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Helping job seekers prepare for technical interviews by enabling context-rich interview feedback

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dc.contributor.authorLu, Yien
dc.contributor.committeechairLee, Sang Wonen
dc.contributor.committeememberChen, Yanen
dc.contributor.committeememberBrown, Dwayne Christianen
dc.contributor.departmentComputer Science and#38; Applicationsen
dc.date.accessioned2024-06-12T08:00:17Zen
dc.date.available2024-06-12T08:00:17Zen
dc.date.issued2024-06-11en
dc.description.abstractTechnical interviews have become a popular method for recruiters in the tech industry to assess job candidates' proficiency in both soft skills and technical skills as programmers. However, these interviews can be stressful and frustrating for interviewees. One significant cause of the negative experience of technical interviews was the lack of feedback, making it difficult for job seekers to improve their performance progressively by participating in technical interviews. Although there are open platforms like Leetcode that allow job seekers to practice their technical proficiency, resources for conducting mock interviews to practice soft skills like communication are limited and costly to interviewees. To address this, we investigated how professional interviewers provide feedback if they were conducting a mock interview and the difficulties they face when interviewing job seekers by running mock interviews between software engineers and job seekers. With the insights from the formative studies, we developed a new system for technical interviews aiming to help interviewers conduct technical interviews with less cognitive load and provide context-rich feedback. An evaluation study on the usability of using our system to conduct technical interviews further revealed the unresolved cognitive loads of interviewers, underscoring the requirements for further improvement to facilitate easier interview processes and enable peer-to-peer interview practices.en
dc.description.abstractgeneralTechnical interview is a common method used by tech companies to evaluate job candidates. During these interviews, candidates are asked to solve algorithm problems and explain their thought processes while coding. Running these interviews, recruiters can assess the job candidate's ability to write codes and solve problems in a limited time. At the same time, the requirements for interviewees to talk aloud help interviewers evaluate their communication and collaboration skills. Although technical interviews enable employers to assess job applicants from multiple perspectives, they also introduce interviewees to stress and anxiety. Among the many complaints about technical interviews, one significant difficulty of the interview process is the lack of feedback from interviewers. As a result, it is difficult for interviewees to improve progressively by participating in technical interviews repeatedly. Although there are platforms for interviewees to practice code writing, resources like mock interviews with actual interviewers for job seekers to practice communication skills are costly and rare. Our study investigated how professional programmers run mock technical interviews and provide feedback when required. The mock interview observations helped us understand the standard procedure and common practices of how practitioners run these interviews. At the same time, we concluded the potential cause of cognitive loads and difficulties for interviewers to run such interviews. To answer the difficulties of conducting technical interviews, we developed a new system that enabled interviewers to conduct technical interviews with less cognitive load and provide enriched feedback. After rerunning mock interviews with our system, we noted that while some features in our system helped make the interview process easier, additional cognitive loads are unresolved. Looking into these difficulties, we suggested several directions for future studies to improve our design to enable an easier interview process for interviewers and support interview rehearsals between job seekers.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:41007en
dc.identifier.urihttps://hdl.handle.net/10919/119392en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttechnical interviewsen
dc.subjectcollaborative editingen
dc.subjectreal-time feedbacken
dc.titleHelping job seekers prepare for technical interviews by enabling context-rich interview feedbacken
dc.typeThesisen
thesis.degree.disciplineComputer Science & Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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