An approach for learning resource recommendation using deep matrix factorization

Điện, Trần Thanh; Nguyen, Hai Thanh; Thai-Nghe, Nguyen

doi:10.1080/24751839.2022.2058250

Cited by 15 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dien et al [25], propose a deep matrix decomposition approach that recommends learning courses depending on learners' skills and needs. The authors provide to researcher with a simpler explanation of conventional matrix factorization and deep matrix factorization.…”

Section: Related Workmentioning

confidence: 99%

DHM-Ocr

Mekala,

TNS,

Rao Tandu

2024

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

In recent years, there has been an increase in online education resources to help learners improve their skills. However, it is difficult to select the right course from available online education resources due to the demands and needs of learners with different knowledge domains. To solve this problem, an online course recommendation model has the important factor of enhancing learner's knowledge. Many existing recommendation systems (RS) use collaborative filtering (CF) to recommend courses to learners. The major problems with the Collaborative Filtering Recommendation System (CFRS) are the sparse preferences and the scalability of the data. According to the similarity of items, many recommendation models are proposed and developed, but none of these provide suggestions to users without their associations or preferences. We propose a deep hybrid model-online course recommendation (DHM-OCR) that uses high-level learner behavior and course objective features. We demonstrate the improvements and efficiency of the model for suggesting online e-learning courses. According to the analysis and evaluation results, it seems that our DHM-OCR outperforms the parallel research recommendation system. Experimental findings from online course data reveal that the suggested model and approach significantly improve classification accuracy and training efficiency, particularly limited available data.

show abstract

Section: Related Workmentioning

confidence: 99%

DHM-Ocr

Mekala,

TNS,

Rao Tandu

2024

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

show abstract

“…And OER could be relative to learning pathways in MOOCs. General OER Recommender systems [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [30], [30], [30], [30], [42] , [44], [45] , [46], [49], [50], [51], [52], [53], [54], [56], [57], [58], [59] [100], [104], [111], [113], [114], [126], [127], [129], [131] OER in MOOCs [28],…”

Section: Figmentioning

confidence: 99%

Systematic Literature Review on Open Educational Resources Recommender Systems

Rima

Meriem²,

Ajhoun³

2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

background: In recent years, recommender systems (RS) have grown in popularity among learning and education stakeholders, while open educational resources (OER) have begun to be deployed to improve the education field. The selection of OERs should not be haphazard; rather, it should be based on the learning objectives and (RS) commonly utilized to overcome the abundance of OER. Objective: The aim of this paper is to examine the current advances in OER recommenders and the different commonly used recommendation approaches and technical features, to position our future research alongside the existing. Method: A systematic literature review with a deductive approach using the grounded theory technique, combining quantitative and qualitative analysis by adopting open coding (inductive and deductive) for content analysis. Databases: we employed Google Scholar as a search engine to crawl data. Results: The findings are presented according to the (Bernard, H. Russell .2000) process. We present the results of the analysis using quotes from interviews that illuminate the theory (exemplars). Conclusion: The review reveals an emerging interest in the area; nevertheless, hybrid interactive systems; chatbot-based, dedicated to course creators are lacking. Further and future research is needed

show abstract

“…At first, the learning resources were modeled based on the Q matrix theory, and the students' learning difficulty was predicted by the long short-term memory (LSTM) algorithm of the learning resource difficulty prediction module; then, according to the requirements of the teaching units, the knowledge points to be learnt were combined for cyclic prediction so as to form a directed path map of learning resources; at last, the shortest path algorithm was used to recommend the minimum learning resources suitable for students' learning level to complete the learning tasks. Dien et al [24] constructed a deep matrix decomposition model which is an extension from standard matrix decomposition, and used it to recommend learning resources based on the capabilities and requirements of learners. The authors tested the model on two sets of experimental data, one dataset was a college student's learning results of recommended courses, another dataset was the learning resource data of five users; at last, the authors gave a few useful suggestions for the learners.…”

Section: Introductionmentioning

confidence: 99%

A New Resource Recommendation Method for Experiential Leaching Based on the Completion Degree of Online Learning Tasks

Jia¹

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Experiential Learning (ExL) is an effective way to consolidate theoretical knowledge and deepen understandings, and the recommendation of ExL resources needs to also take the effect of students’ theoretical learning into consideration. However, existing studies generally ignore the stage-by-stage assessment of students’ completion of online learning tasks, and the recommendation performance of existing resource recommendation models for ExL is not satisfactory enough. Therefore, the recommendation method needs to be innovated, and the interpretability of recommendation results is facing challenges. To respond to these issues, this paper studied a new resource recommendation method for ExL based on the completion degree of online learning tasks. At first, the paper gave the principle of recommending ExL resources based on the completion degree of online learning tasks, and built an online learning task completion degree prediction model. Then, this paper adopted a bi-directional GRU network model based on attention mechanism to analyze the recent online learning behavior sequence of students and attain the completion degree of students’ short term learning tasks. After that, a knowledge map representing ExL resources and the correlation of knowledge attributes was drawn; by combining the completion degree of both the short-term and long-term learning tasks, the ExL resources suitable for students were recommended to them. At last, experimental results verified the effectiveness of the constructed model.

show abstract

An approach for learning resource recommendation using deep matrix factorization

Cited by 15 publications

References 27 publications

DHM-Ocr

DHM-Ocr

Systematic Literature Review on Open Educational Resources Recommender Systems

A New Resource Recommendation Method for Experiential Leaching Based on the Completion Degree of Online Learning Tasks

Contact Info

Product

Resources

About