Matches in SemOpenAlex for { <https://semopenalex.org/work/W4319593775> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W4319593775 endingPage "182" @default.
- W4319593775 startingPage "172" @default.
- W4319593775 abstract "Prediction of learners’ learning styles in online environments has several advantages, including steering learners on the proper path, motivating and engaging them while learning, and improving their learning results. It also helps instructors in the formation of personalized resource recommendations. As a result, predicting learners learning styles is necessary to aid in the personalization process. Existing approaches use either conventional or automatic approaches for learning style identifications. However, the large volume of data stored in online platforms has become a challenge in analyzing the behavior of learners and predicting their learning styles in the real world. Also, most of the existing approaches rely on a particular learning platform and can not be used in other platforms without technical assistance. In this paper, we propose GNN-LS, a new approach to identify and predict learners learning styles using a graph neural network. First, the graph embedding technique is used to capture the representation of learners and resources as a bipartite graph and encode them into low-dimensional representation. The encoded L-R sequences were given as input to the K-means clustering algorithm to identify and obtain labels as per FSLSM dimensions. Then, Graph neural network is trained to predict the learner’s learning style in the real world. The GNN-LS technique can be applied in a variety of educational systems and adapted to fit a variety of learning style models. Extensive experiments are run using the 2015 KDD Cup public available dataset to demonstrate the capabilities of GNN-LS. 5.31-15.68% improvements are achieved across all four FSLSM dimensions in accuracy." @default.
- W4319593775 created "2023-02-09" @default.
- W4319593775 creator A5009952250 @default.
- W4319593775 creator A5023191109 @default.
- W4319593775 creator A5025010101 @default.
- W4319593775 creator A5042805038 @default.
- W4319593775 creator A5090815103 @default.
- W4319593775 date "2022-01-01" @default.
- W4319593775 modified "2023-09-30" @default.
- W4319593775 title "GNN-LS: A Learning Style Prediction in Online Environments using Graph Neural Networks" @default.
- W4319593775 doi "https://doi.org/10.33969/j-nana.2022.020405" @default.
- W4319593775 hasPublicationYear "2022" @default.
- W4319593775 type Work @default.
- W4319593775 citedByCount "0" @default.
- W4319593775 crossrefType "journal-article" @default.
- W4319593775 hasAuthorship W4319593775A5009952250 @default.
- W4319593775 hasAuthorship W4319593775A5023191109 @default.
- W4319593775 hasAuthorship W4319593775A5025010101 @default.
- W4319593775 hasAuthorship W4319593775A5042805038 @default.
- W4319593775 hasAuthorship W4319593775A5090815103 @default.
- W4319593775 hasBestOaLocation W43195937751 @default.
- W4319593775 hasConcept C104317684 @default.
- W4319593775 hasConcept C119857082 @default.
- W4319593775 hasConcept C132525143 @default.
- W4319593775 hasConcept C136197465 @default.
- W4319593775 hasConcept C136764020 @default.
- W4319593775 hasConcept C145420912 @default.
- W4319593775 hasConcept C154945302 @default.
- W4319593775 hasConcept C183003079 @default.
- W4319593775 hasConcept C185592680 @default.
- W4319593775 hasConcept C197657726 @default.
- W4319593775 hasConcept C2781285556 @default.
- W4319593775 hasConcept C33923547 @default.
- W4319593775 hasConcept C41008148 @default.
- W4319593775 hasConcept C41608201 @default.
- W4319593775 hasConcept C50644808 @default.
- W4319593775 hasConcept C55493867 @default.
- W4319593775 hasConcept C59404180 @default.
- W4319593775 hasConcept C66746571 @default.
- W4319593775 hasConcept C73555534 @default.
- W4319593775 hasConcept C80444323 @default.
- W4319593775 hasConceptScore W4319593775C104317684 @default.
- W4319593775 hasConceptScore W4319593775C119857082 @default.
- W4319593775 hasConceptScore W4319593775C132525143 @default.
- W4319593775 hasConceptScore W4319593775C136197465 @default.
- W4319593775 hasConceptScore W4319593775C136764020 @default.
- W4319593775 hasConceptScore W4319593775C145420912 @default.
- W4319593775 hasConceptScore W4319593775C154945302 @default.
- W4319593775 hasConceptScore W4319593775C183003079 @default.
- W4319593775 hasConceptScore W4319593775C185592680 @default.
- W4319593775 hasConceptScore W4319593775C197657726 @default.
- W4319593775 hasConceptScore W4319593775C2781285556 @default.
- W4319593775 hasConceptScore W4319593775C33923547 @default.
- W4319593775 hasConceptScore W4319593775C41008148 @default.
- W4319593775 hasConceptScore W4319593775C41608201 @default.
- W4319593775 hasConceptScore W4319593775C50644808 @default.
- W4319593775 hasConceptScore W4319593775C55493867 @default.
- W4319593775 hasConceptScore W4319593775C59404180 @default.
- W4319593775 hasConceptScore W4319593775C66746571 @default.
- W4319593775 hasConceptScore W4319593775C73555534 @default.
- W4319593775 hasConceptScore W4319593775C80444323 @default.
- W4319593775 hasIssue "4" @default.
- W4319593775 hasLocation W43195937751 @default.
- W4319593775 hasOpenAccess W4319593775 @default.
- W4319593775 hasPrimaryLocation W43195937751 @default.
- W4319593775 hasRelatedWork W2912933387 @default.
- W4319593775 hasRelatedWork W2997669297 @default.
- W4319593775 hasRelatedWork W3014828506 @default.
- W4319593775 hasRelatedWork W3103753037 @default.
- W4319593775 hasRelatedWork W3114961909 @default.
- W4319593775 hasRelatedWork W3217171255 @default.
- W4319593775 hasRelatedWork W4226112616 @default.
- W4319593775 hasRelatedWork W4317655900 @default.
- W4319593775 hasRelatedWork W4319593775 @default.
- W4319593775 hasRelatedWork W1629725936 @default.
- W4319593775 hasVolume "2" @default.
- W4319593775 isParatext "false" @default.
- W4319593775 isRetracted "false" @default.
- W4319593775 workType "article" @default.