A Method for Personalized C Programming Learning Contents Recommendation to Enhance Traditional Instruction

Yan, Yu; Hara, Kohei; Kazuma, Takenobu; He, Aiguo

doi:10.1109/aina.2017.13

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…From the analysis, it can then be observed that RSs take into account user preferences when making suggestions based on recommendations from similar users, while [21,30,35,45,53] make recommendations based on learning style, and [24,31,46,50,61,64] based on diagnosis/student progress and the knowledge group. Likewise, another element they take into account are user skills and/or competences [40,42,56,69] and competences related to work associated with their profile within Internet job search portals.…”

Section: Discussionmentioning

confidence: 99%

“…The percentage of articles according to type of education can be observed in Table 5. In most cases, the area of knowledge to which the recommendations are geared is not specified, while the works [32,53,54,109,113] indicate that studies in their case recommend resources pertaining to the field of architecture and engineering.…”

Section: Types Of Education Covered By the Recommendation Systemsmentioning

confidence: 99%

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Recommendation Systems for Education: Systematic Review

2021

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Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

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