Indigenous Pre Recruitment Course 2016

Indigenous Pre Recruitment Course 2016 – Open Access Guidelines Institutional Open Access Program Special Issues Guidelines Editorial Process Research and Publication Ethics Article Processing Fee Pricing Certification

All published articles are immediately available worldwide under an open access license. Permission is not required to reuse a published article in whole or in part, including figures and tables. For articles published under the Creative Commons CC BY open access license, any part of the article may be reused without permission, as long as the original article is clearly cited.

Indigenous Pre Recruitment Course 2016

Featured papers represent the most advanced research with the potential to have a high impact on the field. Featured papers are submitted by individual invitation or on the recommendation of scientific editors, who are co-authored before publication.

Pdf) Cultural Continuity As A Determinant Of Indigenous Peoples’ Health: A Metasynthesis Of Qualitative Research In Canada And The United States

A featured paper can be an original research article, a comprehensive new research study that incorporates multiple methods and approaches, or a comprehensive review article with a concise and thorough update on the latest advances in the field. scientific literature. This type of paper provides perspectives on future research directions or potential applications.

Editors’ Choice articles are based on recommendations from scientific editors of journals from around the world. The editors select a small number of recently published articles in the journal that will be of interest to the authors or important to the field. The aim is to provide a snapshot of the most exciting works published in the journal’s various research areas.

Received: April 12, 2022 / Revised: May 26, 2022 / Accepted: June 10, 2022 / Published: June 17, 2022

With millions of students/employees browsing course information and job adverts every day, the need for an accurate, efficient, meaningful and transparent course and job recommendation system is more evident than ever. Current recommendation research has attracted considerable attention in academia and industry. However, existing studies have mainly focused on content analysis and deconstructing user characteristics of courses or jobs, and have failed to explore cross-domain data integration between careers and education. At the same time, not fully exploiting the relationship between training, skills, and jobs helps improve the accuracy of recommendations. Therefore, this study aims to propose a new cross-domain recommendation model to help students/employees search for suitable courses and jobs. Using heterogeneous graphs and community detection algorithms, this study presents a Graph-Community-Enabled (GCE) model that integrates data from course profiles and recruitment data. Specifically, to bridge skill gaps between occupations and curricula, skill groups calculated by community detection algorithms are used to link curriculum and job information. Then, an innovative heterogeneous graph method and a random walk algorithm enable cross-domain information recommendations. The proposed model is evaluated on real-world job datasets from recruitment websites, MOOCs, and higher education training datasets. Experiments show that the model is clearly superior to the classical baseline. The approach described can be replicated in a variety of educational/career contexts.

See also  R&m Fencing

We’re Very Much Part Of The Team Here”: A Culture Of Respect For Indigenous Health Workforce Transforms Indigenous Health Care

Education; career; heterogeneous data / heterogeneous graph mining; information recommendations; cross-domain education; career; heterogeneous data / heterogeneous graph mining; information recommendations; throughout the domain

What are the benefits of education for career planning? Finding a fulfilling career is a convenient and obvious popular answer. Student/employee productivity, employability, and career satisfaction are enhanced through lifelong learning, or the acquisition of knowledge to meet personal and professional goals. Students/employees are constantly exploring various educational opportunities to enhance their knowledge in order to achieve their career goals. Education should generally support the ecological employment system [1], while reducing the skills gap between academia, school and industry [2].

With the rapid development of internet technology, online resources have made it easier for students/employees to find educational and job related information. At the same time, it brings with it the problem of “information overload,” confusing students and employers with the vast amount of online material that prevents them from quickly identifying the courses and jobs that are best for them. Recommender systems that help users find the most relevant items are a promising way to filter information. It will provide a variety of specialized recommendations based on each user’s personal needs and preferences.

Recommender systems in education and employment can help students/employees make better and more informed decisions, thereby influencing their future. Many classic job/career and curriculum/educational recommendation (JCR) systems have been proposed, including the CourseAgent system [3], the CourseRank system [4] and the CaPaR framework [5]. Although they are compliant, existing JCRs have individual student/staff requirements and limited impact. Furthermore, most algorithms in previous studies have used user-based modeling (UBM), content-based modeling (CBM) and collaborative filtering (CF). While UBM focuses on analyzing learner or job applicant profiles, CBM focuses on examining course or job content features, and CF primarily examines course evaluations, user learning history, and employment history. However, important implicit information that helps to increase the validity of the proposition, such as links between courses, skills and jobs, is not adequately exploited [6, 7]. More sophisticated routes such as job-skills-skills-course-course can be used to directly link courses and jobs. According to this view, the links between courses, skills and jobs may be much more complex than traditional CF approaches suggest. Furthermore, numerous studies have been conducted on course recommendations and job recommendations [8], but cross-domain data integration between the two has rarely been considered.

See also  Hua Chang Heritage Hotel Bangkok

Recruiting And Training Is The Number One Concern For Smes

Therefore, this study proposes a new Graph-Community Enabled (GCE) method to solve the cross-domain career education recommendation problem from a heterogeneous graph mining perspective. The two domains have three different nodes that interact through four types of relationships. Therefore, the recommendation policy is changed to a graph-based random walk policy. Figure 1 shows the integration of two different information sources, education and career, into a heterogeneous network using skills as a bridge. However, there is a difference between job skills and course skills because different vocabularies are used. We use the Infomap algorithm to calculate skill groups, which helps link job and course data. Finally, five meta-pathway features were manually constructed for recommendation based on the appropriate indexed heterogeneous graphs. Ranking predictions are expressed for each feature. After that, using a random walk algorithm, we can deliver courses and job offers that are more tailored to your future career aspirations or intended educational level.

The significance and uniqueness of this research lies in the aggregation and indexing of work and education data through multiple networks and community discovery to achieve cross-domain information recommendations. Experiments were conducted using MOOC and university course data as well as job postings in the IT industry to demonstrate how students/employees can benefit from graph-based data integration. Findings suggest that the strategy is effective in making curriculum recommendations based on predetermined career goals. The proposed method can be used in various educational and professional settings.

We note that an earlier version of this paper was presented at an international conference [7] and is available on arXiv (, accessed 11 April 2022) [8]. Our previous conference paper conducted only simple preliminary tests on the course recommendation task and did not fully validate the proposed model. This manuscript aims to improve the connectivity quality of heterogeneous graphs by combining Word2Vec/Bert technology with community detection algorithms to solve the skill gap problem in training and work contexts. In addition, several different meta-tracks have been developed for course and work recommendation tasks according to different types of user application scenarios, using several data (ie, MOOCs added) and several basic methods to analyze and verify effectiveness. of the proposed model for enriching test content. More details are provided in Section 3.

See also  12v Latching Relay Automotive

The structure of this paper is as follows: Section 2 reviews the relevant literature; Chapter 3 describes the data collection process and the proposed community-based graphical method; Section 4 discusses the results and provides conclusions and suggestions for future work.

Robust And Prototypical Immune Responses Toward Influenza Vaccines In The High Risk Group Of Indigenous Australians

Recommender systems have been widely used in lesson planning [6, 9, 10]. In most of these studies, courses were recommended to end users based on feedback from other users [ 11 , 12 ], overall user performance [ 13 , 14 ] or similarity of course content [ 15 , 16 ]. For example, Nguyen et al. [17] used a sequential rule to find the optimal subject pair and grade. Recurrent Neural Networks were used by Morsi and Karipis [18] to suggest lessons to help students maintain or improve their GPA. In general, training recommendation systems rarely consider the target profession [1, 19].

Similarly, job recommendation systems have attracted much attention from the academic community in recent decades. Some studies have examined job recommendations related to career orientation. Paparrizos et al. [20] trained machine learning models that use previous job experiences to predict candidates’ next job transitions. Patel et al. [5] presented a CaPaR or “career path recommendation” framework that explores users’ work experiences using text mining and collaborative filtering methods.

Pre screening process in recruitment, recruitment and selection course, pre recruitment process, pre course, pre recruitment training, indigenous recruitment, recruitment consultant course, pre recruitment, recruitment course online, indigenous recruitment agencies, recruitment agency course, recruitment course

Leave a Reply

Your email address will not be published. Required fields are marked *