This study aimed to identify target lexical bundles (e.g., on the other hand, at the same time) for argumentative essay writing and rank them in order of teaching priority for Japanese learners. Despite significant functional roles of lexical bundles in academic writing, the inclusion of lexical bundles in argumentative writing had been underexplored. Since argumentative writing skills help undergraduate students prepare for their academic careers (e.g., writing papers), the lexical bundles under this genre deserve more attention. This study first extracted 78 target bundles from L1 argumentative essay corpora (International Corpus Network of Asian Learners of English: ICNALE and Louvain Corpus of Native English Essays: LOCNESS). The study then classified the bundles according to their discourse functions and semantic transparency to estimate the learnability for Japanese learners in L2 compatible corpora with the ICNALE. The results showed that learners had difficulty using the bundles with referential functions (e.g., in the form of) and semantic opaqueness (e.g., when it comes to), suggesting that the bundles in these two categories should be prioritized among the 78 bundles.

This research centers on the Taiwan Eye-Movement Corpus of Spanish (TECS), a specially created corpus comprising eye-tracking data from Chinese-speaking learners of Spanish as a third language in Taiwan. Its primary purpose is to explore the broad utility of TECS in understanding language learning processes, particularly the initial stages of language learning. Constructing this corpus involves gathering data on eye-tracking, reading comprehension, and language proficiency to develop a machine-learning model that predicts learner behaviors, and subsequently undergoes a predictability test for validation. The focus is on examining attention in input processing and their relationship to language learning outcomes. The TECS eye-tracking data consists of indicators derived from eye movement recordings while reading Spanish sentences with temporal references. These indicators are obtained from eye movement experiments focusing on tense verbal inflections and temporal adverbs. Chinese expresses tense using aspect markers, lexical references, and contextual cues, differing significantly from inflectional languages like Spanish. Chinese-speaking learners of Spanish face particular challenges in learning verbal morphology and tenses. The data from eye movement experiments were structured into feature vectors, with learner behaviors serving as class labels. After categorizing the collected data, we used two types of machine learning methods for classification and regression: Random Forests and the k-nearest neighbors algorithm (KNN). By leveraging these algorithms, we predicted learner behaviors and conducted performance evaluations to enhance our understanding of the nexus between learner behaviors and language learning process. Future research may further enrich TECS by gathering data from subsequent eye-movement experiments, specifically targeting various Spanish tenses and temporal lexical references during text reading. These endeavors promise to broaden and refine the corpus, advancing our understanding of language processing.