Multi-level classification of literacy of educators using PIAAC data

被引:0
|
作者
Yalcin, Seher [1 ]
机构
[1] Ankara Univ, Fac Educ Sci, Ankara, Turkey
关键词
PIAAC; teachers; educators; literacy; multilevel latent class analysis; TEACHERS MATHEMATICAL KNOWLEDGE; STUDENT-ACHIEVEMENT; GENDER GAPS; PERFORMANCE; SKILLS;
D O I
10.1080/02671522.2020.1849375
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This study aims to identify the literacy skills of individuals whose highest level of education was in the field 'teacher training and educational sciences'. The study sample comprised 10,618 individuals in the field of teacher training and educational sciences, selected from 31 countries (participating in the International Adult Skills Assessment Programme during the 2014-2015 survey) using a multi-stage sampling method. The study employed multi-level latent class analysis and three-step analysis in order to determine both the number of multi-level latent classes of educators' literacy scores as well as the selected independent variables' success in predicting those latent classes. The analysis revealed that educators in Germany constituted the group with the highest literacy skills while educators from Singapore comprised the group with the lowest literacy skills.
引用
收藏
页码:441 / 456
页数:16
相关论文
共 50 条
  • [1] NOTE ON CLASSIFICATION OF MULTI-LEVEL DATA
    LANCE, GN
    WILLIAMS, WT
    COMPUTER JOURNAL, 1967, 9 (04): : 381 - &
  • [2] Using IPSec to secure multi-level data classification in MLS networks
    Morsi, Wissam
    El-Fouly, Tarek
    Badr, Ahmed
    2006 6TH INTERNATIONAL CONFERENCE ON ITS TELECOMMUNICATIONS PROCEEDINGS, 2006, : 817 - +
  • [3] Exciting event detection using multi-level multimodal descriptors and data classification
    Chen, Shu-Ching
    Chen, Min
    Zhang, Chengcui
    Shyu, Mei-Ling
    ISM 2006: EIGHTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA, PROCEEDINGS, 2006, : 193 - 200
  • [4] Hierarchical Bayesian learning framework for multi-level modeling using multi-level data
    Jia, Xinyu
    Papadimitriou, Costas
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 179
  • [5] Offensive Language Detection Using Multi-level Classification
    Razavi, Amir H.
    Inkpen, Diana
    Uritsky, Sasha
    Matwin, Stan
    ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2010, 6085 : 16 - +
  • [6] A multi-level refinement approach towards the classification of quotidian activities using accelerometer data
    Ortega-Anderez, Dario
    Lotfi, Ahmad
    Langensiepen, Caroline
    Appiah, Kofi
    JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2019, 10 (11) : 4319 - 4330
  • [7] A multi-level refinement approach towards the classification of quotidian activities using accelerometer data
    Dario Ortega-Anderez
    Ahmad Lotfi
    Caroline Langensiepen
    Kofi Appiah
    Journal of Ambient Intelligence and Humanized Computing, 2019, 10 : 4319 - 4330
  • [8] Classification of medical data with a robust multi-level combination scheme
    Tsirogiannis, GL
    Frossyniotis, D
    Stoitsis, J
    Golemati, S
    Stafylopatis, A
    Nikita, KS
    2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2004, : 2483 - 2487
  • [9] Using PIAAC data to learn more about the literacy practices of adults
    Nienkemper, Barbara
    Grotlueschen, Anke
    INTERNATIONAL JOURNAL OF LIFELONG EDUCATION, 2019, 38 (04) : 393 - 405
  • [10] Multi-level lecture video classification using text content
    Agziyagli, Veysel Sercan
    Ogul, Hasan
    2020 IEEE 14TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT2020), 2020,