Choosing a career path after college may no longer be a subjective decision. Researchers at ESADE, BarcelonaTech and the University of Vic have developed a new software tool to help college students make decisions on their first job experience. "Internships may be a student's first experience in the job market, and thus it may be hard for him or her to express preferences clearly when searching for a job. Our software tool analyzes students' interests based on the words they use when verbally expressing their job preferences," says ESADE professor Núria Agell. "The software tool helps students by identifying internships related to their interests when searching for a job position for the first time. The system automatically proposes a shortlist of jobs based on individual preferences to help students decide where to focus their attention," says co-author and ESADE professor Xari Rovira. The software calculates the best job matches automatically Beginning offline, the algorithm analyzes each student's resume to build a preference profile. It then defines a position profile from each job description. When logged into the system, a student can view his/her auto-generated profile and adjust preferences accordingly. Students can convey their inclinations based on different degrees of linguistic terms. The software then analyzes indecision and hesitance in the decision-making process and calculates the best job matches automatically. A real case example with business students In order to test their software, the authors used a 'real life' example based on the internship program for the Bachelor of Business Administration at ESADE Business School. The researchers matched 275 student resumes and 549 available internships (in English) at a national and international level. From these resumes, a set of features was determined to represent the main interests of the student body and define features for positions. A set of five features (Client and team oriented, Strategy, Sales and marketing, Technical skills, and Finance) was selected. With these features, the system created the student profiles, assigning linguistic terms 'Low', 'Medium', and 'High' for each one of the features. Finally, for each student, the system used linguistic terms to evaluate the fit between the student and each position. The set of positions with a degree of satisfaction equal to 'High' was shown to the student. On average, 22 positions were proposed to each student. By narrowing the focus for the internship search, students save a lot of time and can work more effectively applying for only those positions that match their interests. Finally, to evaluate the advantages and drawbacks of their method, the authors compared it to the Hellinger ranking method. The results showed higher performance in their method. While the Hellinger Method recommended 65 or more positions to the majority of students, the new method was able to narrow the number of recommended positions for most students to fewer than 40, letting students focus on those positions that most closely matched their interests. The new software tool was also compared to TOPSIS, a decision analysis method developed in 1981 to help people with decision-making processes. Compared to the TOPSIS Method, the new method was able to identify more positions matching what kinds of jobs students wanted. A better decision-making process Personnel selection processes are subjective by nature and tend to focus on the position and job requirements instead of a candidate's preferences. "Our method matches students and internships from the perspective of the job candidate rather than the position itself. A job position that is closely aligned with the interests of a candidate may lead to greater employee loyalty compared to a selection process in which only job requirements are taken into account." The findings could make decision-making processes more efficient in settings such as headhunting firms, online job boards, and corporate recruiting to uncover the interests of a job candidate prior to the interview process. You may also like: Will Artificial Intelligence take your job?

ESADE

<< Back to home

Software tool helps college students find the best job match

10/2018

Choosing a career path after college may no longer be a subjective decision. Researchers at ESADE, BarcelonaTech and the University of Vic have developed a new software tool to help college students make decisions on their first job experience.


"Internships may be a student's first experience in the job market, and thus it may be hard for him or her to express preferences clearly when searching for a job. Our software tool analyzes students' interests based on the words they use when verbally expressing their job preferences," says ESADE professor Núria Agell.


"The software tool helps students by identifying internships related to their interests when searching for a job position for the first time. The system automatically proposes a shortlist of jobs based on individual preferences to help students decide where to focus their attention," says co-author and ESADE professor Xari Rovira.


The software calculates the best job matches automatically


Beginning offline, the algorithm analyzes each student's resume to build a preference profile. It then defines a position profile from each job description. When logged into the system, a student can view his/her auto-generated profile and adjust preferences accordingly. Students can convey their inclinations based on different degrees of linguistic terms. The software then analyzes indecision and hesitance in the decision-making process and calculates the best job matches automatically.


A real case example with business students



In order to test their software, the authors used a 'real life' example based on the internship program for the Bachelor of Business Administration at ESADE Business School. The researchers matched 275 student resumes and 549 available internships (in English) at a national and international level. From these resumes, a set of features was determined to represent the main interests of the student body and define features for positions. 


A set of five features (Client and team oriented, Strategy, Sales and marketing, Technical skills, and Finance) was selected. With these features, the system created the student profiles, assigning linguistic terms 'Low', 'Medium', and 'High' for each one of the features. Finally, for each student, the system used linguistic terms to evaluate the fit between the student and each position. The set of positions with a degree of satisfaction equal to 'High' was shown to the student. On average, 22 positions were proposed to each student. By narrowing the focus for the internship search, students save a lot of time and can work more effectively applying for only those positions that match their interests.


Finally, to evaluate the advantages and drawbacks of their method, the authors compared it to the Hellinger ranking method. The results showed higher performance in their method. While the Hellinger Method recommended 65 or more positions to the majority of students, the new method was able to narrow the number of recommended positions for most students to fewer than 40, letting students focus on those positions that most closely matched their interests.


The new software tool was also compared to TOPSIS, a decision analysis method developed in 1981 to help people with decision-making processes. Compared to the TOPSIS Method, the new method was able to identify more positions matching what kinds of jobs students wanted.


A better decision-making process


Personnel selection processes are subjective by nature and tend to focus on the position and job requirements instead of a candidate's preferences.


"Our method matches students and internships from the perspective of the job candidate rather than the position itself. A job position that is closely aligned with the interests of a candidate may lead to greater employee loyalty compared to a selection process in which only job requirements are taken into account."


The findings could make decision-making processes more efficient in settings such as headhunting firms, online job boards, and corporate recruiting to uncover the interests of a job candidate prior to the interview process.


You may also like: Will Artificial Intelligence take your job?


More Knowledge
Consensus, dissension and precision in group decision making by means of an algebraic extension of hesitant fuzzy linguistic term sets
Montserrat Adell, Jordi; Agell Jané, Núria; Sánchez Soler, Mònica; Ruiz Vegas, Francisco Javier
Information Fusion
Nº 42, 07/2018, p. 1 - 11
A linguistic multi-criteria decision making system based on FOWA operators to support university career services
Nguyen, Jennifer; Sánchez Hernández, German; Armisen Morell, Albert; Agell Jané, Núria; Rovira Llobera, Xari; Angulo Bahón, Cecilio
Applied Soft Computing
Nº 67, 06/2018, p. 933 - 940
Influential factors in water planning for sustainable tourism destinations
Vila Fernández-Santacruz, Mar; Afsordegan , Arayeh; Agell Jané, Núria; Sánchez Soler, Mònica; Costa Guix, Gerard
Journal of Sustainable Tourism
Vol. 26, nº 7, 07/2018, p. 1241 - 1256
<< Back to home