Learning Content Recommendations on Personalized Learning Environment Using Collaborative Filtering Method

Hidayat, Adhyfa Fahmy; Suwawi, Dawam Dwi Jatmiko; Laksitowening, Kusuma Ayu

doi:10.1109/icoict49345.2020.9166371

Cited by 11 publications

(11 citation statements)

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“…1 Student [Aarthi et al, 2019, Angelova et al, 2018, Baskota and Ng, 2018, Bhumichitr et al, 2017, Bourkoukou et al, 2017, Cárdenas-Cobo et al, 2020, Chang et al, 2011, Chen et al, 2020, Dahdouh et al, 2019, Praserttitipong and Srisujjalertwaja, 2018, Dussan-Sarria and Leon-Guzman, 2012, Dwivedi and Roshni, 2017, Esteban et al, 2020, Ezz and Elshenawy, 2020, Heras et al, 2020, Hidayat et al, 2020, Hoic-Bozic et al, 2016, Hsu et al, 2013, Iatrellis et al, 2017, Ibrahim et al, 2017, Ibrahim et al, 2020, Isma'il et al, 2020, Janpla and Kularbphettong, 2016, Kanika et al, 2019, Kopeinik, 2018, Liao et al, 2020, Lin et al, 2018, Liu et al, 2017, Ma et al, 2020, Mokarrama et al, 2020, Nho et al, 2020, Nuswantari et al, 2020, Obeid et al, 2018, Palilingan and Batmetan, 2018, Pardos and Jiang, 2020, Pariserum Perumal et al, 2019, Pereira et al, 2018, Porcel et al, 2020, Putri and Zulkarnain, 2...…”

Section: What Kind Of Users Is the Recommendation Aimed At?mentioning

confidence: 99%

“…2 Secondary [Aarthi et al, 2019, Baskota and Ng, 2018, Chang et al, 2011, Ezz and Elshenawy, 2020, Hidayat et al, 2020, Hsu et al, 2013, Janpla and Kularbphettong, 2016, Jiang et al, 2014, Mokarrama et al, 2020, Obeid et al, 2018, Palilingan and Batmetan, 2018, Samin and Azim, 2019, Selvam et al, 2020, Tilahun and Sekeroglu, 2020, Xu, 2019, Yuliansyah et al, 2020 20.00%…”

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confidence: 99%

“…51.25% Learning Objects and material [Bai and Yang, 2019, Benhamdi et al, 2017, Bourkoukou et al, 2017, Cerna, 2020, Chen et al, 2020, De Medio et al, 2020, Ferreira et al, 2020, Heras et al, 2020, Hidayat et al, 2020, Kopeinik, 2018, Liao et al, 2020, Lin et al, 2019, Pariserum Perumal et al, 2019, Peralta et al, 2018, Pereira et al, 2018, Putri and Zulkarnain, 2020, Salehi et al, 2014, Sergis and Sampson, 2016, Wan and Niu, 2020, Wang et al, 2017, Xu, 2019, Zheng et al, 2019, Zhuhadar et al, 2009] Exercises, activities [Cárdenas-Cobo et al, 2020, Hoic-Bozic et al, 2016, Hsu et al, 2013, Porcel et al, 2020, Troussas et al, 2020 Resources [Wenige andRuhland, 2018, Tawfik et al, 2020] Learning Designs [Cobos et al, 2013, El-Bishouty et al, 2014, García et al, 2009, Karga and Satratzemi, 2018, Karga and Satratzemi, 2018] Remote Virtual Labs [Zervas et al, 2015] 2 To recommend courses [Almutairi et al, 2017, Bhumichitr et al, 2017, Dahdouh et al, 2019…”

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confidence: 99%

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The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

Ugarte¹,

Larrañaga²,

Arruarte³

2022

jucs

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Recommender Systems provide users with content or products they are interested in. The main purpose of Recommender Systems is to find, among the vast amount of information that is available or advertised on the Internet, content that meets the user’s needs i.e., a product or content that satisfies his or her wishes. These systems are being used more and more in many of the services of our daily lives. In this paper, a systematic mapping review that explores the use of Rec- ommender Systems in formal learning stages is presented. The paper analyzes what kinds of items the Recommender Systems suggest, who the users that receive the recommendations are, what kinds of information the Recommender Systems use to carry out the recommendation process, the algorithms and techniques the Recommender Systems employ and, finally, how the Recommender Systems have been evaluated. The results obtained in the review will make it possible to iden- tify not only the current situation in this field but also some of the challenges that are still to be faced.

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Section: What Kind Of Users Is the Recommendation Aimed At?mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

Ugarte¹,

Larrañaga²,

Arruarte³

2022

jucs

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show abstract

“…This may allow the students to focus more on special areas which they need to improve more. These virtual assistants can therefore set up their communication style specifically for each student [9]. Furthermore, chatbots and virtual assistants can have a positive impact on teachers' life as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of an Educational Virtual Assistant Software

Halvoník

Pšenák

2021

Int. J. Emerg. Technol. Learn.

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Despite the great creators’ efforts of e-learning educational materials, it is not possible to define the content of these materials specifically for all students. Based on this, it is necessary to provide in e-learning education the possibility of a more detailed interpretation of specific parts of the educational material that may be unclear to specific students. Based on this fact, we decided to take the first step in the form of software design, which will represent a virtual assistant in teaching computer science. The role of this assistant will be the ability to answer technical questions related to the presented curriculum. From an architectural point of view, it will be a set of micro-services, each of which will serve a specific task. The prerequisite is the use of decision trees to determine a specific micro-service, which will be implemented in the form of a neural network. Main aim of this paper is to provide detail description of global software architecture for such a virtual assistant

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“…Goram and Veiel [19] used legal-and domain-specific requirements in a context-based adaptive PLE. In another study, collaborative filtering method was used to recommend learning content for PLEs [20]. McCarthy et al [21] developed iSTART (Interactive Strategy Training for Active Reading and Thinking) system for PLEs.…”

Section: Introductionmentioning

confidence: 99%

Automatic e-content sequencing system for personalised learning environments by using fuzzy AHP based on multiple intelligences

İnce

2021

Journal of Information Science

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The use of personalised learning environments (PLEs) and adaptive learning environments (ALEs) in education has increased during the COVID-2019 pandemic. Thus, the need of effective and high-quality PLEs and ALEs has emerged but developing such learning environments has some difficulties such as the need for a huge number of qualified e-contents. Generation of e-contents for these systems is a time-consuming and costly process. Moreover, presenting the same e-content for each student is not effective because each student can have different intelligence type. To overcome these problems, low cost, reusable and dynamic e-contents can be used for PLE and ALE systems. For this purpose, fuzzy analytic hierarchy process (FAHP) was used to provide sequenced e-contents for PLE and ALE systems. This proposed system was used in a case study to investigate its impact on learning process. The performance of the proposed study was compared with analytical hierarchy process (AHP), which is another multi-criteria decision-making (MCDM) method, also further statistical analyses were investigated. The results showed that 84.6% of students showed interest in the proposed system, so the FAHP method can be used for presenting personalised e-content effectively.

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Learning Content Recommendations on Personalized Learning Environment Using Collaborative Filtering Method

Cited by 11 publications

References 5 publications

The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

Design of an Educational Virtual Assistant Software

Automatic e-content sequencing system for personalised learning environments by using fuzzy AHP based on multiple intelligences

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