吳俊育　兼任教授(113.9.1起)

 

吳俊育教授(CV)於2001與2003年取得國立交通大學電信工程學系學士與碩士學位， 2010年獲得德州農工大學(Texas A&M University, College Station)教育心理學系博士學位，專研研究方法、心理量測與應用統計法（Research, Measurement & Statistics），畢業後即至國立交通大學教育研究所/師資培育中心任職，現為兼任教授。

他的研究著重於發展新穎數據演算法與統計建模策略，用以分析學習者在個人學習環境(Personal Learning Environment, PLE)內注意力狀態與注意力調控意願，及其對學習成效、生活適應相關表現。他嘗試深入了解學習者於開放式網路環境中使用者行為、調控策略使用、認知能力與學習成效表現之間機制，希冀找出影響學習者在PLE中選擇性注意力分配的機制。具體來說，他使用新穎的數據分析技術，例如：機械學習，學習分析 、行為分析、視覺化、交互式圖形學等，及心理認知實驗與心理量測工具，全方位評估學習者在PLE中的注意力情況，並收集學習者在社交媒體中的心理變化、行為過程、生活適應與學業成效之大型結構與非結構數據進行統計與理論建模 。他的學術著作發表於具有影響力的期刊上，如Computers & Education, Computers in Human Behavior,  Journal of Educational Psychology, Child Development, Structural Equation Modeling, Journal of School Psychology, Journal of Computer-Assisted Learning, Interactive Learning Environment, Learning and Individual Differences, Journal of Experimental Education, School Psychology Quarterly…等。研究表現榮獲108 年度科技部吳大猷先生紀念獎肯定。

除大學部教育心理學課程外，他以中文或英文教授研究所層級之基礎和高等應用統計課程，包括：多變量分析，結構方程模型，階層線性建模和多階層建模等。他積極投入地方教育服務，連續四年帶領交大師培中心史懷哲偏鄉服務團隊為國中生進行暑期教育專業服務(2014-2017)，與地區國高中合作開發數學、資訊等跨領域彈性課程等。教學績效榮獲交通大學106學年度傑出教學獎與105學年度優秀教學獎。

他執行多個科學研究計畫，曾任台灣PISA 2015中心計畫共同主持人。學術服務方面，他擔任Frontiers in Psychology (Special issue of Recent advancement in SEM)客座編輯，現為Journal of Clinical Psychology(SSCI), Journal of School Psychology(SSCI), International Journal of STEM Education(ESCI) 和 中國測驗學會測驗學刊 (TSSCI)編輯委員。積極從事學術論文審查服務，榮獲Computers & Education (2017), Journal of School Psychology(2017)與Computers in Human Behavior(2015)傑出審稿人肯定。

分析專長與運算工具

Statistics	R, SAS, SPSS/PASW, STATA, HLM
SEM	Mplus, R,
HLM/MLM	Mplus, R, HLM, SAS, SPSS
Data Crawling	R, Python
Text Mining	R, Python
Machine Learning	R, Python, Matlab
Voice and Signal Processing	R, Python, Matlab
Data Visualization	R, Python, Matlab

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發表

• 英文期刊論文 （Google Scholar *Correspondent †Advisee）
  1. Wu, J.-Y.*, & Cheng, T.† (2019). Who is better adapted in learning online within the personal learning environment?Relating gender differences in cognitive attention networks to digital distraction. Computers & Education,128, 312-329. (SSCI: 2017 IF: 4.538)
  2. Wu, J.-Y.*, Hsiao, Y.-C.†, & Nian, M.-W.† (Online first). Using supervised machine learning on large-scale online forums to classify course-related facebook messages in predicting learning achievement within the personal learning environment. Interactive Learning Environments. (SSCI: 2017 IF: 1.604)
  3. Wu, J. Y.*, & Xie, C.† (2018). Using time pressure and note-taking to prevent digital distraction behavior and enhance online search performance: Perspectives from the load theory of attention and cognitive control. Computers in Human Behavior, 88, 244-254. (SSCI: 2017 IF: 3.536)
  4. Wu, J.-Y.*, Lee, Y.-H., & Lin, J. J. H.† (2018). Using iMCFA to perform the CFA, multilevel CFA, and maximum model for analyzing complex survey data. Frontiers in Psychology, 9, 2. https://doi.org/10.3389/fpsyg.2018.00251 (SSCI: 2017 IF: 2.089)
  5. Wu, J.-Y. (2017). The indirect relationship of media multitasking self-efficacy on learning performance within the personal learning environment: Implications from the mechanism of perceived attention problems and self-regulation strategies. Computers & Education, 106, 56-72. https://doi.org/10.1016/j.compedu.2016.10.010 (SSCI: 2017 IF: 4.538)
  6. Wu, J.-Y.*, Lin, J. J. H.†, Nian, M.-W.†, & Hsiao, Y.-C.† (2017). A solution to modeling multilevel confirmatory factor analysis with data obtained from complex survey sampling to avoid conflated parameter estimates. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01464 (SSCI: 2017 IF: 2.089)
  7. Wu, J.-Y.*, & Peng, Y.-C.† (2017). The modality effect on reading literacy: Perspectives from students’ online reading habits, cognitive and metacognitive strategies, and web navigation skills across regions. Interactive Learning Environments, 25(7), 859-876. https://doi.org/10.1080/10494820.2016.1224251 (SSCI: 2017 IF: 1.604)
  8. Clemens, N. H.*, Lai, M. H. C., Burke, M., & Wu, J.-Y. (2017). Interrelations of growth in letter naming and sound fluency in kindergarten and implications for subsequent reading fluency. School Psychology Review, 46(3), 272–287. https://doi.org/10.17105/SPR-2017-0032.V46-3 (SSCI: 2017 IF: 1.521)
  9. Wu, J.-Y. (2015). University students’ motivated attention and use of regulation strategies on social media. Computers & Education, 89, 75-90. doi: 10.1016/j.compedu.2015.08.016. (SSCI, 2015 IF: 2.881)
  10. Wu, J.-Y.*, & Hughes, J. N. (2015). Teacher network of relationships inventory: Measurement invariance of academically at-risk students across ages 6 to 15. School Psychology Quarterly, 30(1), 23-36. doi: 10.1037/spq0000063 (SSCI, 2015 IF: 2.750)
  11. Liu, L. –F.*, Weng, R. –H, & Wu, J.-Y.* (2014). Exploring factors influencing residents’ health outcomes in long-term care facilities: one year follow-up using latent growth curve model. Quality of Life Research, 23(9), 2613-2627. doi 10.1007/s11136-014-0710-z (as one of corresponding authors with equal contributions) (SSCI, 2014 IF: 2.486)
  12. Chen, C. T.†, She, H. C., Chuang M. H., Wu, J.-Y., Tsai, J. L. & Jung, T.P. (2014). Eye movements predict students’ computer-based assessment performance of physics concepts in different presentation modalities. Computers & Education, 74, 61-72. (SSCI, 2014 IF: 2.556)
  13. Wu, J.-Y. (2014). Gender differences in online reading engagement, metacognitive strategies, navigation skills and reading literacy. Journal of Computer Assisted Learning, 30(3), 252-271. doi: 10.1111/jcal.12054 (SSCI, 2014 IF: 1.360)
  14. Wu, J.-Y.*, Kwok, O. & Wilson, V. (2014). Using design-based latent growth curve modeling with cluster-level predictors to address dependency. Journal of Experimental Education, 82(4), 431–454. doi:10.1080/00220973.2013.876226 (SSCI, 2014 IF: 1.087)
  15. Clemens, N. H., Shapiro, E. S., Wu, J.-Y., Taylor, A., & Caskie, G. L. (2014). Monitoring early first-grade reading progress: A comparison of two measures. Journal of Learning Disabilities. 47(3), 254-270. doi: 10.1177/0022219412454455 (SSCI, 2014 IF: 1.901)
  16. Lee, Y. & Wu, J.-Y.* (2013). The indirect effects of online social entertainment and information seeking activities on knowledge of metacognitive strategies and reading literacy. Computers & Education, 67, 168-177. doi:10.1016/j.compedu.2013.03.001 (SSCI&SCIE, 2013 IF: 2.630)
  17. Lee, Y., Waxman, H. C., Wu, J., Michko, G. M., & Lin, G. (2013). Revisit the effect of teaching and learning with technology. Educational Technology & Society, 16(1), 133-146. (SSCI, 2013 IF: 0.824)
  18. Spilt, J., Hughes, J. N., Wu, J., & Kwok, O. (2012). Dynamics of teacher-student relationships: Stability and change across elementary school and the influence on children’s academic success. Child Development, 83(4), 1180–1195. doi: 10.1111/j.1467-8624.2012.01761.x.(SSCI, 2012 IF: 4.915)
  19. Lee, Y., & Wu, J.*(2012). The effect of individual differences in the inner and outer states of ICT on engagement in online reading activities and PISA 2009 reading literacy: Exploring the relationship between the old and new reading literacy. Learning and Individual Differences, 22(3), 336-342. doi:10.1016/j.lindif.2012.01.007. (SSCI, 2012 IF: 1.507)
  20. Hughes, J. N.*, Wu, J., Kwok, O., Villarreal, V. & Johnson, A. Y. (2012). Indirect effects of child reports of teacher-student relationship on achievement. Journal of Educational Psychology, 104(2), 350-365. doi:10.1037/a0026339 (SSCI, 2012 IF: 3.158)
  21. Wu, J.*, & Kwok, O.* (2012). Using structural equation modeling to analyze complex survey data: A comparison between design-based single-level and model-based multi-level approaches. Structural Equation Modeling-A Multidisciplinary Journal, 19(1), 16–35. doi:10.1080/10705511.2012.634703. (SCI/SSCI, 2012 IF: 4.242)
  22. Wu, J.*, Hughes, J. N., & Kwok, O. (2010). Teacher-student relationship quality type in elementary grades: Effects on trajectories for achievement and engagement. Journal of School Psychology, 48(5), 357-387. doi: 10.1016/j.jsp.2010.06.004. (SSCI, 2010 IF: 2.680)
  23. Zhang, D.*, Willson, V., Katsiyannis, A., Barrett, D. E., Ju, S., & Wu, J.(2010). Truancy offenders in the juvenile justice system: A multi-cohort replication study. Behavioral Disorders, 35(3), 229-242.(SSCI, 2010 IF: 0.684)

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榮譽