Feature Presentation
Building Essential and State-of-the-art
Vocabulary in Information Science:
Observations from Classes at aJapanese University
Hiroshi Kohsaka
The University of Electro-Communications
The terminology of information science has increased significantly, which makes it difficult
to effectively cover all essential technical terms in a class in one given semester. To provide
students with an opportunity to improve their English proficiency, an autonomous classactivity to collect keywords in information science was incorporated into an English course
at a Japanese university. Results suggest that students in their second year and above canbuild both basic and state-of-the-art vocabulary autonomously.

The vocabulary in information science has expanded at a rapid pace in recentyears. Since the invention of the first programmable digital computer ENIAC(Electronic Numerical Integrator and Computer) in 1946, novel technicalterms have been added to the vocabulary of information science (Comer, 2017).
Furthermore, the number of technical words is increasing in association withthe exponential development of new technology, including artificial intelligence(Russell & Norvig, 2022) and quantum computation (Nielsen & Chuang, 2010).
Vocabulary acquisition is one of the core elements of language learning (Hu& Nation, 2000; Thornbury, 2002). A number of teaching strategies to support
building vocabulary have been developed, such as verbal interaction with others(Wang, 2015), consulting dictionaries (Zhang et al., 2021), note-taking ( Jin& Webb, 2021), and guessing from context (Nassaji, 2006). These effective
learning strategies are implemented based on reliable vocabulary lists built
in existence (Nation & Beglar, 2007; Browne et al., 2013). Thus, establishing21

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relevant vocabulary lists is prerequisite to conduct vocabulary building
activities in ESP (English for Specific Purposes) classes. However, in the field of
information science, although several vocabulary lists of the technical terms havebeen published (Esteras & Fabre, 2007; Hirai, 2012), it is still challenging foruniversity teachers and students to follow the rapid pace of the appearance of
new words due to the fact that published lists can become obsolete quickly.
This paper reports a survey and the results of an English class in a Japanese
university aiming at the development of vocabulary in information science by astudent-centered activity. Within a theme selected by a teacher, students selected
relevant keywords at their discretion. The comparison between the keywords
collected by students and a vocabulary textbook demonstrates two-fold results.
First, the vocabulary developed by a large majority of students successfully
nominated basic keywords in information science. Second, the students’ list
covers state-of-the-art words related to novel computer technology. Theseobservations suggest that an autonomous class activity would be a viable option
to tie together two areas of vocabulary in information science: a) permanent andfoundation vocabulary and b) new and potentially transient vocabulary.

Methods

The data for this study were collected in ESP classes taught at the University ofElectro-Communications in Tokyo, Japan. The classes were elective-compulsory
subjects; each student selected one class among other compulsory language
classes. The students were in their second year or above and have passed bothAcademic Written English and Academic Spoken English classes (taught by otherinstructors) in their first year. Therefore, the students possess adequate commandof English for the specific needs of information science. The information scienceclasses were taught face-to-face in the standard 15-week semester, and each classmeeting lasted 90 minutes once per week. The students’ proficiency in English isintermediate level, and they possess high literacy in information science.
A typical instructor-supervised student-centered activity took the following
form throughout the course. The instructor assigned one topic from information
science in each class (Table 1).
22Building Essential Vocabulary in Information Science, OCJSI 5, pages21-29

Table 1The Topic of Each Class
WeekTopic

WeekTopic

1

Discrete math7

Artificial intelligence2

Digital logic

8

Deep learning

3Data representation

9Computer vision

4Processors

10

Robotics

5Memories

11

Quantum computing

6

Input/Output devices

At the beginning of the class, each student brainstormed the topic for fiveminutes to find a keyword about the topic. The instructor allowed the students
to access the Internet during this activity. Students conducted group discussions
with two to four classmates to share their keywords. At the end of the class,each student searched for one keyword in five minutes and submitted boththe keyword and its explanation to Google Forms. In total, 306 keywords werecollected in 11 weeks. In the last week of the course (week 15), the instructor
conducted a survey with a questionnaire to ask about the rigor of the course
for reference to improve next year’s class, in particular, to adjust the speed andthe level of the course to the students’ demand. The survey included multiplechoice single-answer questions (Figure 1). Eighty out of 87 students agreed toparticipate in this study. The instructor received informed consent from all thestudents whose work was used in this report. Python 3.7 was used to perform all
the statistical analyses. The questionnaire responses about the level of the course
were analyzed using the one-way ANOVA (analysis of variance) and post hocTukey test.

Results

The 306 keywords collected in the class were compared with the vocabulary list
from a published book listing the essential vocabulary in information science (N23Kohsaka

Figure 1. Part of the questionnaire conducted to ask about the level of thecourse= 512 words in Hirai, 2012). Figure 2 shows a Venn diagram of the number ofoverlapping words between student-selected words and the textbook vocabulary.
The students’ list included 27 essential keywords in information science (Figure3).
To test whether the students could acquire state-of-the-art terminology, theinstructor included four modern technology fields in the topic list (“Artificialintelligence,” “Deep learning,” “Computer vision,” and “Quantum computing”,
Table 1). Students could find some state-of-the-art words through this classactivity. As a consequence, several state-of-the-art keywords were listed in the
vocabulary selected by students but not in the basic textbook (Figure 4): “objectrecognition” and “face authentication” from artificial intelligence; “quantuminterference” and “q-bit” from quantum computing.
24Building Essential Vocabulary in Information Science, OCJSI 5, pages21-29

Figure 2.Comparison of the vocabulary lists created by the students and described in the textbook

Figure 3. A list of terms selected by the students overlapping the vocabulary inthe textbook
This observation demonstrates that autonomous vocabulary building iscapable of developing a word list including keywords from new technology ininformation science. One of the advantages of this strategy is that teachers do
not have to prepare the comprehensive vocabulary list beforehand. In reality,
it is impossible for English teachers to cover the wide range of information
science fields. The student-centered activity is not only effective for students tolearn vocabulary but also practical for teachers to provide keywords from newtechnology.
To evaluate the course level for the students, the instructor conducted asurvey with a questionnaire to ask students about the difficulty of the class25

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Figure 4. A list of terms selected by the students that did not overlap the vocabulary in the textbook
activity (Figure 1). Students felt the class level was neither too easy nor too
difficult for most topics (Table 2).
It should be noted, however, that the one-way ANOVA yielded significant
variation in difficulty among topics, F(9, 744) = 9.226, p < 10-12. The post hoc
Tukey test demonstrated that quantum computing was challenging for somestudents with a significance level at p < 0.001. In sum, whereas the topics andother class design, such as the progression and structure of content, should be
carefully chosen, as a whole, it appears that the topics selected in the classes
suited the students.

Discussion

This article has reported results from a survey in ESP classes that attempted tobuild a vocabulary list for information science with an autonomous class activity
at a Japanese university. The list developed in classes included both essential
keywords and state-of-the-art terminology. Furthermore, the results from thequestionnaire show that the topics and rigor of the autonomous class activity
matched the level of the students in their second year or above majoring incomputer and information science. These observations indicate that building
vocabulary through students’ autonomous activity could constitute one learning
method to keep up with the ever-growing vocabulary in information technology.
This strategy will be applicable to vocabulary building in other fields evolving
at a fast pace where English teachers cannot keep up with the state-of-the-art
terminology.
A couple of issues remain unexplored. First, the basic vocabulary used inthis article was from a book published over 10 years ago (Hirai, 2012). That age
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Building Essential Vocabulary in Information Science, OCJSI 5, pages21-29

Table 2
Results of the Questionnaire about the Level of the Course
Topic

Too easy

Easy

Just right

DifficultToodifficult

Digital logic

2 (3%)

22 (28%)41 (51%)13 (16%)2 (3%)

Data representation

2 (3%)

12 (15%)49 (61%)

16 (20%)1 (1%)

Processor

1 (1%)

15 (19%)46 (58%)18 (23%)0 (0%)

Memories

2 (3%)

21 (26%)46 (58%)9 (11%)

2 (3%)

Input/Output device

4 (5%)

18 (23%)44 (55%)

11 (14%)3 (4%)

Artificial intelligence

2 (3%)

20 (26%)42 (54%)13 (17%)

1 (1%)

Deep learning

2 (3%)

7 (9%)

41 (51%)27 (34%)3 (4%)

Computer vision0 (0%)

11 (14%)45 (56%)24 (30%)0 (0%)

Robotics

1 (3%)

9 (24%)

21 (57%)6 (16%)

0 (0%)

Quantum computing

0 (0%)

4 (5%)

28 (35%)37 (47%)10 (13%)

may cause an underestimation of the number of keywords between the students’choices and the basic vocabulary. Thus, an updated essential vocabulary list will
be required for adequate evaluation of the word list. Second, although several
state-of-the-art words were found in the students’ list, the quality of the words
needs to be verified. To implement autonomous vocabulary building in classes,
it would be necessary to consider a way to verify the adequacy of the keywords
in the list. One possible strategy to guarantee the quality of words would beteachers’ helpful intervention so that the students can recognize what vocabulary
is more important. Lastly, building vocabulary lists is not the goal and should
be followed by the next step—to design effective and practical methods to use
the vocabulary list aiming at the improvement of students’ proficiency. The
utility of the vocabulary list should be amplified when it is used with proper
strategies. Adopting efficient learning strategies can enable students majoring
in information science to master the requisite vocabulary in their ever-growing
27

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field. In particular, the students’ ability to update their vocabulary is undoubtedly
essential after they graduate from university.

Conclusion

An autonomous class activity was implemented in ESP classes in a Japanese
university to develop a functional vocabulary list in the field of information
science. The list built through the classes included both essential and state-of-theart vocabulary in information science. This pedagogical intervention will help
teachers and students in design and implement effective procedures to acquire
the necessary vocabulary with greater facility in the actual application outsideof the classroom environment. Having an ability to learn novel vocabulary bythemselves is a critical skill to survive in an ever-growing and competitive world.

References

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Jin, Z., & Webb, S. (2021). Does writing words in notes contribute to
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Author bio

Hiroshi Kohsaka is an associate professor at the University of ElectroCommunications, Tokyo, Japan. He has an interest in developing materials forEnglish for science and technology. kohsaka@uec.ac.jp
Received: January 20, 2023
Accepted: May 18, 2023

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