The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
An AI-based open recommender system for personalized labor market driven education
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“AI-powered solutions can assess an individual’s existing skill-set, career vision, and in-demand skills in their locality to provide specific training and certification programs that link up with particular employment opportunities.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [52]
In the citing paper
“AI-based learning systems can also, in real time, give room to a person to learn new skills at their place of work, therefore being ready to shift and adapt amid ever-changing economic conditions52.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [52]
In the citing paper
“For example, AI can analyze labor market trends to identify a list of in-demand skills and then recommend it to an individual based on those recommendations, thereby positioning the individual in a more sustainable employment position.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [52]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6894-4 · Cited by 2,144
Bayesian estimation supersedes the t-test
Kruschke, J. K. (2013) · Journal of Experimental Psychology: General · doi:10.1037/a0029146 · Cited by 1,530
Using Learning Style Instruments to Enhance Student Learning
Hawk, T. F., & Shah, A. J. (2007) · Decision Sciences Journal of Innovative Education · doi:10.1111/j.1540-4609.2007.00125.x · Cited by 551
Addressing cold-start problem in recommendation systems
Lam, X. N., Vu, T., Le, T. D., & Duong, A.-D. (2008) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1145/1352793.1352837 · Cited by 412
2017 IEEE International Conference on Big Data (Big Data)
Jian-Yun Nie, Z. O., Nie, J.-Y., Zoran Obradovic, A., Suzumura, T., Ghosh, R., Nambiar, R., Wang, C., Zang, H., Baeza-Yates, R., Hu, X., et al. (2017) · doi:10.1109/bigdata41644.2017 · Cited by 302
Text Mining in Organizational Research
Kobayashi, V. B., Mol, S. T., Berkers, H., Kismihók, G., & den Hartog, D. D. (2017) · Organizational Research Methods · doi:10.1177/1094428117722619 · Cited by 253
Self-citationAn e-learning recommendation approach based on the self-organization of learning resource
Wan, S., & Niu, Z. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.06.014 · Cited by 131
Skills in demand for ICT and statistical occupations: Evidence from web‐based job vacancies
Lovaglio, P. G., Cesarini, M., Mercorio, F., & Mezzanzanica, M. (2018) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11372 · Cited by 76
Skills and Vacancy Analysis with Data Mining Techniques
Wowczko, I. (2015) · Informatics · doi:10.3390/informatics2040031 · Cited by 73
Research works printed but not linked: 1
- Tavakoli, 2020, Oer recommendations to support career development, IEEE Frontiers In Education (FIE)
Cited by
Sorted by citations
AI-powered ensemble machine learning to optimize cost strategies in logistics business
Yaiprasert, C., & Hidayanto, A. N. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100209 · Cited by 122
AI-Driven Personalized Learning Paths: Enhancing Education Through Adaptive Systems
Yekollu, R. K., Ghuge, T. B., Biradar, S. S., Haldikar, S. V., & Kader, O. F. M. A. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-3191-6_38 · Cited by 92
The Application of Artificial Intelligence in Engineering Education: A Systematic Review
Liu, C., Wang, G., & Wang, H. (2025) · IEEE Access · doi:10.1109/access.2025.3532595 · Cited by 73
Multi-objective optimization with recommender systems: A systematic review
Zaizi, F. E., Qassimi, S., & Rakrak, S. (2023) · Information Systems · doi:10.1016/j.is.2023.102233 · Cited by 71
Recommender systems for sustainability: overview and research issues
Felfernig, A., Wundara, M., Tran, T. N. T., Erdeniz, S. P., Lubos, S., Mansi, M. E., Garber, D., & Le, V.-M. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1284511 · Cited by 66
Metadata
- Authors
- Tavakoli, M., Faraji, A., Vrolijk, J., Molavi, M., Mol, S. T., & Kismihók, G.
- Affiliations
- Technische Informationsbibliothek (TIB) (DE)
- University of Amsterdam (NL)
- Amirkabir University of Technology (IR)
- Corresponding author
- Mol, Stefan T.
- ORCID
- Venue
- Advanced Engineering Informatics
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 52
- Pages
- 101508
- ISSN
- 1474-0346 (ISSN-L)
- OpenAlex
- W4213432396
- Cited by
- 113
- References
- 49
- Citations by year
- 2026: 29 · 2025: 30 · 2024: 28 · 2023: 17 · 2022: 9
- FWCI
- 20.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Advanced Engineering Informatics (publishedVersion, cc-by)
- Corvinus Research Archive (Corvinus University of Budapest) (acceptedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- RENATE (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- RENATE (cc-by)
- Funders
- European Commission
- Erasmus+ (2019-1-HR01-KA203-060984, 2020-1-DE01-KA203-005713)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- personalized learning, labor market skills, open educational resources, learning trajectories, learning analytics dashboards, data science education, statistics learning, student progress assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
