Massive Open Online Courses (MOOCs) Recommendation Modeling using Deep Learning

Sakboonyarat, Siriporn; Tantatsanawong, Panjai

doi:10.1109/icsec47112.2019.8974770

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…It takes into consideration the various connections between courses by employing clustering techniques from network analysis. Ng et al [31] employed matrix factorization and content-based algorithms to generate recommendations for children's literature.…”

Section: Conventional Course Recommendation Algorithmsmentioning

confidence: 99%

Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model

Madhavi,

Nagesh,

Govardhan

2023

EAI Endorsed Trans e-Learn

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The exponential development of online learning resources has led to an information overload problem. Therefore, recommender systems play a crucial role in E-learning to provide learners with personalised course recommendations by automatically identifying their preferences. In addition, e-Learning platforms such as MOOCs and LMS have been criticised for their low course completion rates, and one of the primary reasons is that they do not provide personalised course recommendations for users with varying interests. Rapidly locating the courses that users are interested in on enormous e-Learning platforms can have a significant impact on the quality of learning and the dissemination of knowledge to the learner. This paper examines the most prevalent recommendation techniques utilised in E-learning. We examined how to apply Deep Transformer based Ensembled Attention Model (DTEAM) on e-Learning system in order to achieve personalized course recommendations. The proposed recommendation model uses BERT as its foundation integrated MLM and Transformers. Predicted course recommendations are more aligned with the interests of users. Our experimental results proved that traditional recommendation algorithms, such as collaborative filtering and item-based filtering are incapable of producing superior results. The consequence of the research can assist students in selecting courses according to their preferences and improve their learning caliber

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Section: Conventional Course Recommendation Algorithmsmentioning

confidence: 99%

Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model

Madhavi,

Nagesh,

Govardhan

2023

EAI Endorsed Trans e-Learn

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“…The course data and learner data in this paper were obtained from a real online learning MOOC platform [33]. We mainly collect course data including video information, audio information, the course name, and introduction of the course.…”

Section: Datasetsmentioning

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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“…In the specific domain of MOOCs recommender systems using neural networks (NN), multiple research directions have been pursued. These include optimizing the accuracy of recommendation [15,16,24,25,41], ensuring fairness [4,5,11,16,18], and augmenting explainability [26,28]. While NN-based approaches have set benchmarks in predictive accuracy, this efficacy frequently comes at the cost of model interpretability, raising concerns about the trade-off between performance and transparency.…”

Section: Introductionmentioning

confidence: 99%

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Frej,

Shah,

Knezevic

et al. 2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners and courses. While these enriched representations allow more accurate and personalized recommendations, explainability remains a significant challenge which is especially problematic for certain domains with significant impact such as education and online learning.Recently, a novel class of recommender systems that uses reinforcement learning and graph reasoning over KGs has been proposed to generate explainable recommendations in the form of paths over a KG. Despite their accuracy and interpretability on e-commerce datasets, these approaches have scarcely been applied to the educational domain and their use in practice has not been studied. In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning. To validate the practical implications of our approach, we conducted a user study examining user perceptions of our new explainable recommendations. We demonstrate the generalizability of our approach by conducting experiments on two educational datasets: COCO and Xuetang.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Neural networks.

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Massive Open Online Courses (MOOCs) Recommendation Modeling using Deep Learning

Cited by 9 publications

References 13 publications

Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model

Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model

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

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

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