Who Knows Academic Vocabulary Better? Which is more Supportive for Developing Academic Vocabulary – Instructor or Computer?

by Daron Benjamin Loo


The development of academic writing at the graduate level is a process affected by many variables (see Lee & Murray, 2015; Nygaard, 2017). To cope with the complex process, and to meet academic expectations, student agency may come in handy. Student agency in writing may be understood through students’ intentions and their abilities to shape their own communication experiences to meet goals, which may be self-determined or outlined by external factors (e.g. requirements of graduate program or supervisors) (McAlpine & Amundsen, 2009). Student agency may be nurtured through appropriate instructor mediation (Huerta, Goodson, Beigi, & Chlup, 2017); it can also be honed through students’ use of automated tools which are free (Li, Meng, Tian, Zhang, Ni, & Xiao, 2019).

Lexical complexity is one of the few variables that affect the development of academic writing. At the graduate level, lexical complexity may be affected by the nature of the task or the topic (Yoon, 2017), and it can be nurtured through overt instructor mediation or student agency (Mazgutova & Kormos, 2015). As mentioned, there are free tools available to students. One such tool is AntWordProfiler, developed by Professor Anthony Lawrence at Waseda Universeity, Japan. This tool allows users to analyse lexical complexity, or academic vocabulary usage, in their own texts. There are three levels of complexity that are highlighted – red (lower range), green and blue (higher range), and black (special or non-academic range). These levels are defined by Paul Nation’s General Service List.

In ES 5000 (Graduate Academic Writing – Basic), I introduced AntWordProfiler as a self-evaluating tool for students to gauge their use of academic vocabulary in a problem-solution essay. Before this essay, students had completed a cause and effect assignment, where academic vocabulary was mediated and evaluated by the instructor.

A survey was distributed to determine whether AntWordProfiler was perceived as supportive towards the development of academic vocabulary use, and more importantly, if it encourages the growth of student agency. Another survey was done after the first assignment, where I evaluated their vocabulary usage (without using other tools). The short survey consisted of scalar (a scale of 7, with 1 being very certain and 7 very uncertain) and open-ended questions.

The first survey saw a majority of the students feeling certain that my feedback was effective and trustworthy (4 students rated 6 and the remaining students rated 7). Qualitative responses echo the positive sentiment:

  • The feedback is given depending on individuals, which helped me a lot. Rather than a direct answer, a progress of thinking is provided instead.
  • I understood how many words in my writing are general or advanced so I should improve them to improve my level of English language
  • Feedback can give me a clearer idea of where I am doing badly, and I can focus on strengthening them later in my studies.

From the responses, it can be seen that personalized feedback was given, with some students being prompted to think and others were given explicit directions on how to improve.

The second survey was conducted after students self-evaluated their essays. While students generally held the same positive sentiment towards AntWordProfiler, their perception of their own ability was varied (Figure 1).

Figure 1. Perception towards ability


The lack of the instructor’s presence may have prompted students to be more cognizant of their own issues (Liu & Chao, 2018). In the planning, writing, and revising stage of the essay, students had to rely on themselves (analyze the results of AntWordProfiler and make necessary changes). The lower levels of certainty seen in Figure 1 is also seen in students’ short responses:

  • My main challenge for learning academic vocabulary is that there is no specific list or set of words that I can study from.
  • It’s hard to define those words with the same meaning but at different levels.
  • Some academic words are very complicated and difficult to remember and understand. It is also rarely used in real life.
  • It’s difficult to clarify whether a word is academic word or not. When writing I still use non-academic words without noticing.


When asked about potential strategies that could help improve their knowledge and use of academic vocabulary, students suggested various independent strategies, but nothing with regards to the use of automated tools:

  • Write as many academic texts as you can. Also, once written, check if you can change words that you have used to more academic ones.
  • Join in a class? Without attending a course, I might not spend time on specialized vocabularies.
  • Record some useful academic words while reading other documents and use them when you write your own articles.
  • Read as much specialist article related to my major, reading books, check the synonym of essential word and use them, use them in daily conversation
  • I think the best way is to read academic articles every day, take note and try to use them when writing something academic.


These responses are interesting as they were collected right after students had worked with AntWordProfiler. What can be observed is that while students may hold a positive disposition towards both instructor and automated tool mediation, presence of an instructor is still vital to sustain a productive learning ecology. Liu and Chao (2018) found that when students are faced with the unfamiliar, they may not know how to respond to teaching and learning opportunities. Perhaps, this is what was observed, as most of the students’ learning backgrounds are defined by a heavy instructor presence. Thus, while perceived as seemingly useful for its novelty, automated tools in academic writing would probably work best in tandem with guidance and support provided by an instructor.



Anthony, L. (2014). AntWordProfiler (Version 1.4.1) [Computer Software]. Tokyo, Japan: Waseda University. Available from https://www.laurenceanthony.net/software

Huerta, M., Goodson, P., Beigi, M., & Chlup, D. (2017). Graduate students as academic writers: writing anxiety, self-efficacy and emotional intelligence. Higher Education Research & Development36(4), 716-729.

Lee, A., & Murray, R. (2015). Supervising writing: Helping postgraduate students develop as researchers. Innovations in Education and Teaching International52(5), 558-570.

Li, R., Meng, Z., Tian, M., Zhang, Z., Ni, C., & Xiao, W. (2019). Examining EFL learners’ individual antecedents on the adoption of automated writing evaluation in China. Computer Assisted Language Learning, 1-21.

Liu, Q., & Chao, C. C. (2018). CALL from an ecological perspective: How a teacher perceives affordance and fosters learner agency in a technology-mediated language classroom. ReCALL30(1), 68-87.

Mazgutova, D., & Kormos, J. (2015). Syntactic and lexical development in an intensive English for Academic Purposes programme. Journal of Second Language Writing29, 3-15.

McAlpine, L., & Amundsen, C. (2009). Identify and agency: Pleasures and collegiality among the challenges of the doctoral journey. Studies in Continuing Education31(2), 109-125.

Nygaard, L. P. (2017). Publishing and perishing: an academic literacies framework for investigating research productivity. Studies in Higher Education42(3), 519-532.

Yoon, H. J. (2017). Linguistic complexity in L2 writing revisited: Issues of topic, proficiency, and construct multidimensionality. System66, 130-141.

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