E-Portfolio Effectiveness in Seeking IT Jobs

The fact that graduate students usually have no experience and similar backgrounds might cause job vacancy placement and applicant evaluation to be complicated and stressful tasks for employers. This research project focuses on the role of e-portfolios in facilitating the hiring process and assisting job seekers in finding IT jobs. By using e- portfolios, employers would be able to assess IT applicants during the evaluation process more efficiently than using conventional CVs. A hybrid method was used to assess the e-portfolio effectiveness involving HR/IT interviewees after the experimentation of using simulated e-portfolios. Accordingly, Interviewees previewed the results of compression between a CV and an e-portfolio conducted in a focus group experimentation to get their feedback. The findings of this research indicate a high probability of acceptance to use the e-portfolio for seeking jobs and evaluation purposes by both employers and job seekers

Linkedin As A Learning Tool In Business Education

This article summarizes the existing research on social media as a learning tool in higher education and adds to the literature on incorporating social media tools into collegiate business education by suggesting specific course content areas of business where LinkedIn exercises and training can be incorporated.  LinkedIn as a classroom tool cannot only reinforce basic concepts, such as branding and relationship-building, but creative use of this tool can increase student engagement and collaboration and encourage students to begin building their professional networks, which can be vital in their career progression

Careering through the Web: the potential of Web 2.0 and 3.0 technologies for career development and career support services

This paper examines the environment that the web provides for career exploration. Career practitioners have long seen value in engaging in technology and the opportunities offered by the internet, and this interest continues. However, this paper suggests that the online environment for career exploration is far broader than that provided by public-sector careers services. In addition to these services, there is a wide range of other players including private-sector career consultants, employers, recruitment companies and learning providers who are all contributing to a potentially rich career exploration environment.UKCE

Technical Job Placement Success of Coding Bootcamps

Studies have addressed the inconsistencies and uncertainty of coding bootcamps despite the recent sensationalization of bootcamps as an opportunity to close the wage gaps. While high variability based on intensity, duration, and delivery exist, many of these bootcamps advertise high job placement rates and guarantee technical competency upon graduation. This study evaluates technical job placement rates for recent coding bootcamp graduates using public LinkedIn profiles, accounting for any technical experience prior to the bootcamp such as a technical undergraduate degree or previous employment. Through regression analysis and propensity-score matching, the study finds that while prior technical experience is the strongest predictor of technical employment, the lack of a technical background will not penalize a bootcamp graduate from landing a technical role in the future. The research shows that bootcamp attendees were not penalized for a non-technical undergraduate degree and that the bootcamp significantly positively increased their chances of success to obtain a future technical role. Furthermore, attending a bootcamp was shown to be unhelpful for participants who already had a technical undergraduate degree. Finally, the research suggests avenues for further exploration with regards to how levels of education (i.e. undergraduate, graduate, and/or bootcamp) impact recruiting for graduates

Social Turing Tests: Crowdsourcing Sybil Detection

As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effectively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explore the feasibility of a crowdsourced Sybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both "experts" and "turkers" under a variety of conditions, and find that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools

Professional Network Matters: Connections Empower Person-Job Fit

Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions. While existing works leverage historical or contextual information, they often disregard a crucial aspect: job seekers' social relationships in professional networks. This paper emphasizes the importance of incorporating professional networks into the Person-Job Fit model. Our innovative approach consists of two stages: (1) defining a Workplace Heterogeneous Information Network (WHIN) to capture heterogeneous knowledge, including professional connections and pre-training representations of various entities using a heterogeneous graph neural network; (2) designing a Contextual Social Attention Graph Neural Network (CSAGNN) that supplements users' missing information with professional connections' contextual information. We introduce a job-specific attention mechanism in CSAGNN to handle noisy professional networks, leveraging pre-trained entity representations from WHIN. We demonstrate the effectiveness of our approach through experimental evaluations conducted across three real-world recruitment datasets from LinkedIn, showing superior performance compared to baseline models.Comment: Accepted at WSDM 202

Learning Based-Approach for Personalized Expert Detection

In recent years, identifying experts has gained significant attention in the research area. The main motivation behind it is to facilitate the process of locating the correct individual capable of answering our queries. There has been a lot of focus on building expert recommendation systems. The main focus of these systems is to effectively build an expert profile in order to facilitate recognition. We argue that definition of an expert is a very subjective term and it has a major dependency on the individual initiating the search. There has also been a lot of research on personalizing search results. The two main methods applied in the design of these techniques are (1) Using explicit feedback (ratings etc.) (2) Using implicit feedback (mouse movements etc.). We propose TAK, a learning-based framework for accurate retrieval of experts based on tacit knowledge of the user placing the request. We focus on defining the tacit knowledge of the user based on implicit features like experience and education to deduce the preference of the user and generate more specific and targeted suggestions. The increasing usage of social media for everyday communication has made it a suitable repository of user specific information. Thus, we base our study on LinkedIn, which is a social media application pervasively being used for exchanging information and locating qualified individuals. We use crowd preference knowledge to create a learning-based framework and augment the result with the expert profile created from LinkedIn to provide expert recommendations to the user. This enables the user to make an informed decision. A comparative analysis of the results of the proposed method to the method applied by LinkedIn proves that the former provides more popular suggestions to the latter. It further proves that cultivated tacit knowledge with years of experience has an impact on expert selection decision

PrivateJobMatch: A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment

Coordination failure reduces match quality among employers and candidates in the job market, resulting in a large number of unfilled positions and/or unstable, short-term employment. Centralized job search engines provide a platform that connects directly employers with job-seekers. However, they require users to disclose a significant amount of personal data, i.e., build a user profile, in order to provide meaningful recommendations. In this paper, we present PrivateJobMatch -- a privacy-oriented deferred multi-match recommender system -- which generates stable pairings while requiring users to provide only a partial ranking of their preferences. PrivateJobMatch explores a series of adaptations of the game-theoretic Gale-Shapley deferred-acceptance algorithm which combine the flexibility of decentralized markets with the intelligence of centralized matching. We identify the shortcomings of the original algorithm when applied to a job market and propose novel solutions that rely on machine learning techniques. Experimental results on real and synthetic data confirm the benefits of the proposed algorithms across several quality measures. Over the past year, we have implemented a PrivateJobMatch prototype and deployed it in an active job market economy. Using the gathered real-user preference data, we find that the match-recommendations are superior to a typical decentralized job market---while requiring only a partial ranking of the user preferences.Comment: 45 pages, 28 figures, RecSys 201