A content-based recommendation algorithm for learning resources

Shu, Jiangbo; Shen, Xiaoxuan; Liu, Hai; Yi, Baolin

doi:10.1007/s00530-017-0539-8

Cited by 213 publications

(84 citation statements)

References 33 publications

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“…Various recommendation techniques were proposed to explore learners' preferences, knowledge and browsing histories for recommendation (Dráždilová et al, 2010;Lytras & Ordóñez de Pablos, 2011;Núñez-Valdéz et al, 2012). Convolutional Neural Network (CBCNN) was used to design content-based learning resource recommendation model (Shu et al, 2018), while it was indicated that using conventional recommendation methods had an important problem in e-learning recommendation systems (Kovanović, Joksimović, Gašević, Siemens & Hatala, 2015). Online users often learn with each other and refer to friends, classmates, lecturers and other sources to make learning choices.…”

Section: Related Work E-learning Communitiesmentioning

confidence: 99%

“…On the one hand, there is not enough work on recommendation problems in the e-learning environment, and most scholars focus more on music recommendation, movie recommendation and other fields (Andjelkovic, Parra & O'Donovan, 2019;Wegba, Lu, Li & Wang, 2018;Zhao et al, 2019;Zheng, Kondo, Zilora & Yu, 2018). Although some scholars' research questions are based on the field of e-learning, they still focus on the recommendation of learning resources, not community recommendation (Shu, Shen, Liu, Yi & Zhang, 2018;Vesin, Ivanović, KlašNja-MilićEvić & Budimac, 2012). On the other hand, the existing community recommendation methods do not consider the users' personality information while personality traits have an important impact on the behavior of users joining the community.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging personality information to improve community recommendation in e‐learning platforms

Sun¹,

Geng²,

Cheng³

et al. 2020

Brit J Educational Tech

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E‐learning platforms are becoming more and more important and they are gradually changing people’s learning ways. In the e‐learning platforms, users actively create and join their favorite communities to share their questions and ideas. With the increase of users of e‐learning platforms, the number of communities is increasing dramatically. In this context, it has become difficult for users to find learning communities that match their interests and preferences. Therefore, how to effectively recommend the learning community for users has become an urgent need. However, compared to learning item recommendation, there is relatively limited work on learning community recommendation, and the existing research on community recommendation often ignores the personality information. Personality is considered one of the primary factors that influence human behavior and social relationships, as it affects how people react and interact with others. Several studies have demonstrated that people with similar personality tend to have similar interests. Furthermore, homophily theory also states that social interactions between similar individuals occur at a higher rate than among dissimilar ones. Since interests and interactions are important driving forces for users to join the learning communities, personality has an important impact on users’ choices of communities. Therefore, this paper aims at shedding some light on the impact of personality information on the accuracy of community recommendations. Particularly, we propose three enhanced matrix factorization models based on the Big Five personality framework. To evaluate the effectiveness of our proposed models, we conducted extensive experiments on myPersonality datasets. The results prove that the personality information can improve the performance of the learning community recommendation model and alleviate the data sparsity problem.

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Section: Related Work E-learning Communitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging personality information to improve community recommendation in e‐learning platforms

Sun¹,

Geng²,

Cheng³

et al. 2020

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…The gained knowledge could be a trained model, or data set, or features. From the point of view of the research in micro learning, because there are very few sufficient and complete public data sets, many studies [20,35,48] are based on the gained knowledge from other domain. Using transfer learning methods to transfer sufficient labelled data from other domain(s) to the target domain is an indirect method to construct models and produce more labelled data.…”

Section: Transfer Learningmentioning

confidence: 99%

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

et al. 2019

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The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people's daily activities. Such development together with the lifelong learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners' fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

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“…However, both datasets contain very few users and only a fraction of subjects. One prior study [15] used a convolutional neural network (CNN) to model the latent factors based on the BookCrossing dataset [16]. However, in the e-learning domain, the type of learning materials could be in the format of video, audio, and text, the conventionally trained model was not sophisticated enough for micro learning.…”

Section: Insufficient Data Sourcementioning

confidence: 99%

Towards the Readiness of Learning Analytics Data for Micro Learning

Lin

Sun

Shen

et al. 2019

Services Computing – SCC 2019

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With the development of data mining and machine learning techniques, data-driven based technologyenhanced learning (TEL) has drawn wider attention. Researchers aim to use established or novel computational methods to solve educational problems in the 'big data' era. However, the readiness of data appears to be the bottleneck of the TEL development and very little research focuses on investigating the data scarcity and inappropriateness in the TEL research. This paper is investigating an emerging research topic in the TEL domain, namely micro learning. Micro learning consists of various technical themes that have been widely studied in the TEL research field. In this paper, we firstly propose a micro learning system, which includes recommendation, segmentation, annotation, and several learning-related prediction and analysis modules. For each module of the system, this paper reviews representative literature and discusses the data sources used in these studies to pinpoint their current problems and shortcomings, which might be debacles for more effective research outcomes. Accordingly, the data requirements and challenges for learning analytics in micro learning are also investigated. From a research contribution perspective, this paper serves as a basis to depict and understand the current status of the readiness of data sources for the research of micro learning.

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A content-based recommendation algorithm for learning resources

Cited by 213 publications

References 33 publications

Leveraging personality information to improve community recommendation in e‐learning platforms

Leveraging personality information to improve community recommendation in e‐learning platforms

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

Towards the Readiness of Learning Analytics Data for Micro Learning

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