A recommender system of open educational resources based on the purpose of learning

Villalba, Klinge; Cuba, Sonia Castro; Deco, Claudia; Bender, Cristina; García‐Peñalvo, Francisco José

doi:10.1109/laclo.2017.8120899

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…It is also necessary that the student knows the use of strategies to make the most of their time and thus the learning of programming is successful and of quality. In order to help both teachers and students to achieve this goal, in [19] a recommender system focused on the pedagogical intention of the learning session that the teacher designs is presented. This recommendation system uses the Biggs SOLO Taxonomy and suggests the most appropriate learning resources according to the educational purpose of the teacher.…”

Section: Resultsmentioning

confidence: 99%

Approaches of Learning and Computational Thinking in Students that get into the Computer Sciences Career

Villalba-Condori

Cuba-Sayco

Chávez

et al. 2018

Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality

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The way in which the student processes the information, codifies it and recovers it, constitutes its learning approach. The learning process differentiates two qualitative ways of dealing with this process: the deep approach and the superficial approach. The use of each approach depends on the context. However, the adoption of a deep learning approach is positively related to the academic performance. Computational Thinking is defined as a competence of the XXI century, which allows solving problems from a computational point of view, and there is a variety of instruments that allow to measure it, and to know the state in which the evaluated student is. In this paper, we verified the existence of the positive significant relationship between the learning approach and computational thinking in students entering the career of Computer Sciences. By applying Pearson correlation test to verify the relationship between Learning Approaches and Computational Thinking, we found that both variables have homogeneous behaviors and that students show similar conditions. Men are in better conditions than women on the evaluated aspects of the Computational Thinking, Also, we found a significantly positive relationship between Computational Thinking and the Learning Approach (r = 0,882). This result shows that the learning approaches that students practice are linked to the computational thinking they demonstrate. According to the results, the teachers of this career must develop active and deep methodological strategies due to the predisposition in these students.

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Section: Resultsmentioning

confidence: 99%

Approaches of Learning and Computational Thinking in Students that get into the Computer Sciences Career

Villalba-Condori

Cuba-Sayco

Chávez

et al. 2018

Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality

Self Cite

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“…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.

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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

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“…Recommendation systems have been successfully used in many applications and recommendation algorithms have achieved great success [5]. e amount of data entered in public curriculum resources and social discourse resources in universities will grow at a faster rate.…”

Section: Introductionmentioning

confidence: 99%

“…It is hoped that learning contents and learning orientations suitable for the development of different learners can be provided accordingly, thus promoting their individual development and thus realizing personalized education in the true sense [ 4 ]. Recommendation systems have been successfully used in many applications and recommendation algorithms have achieved great success [ 5 ]. The amount of data entered in public curriculum resources and social discourse resources in universities will grow at a faster rate.…”

Section: Introductionmentioning

confidence: 99%

The Design of Personalized Education Resource Recommendation System under Big Data

Tian

Tang

2022

Computational Intelligence and Neuroscience

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With the advent of the Internet and the era of big data, education is increasingly dependent on data resources to support product and business innovation, and the lack of data resources has severely limited the areas involved. As a general information filtering method, personalized recommendation systems analyze the historical interaction data between users and items to build user interest models in an environment of “information overload”, allowing users to discover and recommend information that interests them. However, the explosive growth of information in the network makes users wander in the sea of information, and it is increasingly difficult to find the information they really need, i.e., information overload. This has given rise to personalized recommendation systems, which currently have more mature applications in industries such as e-commerce, music services, and movie services. To this end, this paper studies and implements a customized educational resource recommendation system that can handle big data. The results show that the values of different similarity calculations all fluctuate with the gradual increase of the number of nearest neighbors, and the algorithm in this paper is maximum at the number of neighbors around 60; then, it is inferred that applying the calculation method to the recommendation algorithm will improve the recommendation accuracy. Therefore, education uses the concept of big data to process the huge amount of education data and find some correlations and laws in education, so as to realize “teaching according to the material, teaching according to the material”.

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A recommender system of open educational resources based on the purpose of learning

Cited by 10 publications

References 3 publications

Approaches of Learning and Computational Thinking in Students that get into the Computer Sciences Career

Approaches of Learning and Computational Thinking in Students that get into the Computer Sciences Career

Systematic Literature Review on Open Educational Resources Recommender Systems

The Design of Personalized Education Resource Recommendation System under Big Data

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