Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems

Dias, Alessandro da Silveira; Wives, Leandro Krug

doi:10.1007/s00779-018-01197-7

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…The LO recommendation approach of this recommender system is presented by (Dias & Wives, 2019). This recommendation approach uses the results of the choices ("how to learn", "with whom to learn", "in which learning pathway to learn", etc.)…”

Section: Usage Of the Results Of The Learner-user Choicesmentioning

confidence: 99%

“…This recommender system has a LO recommendation approach that uses the result of the learner-user choices as implicit feedback. (Dias & Wives, 2019) presents this LO recommendation approach, and an experimental evaluation which shows that it presents higher prediction accuracy than baseline recommendation approaches in the AdaptWeb. This result is statistically significant.…”

Section: Discussionmentioning

confidence: 99%

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Learner-User Satisfaction Survey in the AdaptWeb Platform using the Learner Choices from Learner-Driven Learning

Dias

Wives

2020

RBIE

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In this paper, we review the definition of the learner choices from the Learner-driven Learning paradigm for e-learning systems. After this, we analyze how different categories of e-learning systems enable the user to make these choices, such as Serious Games. We present in detail how AdaptWeb platform makes available these choices to learner users. Additionally, we present a satisfaction survey performed after an online course on AdaptWeb platform. The survey questions were about making choices during learning and about the way AdaptWeb makes the choices available to learner-users. Summarizing the results, students enjoyed being able to make choices about their own learning and felt that this possibility was beneficial to their learning. Moreover, they liked the way AdaptWeb makes the choices available to students. Most of the students found the system easy to use, intuitive, and the student's choices were explicit and easy to take.

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Section: Usage Of the Results Of The Learner-user Choicesmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Learner-User Satisfaction Survey in the AdaptWeb Platform using the Learner Choices from Learner-Driven Learning

Dias

Wives

2020

RBIE

Self Cite

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“…Currently, there are many problems with course recommendations. Only considering the user's nearest neighbor recommendation method and integrating basic personal information and social preferences for the recommendation, the user's implicit feedback data is ignored [2]. However, the multimodal graph convolution model is used to generate the specific modal representation of users and videos to capture better the implicit feedback data of users [3].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention

et al. 2022

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With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.

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A joint neural network model for combining heterogeneous user data sources: An example of at‐risk student prediction

Qiao¹,

Hu²

2019

Asso for Info Science & Tech

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Information service providers often require evidence from multiple, heterogeneous information sources to better characterize users and offer personalized service. In many cases, statistic information (for example, users' profiles) and sequentially dynamic information (for example, logs of interaction with information systems) are two prominent sources that can be combined to achieve optimized results. Previous attempts in combining these two sources mainly exploited models designed for either static or sequential information, but not both. This study aims to fill the gap by proposing a novel joint neural network model that can naturally fit both static and sequential user data. To evaluate the effectiveness of the proposed method, this study uses the problem of at‐risk student prediction as an example where both static data (personal profiles) and sequential data (event logs) are involved. A thorough evaluation was conducted on an open data set, with comparisons to a range of existing approaches including both static and sequential models. The results reveal superb performances of the proposed method. Implications of the findings on further research and applications of joint models are discussed.

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Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems

Cited by 11 publications

References 19 publications

Learner-User Satisfaction Survey in the AdaptWeb Platform using the Learner Choices from Learner-Driven Learning

Learner-User Satisfaction Survey in the AdaptWeb Platform using the Learner Choices from Learner-Driven Learning

A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention

A joint neural network model for combining heterogeneous user data sources: An example of at‐risk student prediction

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