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In our work, we deal with the question of what impact the Erasmus programme has on linguistic and personal competences and how it helps the economy forward. We look around this topic in the literature review. We managed to get almost 100 surveys filled out among the participants however, the representatives of the companies proved to be quite passive, with only nearly 30 of them replying. In the form of a survey, we found that it has a positive effect on improving language skills and helping with independence or problem-solving ability. In the quantitative survey we found that companies often prefer other factors than the people in a mobility programme, and they do not show favour toward a mobility student in a job interview, although the skills acquired during the programme help the individual. Therefore though indirectly, but the programme has a positive impact on the economy as well.

There has been a lack of research on the third area of impairment noted in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition-"occupational functioning." It is important to understand the impact of common treatments for attention-deficit/hyperactivity disorder (ADHD) in occupational settings. Twenty individuals with ADHD between ages 16 and 25 participated in a double-blind, placebo controlled evaluation of 40 mg lisdexamfetamine dimesylate in a setting designed to approximate a restaurant workplace with associated, simulated food delivery. Outcome measures included ratings of performance, as well as behavioral productivity. Results indicated that participants completed more workplace tasks when on medication, relative to placebo. Ratings of job application quality, job interview performance, and delivery outcomes were not significantly different on medication versus placebo. These results suggest positive effects of medication in a workplace environment, but also a need for study of additional interventions to support workplace-related behavior and functioning. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The expansion of MOOCs (massive open online courses) is very much associated with instructors interested in the craft of teaching, innovating, and experimenting with different methods to improve and expand students’ learning experience. The Erasmus+ project MOONLITE has worked to create cross-institutional scenarios and new educational pathways for migrants and refugees, devising, among other things, two Spanish language MOOCs (LMOOCs). They are the product of the joint efforts of university academics, non-governmental organizations (NGOs), and refugee support groups (RSGs), together with volunteers, refugees, and migrants, who formed a community in order to design courses that effectively addressed the needs of refugees and migrants arriving in Spain. Using a design thinking process, all parties involved attempted to collaboratively identify strategies and solutions to a given problem that might not be obvious upon first inspection; in this case, the specific linguistic needs of migrants and refugees arriving in a new country. Results show that this continuum of academics— NGOs/RSGs—volunteers—refugees/migrants was highly effective for the learners, with an overall completion rate in both MOOCs of 96%. The participants’ perception is that these MOOCs helped them in their goal of integrating into life in Spain in key situations, such as communicating in a job interview or understanding the process of looking for accommodation and completing bureaucratic procedures.

PurposeThe purpose of this paper is to explore the knowledge of lexical items in a job interview by recent graduates.Design/methodology/approachThe data were collected from one of the organisations in the Klang Valley, Malaysia. Twenty-seven recent graduates participated in the study. The structured standard interview questions were used to elicit the job interview data. The data were recorded and analysed qualitatively by using Allwood's (1999a, b, c) communicative behaviour theory.FindingsThe findings showed that the lexical items used by the interviewees varied and revealed their knowledge of lexical items in relation to these five characteristics: personality, skills, capability, experience and self-motivation. The successful interviewees reflected their knowledge of lexical items that indicated their confidence in in the job interviews. The lexical items used by the reserved interviewees were limited that displayed their personalities and reflected uncertainty and lack of confidence. The unsuccessful interviewees used limited lexical items and were vague and evasive in answering questions. This could also be not convincing enough to influence the outcome of the interviews positively.Practical implicationsThe findings of this study can assist policymakers such as officials of higher learning institutions to integrate interview workshops and mock interviews into their current curriculum as a form of preparation for undergraduates. These mock interviews can be more appropriate if experienced interviewers are utilised from the outsourcing organisations.Social implicationsIt creates an awareness for job applicants especially undergraduates after completing their studies that knowledge of lexical items are important in job interviews.Originality/valueThe use of appropriate lexical items play an important role in job interviews as they have shown that all the successful interviewees had used them to good effect in their interactions with the interviewer.

This article is the result of secondary analysis of data obtained from a research based on the partnership between the University of Craiova and the Chamber of Commerce and Industry of Dolj County. The information obtained was placed in a wider context, in which there were added the specific influences of globalization and Romania's membership of the European Union. The main objective of the research was to identify the employers' requirements regarding the quality of human resources. Empirical exploration of the attitude of jobseekers revealed a high level of mismatch between employer searches and what is identified on the labor market. Also, you can see the need to prepare those looking for a job in order to attend the job interview with the best chances. The study we refer to in this article shows that professionalism is not enough for a job, looking for the specific skills and personality traits of the candidates, which often provide the score that makes the difference between the candidates. In such a context, the repeated failures of the applications, the low number of jobs and the low remuneration in relation to the European labor market will favor the renunciation of the personal development and the search for professional alternatives abroad, even if activities below the qualification level are performed but are generating higher incomes. The temptation of immediate income becomes stronger than the desire to build a career, which will have negative consequences in the long term.

First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art.

Self-presenting effectively at job interviews presents significant challenges for students at urban community colleges, especially those identified as low-income Black and Latinx. Current practices provide minimal support, if any, for enhancing the self-presentation of these students at job interviews. Too often, instead, students simply do not perform well, fail to achieve their job objectives, and remain marginalized by society. This article describes the experiences of urban community college students involved in an innovative effort to enhance their performance in job interviews. This innovative approach features identity work and highlights the value of role play and improvisation in a range of dramatic activities. It utilizes insights gained from research into Freirian conscientization, critical pedagogy, and critical race theories as well as participatory action research (PAR) methodology. This study highlights the eloquent voices and dramatic concerns of the students involved in two vignettes, “Acting White” and “Acting Phony,” expressing the plight of these students. Providing insights for teachers, the article reviews identity work to facilitate successful self-presentation conducted by a wide range of educators dealing with similar challenges involving racial and ethnic minorities. Concluding observations are presented and future debate and research is encouraged.

The study investigated the vocational education students’ pronunciation problems, especially on English consonant sounds. More specifically, this study looked at 1) what are the students’ problems on English consonant sound, 2) what are the dominant problems affecting and 3) how do they resolve their pronunciation problems. The participants were 69 vocational higher education freshmen in Indonesia. The qualitative description approach was used to collect the data. Field observation, questionnaire, and job interview video practice were conducted in the study. The study revealed that most students had problem on the consonants sounds such as: /θ/, /ð/, /dʒ/, / tʃ/, /ʃ/, /j/, etc. Three factors affecting their pronunciation were mother tongue influence, non-corresponding spelling, and age. The most dominant factor was the mother tongue influence. After discussing their partner’s feedback, the students said to have a willingness to resolve their difficulties. The results can be used as a reference in making pronunciation instructions to minimize students’ pronunciation errors.

During recruitment, human resource departments face two challenges: finding the right people for the job and attracting talent. Therefore, the hiring process requires both the ability to communicate a good company brand image and to understand the characteristics and potential of candidates. In this study, we used a neuroscientific approach to measure the experience of candidates during a job interview. The experiment involved 30 participants that individually took part in a job interview lasting 40 min. During the experiment, their engagement and stress levels were measured in real-time with skin conductance and electroencephalographic (EEG) data. From the results, we identified both the most stressful phases (the second and the fourth parts, relating to the explanation of the job and remuneration) and the most engaging phases (the first and the third phases, relating to the presentation of the company and the explanation of the career process) of the interview, suggesting implications for the assessment process. This study is a contribution to the field of neuromanagement, as a neuroscientific approach was applied to management issues in light of work and organizational psychology.

Nowadays the number of students studying abroad increases rapidly. Migrant students represent a highly motivated and achieving category and contribute a lot to the economies of the host countries. Coming to the host country they face different challenges, one of them being employment. Previous studies reveal that migrant students during the period of their acculturation undergo various difficulties, and even after graduation, there are such hurdles as pressure, immigration policies, and an uncertain future. If they want to pursue a career in the host country, new barriers such as documentation requirements, problems with language and discrimination could appear. The necessity to create a safe environment that can help them acquire the necessary skills needed at a job interview made the research authors consider the use of a VR tool. Virtual reality simulators make it possible to repeat actions as many times as necessary to achieve the desired result and proved their efficiency applied to other vulnerable categories of citizens. In this study, a needs analysis was conveyed to depict the barriers migrant students face while seeking a job in a host country and to discuss the most useful architecture of an effective computer simulator “Employment navigator”. 171 migrant students took part in it. The outcome of the research resulted in the architecture design of the ‘Employment Navigator’ virtual reality simulator that can be used as a tool to assess the readiness of a migrant student to undergo a job interview successfully.

Face serves as the primary source of contact for humans while interacting with each other. Facial Expressions fall under the bucket of non-verbal type of communication and plays a vital role in understanding the emotional state of a person. Identifying emotions through Facial Expressions can be used in various fields like revealing the Behavior of a candidate in a Job Interview, Understanding the comprehension level of the candidates in a classroom, Healthcare, Electoral campaign etc.; In additions to images fed to the Neural Network, Open Face tool is also used to extract the Facial Action Units of the subject in the dataset which contributes in training a neural network. We have designed and built a model-based technique with a high accuracy to classify the Facial based emotions. The data is trained on a Temporal Relations based Neural Network. Emotions can help us make decisions and it has a wide use case.

The paper built on First Impression Challenge from Chalearn V2 Workshop on Explainable Computer Vision Multimedia and Job Candidate Screening Competition CVPR17 by focusing solely on Textual Input in contrast to other Challenge’s participants who considered video or audio modalities. Therefore, the paper aims to develop a new deep learning architecture capable of predicting human personality traits and job interview from the video transcripts. Several feature representations that involve statistical and deep learning have contrasted. Our approach achieved the best score when text modality alone were employed, yielding an average of 89% score in human personality traits and 89.10% value for job interview. The research results will help companies and other organization studying human personality to assess a human personality using a minimum textual resources from the job candidates

There has always been pressure on higher education institutions to enhance the employability of graduates and to instil knowledge, skills, and attributes that will be beneficial to future employers. The impact of Covid‑19 on a global, national, and local level is placing even more pressure on the topic of employability. Graduates are uncertain about job availability and there is a need for career guidance. After a national and local shutdown of university campuses in 2015 and 2016 due to #FeesMustFall, Career Services staff at the University of the Free State designed and created online work readiness programmes in order for students to continue with work preparations, no matter what the circumstances. In 2018, the first topics on CV‑writing and job interview skills were rolled out online and made available to all registered students; each semester, two additional topics were added. By the time Covid‑19 led to a national lockdown in South Africa in March 2020, the transition to online work readiness programmes was fairly easy. The purpose of this study is to determine the impact of the online work readiness programmes offered on the Blackboard platform from April to June 2020 during the Covid‑19 pandemic and national lockdown. This article will provide an analysis of a questionnaire conducted with willing participants who have engaged and worked through the online work readiness programmes from April to June 2020, to investigate the impact on graduates’ readiness for the world of work. Through the survey, students shared their learning experiences and the influence it has had on their career planning. It is believed that the findings of this research study will create a deeper understanding of how career services, as a particular functional area in student affairs, can reposition itself during uncertain times to remain responsive to the needs of students.

In an effort to bridge the scientist–practitioner gap in the employee selection, some researchers have advocated telling stories to better communicate the value of evidence‐based hiring practices to human resource (HR) professionals. In this paper, we conducted two experiments that examine the efficacy of storytelling for overcoming managers’ resistance to use structured job interviews. In two experiments, we found that participants who read a story regarding the effectiveness of structured interviews, as opposed to receiving evidence‐based advice, reported more favorable attitudes toward structured job interviews. Serial mediation analysis revealed that the observed attitude change was mediated by an increase in narrative transportation and reduction in counterarguing. Implications for using stories in communicating the value of evidence‐based HR practices are discussed.

Current automatic deception detection approaches tend to rely on cues that are based either on specific lexical items or on linguistically abstract features that are not necessarily motivated by the psychology of deception. Notably, while approaches relying on such features can do well when the content domain is similar for training and testing, they suffer when content changes occur. We investigate new linguistically defined features that aim to capture specific details, a psychologically motivated aspect of truthful versus deceptive language that may be diagnostic across content domains. To ascertain the potential utility of these features, we evaluate them on data sets representing a broad sample of deceptive language, including hotel reviews, opinions about emotionally charged topics, and answers to job interview questions. We additionally evaluate these features as part of a deception detection classifier. We find that these linguistically defined specific detail features are most useful for cross-domain deception detection when the training data differ significantly in content from the test data, and particularly benefit classification accuracy on deceptive documents. We discuss implications of our results for general-purpose approaches to deception detection.

Design of appropriate assessment tools to measure student achievement of program learning outcomes (PLOs) represents a challenge to higher education institutions. Typical outcome-based assessment tools examine student outcomes at the course level, i.e. course learning outcomes (CLOs), and are then mapped to PLOs. The validity of this approach depends on the correctness and accuracy of the mapping between CLOs and PLOs. To directly evaluate student achievement of PLOs, curriculum-based exit exams have been developed and adopted by engineering programs. Such exams play a critical role in the assessment and improvement of engineering programs. Yet, their validity as a direct measure of PLOs is uncertain. This paper aims to fill this gap by assessing the appropriateness of using curriculum-based exit exams to evaluate student attainment of PLOs at the time of graduation. A student survey was developed to obtain the perceptions of 92 senior civil engineering students over the course of three semesters. Findings revealed that students generally did not believe that the exit exam was an appropriate tool to evaluate their attainment of PLOs nor did it improve their knowledge in civil engineering disciplines. However, it helped prepare them for a job interview. Student performance in various disciplines was attributed to the level of preparedness, interest in the topics, and difficulty of exam questions. Additionally, students believed having more sample questions or increasing the contribution of the exit exam to their grade point average would help improve the overall performance. The results also showed that the majority of students prepared for the exit exam by studying and solving questions from previous exit exam samples. A continuous improvement process was proposed to enhance the overall exit exam experience. Remedial actions were also suggested to render it a more valid direct assessment tool and inspire a positive attitude in students towards it.

Gaze and language are major pillars in multimodal communication. Gaze is a non-verbal mechanism that conveys crucial social signals in face-to-face conversation. However, compared to language, gaze has been less studied as a communication modality. The purpose of the present study is 2-fold: (i) to investigate gaze direction (i.e., aversion and face gaze) and its relation to speech in a face-to-face interaction; and (ii) to propose a computational model for multimodal communication, which predicts gaze direction using high-level speech features. Twenty-eight pairs of participants participated in data collection. The experimental setting was a mock job interview. The eye movements were recorded for both participants. The speech data were annotated by ISO 24617-2 Standard for Dialogue Act Annotation, as well as manual tags based on previous social gaze studies. A comparative analysis was conducted by Convolutional Neural Network (CNN) models that employed specific architectures, namely, VGGNet and ResNet. The results showed that the frequency and the duration of gaze differ significantly depending on the role of participant. Moreover, the ResNet models achieve higher than 70% accuracy in predicting gaze direction.

This article deals with the future of work. It addresses the challenges posed by digitization and the use of artificial intelligence in employment. Digitalisation is changing the world of work and the demands on employees. Lifelong learning becomes a central topic. Computer programs play an important role in more and more decisions. For example, when it comes to the question of whether an applicant is suitable and is even invited to a job interview. Personnel decisions are to be solved more efficiently and in a more time-saving way by using AI. However, this bears the danger of disadvantaging individuals or groups of individuals. It is shown where AI is already used and where known problem areas exist. The dependence of supposedly objective data on the quality of data input is discussed. Fields with discrimination potential are shown.

Employment of autistic individuals is strikingly low in relation to the skill level and capabilities of this population. Roughly 65% of autistic adults are either unemployed or underemployed relative to their abilities but there is increasing recognition that this number could be greatly improved through empowering autistic individuals while simultaneously providing a boost to the economy. Much of this disparity can be attributed in part to the lack of awareness and understanding among employers regarding behavior of autistic individuals during the hiring process. Most notably, the job interview—where strong eye contact is traditionally expected but can be extremely uncomfortable for autistic individuals—presents an unreasonable initial barrier to employment for many. The current work presents a data visualization dashboard that is populated with quantitative data (including eye tracking data) captured during simulated job interviews using a novel interview simulator called Career Interview Readiness in Virtual Reality (CIRVR). We conducted a brief series of case studies wherein autistic individuals who took part in a CIRVR interview and other key stakeholders provided lived experiences and qualitative insights into the most effective design and application of such data visualization dashboard. We conclude with a discussion of the role of information related to visual attention in job interviews with an emphasis on the importance of descriptive rather than prescriptive interpretation.

Assessment and evaluation methods in a job interview have always been very much focused on a person’s performance, knowledge, and experience. However, excelling at a challenging job often requires skills that go beyond the skills and background of a person. And while psychometrics in the evaluation process is nothing new, gamifying the interview process is still considered a novel approach to candidate evaluation. As part of exploring new potentialities for Chemistry’s innovative card game, BossUP!, a study was conducted with the purpose of prototyping a new format of interview engagement and evaluation for the organization. In combination with a pre- and post-questionnaire, coupled with face-to-face gameplay during the interview process, this study explores the boundaries of how interviews can break free of traditional methods, by not only making it a fun and engaging experience via gamification, but to also enhance the evaluation method beyond performance, knowledge, and experience.

An essential component of the job recruitment process involves conducting interviews, during which decision is made on the appropriateness of a candidate for a particular job. An important challenge faced by interviewers during the recruitment process relates to determining whether or not selected candidates will attend the job interview. Candidates failing to attend interviews because of various reasons lead to time wasting and as such, effective prediction is needed by human resource managers about whether or not a candidate will attend an interview. In other words, if the human resource department knows exactly whether or not the job seeker is attending the interview, a more efficient interview invitation strategy could be worked upon. However, even though different machine learning techniques are available for prediction, limited work has been undertaken to address this recruitment process related issue. Taking cognizance of this problem, this paper investigates, analyzes, and predicts candidate attendance during interviews in order to optimize recruitment process within businesses. For this, C4.5 algorithm was applied on an open dataset related to job interview attendance. Following generation of a classification tree, results showed some insightful patterns on when candidates do not attend job interviews.

With the increasing presence in our lives of interactive and technological items, the concept of “experience” becomes crucial in both the design and the evaluation phases. When aiming to measure such experience, researchers typically adopt self-report methodology. However, it has been established that also unconscious motives (i.e., emotions, implicit attitudes) - which people are not always aware of - need to be taken into account in understanding users’ perception, by means of indirect measures. The present study investigates the possibility of measuring UX by both implicit and explicit attitudes measures. In an experimental study (N = 36), we asked participants to evaluate a conversational chatbot prototype, which had the fictional goal of pre-selecting candidates for a job, and to fix a date for the job interview. We manipulated between-participants the chatbot’s gender (male vs. female) and the tone of voice (formal vs. informal). Explicit user experience’s evaluation (i.e., UEQ) and implicit attitudes towards the chatbot (i.e., IAT) were the dependent variables, as well as self-reported measures of chatbot’s efficacy and emotional experience. Our findings showed an implicit preference towards the informal version of the chatbot, as revealed by the IAT scores, whereas no differences emerged on explicit measures of the UEQ. Results are discussed in light of the effect of implicit attitudes on overall experience, and on the importance of comparing implicit and explicit measures, as well as objective usability measures, when evaluating the UX.

Objectives: The involvement of patient and family representatives in job interview panels is sparsely documented. This study was conducted at a newly established university hospital in Denmark. The aim was to identify different perspectives on attitudes and experiences associated with involving patient and family representatives in the recruitment process for senior staff. Furthermore, the aim was to highlight considerations and reservations related to the subsequent implementation process. Methods: Inspiration was drawn from formative evaluation research. Data Sources: Seventeen telephone interviews with applicants, 49 e-mail responses from staff, and unsolicited e-mails to the researcher. Analysis Strategy: Interpretive description. Results: Learnings from the study showed among other things that the participating staff experienced widespread skepticism before participation in the job interview panels, but their experience in the panels led them to consider the patients’ and families’ input to be beneficial to the entire recruitment process. The considerations and reservations raised were divided into 5 themes. Conclusions: The results provide a relevant starting point to negotiate and refine the aims of collective patient involvement related to a given situation—such as health-care recruitment processes.

Job interview is one of the stages that must be passed by job applicants before getting a job. However, the manual interview process resulted in a large amount of cost and time of selection, so we need a system that can provide recommendations of which applicants are qualified. Currently there are many studies of text classification using the Naive Bayes method, K-Nearest Neighbor, Support Vector Machine, and Deep Learning. Therefore, algorithms that are used less frequently will be tested in this study, such as the Decision Tree. Then the Swarm Intelligence method, Particle Swarm Optimization, is implemented to improve the performance of the method. So that this research focuses on testing and comparing ordinary Decision Tree and Decision Tree methods that are optimized with Particle Swarm Optimization. From the test results, the accuracy of the optimized model increased by 7.1%, and the highest accuracy achieved was 74.3%.

The Software Engineering (SE) curriculum in undergraduate computer science (CS) education is designed to train students in the process of software and systems development. Traditionally, topics such as software development methodologies, industry nomenclatures, and solution analysis are delivered through lectures and group projects. We propose a novel approach in teaching SE that we call MACROVR: MAchine learning to select project team members; Cloud technologies required for project control, code versioning, and team communications; ROtational schedules in Agile/Scrum roles; an individual Video of the team project story board; and Rubrics for all presentations. Our teaching strategy with this approach utilizes the latest technologies currently employed in industry and corresponds to soft skills commonly assessed in interviews. The goal of our study is to measure if using the MACROVR approach contributes to preparedness for a computing job interview. Most often, this course is taken towards the end of a four-year CS degree program while students are job hunting or seeking an internship in the computing industry. We use an anonymous, fifteen question survey instrument sent to volunteers that indicated they are seeking a computing job and have successfully completed the SE course. The sample is comprised of three sections of the SE course using the MACROVR approach (135 students) and four sections that did not use all of the required strategies and technologies, which we call MACROVR-lite (184 students). Our two cohorts, MACROVR and MACROVR-lite, are each given the same survey questions. We analyze their Likert scale data responses using non-parametric methods. Our findings indicate the MACROVR approach better prepares students with the skills and highly valued qualities for success in computing industry interviews.

Part 2 of the Transitions to Adulthood Center for Research's An Interview Tale Comic depicts questions in an interview that a young adult may encounter. Please see our tip sheet, Do I Tell My Boss?: Disclosing My Mental Health Condition at Work for more information. Part 1 of An Interview Tale is here.

Part 1 of the Transitions to Adulthood Center for Research's An Interview Tale Comic demonstrates questions that an interviewer can and cannot ask someone during an interview. Please see our tip sheet, "Do I Tell My Boss?: Disclosing My Mental Health Condition at Work" for more information.

Concluding one’s educational career and venturing into the arena of employment in the veterinary profession can be a daunting, yet exhilarating apex in life. Unfortunately, our personalities often set us up as achievers who assume the perfect job must be obtained in order to be professionally fulfilled. The perfect job probably doesn’t exist. This paper will discuss navigating the first year of employment following graduation from the perspective of the recent graduate. Suggestions will be made about how to reverse the traditional job interview and use the opportunity to find a position that fits your goals best., American Association of Bovine Practitioners Proceedings of the Annual Conference, 2020

Artificial Intelligence (AI) is playing an increasingly prevalent role in our lives. Whether its landing a job interview, getting a bank loan or accessing a government program, organizations are using automated systems informed by AI enabled technologies in ways that have significant consequences for people. At the same time, there is a lack of transparency around how AI technologies work and whether they are ethical, fair or accurate. This paper examines a body of literature related to the ethical considerations surrounding the use of artificial intelligence and the role of ethical codes. It identifies and explores core issues including bias, fairness and transparency and looks at who is setting the agenda for AI ethics in Canada and globally. Lastly, it offers some suggestions for next steps towards a more inclusive discussion.

Selecting the best talents from a large number of job applicants is challenging, especially for big companies that usually receive tens of thousands of applicants for every job opening. One of the most costly and time-consuming applicant selection stages is the interview process, since it usually performs face to face meetings and involves third parties to do the interviews and analyze the result. To this end, Human Capital Directorate at Telkom Indonesia adopts AI technology to automate some stages of job applicant selection to reduce manual process and third-party involvement. In this paper, we investigate appropriate feature extraction methods to automate job interview grading for reducing bias and human errors. TFIDF, one of the most popular feature extractions, is compared with word embedding to find the optimal method and parameters in classifying interview verbatims with ANN classifier. Based on the test results, the average accuracy for TFIDF outperforms word embedding by 85.22% against 74.88%, respectively. Therefore, for the case of job interview grading using our dataset, TF-IDF performs better to reduce the number of dimensions.

Identifying an effective and scalable approach for student career management is one of the top priorities of Higher Education Institutions (HEI) through their career centers. Machine learning in the form of predictive analytics helps in achieving this goal. The ability to predict which students will most likely to be employed and why is imperative. This paper is focused on identifying why a student is most likely to be employed based on the career center data. In other words, the paper aims to determine in a scientific way which among the input attributes of are most predictive. The datasets used are from the career center of the Technological Institute of the Philippines and its school registrar. Datasets involved 27,000 information consists of (3,000 observations and 9 attributes) of students' mock job interview evaluation results, students' performance rating from the internship and general percentage average (GP A) of students enrolled from 2015 to 2018. To determine the most predictive attributes, three methods are used and compared. These are univariate selection (US), recursive feature elimination (RFE) and principal component analysis (PCA). Results show that mental alertness and manner of speaking are the two most predictive attributes. This study will help enhance the quality of career coaching by identifying students in need of improvement and tailoring guidance to help them.

Thisstudy explores the extent to which theenvironmentally-friendly job preferences of human resource managers (interviewers)and job applicants (interviewees) match in case of Turkey’s resource-dependentbusiness organizations. The study covers 63 human resource managers from 11medium/large-sized businesses that have several joint-plants producing cementand variety of sorts of metal/mineral supplies in different cities in Turkeyand 75 job applicants that these human resource managers interviewed. Consequently,138 participants were asked to quantitively rank the importance of 22-itemcriteria when they choose where to work. The comparison of the mean scoresdemonstrates that both interviewers and interviewees are not that concernedwith the green workplace, green business, and eco-friendliness. Rather, salaryand other benefits, promotion and career opportunities, organizationalreputation, and match of skills/qualifications to job requirements are found asthe key factors affecting the work/job decisions of both interviewers andinterviewees. Moreover, the only demographic difference is found in gender amonginterviewers that female managers more tend to prefer environmentally-friendlyjobs/works. Emphasizing the roles of regulations and environmental standards tomake business organizations greener, the study concludes with recommendationsfor practice within the green human resource management and green business perspective., This study explores the extent to which the environmentally-friendly job preferences of human resource managers (interviewers) and job applicants (interviewees) match in case of Turkey’s resource-dependent business organizations. The study covers 63 human resource managers from 11 medium/large-sized businesses that have several joint-plants producing cement and variety of sorts of metal/mineral supplies in different cities in Turkey and 75 job applicants that these human resource managers interviewed. Consequently, 138 participants were asked to quantitively rank the importance of 22-item criteria when they choose where to work. The comparison of the mean scores demonstrates that both interviewers and interviewees are not that concerned with the green workplace, green business, and eco-friendliness. Rather, salary and other benefits, promotion and career opportunities, organizational reputation, and match of skills/qualifications to job requirements are found as the key factors affecting the work/job decisions of both interviewers and interviewees. Moreover, the only demographic difference is found in gender among interviewers that female managers more tend to prefer environmentally-friendly jobs/works. Emphasizing the roles of regulations and environmental standards to make business organizations greener, the study concludes with recommendations for practice within the green human resource management and green business perspective.

People behave differently in different situations. With the advances in ubiquitous sensing technologies, it is now easier to capture human behavior across multiple situations automatically and unobtrusively. We investigate human behavior across two situations that are ubiquitous in hospitality (job interview and reception desk) with the objective of inferring performance on the job. Utilizing a dataset of 338 dyadic interactions, played by students from a hospitality management school, we first study the connections between automatically extracted nonverbal cues, linguistic content, and various perceived variables of soft skills and performance in these two situations. A correlation analysis reveals connection between perceived variables and nonverbal cues displayed during job interviews, and perceived performance on the job. We then propose a computational framework, with nonverbal cues and linguistic style from the two interactions as features, to infer the perceived performance and soft skills in the reception desk situation as a regression task. The best inference performance, with R2 = 0.40, is achieved using a combination of nonverbal cues extracted from the reception desk setting and the human-rated interview scores. We observe that some behavioral cues (greater speaking turn duration and head nods) are positively correlated to higher ratings for all perceived variables across both situations. The best performance using verbal content is achieved by fusion of LIWC and Doc2Vec features with R2 = 0.25 for perceived performance. Our work has implications for the creation of behavioral training systems with focus on specific behaviors for hospitality students.

This study aims to apply an approach using machine learning for predicting students' employability. The researchers conducted a case study that involved 27,000 information (3000 observations and 9 features) of students' Mock Job Interview Evaluation Results, On-the Job Training (OJT) Student Performance Rating and General Point Average (GPA) of students enrolled in OJT course of School Year 2015 to School Year 2018. Three learning algorithms were used such as Decision Trees (DT), Random Forest (RF), and Support vector machine (SVM) in order to understand how students get employed. The three algorithms were evaluated through the performance matrix as accuracy measures, precision and recall measures, f1-score and support measures. During the experiments Support Vector machine (SVM) obtained 91.22% in accuracy measures which was significantly better than all of the learning algorithms, DT 85%, RF 84%. The learning curve produced during the experiment displays the training error results which were above the one for validation error while the validation curve displays the testing output where gamma was best at 10 to 100 in gamma 5. This concludes that the model produced with SVM was not underfit and over-fit. This study is very promising that lead to the researchers to be motivated to enhanced the process and to validate the produced predictive model for further study.

Various job interview skills training have been carried out to prepare students for employment. However, the outcome of job interviews is questionable, as one of the reasons for unemployment among Malaysian graduates is their inability to communicate competently in job interviews. The Malaysia Education Blueprint 2015-2025 (Higher Education) stated that unemployed graduates were also lacking in a strong command of English. Since job interviews are placed heavily on verbal exchanges between the interviewer and job candidate, the role that language plays in interaction is worth exploring. This paper investigates spoken metadiscourse in 16 actual first-stage technical and non-technical Malaysian ESL job interviews. Corpus linguistics was employed for data analysis. An adapted framework of spoken metadiscourse is employed to analyse the types and frequencies of textual and interpersonal metadiscourse in the corpus. The findings revealed that both textual and interpersonal metadiscourse were used, but there are variations in the distribution and composition of metadiscourse in the two categories across disciplines. Excessive use of metadiscourse was found to impede communication flow instead of assisting speakers to be persuasive in their speech. This implies the need for targeted instruction on metadiscourse, specifically among language learners in higher learning institutions to facilitate appropriate usage of metadiscourse in speech.

This study identified situations where communication was a barrier to employment for adults with autism spectrum disorders (ASD). Informant interviewees were six college graduates with ASD who have been employed for one year. Following the qualitative inductive analysis, five communication-related themes emerged: (1) job interviewing success, (2) negotiating co-worker interactions, (3) supervisory interactions, instruction, and feedback, (4) handling customer intercommunication, and (5) communication-oriented coping strategies. Participants were relatively more successful in communication coping strategies and supervisory interaction compared to difficulties with job interviewing and co-worker interactions. Vocational rehabilitation counsellors assisting clients with ASD should prioritize job interview communication skills and social skill education specific to appropriate interactions with co-workers and supervisors utilizing role play, social skill training, and job coaching.

Vocational high schools (VHS) mainly prepare the students to engage in employment with a number of specific skills even though some students may continue to study in university/ college level. Various job vacancy advertisements require English skill in completing either the documents or interview. In competing the documents such as CV and cover letter, the students may adopt and adapt some templates even consult to the experts to get the best quality. However, in the interview, the students should perform themselves in live interaction. If the students are not well prepared, they may pass the documents selection but fail the interview step. Therefore, English job interview preparation is needed to equip the students ready in facing the job interview. Nowadays, artificial intelligence (AI) technology can help to improve the students’ speaking skill including job interview. The use of AI becomes higher due to the advantages such as flexibility, accurateness, and direct feedback. This program applied AI technology to develop English job interview skill in of the students in SMK AAG Penerbangan Adisutjipto Yogyakarta. The result was the selected AI successfully improved the students’ job interview skills indicated on the increased mark after applying the technology.

AimsWe often have patients who are admitted to the ward wearing only the clothes they came in. These patients have no way of going to get more clothes due to being detained, poverty/ homelessness or covid restrictions. Many do not have friends or family who can bring them clothes. As such they might wear one set of clothes for a number of weeks which is bad for their physical and mental health. We are creating a clothes bank to provide a change of clothes for these patients, and help their recovery back into the community. Many have clothes that are inappropriate for the current weather, or do not have a set of smart enough clothes for a job interview. We feel that this simple intervention will have a big community impact.MethodWe have obtained support from a number of charities and companies to supply donations. The project will be led by a team of staff and patients.ResultWe will review the usage of this scheme in 6 months timeConclusionWe hope this intervention will tackle the issue of clothing on mental health wards. In the future we wish to expand this to outpatient mental health service users. We would then like to expand this project countrywide as are unaware of any other areas providing something similar.

In the context of a Transition to Work course for fourth-year psychology majors, we had students use an e-portfolio to self-reflect on the learning experiences they deemed most significant during their degree. Such significant learning experiences can be drawn upon when answering behavioral job interview questions. We examined whether students would show improvement in mock behavioral interview performance over time, and whether any potential improvement was related to performance on self-reflective course assignments. Students indicated that the majority of their significant learning experiences had taken place outside of the classroom (e.g., paid and unpaid work, extracurricular activities). Across the duration of the course, students improved on all metrics of interview performance, and final interview performance was predicted by student grades on a self reflective e-portfolio, but not other course assignments.

Whether you are a leader, a programmer, or just someone aspiring to be better, I am sure there are some smart takeaways from our conversation that will help you grow in your role. Oh, and if you read to the end, you can find out what his favorite job interview question is - and see if you would be able to pass his test.

Neoliberal politikaların yarattığı dönüşüm hukuk alanına da yansımış ve avukatlık mesleğinin yapılış biçimini değiştirmiştir. Neoliberalizm ile birlikte ortaya çıkan yeni ve karmaşık hukuki sorunların çözümü konusunda uluslararası şirketlerin taleplerine ve beklentilerine cevap verebilecek yeni bir avukatlık yapılanmasına ihtiyaç duyulmuştur. Bu ihtiyaca cevap vermek üzere kurulan büroların ayırt edici özelliği ise şirketlerin farklı alanlardaki hukuki sorunlarına çözüm üretebilmek amacıyla iyi eğitimden geçmiş, birden çok yabancı dile hakim, genç avukatları istihdam etmesidir. Bu bürolarda çalışmak, yüksek maaş ve kariyer imkanları sebebiyle cezbedici olmakla beraber adliyelerle ilişkisi sınırlı, sadece belli alanlarda uzmanlaşmış, dava dosyalarının yalnızca belirli aşamalarıyla ilgilenen yeni bir avukatlık biçiminin doğmasına neden olmaktadır. Çalışmamızın ana eksenini ise ülkemizde faaliyet gösteren bu tür bürolarda çalışan ve yukarıda anılan özellikleri haiz kadın avukatlar oluşturmaktadır. Bahsi geçen büroların çalışma koşullarına içkin olan ve neoliberalizm ile güçlenen rekabet, yabancılaşma, kademelenme gibi unsurların iş yerinde ve iş dışında kadınların yaşamlarına ne şekilde yansıdığı yapılan görüşmeler kapsamında değerlendirilmiştir. Bu bağlamda, adalet sisteminin taşıdığı eril yapının devamı olan bu tür bürolarda, kadın avukatların iş görüşmelerinden işten çıkarılmaya dek maruz kaldıkları cinsiyetçi uygulamalara yer verilmiştir. Ücret ve kariyer bakımından iyi bir statüde bulunan kadın avukatların ev içi işbölümünde yaşadıkları deneyimler yapılan görüşmeler çerçevesinde ele alınmıştır The transformation created by the neoliberal policies has also affected the field of law and changed the way the law profession is practiced. By implications, neoliberalism brought in a new type of attorneyship that could meet the expectations and fulfill the requirements of international companies with regard to the novel and complex legal issues. The distinguishing characteristic of these law firms which are established to meet such needs is that these firms employ well educated, multilingual young attorneys in order to address the companies' diverse set of legal matters. Given the fact that it is tempting to work for these firms with all their career and six-figure income opportunities, it, however creates a new style of attorneyship that has weak ties with the courthouse--also based on specialized attorneys dealing only with certain instances of a given case. The focus of our article will be on the women attorneys who are working in such law firms, and having acquired the qualifications mentioned above. The reflections of the factors--competition, alienation, pecking order--on the lives of women are evaluated within the context of interviews made whether it concerns their lives in or out of the office. In this sense, a vast array of sexist applications starting from the job interview until the dismissal at these law firms are the extensions of the patriarchal structure inherent to the justice system.. Hence, the division of domestic labor and the related experiences of women attorneys with respectable salaries and careers are discussed in the scope of the interviews

The impact of non verbal behaviour in a hiring decision remains an open question. Investigating this question is important, as it could provide a better understanding on how to train candidates for job interviews and make recruiters be aware of influential non verbal behaviour. This research has recently been accelerated due to the development of tools for the automatic analysis of social signals, and the emergence of machine learning methods. However, these studies are still mainly based on hand engineered features, which imposes a limit to the discovery of influential social signals. On the other side, deep learning methods are a promising tool to discover complex patterns without the necessity of feature engineering. In this paper, we focus on studying influential non verbal social signals in asynchronous job video interviews that are discovered by deep learning methods. We use a previously published deep learning system that aims at inferring the hirability of a candidate with regard to a sequence of interview questions. One particularity of this system is the use of attention mechanisms, which aim at identifying the relevant parts of an answer. Thus, information at a fine-grained temporal level could be extracted using global (at the interview level) annotations on hirability. While most of the deep learning systems use attention mechanisms to offer a quick visualization of slices when a rise of attention occurs, we perform an in-depth analysis to understand what happens during these moments. First, we propose a methodology to automatically extract slices where there is a rise of attention (attention slices). Second, we study the content of attention slices by comparing them with randomly sampled slices. Finally, we show that they bear significantly more information for hirability than randomly sampled slices., Comment: Accepted at 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

We report on a job interview simulation game developed as part of the BRAINTRAIN project funded by the EU. The on-line accessible single-player 2D narrative game is adaptive to the trainees’ (a) answering responses to questions by a virtual interviewer, (b) point of gaze towards the interviewer, and (c) his/her stress levels. Adaptiveness was realized by changing the game world in terms of more or less abstract interviewing questions, increasing or decreasing visual and audio clutter in the game interface. The game is intended to be an intervention instrument aiming to improve social communication and interaction skills. The target group are high functioning Autism Spectrum Disorder (ASD) adults. To test the feasibility of such a tool, we report on findings from a market study in terms of a SWOT analysis, and we show the results from a user study in terms of hypothesized effects on social skills improvements.

Job interviews are one of the most common methods of selecting employees. However, most people experience a certain amount of anxiety during a job interview. This study aims to help people overcome this anxiety by using a virtual reality job-interview simulation system. This system utilizes voice recognition, a dialogue knowledge base, and morphological analysis to generate new questions that include nouns from the user's answers. We expect the user's sense of presence and anxiety to increase with interactive questions. This research used self-assessment questionnaires and psychophysiological measures to investigate two study conditions: (a) using developing questions that included part of the user's answer (n = 10) and (b) using a fixed developing question (n = 10). The results suggested that using developing questions that included the user's answer provided a greater sense of presence and anxiety.

In this paper, we propose a sentiment analysis model using machine learning for the support on decision-making in the process of job interviews. To do this, a characterization of the analysis of sentiments, job interviews and machine learning algorithms is first performed. Then, supervised machine learning with artificial neural networks is implemented in a prototype, due to the non-linear behavior described in the variables taken in the study and applying the Eye tracking technique. Finally, tests are carried out with people, in which, by asking questions of these, the involuntary movements of the pupil of the eye are analyzed, through the processing of a volume of data and the results of the ocular patterns are interpreted. Correlated with the questions of the test and with it, a final judgment is presented for the support of the decision making.

Social psychologists have long studied job interviews with the aim of knowing the relationships between behaviors, interview outcomes, and job performance. Several companies give great importance to psycho-test based on observation of the candidate is behavior more than the answers they even especially in sensitive positions like trade, marketing, investigation, etc. Our work will be a combination between two interesting topics of research in the last decades which are social psychology and affective computing. Some techniques were proposed until today to analyze automatically the candidate is non verbal behavior. This paper concentrates in body gestures which is an important non-verbal expression channel during affective communication that is not very studied in comparison to facial expressions. We proposed in this work a deep Spatio-temporal approach that merges the temporal normalization method which is the energy binary motion information (EBMI) with deep learning based on stacked auto-encoder (SAE) for emotional body gesture recognition in job interview and the results prove the efficiency of our proposed approach.