algorithms Applied in Educational Data Mining

Akıllı Biçimlendirici Değerlendirme Sisteminin Akademik Başarı ve Ders Sürecine Etkisi

An Intelligent Learning Support System

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