TY - JOUR

T1 - An AI-based open recommender system for personalized labor market driven education

AU - Tavakoli, Mohammadreza

AU - Faraji, Abdolali

AU - Vrolijk, Jarno

AU - Molavi, Mohammadreza

AU - Mol, Stefan T.

AU - Kismihók, Gábor

N1 - Funding Information: The authors gratefully acknowledge the financial support from the following projects that have helped in developing the eDoer platform: ADSEE - Applied Data Science Educational Ecosystem, European Commission - Erasmus Plus Programme , 2019-1-HR01-KA203-060984 ; OSCAR - Online, open learning recommendations and mentoring towards Sustainable research CAReers, European Commission - Erasmus Plus Programme , 2020-1-DE01-KA203-005713 ; BIPER - Business Informatics Programme Reengineering, European Commission - Erasmus Plus Programme , 2020-1-HU01-KA226-HE-093987 ; ADAPT - Implementation of an Adaptive Continuing Education Support System in the Professional Field of Nursing German Federal Ministry of Education and Research BMBF - INVITE 21INVI0501 ; WBsmart - AI-based digital continuing education space for elderly care, German Federal Ministry of Education and Research BMBF - INVITE 21INVI2101 .

PY - 2022/4

Y1 - 2022/4

N2 - Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements; (2) decomposes skills into learning topics; (3) collects a variety of open online educational resources that address those topics; (4) checks the quality of those resources and topic relevance with three intelligent prediction models; (5) helps learners to set their learning goals towards their desired job-related skills; (6) recommends personalized learning pathways and learning content based on individual learning goals; and (7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by means of a pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal recommendations provided by eDoer to acquire knowledge of basic statistics, attained higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported.

AB - Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements; (2) decomposes skills into learning topics; (3) collects a variety of open online educational resources that address those topics; (4) checks the quality of those resources and topic relevance with three intelligent prediction models; (5) helps learners to set their learning goals towards their desired job-related skills; (6) recommends personalized learning pathways and learning content based on individual learning goals; and (7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by means of a pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal recommendations provided by eDoer to acquire knowledge of basic statistics, attained higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported.

KW - Educational data mining

KW - Open educational resources

KW - Recommender systems

UR - http://www.scopus.com/inward/record.url?scp=85125135025&partnerID=8YFLogxK

U2 - 10.1016/j.aei.2021.101508

DO - 10.1016/j.aei.2021.101508

M3 - Article

AN - SCOPUS:85125135025

VL - 52

JO - Advanced engineering informatics

JF - Advanced engineering informatics

SN - 1474-0346

M1 - 101508

ER -