Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

Khalid, Asra; Lundqvist, Karsten; Yates, Anne; Ghzanfar, Mustansar Ali

doi:10.1371/journal.pone.0245485

Cited by 17 publications

(9 citation statements)

References 20 publications

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“…At the same time, big data technologies such as spark were used to improve the efficiency of system recommendation. To solve the problem that traditional recommender system models cannot be incrementally updated with increasing data, Khalid et al [ 16 ] proposed a novel online recommendation algorithm that votes on active learners and hyperspheres. In order to solve the performance problem of course recommendation in big data scenarios, Zhang et al [ 17 ] proposed a distributed association rule mining algorithm, and used Hadoop for distributed storage and spark for distributed memory computing, which improved the recommendation effect and efficiency of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Yang

Li²,

Zhou³

et al. 2022

PLoS ONE

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Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, so how to obtain various features with different dimensions, i.e., how to integrate the advantages of each model and improve the model inference efficiency, becomes the focus of this paper. In this paper, a better deep learning model is obtained by integrating several cutting-edge deep learning models. Meanwhile, to make the integrated learning model converge better and faster, the parameters of the integrated module are initialized, constraints are imposed, and a new activation function is designed for better integration of the sub-models. Finally, the integrated large model is distilled for knowledge distillation, which greatly reduces the number of model parameters and improves the model inference efficiency.

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Section: Related Workmentioning

confidence: 99%

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Yang

Li²,

Zhou³

et al. 2022

PLoS ONE

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show abstract

“…Machine learning was also found in literature to recommend courses. Aher and Lobo [55], Li et al [118] and Mondal et al [155] [167] proposed Novel online recommendation algorithm for course recommendation. Hybrid approach in to recommend courses were also found in the literature.…”

Section: Rq1 How Many Studies Supported Their Claim With Experiments and Which Datasets Were Used In The Studies?mentioning

confidence: 99%

“…Furthermore, clustering and k-means for learning resource in Chakraborty et al [106], Cooper et al [116] utilized sequential pattern mining and Chang et al [73] used watch time log for video recommendation. Finally, Khalid et al [167] [97]. Further, user profile, user similarity and their combination were used in Estrela et al [80] for course recommendation.…”

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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Online learning environments (OLE) are gaining popularity, including learning management systems (LMS) and massive open online courses (MOOCs), which are the best modern alternate solutions available for education in the current era. The luxury to learn irrespective of geographical and temporal restrictions makes it an attractive resource. At the start of 2020, the global pandemic enforced social distance practice worldwide, changing the work environment dynamics, leaving the people with options like online trading, work from home, and online education. The online learning environments gained particular attention in the educational sector, where users could access the online learning resources to fulfil their academic requirements during the lockdown. From massively available content such as MOOC, the learners are overwhelmed with the available choices. In this scenario, recommender systems (RS) come to the rescue to help the learner make appropriate choices for completing the enrolled course. There is tremendous scope and a multitude of opportunities available for researchers to focus on this domain. An exhaustive analysis is required to spotlight the opportunities in this realm. Various studies have been performed to provide such solutions in multiple areas of the MOOC recommendation systems (MOOCRS) such as course recommendation, learner peer recommendation, resource recommendations, to name a few. This is a compendious study into the research conducted in this area, identifying 670 articles out of 116 selected for analysis published from 2013 to 2021. It also highlights multiple areas in MOOC, where the recommendation is required, as well as technologies used by other researchers to provide solutions over time.

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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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Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

Cited by 17 publications

References 20 publications

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

A Systematic Mapping Review on MOOC Recommender Systems

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

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