Machine Learning for Educational Metaverse: How Far Are We?

被引：10
|
作者：
Bilotti, Umberto [1 ]
Di Dario, Dario [1 ]
Palomba, Fabio [1 ]
Gravino, Carmine [1 ]
Sibilio, Maurizio [1 ]
机构：
[1] Univ Salerno, Fisciano, Italy
来源：
2023 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, ICCE | 2023年
关键词：
Consumer Technology for Metaverse; Education; Machine Learning; Deep Learning;
D O I：
10.1109/ICCE56470.2023.10043465
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The concept of metaverse is becoming pervasive and promises to revolutionize the way people will interact with each other in a sustainable manner. The educational context seems to represent an ideal use case, as the metaverse may provide a digital environment empowered by analytical instruments able to monitor the social and psychological needs of students, other than lowering the entry barriers of students with disabilities. Machine learning will represent a key component of such a new consumer technology, yet little is known about its adoption within an educational metaverse. This paper overviews the current state of the art and provides a discussion about its suitability, in an effort of highlighting future research avenues and challenges.
引用
收藏
页数：2
相关论文
共 50 条
  • [1] API Recommendation for Machine Learning Libraries: How Far Are We?
    Wei, Moshi
    Huang, Yuchao
    Wang, Junjie
    Shin, Jiho
    Harzevili, Nima Shiri
    Wang, Song
    PROCEEDINGS OF THE 30TH ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2022, 2022, : 370 - 381
  • [2] API recommendation for machine learning libraries: how far are we?
    Wei, Moshi
    Huang, Yuchao
    Wang, Junjie
    Shin, Jiho
    Harzevili, Nima Shiri
    Wang, Song
    ESEC/FSE 2022 - Proceedings of the 30th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2022, : 370 - 381
  • [3] Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?
    Del-Coco, Marco
    Leo, Marco
    Carcagni, Pierluigi
    INFORMATION, 2024, 15 (06)
  • [4] Fairness-aware machine learning engineering: how far are we?
    Ferrara, Carmine
    Sellitto, Giulia
    Ferrucci, Filomena
    Palomba, Fabio
    De Lucia, Andrea
    EMPIRICAL SOFTWARE ENGINEERING, 2024, 29 (01)
  • [5] Machine Learning Based Recommendation of Method Names: How Far Are We
    Jiang, Lin
    Liu, Hui
    Jiang, He
    34TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2019), 2019, : 614 - 626
  • [6] Fairness-aware machine learning engineering: how far are we?
    Carmine Ferrara
    Giulia Sellitto
    Filomena Ferrucci
    Fabio Palomba
    Andrea De Lucia
    Empirical Software Engineering, 2024, 29
  • [7] Machine learning for seismic exploration: Where are we and how far are we from the holy grail?
    Anjom, Farbod Khosro
    Vaccarino, Francesco
    Socco, Laura Valentina
    GEOPHYSICS, 2024, 89 (01) : WA157 - WA178
  • [8] How far are we with automated machine learning? characterization and challenges of AutoML toolkits
    Al Alamin, Md Abdullah
    Uddin, Gias
    EMPIRICAL SOFTWARE ENGINEERING, 2024, 29 (04)
  • [9] Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?
    Wang, Song
    Shrestha, Nishtha
    Subburaman, Abarna Kucheri
    Wang, Junjie
    Wei, Moshi
    Nagappan, Nachiappan
    2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2021), 2021, : 1548 - 1560
  • [10] Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?
    Gianti, Eleonora
    Percec, Simona
    BIOMACROMOLECULES, 2022, 23 (03) : 576 - 591
12345