Assessment of Optimal Pedagogical Factors for Canadian ESL Learners’ Reading Literacy Through Artificial Intelligence Algorithms

Abstract

The current study explored the effective pedagogical factors that distinguish high-achieving from low-achieving ESL (English as a second language) primary school learners in reading literacy in Canada. In total, 203 samples (167 high-achieving learners and 36 low-achieving learners from 128 primary schools) in the 4th grade were drawn from the public database of Progress in International Reading Literacy Study (PIRLS) 2016, which is the benchmark for large-scale assessments of reading literacy targeting fourth-grade students. For the first time in the ESL-related research, this study applied an artificial intelligence approach, support vector machine (SVM), to concurrently analyze 41 pedagogical factors associated with reading materials, classroom organization, reading strategies, in-class reading activities and post-reading activities. The overall 41 factors collectively distinguished the high-achieving readers from the low-achieving readers with a high accuracy score (0.793) via SVM. Then, these 41 factors were ranked according to their contribution to the SVM model through SVM-based recursive feature elimination (SVM-RFE). Eventually, an optimal factor set was selected by the SVM-RFE cross validation, which contained 10 effective pedagogical factors centered on reading materials, reading strategies and in-class reading activities for fourth-grade high-achieving ESL learners in reading literacy. Suggestions based on solid data analysis would facilitate infrastructural and pedagogical improvements in ESL reading education.

An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches

“…The learning time that students spent on test language (LMINS) was a strong indicator of high-and low-achieving readers. Generally, students' learning time could be extended to homework and other extracurricular activities (Xiao & Hu, 2019a). Caution was paid to the learning time on test language per week within school, which was a significant feature to distinguish high-and low-level readers.…”

“…Considering principals' guide, teachers managed to change their instructions with students' actual needs (ADINST) in many lessons and thus improved students' reading performance. In teaching process, designing classes according to students' interests and need was effective in cultivating high-achieving readers (Xiao & Hu, 2019a). Moreover, appropriate materials might help students to foster intrinsic motivation and motivate them to participate extrinsic engagement (Johnson & Blair, 2003;Xiao & Hu, 2019a).…”

“…In teaching process, designing classes according to students' interests and need was effective in cultivating high-achieving readers (Xiao & Hu, 2019a). Moreover, appropriate materials might help students to foster intrinsic motivation and motivate them to participate extrinsic engagement (Johnson & Blair, 2003;Xiao & Hu, 2019a). What's more, excellent readers usually received teacher feedback (PERFEED) during their learning process.…”

Regression Analysis of ICT Impact Factors on Early Adolescents’ Reading Proficiency in Five High-Performing Countries

“…Certain previous studies have investigated the relationship between impact factors and excellent subject performance by students. For instance, pedagogical impact factors of 4th-grade students with excellent reading proficiency were identified based on Progress in International Reading Literacy Study (PIRLS) (Xiao and Hu, 2019). Regarding the PISA-based analysis, a set of impact factors influencing top students’ science performance was explored (e.g., Chen et al, 2019).…”

Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

“…Biglan’s classification scheme of academic disciplines (hard (H) vs. soft (S) disciplines and pure (P) vs. applied (A) disciplines) has been credited as the most cited organizational system of academic disciplines in tertiary education [ 68 – 70 ]. Many studies have also provided evidence supporting the validity of this classification [ 69 ].…”