ResearchGate
Zhan Jan Wang
I am a lecturer and researcher at the Analytics\Assessment Research Centre, The Education University of Hong Kong. My research interests are: theoretical and applied linguistics of Chinese and English (both 1st and 2nd language acquisition), AI in education, and computational linguistics (NLP, LLMs). I taught relevant BEd and MA courses in the Department of Chinese Language Studies and The Department of English at EdUHK and CUHK. I program with the languages of C++ Python etc.
Forthcoming in Language Teaching Research
Wang, Z., Morakhovski, A., & Chiu, M. M. (Forthcoming). Improve student stories: An AI-powered feedback tool for narrative speaking tasks. Language Teaching Research.
Sample Paper
Paper 1
Skehan P, Wang Z. (2026) Exploring new measures of accuracy in second language task-based spoken performance. Studies in Second Language Acquisition. Published online 2026:1-20. doi:10.1017/S0272263126101892
Paper 2
Wang, Z., & Chiu, M. M. (2024). Multi-discourse modes in student writing: Effects of combining narrative and argument discourse modes on argumentative essay scores. Applied Linguistics. https://doi.org/10.1093/applin/amac073
Motivation of the study
Working with Hong Kong Examinations and Assessment Authority (HKEAA) to analyze HKDSE (a university entrance examination) Chinese paper, we sampled student writing on the following two argumentative task prompts:
Prompt 1
Write an essay entitled ‘Not to be the first and not to be the last either’ to express your opinion of this attitude toward lives
Prompt 2
Write an essay entitled ‘The sun and the shadow’ according to the pictures
N = 695 argumentative essays
So, our purposes are:
1) What makes a good student argumentative writing? (We have official ratings from HKEAA)
2) Are there task differences? (fairness)
Analysis
- Linguistic complexity (using Chinese Coh-Metrix): essay total words, % connectives, sentence length, word frequency, MTLD, word concreteness
- Discourse strategy: Multi-discourse Modes (MDMs): argument + narrative
Results
- Good writing: Total Words, Word Frequency, Word Concreteness, Sentence Length, Multi-discourse Modes
- What inspires our discovery of MDM
- Lingusitic theories (Goldberg, 1995; Huang, et al., 2000; Smith, 1991; Vendler, 1967) verbal semantics/discourse modes/lexical aspects/construction grammar—a verb has internal meaning related to action (narrative) vs states (argument)
- Language use changed by social interactions (facebook, twitter, instagram, Tik Tok)
Paper 3
Wen, Y., Chiu, M., Guo, X., & Wang, Z. (2024). AI‐powered vocabulary learning for lower primary school students. British Journal of Educational Technology.
Data
- 140 second-grade students, 6 teachers used ARCHe for both homework and classwork for two terms (lasting 8 to 10 weeks) in 3 government primary schools in Singapore
- ARCHe has 4 AI functions: feedback for pronunciation and handwriting, scoring of sentences, recommendations
Measures
- Linguistic analysis of Chinese sentences students made in the classroom group activities and at home
- Questionnaire
Results
- Students perceived more usefulness of ARCHe feedback towards pronunciation showed greater emotional engagement
- Students perceived more usefulness of ARCHe feedback towards handwriting showed greater cognitive engagement
- Students in groups that wrote more sentences during their class activities showed more learning gains (unlike the non-significant effects of home-based individual activities).
Taking-home message
- Usefulness of ARCHe
- Learning in social context matters!
https://orcid.org/0000-0002-8684-3767