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.
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AI-powered solutions can assess an individual's existing skill-set, career vision, and local in-demand skills to provide specific training and certification programs linked to employment opportunities.
“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]
AI-based learning systems can enable real-time workplace learning, helping individuals adapt to changing economic conditions.
“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]
AI can analyze labor market trends to identify in-demand skills and recommend them to individuals, positioning them in more sustainable employment.
“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]
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Cites
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X.-H., Li, Y., & Zhao, L. (2017) · Multimedia tools and applications · doi:10.1007/s11042-018-6894-4 · Cited by 1,875 · see citing works
Bayesian estimation supersedes the t-test
Kruschke, J. K. (2013) · Journal of Experimental Psychology: General · doi:10.1037/a0029146 · Cited by 1,530 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Skills and Vacancy Analysis with Data Mining Techniques
Wowczko, I. (2015) · Informatics · doi:10.3390/informatics2040031 · Cited by 73 · see citing works
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 70 · see citing works
2018 IEEE Frontiers in Education Conference (FIE)
(2018) · doi:10.1109/fie38866.2018 · Cited by 62 · see citing works
Analyzing Computer Programming Job Trend Using Web Data Mining
Smith, D. T., & Ali, A. (2014) · Issues in Informing Science and Information Technology · doi:10.28945/1989 · Cited by 45 · see citing works
2018 IEEE Global Engineering Education Conference (EDUCON)
(2018) · doi:10.1109/educon42241.2018 · Cited by 44 · see citing works
Sun, G., Cui, T., Beydoun, G., Chen, S., Dong, F., Dongming Xu, A., & Shen, J. (2017) · Sustainability · doi:10.3390/su9060898 · Cited by 38 · see citing works
Open Educational Resources: Motivations, Logistics and Sustainability
Sclater, N. (2010) · Springer eBooks · doi:10.1007/978-1-4419-6959-0_10 · Cited by 38 · see citing works
An Open and Data-driven Taxonomy of Skills Extracted from Online Job AdvertsPreprint
Djumalieva, J., & Sleeman, C. (n.d.) · RePEc: Research Papers in Economics · Cited by 34 · see citing works
Li, X., Jiang, Z., Guan, Y., Li, G., & Wang, F. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100927 · Cited by 33 · see citing works
Kim, W. H., & Choi, H.-J. (2008) · doi:10.1145/1352793 · Cited by 33 · see citing works
Fabbri, A. R., Li, I., Trairatvorakul, P., He, Y., Ting, W. T., Tung, R., Westerfield, C., & Radev, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1057 · Cited by 32 · see citing works
Measuring completeness as metadata quality metric in Europeana
Király, P., & Buchler, M. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622487 · Cited by 32 · see citing works
A Recommender System For Open Educational Videos Based On Skill Requirements
Tavakoli, M., Hakimov, S., Ewerth, R., & Kismihók, G. (2020) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt49669.2020.00008 · Cited by 29 · see citing works
Self-citationFuzzy clustering of lecture videos based on topic modeling
Basu, S., Yu, Y., & Zimmermann, R. (2016) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2016.7500264 · Cited by 29 · see citing works
An Ontology-Based Approach for the Semantic Representation of Job Knowledge
Khobreh, M., Ansari, F., Fathi, M., Vas, R. F., Mol, S. T., Berkers, H. A., & Varga, K. (2015) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2015.2449662 · Cited by 28 · see citing works
Self-citationRecommendation of open educational resources. An approach based on linked open data
Chicaiza, J. A., Piedra, N., López, J., & Tovar, E. (2017) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon.2017.7943018 · Cited by 27 · see citing works
Metadata Analysis of Open Educational Resources
Tavakoli, M., Elias, M., Kismihók, G., & Auer, S. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448208 · Cited by 25 · see citing works
Self-citationContent Management for E-Learning
Ferrer, N. F., & Minguillón, J. (2010) · Springer eBooks · doi:10.1007/978-1-4419-6959-0 · Cited by 24 · see citing works
Cognitive factors of the transfer of empirical engineering knowledge: A behavioral and fNIRS study
Wang, F., Jiang, Z., Li, X., & Li, G. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101207 · Cited by 23 · see citing works
Quality Prediction of Open Educational Resources A Metadata-based Approach
Tavakoli, M., Elias, M., Kismihók, G., & Auer, S. (2020) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt49669.2020.00007 · Cited by 21 · see citing works
Self-citationKeyword extraction from Educational Video transcripts using NLP techniques
Shukla, H., & Kakkar, M. (2016) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence.2016.7508096 · Cited by 21 · see citing works
Extracting Topics from Open Educational Resources
Molavi, M., Tavakoli, M., & Kismihók, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57717-9_44 · Cited by 20 · see citing works
Self-citationLabour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
Tavakoli, M., Mol, S. T., & Kismihók, G. (2020) · International Conference on Computer Supported Education · doi:10.5220/0009420300960104 · Cited by 19 · see citing works
Self-citationFloriano, A. G., Ferreira-Santiago, Á., Yáñez-Márquéz, C., Camacho-Nieto, O., Aldape-Pérez, M., & Villuendas-Rey, Y. (2017) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v18i1.2646 · Cited by 16 · see citing works
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Horita, K., Kimura, F., & Maeda, A. (2015) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2016.v8.1015 · Cited by 16 · see citing works
A Proposal of Quality Assessment of OER Based on Emergent Technology
Romero-Peláez, A., Segarra-Faggioni, V., Piedra, N., & Tovar, E. (2019) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon.2019.8725067 · Cited by 14 · see citing works
Sun, G., Cui, T., Dongming Xu, A., Shen, J., & Chen, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91464-0_21 · Cited by 13 · see citing works
Metadata Quality Assessment Metrics into OCW Repositories
Romero-Peláez, A., & Alarcón, P. P. (2017) · International Conference on Education Technology and Computer · doi:10.1145/3175536.3175579 · Cited by 13 · see citing works
Romero-Peláez, A., Segarra-Faggioni, V., & Alarcón, P. P. (2018) · International Conference on Education Technology and Computer · doi:10.1145/3290511.3290540 · Cited by 13 · see citing works
Topic-Specific Recommendation for Open Education Resources
Wang, J., Xiang, J., & Uchino, K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25515-6_7 · Cited by 11 · see citing works
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Kobayashi, V., Mol, S. T., & Kismihók, G. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.13.24 · Cited by 8 · see citing works
Self-citationFinding out topics in educational materials using their components
Saraiva, M. D. C., & Medeiros, C. B. (2017) · 2022 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2017.8190705 · Cited by 5 · see citing works
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Aryal, S., Porawagama, A. S., Hasith, M. G. S., Thoradeniya, S. C., Kodagoda, N., & Suriyawansa, K. (2018) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs.2018.8913347 · Cited by 5 · see citing works
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Fensel, A., Fensel, A., Zaveri, A., Hellmann, S., Pellegrini, T., Germany, S. 2. 1. I. C. O. S. S. 2. L., & Association for Computing Machinery-Digital Library (2016) · doi:10.1145/2993318 · Cited by 4 · see citing works
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Castello, V., Flores, E., Gabor, M. R., Caballero, J. G., Guspini, M., Luna, J. M., Mahajan, L., McGartland, K., Szabó, I. B., & Ramos, F. J. V. (2014) · Cited by 1 · see citing works
Flagged first, then by citations
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- M. Tavakoli, A. Faraji, S.T. Mol, G. Kismihók, Oer recommendations to support career development, IEEE Frontiers In Education (FIE) (2020).
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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 · see citing works
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Федушко, С., Ustyianovych, T., & Syerov, Y. (2022) · Journal of Intelligence · doi:10.3390/jintelligence10020032 · Cited by 26 · see citing works
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Qi, G. (2024) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978241299626 · Cited by 3 · see citing works
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Fahyuni, E. F., Taurusta, C., Rindaningsih, I., Burhan, N. B. M., & Khuluqo, I. E. (2023) · Academia Open · doi:10.21070/acopen.8.2023.7201 · Cited by 3 · see citing works
Los factores que influyen en la deserción universitaria
Gutiérrez, D. D. J. V., Gutiérrez, G. M. V., Díaz, A. T., Achuy, M. L., Chagua, J. J. G., & Barrantes, J. E. A. (2023) · Revista de Climatología · doi:10.59427/rcli/2023/v23cs.1869-1877 · Cited by 2 · see citing works
Wang, F. (2024) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis61070.2024.10594603 · Cited by 2 · see citing works
Feng, Z., & Zhang, J. (2025) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn64258.2025.10934182 · Cited by 2 · see citing works
Molavi, M., Moein, M. M., Tavakoli, M., Faraji, A., Kismihók, G., & Mol, S. T. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785073 · Cited by 2 · see citing works
Self-citationMardiati, M. (2025) · Journal of Paddisengeng Technology · doi:10.65224/jopate.v1i1.190 · Cited by 2 · see citing works
Ilina, A. V., Belov, S., Filozova, I., Gavrilenko, Y., Javadzade, J., Kadochnikov, I., Korenkov, V., Pelevanyuk, I., Priakhina, D., Semenov, R. N., et al. (2022) · International Workshop on Deep Learning in Computational Physics (DLCP) · doi:10.22323/1.429.0028 · Cited by 2 · see citing works
A Comprehensive Analysis of Machine Learning and Deep Learning Based Product Recommendation System
Chakravarthi, G. K., Kumar, R., & Addagarla, S. K. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-77075-3_25 · Cited by 2 · see citing works
Calidad de servicios educativos en los centros de formación de educación superior
Gutiérrez, D. D. J. V., Díaz, A. T., & Achuy, M. L. (2023) · Horizontes Revista de Investigación en Ciencias de la Educación · doi:10.33996/revistahorizontes.v7i30.650 · Cited by 2 · see citing works
A User-Centric Evaluation of a Continuing Education Course Recommender System
Scherb, F. K., Ifenthaler, D., Bachmann, P. V., & Diestelkämper, R. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016659 · Cited by 2 · see citing works
Development of Recommender Systems for Better Services and Products using Data Science
Padmapriya, S., Thamizhamuthu, R., Jagadeesh, S., Selvi, D. M. K., & Shariff, M. A. (2023) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc57686.2023.10193328 · Cited by 2 · see citing works
Yi, J. (2023) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2023.2.00633 · Cited by 2 · see citing works
Hosseinkhani, N. T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5385537 · Cited by 2 · see citing works
AI-Driven Innovations in Pedagogical and Andragogical Approaches
Asrifan, A., Shafa, S., Octaberlina, L. R., Ersyad, Z. M., Hermansyah, S., & Amri, N. A. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-6130-6.ch009 · Cited by 1 · see citing works
Zhang, F. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251361837 · Cited by 1 · see citing works
Alvarado, A., & Portillo, J. R. (2025) · World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) · doi:10.1109/weef-gedc66748.2025.11256341 · Cited by 1 · see citing works
A KNN-Based Bilingual Book Recommendation System with Gamification and Learning Analytics
Kassenkhan, A. M. (2026) · Information · doi:10.3390/info17020120 · Cited by 1 · see citing works
Zhang, Y. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01236-w · Cited by 1 · see citing works
Research on an AI-Based Sustainable Education Content Recommendation System
Wei, L., Kakade, K., Cheng, Y., & Yang, S. (2026) · International Conference on Recent Trends in Computer Science and Technology (ICRTCST) · doi:10.1109/icrtcst68392.2026.11545234 · Cited by 1 · see citing works
Bucchiarone, A., Chiarello, F., Raffaghelli, J. E., Giordano, V., Vázquez‐Ingelmo, A., Schiavo, G., Antonaci, A., Grion, V., & Guadagni, A. (2024) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon60312.2024.10578861 · Cited by 1 · see citing works
Natural Language Processing Techniques to Identify Work Profiles from Online Job Postings
Ferreira, A., Gómez, W., & Grünewald, I. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-83207-9_28 · Cited by 1 · see citing works
Dongna, L. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251363147 · Cited by 1 · see citing works
Phadtare, A. S., Kulkarni, N. G., & Devare, M. H. (2026) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00687-y · Cited by 1 · see citing works
Sustainability-aware evaluation metrics for recommender systems
Felfernig, A., Garber, D., Helić, D., Le, V.-M., Lubos, S., & Tran, T. N. T. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01045-8 · Cited by 1 · see citing works
Chaturved, E. S. S., & Gladence, L. M. (2024) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns62112.2024.10691127 · Cited by 1 · see citing works
Li, L. (2024) · International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) · doi:10.1109/iacis61494.2024.10721685 · Cited by 1 · see citing works
Domenichini, D., Giordano, V., Fantoni, G., & Chiarello, F. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74627-7_5 · Cited by 1 · see citing works
Bautista, M., Alfaro, S., & Wong, L. (2024) · Journal of Computer Science · doi:10.3844/jcssp.2024.522.534 · Cited by 1 · see citing works
Seoane, F. J. F., Villares, M. O. D. C., Abad-Itoiz, N., & Ferreiro, E. J. (2024) · Administrative Sciences · doi:10.3390/admsci14070154 · Cited by 1 · see citing works
Aplicación de herramientas tecnológicas en la enseñanza de la ingeniería estructural
González-Durán, M., Hernández-Sánchez, T. I., Arballo, O. C., Enríquez, L. I. V., & Ortega-Medrano, D. (2022) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v6i6.3940
RYNEK PRACY BIG DATA: PRZEGLĄD WYMAGAŃ W BRANŻY
Bernaś, M., Witos, K., KŁOS-WITKOWSKA, A., MARTSENYUK, V., DIMITROV, G., Rančić, D., & BYCHKOV, O. (2022) · Akademia Techniczno-Humanistyczna w Bielsku-Białej eBooks · doi:10.53052/9788367652018.03
Reviews in Recommender Systems: 2022
(2024) · Frontiers research topics · doi:10.3389/978-2-8325-4766-3
The application of artificial intelligence technologies in management
Čyras, G., & Janušauskienė, V. M. (2024) · Business and management · doi:10.3846/bm.2024.1183
Bramley, D. R. (2025) · EDUCATION SCIENCES AND SOCIETY · doi:10.3280/ess2-2024oa18383
Design and Implementation of an AI-Driven Market Analysis System
Zhang, X. (2024) · International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC) · doi:10.1109/iiotbdsc64371.2024.00067
Bisaso, S., & Gilbert, M. (2025) · International Journal Of Scientific Research In Engineering & Technology · doi:10.59256/ijsreat.20250501001
TripAI : A personalized tourist recommender app for streamlined trip planning using machine learning
Shaik, N., Shaik, M. I., Avvari, P., Thimmapuram, T. K., & Reddy, K. R. K. (2025) · AIP conference proceedings · doi:10.1063/5.0247160
Transforming Workforce Development through AI-Driven Education Networks
Mustafa, S. W., Sari, H., Asriany, A., & Hajeni, H. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5786-7.ch007
Thoppil, I. J., Gopu, A., & Saha, J. (2025) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct65861.2025.11306811
Karthikeyan, K., T, S. J., & Saranya, T. (2025) · International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis66685.2025.11315493
Deep collaborative filtering recommender systems in smart cities: a systematic review
Abakarim, S., Qassimi, S., & Rakrak, S. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02644-7
Yu, L. (2025) · International Conference on Communication, Computer, and Information Technology (IC3IT) · doi:10.1109/ic3it66137.2025.11341437
Klostermann, M., Brenzke, M., Dietz, A., Kluge, A., & Radüntz, T. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2617137
Employing artificial intelligence to bridge the gap between education outcomes and the labor market
Alhami, A., & Zabian, A. (2026) · AIP conference proceedings · doi:10.1063/5.0318418
Jebakerupa Roslin, A., A, V. P., Harikrishnan, S., Kamaleshwaran, P., Suwathi, S., & Kumaran, S. (2025) · International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) · doi:10.1109/icaiccit68829.2025.11434060
Fischer, C. E., Juliani, D. P., & Bleicher, S. (2026) · Informática na educação teoria & prática · doi:10.22456/1982-1654.148782
The Role of Recommender Systems in Everyday Life
Escorial, R. B., & Diamano, N. F. B. (2026) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl67539.2026.11451856
- 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
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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.
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