Synergistic effects of instruction and affect factors on high- and low-ability disparities in elementary students' reading literacy

被引:26
作者
Chen, Jiangping [2 ]
Zhang, Yang [3 ]
Hu, Jie [1 ]
机构
[1] Zhejiang Univ, Sch Int Studies, Dept Linguist, Hangzhou 310058, Zhejiang, Peoples R China
[2] Univ Hong Kong, Fac Educ, Hong Kong 999077, Peoples R China
[3] Peking Univ, Beijing Inst Big Data Res, Beijing 100085, Peoples R China
关键词
Data mining; Effective reading pedagogies; Elementary reading literacy; PIRLS; 2016; Reading self-concepts; SELF-CONCEPT; COMPREHENSION; ACHIEVEMENT; SCIENCE; PIRLS; MOTIVATION; MATH;
D O I
10.1007/s11145-020-10070-0
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This study examined the combined effects of teachers' instructional practices and students' reading-related affective engagement on predicting the high and low levels of elementary reading literacy from a linguistically and culturally comparative perspective. Data were based on 9748 students from 4 English-speaking and 3 Chinese-speaking education systems participating in the Progress in International Reading Literacy Study 2016. A mixed theory-based and data-driven approach was adopted. Four machine learning algorithms, specifically logistic regression, support vector machine, decision tree, and extreme gradient boosting, were simultaneously used to classify and predict high- and low-proficiency readers and to identify the most important factors for the ability separation. The findings showed that for both system groups, those factors together were sufficiently powerful to discriminate the readers and that the affective constructs, particularly students' self-concepts, played a predominant role. The Chinese-speaking systems, compared with their English counterparts, applied more effective pedagogies to nurture sophisticated readers.
引用
收藏
页码:199 / 230
页数:32
相关论文
共 45 条
  • [1] Country, School and Students Factors Associated with Extreme Levels of Science Literacy Across 25 Countries
    Alivernini, F.
    Manganelli, S.
    [J]. INTERNATIONAL JOURNAL OF SCIENCE EDUCATION, 2015, 37 (12) : 1992 - 2012
  • [2] [Anonymous], 2013, EDUC STUD-UK, DOI DOI 10.1080/03055698.2013.767187
  • [3] Basol G., 2009, International Journal of Human Sciences, V6, P99
  • [4] Discrimination of the Contextual Features of Top Performers in Scientific Literacy Using a Machine Learning Approach
    Chen, Jiangping
    Zhang, Yang
    Wei, Yueer
    Hu, Jie
    [J]. RESEARCH IN SCIENCE EDUCATION, 2021, 51 (SUPPL 1) : 129 - 158
  • [5] Chen T., 2016, KDD16 P 22 ACM, P785, DOI [DOI 10.1145/2939672.2939785, 10.1145/2939672.2939785]
  • [6] Progress in International Reading Literacy Study 2006 (PIRLS): pedagogical correlates of fourth-grade students in Hong Kong
    Cheung, Wai Ming
    Tse, Shek Kam
    Lam, Joseph W. I.
    Loh, Elizabeth Ka Yee
    [J]. JOURNAL OF RESEARCH IN READING, 2009, 32 (03) : 293 - 308
  • [7] Exploring online students' self-regulated learning with self-reported surveys and log files: a data mining approach
    Cho, Moon-Heum
    Yoo, Jin Soung
    [J]. INTERACTIVE LEARNING ENVIRONMENTS, 2017, 25 (08) : 970 - 982
  • [8] SUPPORT-VECTOR NETWORKS
    CORTES, C
    VAPNIK, V
    [J]. MACHINE LEARNING, 1995, 20 (03) : 273 - 297
  • [9] Farzan I. A, 2018, THESIS
  • [10] Fraser B.J., 1987, INT J EDUC RES, V11, P145, DOI [DOI 10.1016/0883-0355(87)90035-8, 10.1016/0883-0355(87)90035-8]