Designing a Learning Recommender System by Incorporating Resource Association Analysis and Social Interaction Computing

Li, Yanyan; Zheng, Yafeng; Kang, Jia; Bao, Haogang

doi:10.1007/978-981-287-868-7_16

Cited by 8 publications

(9 citation statements)

References 2 publications

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“…[21]- [23], [39], [50], [55], [62], [69]- [72], [79], [80], [86] Cameras 11 (13.41%) [20], [34], [35], [43], [44], [47], [49], [52], [63], [65], [66] Microcontrollers 7 (10.29%) [34], [47], [48], [58], [59], [66], [67] Virtual/remote laboratories 6 (8.82%) [20], [47], [53], [73], [74], [82] Biometric sensors 5 (7.35%) [34], [52], [66], [67], [71] Environmental sensors 5 (7.35%) [41], [53], [59], [66],…”

Section: B Technologies Involved In Slesmentioning

confidence: 99%

“…[21]- [23], [30], [31], [36], [38], [41], [50], [53], [57], [61], [68], [70], [71], [79], [80] Data mining 9 (13.24%) [22], [27], [31], [36], [54], [67], [69], [72], [80] Ontologies 6 (8.82%) [21], [22], [25], [32], [60], [62] Artificial intelligence 6 (8.82%) [19], [54], [59], [66], [75], [78] Cloud computing 5 (7.35%) [34], [44], [55], [56] favored the inclusion of data processing and analysis techniques in SLEs (see Table V). The results of the analysis are presented considering that some of these data processing techniques might overlap in specific taxonomies.…”

Section: B Technologies Involved In Slesmentioning

confidence: 99%

See 1 more Smart Citation

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Tabuenca

Serrano-Iglesias

Martín

et al. 2021

IEEE Trans. Learning Technol.

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Smart learning environments (SLEs) have gained considerable momentum in the last 20 years. The term SLE has emerged to encompass a set of recent trends in the field of educational technology, heavily influenced by the growing impact of technologies such as cloud services, mobile devices, and interconnected objects. However, the term SLE has been used inconsistently by the technology-enhanced learning (TEL) community, since different research works employ the adjective "smart" to refer to different aspects of novel learning environments. Previous surveys on SLEs are narrowly focused on specific technologies, or remain at a theoretical level that does not discuss practical implications found in empirical studies. To address this inconsistency, and also to contribute to a common understanding of the SLE concept, this paper presents a systematic literature review (SLR) of papers published between 2000 and 2019 discussing SLEs in empirical studies. Sixty eight papers out of an initial list of 1,341 papers were analyzed to identify: 1) what affordances make a learning environment smart; 2) which technologies are used in SLEs; and 3) in what pedagogical contexts are SLEs used. Considering the limitations of previous surveys, and the inconsistent use of the SLE concept in the TEL community, this paper presents a comprehensive characterization Manuscript sent for review July 30, 2020. Revised month day, year; Accepted month day, year. Date of publication month day, year.

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Section: B Technologies Involved In Slesmentioning

confidence: 99%

Section: B Technologies Involved In Slesmentioning

confidence: 99%

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Tabuenca

Serrano-Iglesias

Martín

et al. 2021

IEEE Trans. Learning Technol.

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show abstract

“…This system has been designed based on hybrid recommendation using the collaborative filtering and the sequential pattern mining. Li et al [22] present a general architecture of learning recommender system for the smart learning environment. By constructing learner models and resource models, the proposed recommender system aims to recommend learning resources by using the clustering and association rule mining and to recommend peers via social interaction computing.…”

Section: Related Workmentioning

confidence: 99%

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

Bourkoukou

Bachari

2018

JOIV : Int. J. Inform. Visualization

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Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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“…In the proposed recommendation approach, ontology is used to model and represent the domain knowledge about the learner and learning resources whereas SPM algorithm discovers the learners' sequential learning patterns. Li et al [16] present a general architecture of learning recommender system for the smart learning environment. By constructing learner models and resource models, the proposed recommender system aims to recommend learning resources by using the clustering and association rule mining and to recommend peers via social interaction computing.…”

Section: Litterature Reveiwmentioning

confidence: 99%

Weighting based approach for learning resources recommendations

Bourkoukou

Achbarou

2018

JOIV : Int. J. Inform. Visualization

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Personalized e-learning systems based on recommender systems refines enormous amount of data and provides suggestions on learning resources which is appealing to the learner. Although, the recommender systems depends on content based approach or collaborative filtering technique to make recommendations, these methods suffers from cold start and data sparsity problems. To overcome the limitations of the aforementioned problems, a weight based approach is proposed for better performance. The main criterion for building a personalized recommender system is to exploit useful content and provide better recommendations with minimal processing time. The proposed system is a web based client side application which uses user profiles to form neighborhoods and calculates predictions using weights. For newcomers a profile is constructed based on learning styles. The resources which might be of interest to the user are predicted from calculated predictions.

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Designing a Learning Recommender System by Incorporating Resource Association Analysis and Social Interaction Computing

Cited by 8 publications

References 2 publications

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

Weighting based approach for learning resources recommendations

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