Content area
Objective
This quasi-experimental study aimed to compare the effectiveness of lecture-based teaching with three corpus-assisted methods—concordancing, collocation, and frequency—in promoting retention of medical terminology among English as a foreign language (EFL) medical students at Iran University of Medical Sciences. The study evaluated short- and long-term retention of eight cardiovascular terms using the Vocabulary Levels Test conducted at three intervals over a month.
Results
Forty EFL medical students were divided into four groups (n = 10 each) and taught the target medical terms using either lecture-based or corpus-assisted methods. Pre-test Vocabulary Knowledge Scale scores showed no significant baseline differences. Results of post-tests showed frequency analysis showed the highest mean score of immediate retention (7.3 ± 0.5), followed by concordancing, collocation, and lecture-based methods. At two weeks, concordancing led (7.0 ± 0.8), followed by frequency, collocation, and lecture-based methods. At one month, concordancing maintained the lead (6.6 ± 0.8), followed by frequency (6.2 ± 0.9), collocation (5.0 ± 1.0), and lecture-based (2.7 ± 1.2). Corpus-assisted methods outperformed lecture-based teaching across all intervals, with concordancing showing the strongest long-term retention.
Introduction
Learning medical terminology poses a significant challenge for English as a foreign language (EFL) medical students, requiring both mastery of complex, specialized vocabulary and the ability to apply it in reading and comprehending English medical texts [1, 2]. Proficiency in medical terminology is essential for medical students to engage with academic literature, participate in clinical discussions, and succeed in their studies and future careers [3, 4]. For underrepresented minorities, such as EFL medical students, a strong foundation in medical terminology is particularly critical to overcome barriers in understanding medical materials and communicating effectively in professional settings [5, 6].
Medical terminology has traditionally been taught through lecture-based methods emphasizing rote memorization [2]. This approach remains common due to its familiarity and ability to accommodate large student-to-instructor ratios [7]. However, research consistently demonstrates that lecture-based methods are less effective for deep understanding and long-term retention compared to active learning approaches [8, 9, 10–11]. To address the limitations of lecture-based, alternative methods, such as corpus-assisted language learning, have emerged that leverages authentic language data to provide contextualized examples of language use [12, 13–14]. While corpus methods have shown effectiveness in teaching general English as a second or foreign language, their application to teaching specialized English (e.g., medical terminology), particularly in non-English-speaking contexts, remains underexplored [15].
To address this gap, the present study focuses on three corpus methods—collocation, frequency analysis, and concordance—to evaluate their potential in teaching medical terminology. While collocation analysis explores frequently co-occurring words and common word combinations essential for fluency in a discourse [16], frequency analysis examines morphological variations, revealing how affixes modify term meanings [17]. On the other hand, concordancing allows students to analyze text excerpts to identify contextual patterns of terms and their usage [18, 19].
Given this, the present study compares the effectiveness of lecture-based teaching with three corpus-assisted methods in teaching medical terminology to EFL medical students at Iran University of Medical Sciences. The data were collected from 40 intermediate-level EFL medical students using the Vocabulary Levels Test [20] to assess retention of cardiovascular-related terminology. The objective was to determine which of the four teaching methods is most effective in teaching medical terminology to EFL medical students.
Method
This quasi-experimental study was conducted at Iran University of Medical Sciences. The quasi-experimental method was chosen for its ability to facilitate controlled comparisons among groups and effectively measure differences in vocabulary retention across the four teaching methods, using one-way ANOVA and Tukey’s HSD post-hoc tests. This study was performed in full accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Iran University of Medical Sciences (IR.IUMS.REC.1403.1133). The written informed consent was obtained from all participants.
Design of the study
To compare the effectiveness of teaching medical terminology through corpus-assisted activities and lecture-based methods, 40 EFL medical students with intermediate English proficiency were selected from a pool of 200 students enrolled in a medical terminology course at Iran University of Medical Sciences (IUMS). All 200 students completed the Oxford Placement Test, identifying 120 with intermediate proficiency. From these, 40 were randomly selected using a random number generator and randomly assigned to four groups (n = 10 each: one lecture-based, three corpus-assisted) in this quasi-experimental study. Each group participated in a session (70-minute teaching and 20-minute post-teaching activities) targeting eight cardiovascular terms, selected for their importance in medical education and prevalence in clinical texts [1]. All groups shared identical learning goals and expected outcomes, differing only in teaching method and post-teaching activities.
A pre-test using the Vocabulary Knowledge Scale (VKS) assessed baseline knowledge, chosen for its high reliability in measuring depth of vocabulary knowledge in EFL contexts (test-retest correlation > 0.8) [21]. Retention was evaluated using a customized Updated Vocabulary Levels Test (VLT) at three intervals: immediately after the session (T1), two weeks later (T2), and one month later (T3). Retention scores were analyzed using SPSS (version 25) with one-way ANOVA to compare mean scores across groups, Tukey’s HSD post-hoc tests to identify specific group differences, and repeated-measures ANOVA and Mauchly’s test to determine changes over time.
Participants
A sample of 40 medical students was randomly selected from 120 intermediate-level EFL students, identified from a pool of 200 enrolled in a medical terminology course at IUMS. Intermediate proficiency was determined using the Oxford Placement Test (OPT), a validated and reliable tool (Cronbach’s α = 0.90), with scores of 30–39 on the 60-point scale, corresponding to CEFR B1–B2 levels. Participants, whose first language was Farsi, included 26 female and 14 male students, evenly distributed across four groups (n = 10 per group).
Materials
We selected Medical Terminology: An Illustrated Guide (9th edition) by Cohen and Jones [1] as the primary teaching material due to its comprehensive coverage, clear explanations, and detailed illustrations, which are well-suited for lecture-based and corpus-assisted teaching methods in an EFL medical context. This textbook is widely adopted in medical education for its structured presentation of terminology, alignment with clinical contexts, and accessibility to non-English speakers [2, 22]. The cardiovascular system (Chap. 10) was chosen for its relevance to medical education and prominence in clinical texts, providing a focused context for comparing teaching methods. Eight target nouns—endocardium, thrombosis, hematocrit, angiogram, fibrillation, embolism, myocardium, and ventricle—were selected from this chapter to ensure consistency in part of speech and word length (2–3 syllables), minimizing retention biases [23]. Following Nation [20], we selected target words based on specific criteria to control for factors affecting retention:
frequency of use was balanced using the Corpus of Contemporary American English (COCA) Medical subcorpus, selecting terms with moderate frequency (10–50 occurrences per million words) to ensure learnability;
semantic complexity was controlled by choosing terms denoting anatomical structures (e.g., endocardium, myocardium, ventricle), conditions (e.g., thrombosis, fibrillation, embolism), or diagnostic measures (e.g., hematocrit, angiogram);
morphological complexity was standardized by selecting terms with one to two affixes (e.g., hema-to-crit, endo-card-ium) via morpheme segmentation.
Designing the lesson plans
We developed four lesson plans, one for each teaching method. Each plan targeted the same eight cardiovascular terms but was tailored to reflect a distinct pedagogical approach. The lecture-based plan followed traditional methods, emphasizing direct instruction, repetition, and rote memorization to facilitate rapid recall [7]. The corpus-assisted concordancing lesson plan focused on analyzing terms in context, the collocation lesson plan emphasized building fluency through common word pairings in medical discourse, and the frequency lesson plan centered on exploring morphological variations to understand term formation [15, 24]. Table 1 summarizes the lesson plans for each teaching method.
Table 1. Overview of lesson plans for lecture-based and three corpus-assisted methods
Teaching method | Learning objective | Materials | Instruction: teaching procedure | Instruction: post-teaching activities |
|---|---|---|---|---|
Lecture-based | Define and recall eight cardiovascular terms with 80% accuracy on an in-class test. | 1. Lecture slides on cardiovascular diseases. 2. Flashcards for eight target terms (endocardium, thrombosis, etc.). 3. In-class test for recall. | 1. Deliver a 30-min lecture on terms, emphasizing definitions (30 min). 2. Conduct a flashcard memorization activity (20 min). 3. Administer a recall test (10 min). 4. Summarize terms and address questions (10 min). | Written activities requiring students to define and match eight terms to meanings, scored for accuracy (20 min). |
Corpus-assisted (Concordancing) | Analyze contextual usage of eight cardiovascular terms in COCA medical subcorpus via concordance lines to improve semantic understanding. | 1. Handouts with 10–15 concordance lines per term from COCA Medical subcorpus. 2. Guiding questions (e.g., “How is fibrillation used in clinical vs. research contexts?”). 3. Highlighters for marking patterns. | 1. Explain concordancing and its role in contextual analysis (10 min). 2. Pairs analyze concordance lines, highlighting syntactic (e.g., subject- verb), collocational (e.g., modifiers), and discourse- level (e.g., diagnostic vs. symptomatic use) patterns (20 min). 3. Discuss contextual pattern examples in groups (10 min). 4. Identify new terms in examples using concordance lines and explain meanings (20 min). 5. Summarize and address questions (10 min). | Activities identifying contextual usage of medical terms from concordance lines, evaluated for accuracy and explanation of meanings in context (20 min). |
Corpus-assisted (Collocations) | Identify and use collocations of eight cardiovascular terms in Chap. 10 and COCA medical subcorpus to build fluency. | 1. Handouts listing top 5 collocations per term from COCA Medical: verb-noun (e.g., treat embolism), adjective- noun (e.g., atrial fibrillation), noun-noun (e.g., ventricular fibrillation). 2. Access to COCA Medical subcorpus. | 1. Explain collocations and their importance (10 min). 2. Pairs analyze collocation data using COCA (20 min). 3. Discuss collocation examples and relevance (10 min). 4. Write sentences using collocations and explain usage (20 min). 5. Summarize and address questions (10 min). | Sentence-writing activities evaluated for correct collocation use and contextual appropriateness (20 min). |
Corpus-assisted (Frequency) | Identify morphological variations of eight cardiovascular terms and their frequency of occurrence in the COCA Medical subcorpus to understand term formation and prevalence. | 1. Handouts with normalized frequencies per million words (COCA Medical) and morphological breakdowns (e.g., fibrill- = 42.1/million, - ation = 38.7/million). | 1. Explain morphological variations and frequency analysis (10 min). 2. Pairs identify affixes and their normalized frequency patterns in texts (20 min). 3. Discuss affix impact on meaning and prevalence (10 min). 4. Identify new terms in examples using affixes and explain meanings (20 min). 5. Summarize and address questions (10 min). | Activities identifying affixes of terms from frequency data, evaluated for accuracy and explanation of meanings based on prevalence in the COCA Medical subcorpus (20 min). |
Assessing retention of medical terminology
To assess short- and long-term retention of the eight target cardiovascular terms, a customized meaning-recall Updated Vocabulary Levels Test (VLT, interactive version), available at https://www.lextutor.ca/tests/, was administered at three intervals: immediately following the teaching session (T1), two weeks later (T2), and one month later (T3). The Updated VLT was selected for its established validity in measuring vocabulary knowledge in EFL contexts and its adaptability to focus on specific lexical items, with our customization targeting only the eight target words [25].
Participants were shown each target term (e.g., fibrillation) and asked: “Write the meaning of this word in English.” Responses were scored binarily: 1 point for a correct and complete definition (e.g., “rapid, irregular heartbeat”), 0 for incorrect, partial, or blank responses. The VKS was used as a pre-test to confirm that the eight target terms were largely unfamiliar across all groups, ensuring valid candidates for instruction and equivalent baselines. The VKS measures depth of knowledge on a 5-point scale (1 = no familiarity; 5 = full mastery) and has demonstrated high test-retest reliability (> 0.8) [21]. To minimize practice effects from repeated testing, the VKS pre-test employed recognition and partial recall formats, while the VLT post-tests used full meaning recall in English, with item orders randomized across the three administrations. This differential testing strategy—sensitive screening (VKS) followed by recall-based outcome assessment (VLT)—is standard in vocabulary retention studies to avoid pre-exposure bias while rigorously evaluating learning gains [26].
Results
Pre-test
One-way ANOVA was conducted to assess baseline equivalence in prior knowledge of the eight target words across the four groups. Prior to analysis, we checked for outliers using boxplots and z-scores in SPSS (version 25); no outliers were identified, as all VKS scores fell within ± 3 standard deviations of the mean for each group. VKS scores showed no significant differences among groups (F(3, 36) = 0.87, p =.462, η² = 0.07), with mean scores ranging from 1.2 to 1.4 (SD = 0.3–0.4) on a 5-point scale where 1 = “I don’t remember having seen this word before,” 2 = “I have seen this word before but I don’t know what it means,” 3 = “I think it means …… (synonym or translation),” 4 = “I know one meaning of this word: ……,” and 5 = “I can use this word in a sentence or explain it in context, confirming minimal prior knowledge and comparable starting points for all groups.
Post-tests
Retention of the eight target words was assessed using the updated VLT at three intervals as discussed above. Table 2 presents the mean retention scores and standard deviations for each teaching method at each interval.
Table 2. VLT retention scores across teaching methods at three intervals
Group (Teaching method) | T1 mean (SD) | T2 mean (SD) | T3 mean (SD) | |
|---|---|---|---|---|
Concordancing | 7.1 (0.7) | 7.0 (0.8) | 6.6 (0.8) | |
Frequency | 7.3 (0.5) | 6.8 (0.7) | 6.2 (0.9) | |
Collocation | 6.3 (0.8) | 6.0 (0.9) | 5.0 (1.0) | |
Lecture-based | 5.4 (1.0) | 4.6 (1.1) | 2.7 (1.2) |
The findings reveal distinct patterns of effectiveness across the three testing intervals. Immediately after the teaching session, the frequency method demonstrated the highest retention, closely followed by concordancing, with collocation and lecture-based methods trailing. At T2, concordancing outperformed other methods, with frequency remaining competitive, while collocation and lecture-based methods performed less robustly. By the one-month interval, concordancing maintained its lead, demonstrating sustained retention, followed by frequency, collocation, and lecture-based methods. These patterns suggest that frequency analysis fosters immediate retention while concordancing promotes retention over time. Collocation shows moderate long-term retention, and the lecture-based method consistently underperformed.
Retention over time
Repeated-measures ANOVA indicated a significant effect of time on retention scores across all groups (F(2, 72) = 15.63, p <.001, η² = 0.30), reflecting a gradual decline from T1 to T3. We checked for outliers using boxplots and z-scores in SPSS (version 25) for VLT scores at each interval (T1, T2, T3); no outliers were identified. Also, Mauchly’s test confirmed that the assumption of sphericity was met (W = 0.92, p =.314); thus, no corrections were applied. A significant interaction between time and teaching method (F(6, 72) = 3.45, p =.005, η² = 0.22) showed that retention patterns differed by method (see Fig. 1). The concordancing group exhibited the smallest decline, followed by frequency and collocation, with the lecture-based group showing the largest drop. Tukey’s HSD post-hoc tests confirmed that concordancing’s retention remained significantly higher than lecture-based at all intervals (p <.001), with frequency and collocation showing moderate advantages over lecture-based, particularly at T3.
[See PDF for image]
Fig. 1
Retention pattern across time intervals
Discussion
The present study compared the effectiveness of lecture-based teaching with three corpus-assisted methods—concordancing, collocation, and frequency—in promoting retention of medical terminology among EFL medical students. Results indicated that corpus-assisted methods outperformed lecture-based instruction, with distinct patterns across testing intervals. The frequency method showed the strongest immediate retention (T1: 7.3 ± 0.5), while concordancing demonstrated superior performance at two weeks (T2: 7.0 ± 0.8) and one month (T3: 6.6 ± 0.8), followed by collocation and lecture-based methods. Overall, concordancing emerged as the most effective for sustained retention.
Findings of the present study align with corpus studies, which underscore the efficacy of data-driven learning and authentic language for specialized vocabulary acquisition. The frequency method’s superior immediate recall can be attributed to its pedagogical focus on breaking down terms into morphemes and presenting normalized frequency data (e.g., fibrill- = 42.1/million), which provides students with a simple, rule-based decoding strategy—identifying affixes and their prevalence—to rapidly construct meanings during the post-teaching activity. This morphological analysis reduces cognitive effort in the short term and facilitates quick retrieval on the meaning-recall VLT administered immediately after instruction [27, 28]. In contrast, concordancing’s lead in long-term retention stems from its emphasis on inferring meaning from multiple authentic contexts via 10–15 concordance lines per term. During the teaching procedure, students actively highlighted syntactic, collocational, and discourse-level patterns (e.g., fibrillation in diagnostic vs. symptomatic contexts) and answered guiding questions, promoting deeper semantic processing. This discovery-based approach creates richer, more interconnected memory traces by linking the term to real-world medical usage, enhancing retrieval strength over time as measured at T2 and T3 [15]. Also, the collocation method showed moderate retention, likely due to its focus on fluency-building through sentence-writing with high-MI pairs (e.g., treat embolism), which strengthens associative networks but may not match concordancing’s depth in semantic encoding [16].
The lecture-based group’s sharp decline (from 5.4 ± 1.0 at T1 to 2.7 ± 1.2 at T3) likely results from a combination of high intrinsic cognitive load from rote memorization of complex terms without contextual support and lower student engagement in the passive lecture format [22]. Unlike corpus methods, which involve active analysis and discovery, lecture-based instruction relies on repetition and recall drills, leading to superficial encoding and rapid forgetting—especially evident in the meaning-recall VLT at T3 [9, 11]. This dual factor of cognitive overload and reduced motivation underscores the limitations of traditional methods in EFL medical education.
From a psychological perspective, corpus-assisted methods, particularly concordancing, likely reduced extraneous load by providing contextualized examples, facilitating efficient schema construction and long-term memory consolidation [9]. The minimal retention decline in the concordancing group aligns with retention studies showing that active, discovery-based learning enhances encoding and retrieval, especially for EFL learners who face linguistic and cognitive challenges [11]. The superior performance of corpus-assisted methods suggests that autonomy and student-driven learning foster greater engagement, potentially enhancing motivation among students who often face limited opportunities for active participation in traditional settings [12].
In addition, the frequency method’s initial advantage supports frequency-based learning models, where exposure to high-frequency morphological variants accelerates early lexical gains [28, 29]. Concordancing’s sustained effectiveness aligns with task-based approaches that promote discovery-learning in authentic texts, enhancing depth of vocabulary knowledge [30]. Findings of the present study suggest that combining frequency analysis for rapid mastery with concordancing for contextual depth could optimize both immediate and long-term retention. Results advocate the application of corpus tools in teaching medical terminology and inform second language practices by prioritizing active, data-driven learning [12, 31].
Limitations
The small sample size (N = 40, n = 10 per group) may limit generalizability and statistical power (observed power for the significant interaction = 0.78), and increase the risk of Type II errors due to potential underpowering, particularly for detecting smaller effect sizes. Also, the study was conducted at a single institution which may affect generalizability. Additionally, the focus on eight cardiovascular terms may not fully represent the breadth of medical terminology required in clinical practice, potentially limiting the generalizability of the teaching methods’ effectiveness. Also, repeated testing of the same medical words across intervals, while methodologically justified, may introduce practice effects, particularly at the two-week interval (T2), which may be insufficient to fully mitigate testing effects for motivated learners, thus affecting the interpretation of long-term retention data. These effects were mitigated through distinct test formats and randomization of test items.
Teaching implications and conclusion
The superior retention outcomes of corpus-assisted methods, particularly concordancing and frequency analysis, advocate for their integration into EFL medical curricula to enhance mastery of specialized terminology. Educators can implement structured activities using corpus tools like Sketch Engine or AntConc [32] to enable students to analyze authentic medical texts that promotes active, data-driven learning. To optimize learning outcomes, medical English programs should adopt a scaffolded curriculum that sequences frequency-based activities for rapid term acquisition, followed by concordancing activities to deepen semantic processing. Such a hybrid model can be feasibly implemented with minimal resources, as many corpus tools are open-access, and can be tailored to diverse medical subfields. Also, professional development programs for instructors should emphasize training in corpus tools to ensure effective classroom implementation.
Author contributions
S.J.E conceptualized and designed the study. S.J.E, E.D., M.B. and H.H. collected and analyzed the data. S.J.E, M.B. and H.H. interpreted the data. S.J.E and E.D. wrote the main manuscript text. All authors have met criteria for authorship and had a role in preparing the manuscript. Also, all authors approved the final manuscript.
Funding
The current study did not receive any form of funding.
Data availability
The datasets used and/or analyzed during the current study are available from the first author on reasonable request.
Declarations
Ethics approval and consent to participate
The present study was conducted in full accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee at Iran University of Medical Sciences (ID: IR.IUMS.REC.1403.1133). Written informed consent was obtained from all participants before the start of the study. Various measures were implemented to ensure the security and confidentiality of the recorded data. Participants’ personal information was separated from the data using a coding system and pseudonyms were employed instead of real names. Access to the data was strictly limited to the researchers. In reporting the results, all identifying information about participants was removed to ensure confidentiality of the data.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Cohen, BJ; Jones, SA. Medical terminology: an illustrated guide; 2021; 9 Burlington, Jones & Bartlett Learning:
2. del Moral BL, VanPutte C, Mccracken B. The use of role-play in the learning of medical terminology for online and face-to-face courses. AJP Adv Physiol Educ. 2024. 48(3): 578–87. 10:1152/advan.00038.2024.
3. Bieliaieva, O; Lysanets, Y; Havrylieva, K; Znamenska, I; Rozhenko, I; Nikolaieva, N. Paronymy in the sublanguage of medicine (linguistic and linguo-didactic aspects). Georgian Med News; 2017; 271, pp. 144-9.
4. Morokhovets, HYMO; Lysanets, YV. Developing the professional competence of future Doctors in the instructional setting of higher medical educational institutions. Wiad Lek; 2017; 70,
5. Dewidar, O; Elmestekawy, N; Welch, V. Improving equity, diversity, and inclusion in academia. Res Integr Peer Rev; 2022; 7,
6. Hood, S; Barrickman, N; Djerdjian, N; Farr, M; Gerrits, RJ; Lawford, H. Some believe, not all achieve: the role of active learning practices in anxiety and academic self-efficacy in first-generation college students. J Microbiol Biol Educ; 2020; 21,
7. Alaagib, NA; Musa, OA; Saeed, AM. Comparison of the effectiveness of lectures based on problems and traditional lectures in physiology teaching in Sudan. BMC Med Educ; 2019; 19,
8. Ehsanzadeh, SJ. Assessing threshold level of L2 vocabulary depth in reading comprehension and incidental vocabulary learning. Lang Educ Assess; 2020; 3,
9. Freeman, S; Eddy, SL; McDonough, M; Smith, MK; Okoroafor, N; Jordt, H. Active learning increases student performance in science, engineering, and mathematics. Proc Natl Acad Sci USA; 2014; 111,
10. Hunter, A-B; Seymour, E; Thiry, H; Weston, T; Holland, D; Harper, R. Talking about leaving revisited: persistence, relocation, and loss in undergraduate STEM education; 2019; Cham, Springer:
11. Theobald, EJ; Hill, MJ; Tran, E; Agrawal, S; Arroyo, EN; Behling, S. Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proc Natl Acad Sci USA; 2020; 117,
12. Boulton, A; Cobb, T. Corpus paradigms and practice in language learning: a meta-analysis. Lang Learn; 2017; 67,
13. Cobb, T; Boulton, A. Classroom applications of corpus analysis. Cambridge handbook of corpus linguistics; 2015; Cambridge, Cambridge University Press: pp. 478-97. [DOI: https://dx.doi.org/10.1017/CBO9781139764377.027]
14. Mizumoto, A; Chujo, K. A meta-analysis of data-driven learning approach in the Japanese EFL classroom. Engl Corpus Stud; 2015; 22, pp. 1-18.
15. Egbert, J; Larsson, T; Biber, D. Doing linguistics with a corpus: methodological considerations for the everyday user; 2020; Cambridge, Cambridge University Press: [DOI: https://dx.doi.org/10.1017/9781108888790]
16. Goulart, L. The use of collocations across proficiency levels: a literature review. BELT-Braz Engl Lang Teach J; 2019; [DOI: https://dx.doi.org/10.15448/2178-3640.2019.2.34129]
17. Egbert, J; Burch, B; Biber, D. Lexical dispersion and corpus design. Int J Corpus Linguist; 2020; 25,
18. Wulff, S; Baker, P. Wulff, S; Baker, P. Analyzing concordances. A practical handbook of corpus linguistics; 2021; Cham, Springer: pp. 161-79.
19. Jackson, H. Corpus and concordance: finding out about style. Teaching and Language corpora; 2014; London, Routledge: pp. 224-39. [DOI: https://dx.doi.org/10.4324/9781315842677-19]
20. Nation ISP. Learning vocabulary in another language. 2nd ed. Cambridge: Cambridge University Press; 2013.
21. Wesche, M; Paribakht, TS. Assessing second language vocabulary knowledge: depth versus breadth. Can Mod Lang Rev.; 1996; 53,
22. Sweller, J. Cognitive load theory, learning difficulty, and instructional design. Learn Instr; 1994; 4,
23. Webb, S; Nation, P. How vocabulary is learned; 2017; Oxford, Oxford University Press:
24. Dudley-Evans, T; St John, MJ. Developments in english for specific purposes; 1998; Cambridge, Cambridge University Press:
25. Stoeckel, T; McLean, S; Nation, P. Limitations of size and levels tests of written receptive vocabulary knowledge. Stud Second Lang Acquis; 2021; 43,
26. Bachman, LF. What does Language testing have to offer? 1. The writings of Lyle F. Bachman; 2024; London, Routledge: pp. 367-99. [DOI: https://dx.doi.org/10.4324/9781315765211-25]
27. Egbert, J; Biber, D; Keller, D; Gracheva, M. Register and the dual nature of functional correspondence: accounting for text-linguistic variation between registers, within registers, and without registers. Corpus Linguist Linguist Theory; 2024; 20,
28. Ehsanzadeh, SJ; Dwyer, E. The scope of vocabulary in multilingual learners’ mainstream schooling: a list of significant words; 2025; London, SAGE Publications Sage UK: 13670069251336328.
29. Laufer, B. Lexical coverage, inferencing unknown words and reading comprehension: how are they related?. TESOL Q; 2020; 54,
30. Ellis, R. The study of second Language acquisition; 2008; 2 Oxford, Oxford University Press:
31. Ehsanzadeh, SJ; Dehnad, A. Analysis of high-frequency errors and linguistic patterns in EFL medical students’ English writing: insights from a learner corpus. BMC Med Educ; 2024; 24, 1264. [DOI: https://dx.doi.org/10.1186/s12909-024-06242-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39501265][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539313]
32. Kilgarriff, A; Baisa, V; Bušta, J; Jakubíček, M; Kovář, V; Michelfeit, J. The sketch engine: ten years on. Lexicography; 2014; 1,
© The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.