This study was conducted to analyse hedging modal verbs in student assignments. Specifically, it was designed to investigate how the native English speaking (NS) students and the non-native English speaking (NNS) students express their hedging in their assignments by focusing on three hedging modal verbs: would, should, and may. The methodology used for the present research was a combined approach of methods from corpus linguistics and discourse analysis (specifically, move analysis). The results of the top-down corpus-based move analysis showed that the different patterns of these three modal verbs have different hedging functions and have a tendency to occur at different move types to fulfil different communicative purposes. The analyses also indicated the existence of both similarities and differences between the NS and NNS groups in the use of hedging modal verbs in terms of both lexico-grammatical and rhetorical features in different contexts. This study contributed to analyse the hedging modal verbs used in students’ assignments from corpus linguistics and move analysis approaches.

Noun phrase (NP) complexity research has shown the effects of both discipline and writing competence on NP complexity in academic writing and has focused more on applied linguistics. Yet few studies examined NPs in the academic writing of computer science (CS), especially in the CS conference abstract writing, in depth. This study fills this gap by investigating the disciplinary preference of NPs through the corpus analysis of 267 published abstracts from a leading CS conference. The authors found that multiple pre-modifiers were the most frequently used device by CS researchers, and attributive adjectives, nouns, and prepositional phrases were fundamental in abstract composition in both CS and applied linguistics. The difference largely lies in the use of devices in later-acquired stages. CS researchers favor more multiple pre-modifiers while their peers in applied linguistics tend to prefer multiple prepositional phrases as post-modifiers. The findings shed light on classroom instruction and future research on NP complexity.

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