Many businesses are burdened with the need to train students for the job instead of finding them prepared for it. Few business leaders feel that colleges prepare students for future jobs from day one. It can be a challenge for colleges to determine if their curricula meet the industry needs. Mapping industry needs to academic courses can be advantageous to both parties as it will allow colleges to be aligned with the industry needs and accordingly satisfy those needs and will allow the industry to hire better prepared graduates. In an attempt to address this, a system prototype that uses a collection of job descriptions from various sites and syllabi of college courses as the input knowledge was developed. The primary goal of the system is to help students to find courses that would be most beneficial in providing them with the skills that match a given job description. The secondary goal is to help faculty to quickly find out information about current skills and tools covered in the existing courses, which accordingly can help them to make decisions about their future courses to satisfy the industry needs. The system was developed using the Natural Language Toolkit (NLTK) and the Python programming language. Two sets of keywords were used to test the system; the first one is the most common keywords and the second one includes the most and least common keywords. Results from testing the system demonstrate that using the former set of keywords allowed for better results with precision equal to 55% and recall equal to 39.61%.
","abstract_html":"<p>Many businesses are burdened with the need to train students for the job instead of finding them prepared for it. Few business leaders feel that colleges prepare students for future jobs from day one. It can be a challenge for colleges to determine if their curricula meet the industry needs. Mapping industry needs to academic courses can be advantageous to both parties as it will allow colleges to be aligned with the industry needs and accordingly satisfy those needs and will allow the industry to hire better prepared graduates. In an attempt to address this, a system prototype that uses a collection of job descriptions from various sites and syllabi of college courses as the input knowledge was developed. The primary goal of the system is to help students to find courses that would be most beneficial in providing them with the skills that match a given job description. The secondary goal is to help faculty to quickly find out information about current skills and tools covered in the existing courses, which accordingly can help them to make decisions about their future courses to satisfy the industry needs. The system was developed using the Natural Language Toolkit (NLTK) and the Python programming language. Two sets of keywords were used to test the system; the first one is the most common keywords and the second one includes the most and least common keywords. Results from testing the system demonstrate that using the former set of keywords allowed for better results with precision equal to 55% and recall equal to 39.61%.</p>","abstract_has_math":false,"creators":["Rockwell, Daniel"],"institution":null,"degree_name":"Master of Science","degree_level":"Thesis","degree_discipline":"TSYS School of Computer Science","degree_department":null,"school":null,"contributors":["Shamim Khan","Kyongseon Jeon","Rania Hodhod"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-01-01T08:00:00Z","date_published":"2019-01-01T08:00:00Z","updated_at":"2026-07-24T01:45:01Z","subjects":["Natural Language Processing","Artificial Intelligence","Natural Language Toolkit","Lemmatization","Course Development","Text Matching","Artificial Intelligence and Robotics","Computer Sciences","Databases and Information Systems"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/356","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shamim Khan","Kyongseon Jeon","Rania Hodhod"]},{"key":"dc:creator","label":"Author","values":["Rockwell, Daniel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-29T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["TSYS School of Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Natural Language Processing","Artificial Intelligence","Natural Language Toolkit","Lemmatization","Course Development","Text Matching","Artificial Intelligence and Robotics","Computer Sciences","Databases and Information Systems"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/356"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Many businesses are burdened with the need to train students for the job instead of finding them prepared for it. Few business leaders feel that colleges prepare students for future jobs from day one. It can be a challenge for colleges to determine if their curricula meet the industry needs. Mapping industry needs to academic courses can be advantageous to both parties as it will allow colleges to be aligned with the industry needs and accordingly satisfy those needs and will allow the industry to hire better prepared graduates. In an attempt to address this, a system prototype that uses a collection of job descriptions from various sites and syllabi of college courses as the input knowledge was developed. The primary goal of the system is to help students to find courses that would be most beneficial in providing them with the skills that match a given job description. The secondary goal is to help faculty to quickly find out information about current skills and tools covered in the existing courses, which accordingly can help them to make decisions about their future courses to satisfy the industry needs. The system was developed using the Natural Language Toolkit (NLTK) and the Python programming language. Two sets of keywords were used to test the system; the first one is the most common keywords and the second one includes the most and least common keywords. Results from testing the system demonstrate that using the former set of keywords allowed for better results with precision equal to 55% and recall equal to 39.61%.
"]},{"key":"dc:title","label":"Title","values":["Curtus: An NLP Tool to Map Job Skills to Academic Courses"]}]}],"canonical_facts":{"dc:contributor":["Shamim Khan","Kyongseon Jeon","Rania Hodhod"],"dc:creator":["Rockwell, Daniel"],"dc:date.available":["2020-01-29T08:00:00Z"],"dc:description.abstract":["Many businesses are burdened with the need to train students for the job instead of finding them prepared for it. Few business leaders feel that colleges prepare students for future jobs from day one. It can be a challenge for colleges to determine if their curricula meet the industry needs. Mapping industry needs to academic courses can be advantageous to both parties as it will allow colleges to be aligned with the industry needs and accordingly satisfy those needs and will allow the industry to hire better prepared graduates. In an attempt to address this, a system prototype that uses a collection of job descriptions from various sites and syllabi of college courses as the input knowledge was developed. The primary goal of the system is to help students to find courses that would be most beneficial in providing them with the skills that match a given job description. The secondary goal is to help faculty to quickly find out information about current skills and tools covered in the existing courses, which accordingly can help them to make decisions about their future courses to satisfy the industry needs. The system was developed using the Natural Language Toolkit (NLTK) and the Python programming language. Two sets of keywords were used to test the system; the first one is the most common keywords and the second one includes the most and least common keywords. Results from testing the system demonstrate that using the former set of keywords allowed for better results with precision equal to 55% and recall equal to 39.61%.
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