Adaptive Social Media Skills Trainer for Vocational Education and Training: Concept and Implementation of a Recommender System

Valentin, Christina Di; Emrich, Andreas; Lahann, Johannes; Werth, Dirk; Loos, Peter

doi:10.1109/hicss.2015.234

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…The objective of the study proposed by [25] was the development of social media skills based on whether users had or lacked these skills. Our proposal not only works with the skills obtained by a user in their academic background or in the course of their job, but also considers their current position and related ones.…”

Section: Discussionmentioning

confidence: 99%

“…A review of the literature on the use of ontologies for RSs highlights the importance of information representation and management. The study by [25] indicated that the amount of information handled is a key factor for recommendations to be successful and proposed that future studies could do tests with real data in order to have a sufficient sample size.…”

Section: Related Studiesmentioning

confidence: 99%

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Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

2021

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Lifelong learning enables professionals to update their skills to face challenges in their changing work environments. In view of the wide range of courses on offer, it is important for professionals to have recommendation systems that can link them to suitable courses. Based on this premise and on our previous research, this paper proposes the use of ontology to model job sectors and areas of knowledge, and to represent professional skills that can be automatically updated using the profiled data and machine learning for clustering entities. A three-stage hybrid system is proposed for the recommendation process: semantic filtering, content filtering and heuristics. The proposed system was evaluated with a set of more than 100 user profiles that were used in a previous version of the proposed recommendation system, which allowed the two systems to be compared. The proposed recommender showed 15% improvement when using ontology and clustering with DBSCAN in recall and serendipity metrics, and a six-point increase in harmonic mean over the stored data-based recommender system.

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Section: Discussionmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

2021

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“…By recommending appropriate apprenticeship location, students will maximally develop their own skills in the industrialized world. There is a recommendation system developed with adaptive methods of using social media to develop individual skills [17]. Research on the use of online forums to develop student skills in apprenticeships [18], then to support the competency of the apprentice participants is done by developing a Portal [10].…”

Section: Discussionmentioning

confidence: 99%

Recommendation System for Selection of Majors and Apprenticeship on Vocational and Training Education Based on Competency to Produce Demand Driven Graduates

Permana¹,

Dewi²,

Setemen³

2017

Proceedings of the 2nd International Conference on Innovative Research Across Disciplines (ICIRAD 2017)

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Abstract-This research aims to produce demand driven human resources through vocational education and training. In the process of realizing it, it is necessary to innovate in several sectors, such as the selection of appropriate majors and apprenticeship process in accordance with the competence of students in the middle to college level. The method applied in this research is to do the selection process for new students by giving recommendation of the department which is in accordance with the competence of the students by taking the majors test. Then the students will take part in the apprenticeship, will take the test and with some component of the value of the course or course, the value of report cards, and the GPA will be processed and produce recommendation of apprenticeship location in accordance with the student's competence. The impact of this research is students who are in the appropriate majors, will affect the comfort in the learning process so as to maximize student potential. The apprenticeship location recommendation aims to provide work comfort, work motivation, good career expectations for students who attend apprenticeships in the industrial world. So the graduates produced by vocational education is demand driven.

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“…In this review, the importance of the representation and management of information is highlighted. Studies [10,11] pointed out that ontologies allow information of interest to users to be inferred, which can be used to enrich recommendations. Likewise, [12] indicated that the amount of information handled is a key factor in the success of recommendations.…”

Section: Introduction and Related Studiesmentioning

confidence: 99%

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

2022

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Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content-based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web-based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained.

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Adaptive Social Media Skills Trainer for Vocational Education and Training: Concept and Implementation of a Recommender System

Cited by 10 publications

References 14 publications

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

Recommendation System for Selection of Majors and Apprenticeship on Vocational and Training Education Based on Competency to Produce Demand Driven Graduates

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

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