Author Information
Noriko Aotani, Tokai Gakuen University, JapanShin’ya Takahashi, Tokai Gakuen University, Japan
Abstract
This study employed Technique Feature Analysis (TFA) criteria to analyze the characteristics of tasks effective for reconstructing Japanese EFL learners’ lexical networks. We focused on the deepening and expansion of knowledge of already-known words. Twenty words were selected from an essay, and participants (73 Japanese university students) evaluated the perceived relationships among these words (Test 1). One week later, they read the essay and completed one of three tasks. Participants in the MCQ group answered ten multiple-choice questions regarding the meanings of the target words. The FIB group filled in ten blanks in the essay using target words provided with their meanings and usage examples. The Comp group completed ten sentences unrelated to the essay with the target words. TFA points assigned to the tasks were calculated as 5 for MCQ, 7 for FIB, and 8 for Comp. After completing the task, participants took the same word-relationship test (Test 2). Finally, four weeks after Test 2, participants completed Test 3. The results showed that word-relationship scores increased from Test 1 to Test 2 and decreased from Test 2 to Test 3 in all groups. No significant effects of group (i.e., task type) were found. The results were also analyzed and visualized using Gephi, a data visualization platform, revealing qualitative features of individual changes in lexical networks. The present findings did not correspond to the learning effects predicted by TFA, suggesting that more appropriate tasks and/or evaluation criteria are necessary for applying the TFA framework to the learning of already-known words.
Paper Information
Conference: ECE2026Stream: Foreign Languages Education & Applied Linguistics (including ESL/TESL/TEFL)
This paper is part of the ECE2026 Conference Proceedings (View)
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