Yueer Wei and Jie Hu


  1. [1] S. Choy and P. Cheah, Teacher perceptions of critical thinking among students and its influence on higher education, International Journal of Teaching and Learning in Higher Education, 20(2), 2009, 198–206.
  2. [2] J. Hu and Y. Wei, The centrality of creativity: A new perspective on English language teaching. English Today, 35(2), 2019, 60–62.
  3. [3] S. Ebadi and M. Rahimi, An exploration into the impact of WebQuest-based classroom on EFL learners’ critical thinking and academic writing skills: A mixed-methods study, Computer Assisted Language Learning, 31(5–6), 2018, 617–651.
  4. [4] Y. Xiao and J. Hu, Assessment of optimal pedagogical factors for Canadian ESL learners’ reading literacy through artificial 163 intelligence algorithms, International Journal of English Linguistics, 9(4), 2019, 1–22.
  5. [5] Y. Xiao and J. Hu, The moderation examination of ICT use on the association between Chinese mainland students’ socioeconomic status and reading achievement, International Journal of Emerging Technologies in Learning, 14(11), 2019, 75–89.
  6. [6] J. Chen and J. Hu, Enhancing L2 learners’ critical thinking skills through a connectivism-based intelligent learning system, International Journal of English Linguistics, 8(6), 2018, 12–21.
  7. [7] Critical Thinking: A Statement of Expert Consensus for Purposes of Educational Assessment and Instruction. The Delphi Research Report (CA: Santa Clara University, 1990).
  8. [8] M. Peng, G. Wang, J. Chen, M. Chen, H. Bai, S. Li, et al., Validity and reliability of the Chinese critical thinking dis- position inventory, Chinese Journal of Nursing, 39(9), 2004, 644–647.
  9. [9] The California Critical Thinking Dispositions Inventory (CCTDI) and the CCTDI Test Manual (Millbrae: California Academic Press, 2003).
  10. [10] X. Chen, Y. Yan, and J. Hu, A corpus-based study of Hillary Clinton’s and Donald Trump’s linguistic styles, International Journal of English Linguistics, 9(3), 2019, 13–22.
  11. [11] Y. Xiao, Y. Li, and J. Hu, Construction of the belt and road initiative in Chinese and American media: A critical discourse analysis based on self-built corpora, International Journal of English Linguistics, 9(3), 2019, 68–77.
  12. [12] Y. Jin, B. Li, N. Chen, X. Li, and J. Hu, The discrimination of learning styles by Bayes-based statistics: An extended study on ILS system, Control and Intelligent Systems, 43(2), 2015, 68–75.
  13. [13] C. Yu, X. Li, H. Yang, Y. Hong, W. Xue, Y. Chen, et al., Assessing the performances of protein function prediction algorithms from the perspectives of identification accuracy and false discovery rate, International Journal of Molecular Sciences, 19(1), 2018, 183.
  14. [14] J. Chen, Y. Zhang, Y. Wei, and J. Hu, Discrimination of the contextual features of top performers in scientific literacy using a machine learning approach, Research in Science Education, 2019, [Preprint], doi: 10.1007/s11165-019-9835-y, Retrieved from https://rdcu.be/btN56.
  15. [15] Y. Wei, Q. Yang, J. Chen, and J. Hu, The exploration of a machine learning approach for the assessment of learning styles changes, Mechatronic Systems and Control, 46(3), 2018, 121–126.
  16. [16] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, 46(1–3), 2002, 389–422.
  17. [17] F. Zhang, H.L. Kaufman, Y.P. Deng, and R. Drabier, Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood, BMC Medical Genomis, 6(S1), 2013, S4.
  18. [18] C.C. Chang and C.J. Lin, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2(3), 2011, 27.

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