Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

Klašnja-Milićević, Aleksandra; Ivanović, Mirjana; Νανόπουλος, Αλέξανδρος

doi:10.1007/s10462-015-9440-z

Cited by 144 publications

(80 citation statements)

References 132 publications

(113 reference statements)

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“…This recommender system suggests the courses, learning the material and interesting subjects to the students of e-learning systems (Klašnja-Milićević, Ivanović, & Nanopoulos, 2015).…”

Section: State Of the Literaturementioning

confidence: 99%

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

Usman

Iqbal

et al. 2017

EURASIA J MATH SCI T

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Machine Learning is a scientific discipline that addresses learning in context is not learning by heart but recognizing complex patterns and makes intelligent decisions based on data. Currently, students have to face the problem of selecting the best suitable university for admission in engineering. There is no predictor system that recommends the students to select the specific category which is best to its academic career. Students have to first appear in the entry test and can't predict whether he/she can pass the entry test to get admitted in University. To tackle this problem the field of Machine Learning develops algorithms that discover knowledge from specific data and experience, based on sound statistical and computational principles. After going through the entry test students have to face problems for selecting the preferences among different categories due to the lack of knowledge of intake merits of preceding years. Another problem arises when students are waiting for admission in specific university, meanwhile, other universities finish their admission processes and select the students, but some students can't take admission in any university due to no prediction system for admission in universities. In this work, we would like to develop an E-Assessment and Computer-Aided Prediction online system that enables the student to predict the entry test numbers by giving the Metric and Intermediate marks and other academic numbers. The suggested scheme has been demonstrated to perform at the maximum speed under MATLAB setup.

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“…This recommender system suggests the courses, learning the material and interesting subjects to the students of e-learning systems (Klašnja-Milićević, Ivanović, & Nanopoulos, 2015).…”

Section: State Of the Literaturementioning

confidence: 99%

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

Usman

Iqbal

et al. 2017

EURASIA J MATH SCI T

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“…Consequently, it is necessary to mention that even there have been developed several survey papers focused on recommender systems both regarding a wide point of view (Adomavicius and Tuzhilin [3], Konstan and Riedl [65], Bobadilla et al [21]), and also focused on specific areas (Campos et al [23], Klašnja-Milićević et al [63], Abbas et al [1], Martínez et al [83]), according to our best knowledge (October 2016), the current paper is the first effort focused on concentrating all the research works focused on recommender systems supported by fuzzy tools.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

151

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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“…Recommenders evaluate user behaviour and preferences and offer the user the most appropriate learning resource. There are different recommender techniques [1] [12] implemented in a number of recommender systems [7] [15]. According to [12] these techniques can be divided into four categories:…”

Section: Introductionmentioning

confidence: 99%

“…• Content-based techniques use for recommendation only information about the users and their histories [12]. • Matrix/tensor factorization techniques consist of decomposition of a tensor to factors.…”

Section: Introductionmentioning

confidence: 99%

Measures for recommendations based on past students' activity

Huptych

Bohuslavek

Hlosta

et al. 2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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This paper introduces two measures for the recommendation of study materials based on students' past study activity. We use records from the Virtual Learning Environment (VLE) and analyse the activity of previous students. We assume that the activity of past students represents patterns, which can be used as a basis for recommendations to current students.The measures we define are Relevance, for description of a supposed VLE activity derived from previous students of the course, and Effort, that represents the actual effort of individual current students. Based on these measures, we propose a composite measure, which we call Importance.We use data from the previous course presentations to evaluate of the consistency of students' behaviour. We use correlation of the defined measures Relevance and Average Effort to evaluate the behaviour of two different student cohorts and the Root Mean Square Error to measure the deviation of Average Effort and individual student Effort.

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Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

Cited by 144 publications

References 132 publications

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

Fuzzy Tools in Recommender Systems: A Survey

Measures for recommendations based on past students' activity

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