Construction and Application of Adaptive Test Bases for College English Listening Comprehension Based on Natural Language Processing

[1] Tavabi, N., Pruneski, J., Golchin, S., Singh, M., Sanborn, R., & Heyworth, B., et al. (2024). Building large-scale registries from unstructured clinical notes using a low-resource natural language processing pipeline. Artificial Intelligence In Medicine, 151. Search in Google Scholar

[2] Pham, H. T. T. L., & Han, S. U. (2023). Natural language processing with multitask classification for semantic prediction of risk-handling actions in construction contracts. Journal of computing in civil engineering(6), 37. Search in Google Scholar

[3] Ko, T., Jeong, H. D., & Lee, J. H. (2023). Natural language processing–driven similar project determination using project scope statements. Journal of management in engineering. Search in Google Scholar

[4] Hoang, H., & Crosthwaite, P. (2024). A comparative analysis of multiword units in the reading and listening input of english textbooks. System, 121. Search in Google Scholar

[5] Babayiit, S., & Shapiro, L. (2020). Component skills that underpin listening comprehension and reading comprehension in learners with english as first and additional language. Journal of Research in Reading, 43(1). Search in Google Scholar

[6] Kuo, Y. C., Chu, H. C., & Tsai, M. C. (2017). Effects of an integrated physiological signal-based attention-promoting and english listening system on students’ learning performance and behavioral patterns. Computers in Human Behavior, 75(oct.), 218-227. Search in Google Scholar

[7] Chang, Chi-Cheng, Warden, Clyde, A., & Liang, et al. (2018). Performance, cognitive load, and behaviour of technology-assisted english listening learning: from call to mall. Journal of Computer Assisted Learning, 34(2), 105-114. Search in Google Scholar

[8] Barenberg, J., Berse, T., Reimann, L., & Dutke, S. (2021). Testing and transfer: retrieval practice effects across test formats in english vocabulary learning in school. Applied Cognitive Psychology. Search in Google Scholar

[9] Lee, J. S. (2020). The role of informal digital learning of english and a high-stakes english test on perceptions of english as an international language. Australasian Journal of Educational Technology(2). Search in Google Scholar

[10] Betül Hazal Dinçer, Antonova-Unlu, E., & Kumcu, A. (2019). Assessing the use of multiple-choice translation items in english proficiency tests: the case of the national english proficiency test in turkey. Applied Linguistics Review. Search in Google Scholar

[11] Shin, S. Y., Lee, S., & Lidster, R. (2021). Examining the effects of different english speech varieties on an l2 academic listening comprehension test at the item level:. Language Testing, 38(4), 580-601. Search in Google Scholar

[12] Okim, K., Ron, T., & Meghan, M. (2019). The effects of international accents and shared first language on listening comprehension tests. TESOL Quarterly, 53. Search in Google Scholar

[13] Milliner, B., & Dimoski, B. (2021). The effects of a metacognitive intervention on lower- proficiency efl learners’ listening comprehension and listening self-efficacy. Language Teaching Research. Search in Google Scholar

[14] Chang, C. C., Warden, C. A., Liang, C., & Chou, P. N. (2018). Performance, cognitive load, and behaviour of technology-assisted english listening learning: from call to mall. Journal of Computer Assisted Learning, 34(2), 105-114. Search in Google Scholar

[15] Vaughn, S., Martinez, L. R., Wanzek, J., Roberts, G., & Anna-Mária Fall. (2017). Improving content knowledge and comprehension for english language learners: findings from a randomized control trial. Journal of Educational Psychology, 109(1). Search in Google Scholar

[16] Safa, M. A., & Motaghi, F. (2021). Cognitive vs. metacognitive scaffolding strategies and efl learners’ listening comprehension development. Language Teaching Research, 136216882110218. Search in Google Scholar

[17] Arendse, D. E., & Maree, D. (2019). Exploring the factors of the english comprehension test:. South African Journal of Psychology, 49(3), 376-390. Search in Google Scholar

[18] O’Grady, S. (2021). Adapting multiple-choice comprehension question formats in a test of second language listening comprehension. Language Teaching Research, 136216882098536. Search in Google Scholar

[19] Du, G., Hasim, Z., & Chew, F. P. (2021). Contribution of english aural vocabulary size levels to l2 listening comprehension. IRAL - International Review of Applied Linguistics in Language Teaching. Search in Google Scholar

[20] Liu, G. Z., Jing℡ao Chen, & Hwang, G. J. (2018). Mobile-based collaborative learning in the fitness center: a case study on the development of english listening comprehension with a context-aware application. British Journal of Educational Technology, 49, págs. 305-320. Search in Google Scholar

[21] Rets, I., & Rogaten, J. (2020). To simplify or not? facilitating english l2 users’ comprehension and processing of open educational resources in english using text simplification. Journal of Computer Assisted Learning. Search in Google Scholar

[22] Lin H X,Han Z W.Achieving strength-ductility synergy in zirconium via ultra-dense twin-twin networks[J]. Acta Materialia,2024,269119825-. Search in Google Scholar

[23] Jianan S,Jiazi C,Yuehui C, et al.ProSE-Pero: Peroxisomal Protein Localization Identification Model Based on Self-Supervised Multi-Task Language Pre-Training Model.[J].Frontiers in bioscience (Landmark edition),2023,28(12):322-322. Search in Google Scholar

[24] Wang C,Yang J,Zhang B.A fault diagnosis method using improved prototypical network and weighting similarity-Manhattan distance with insufficient noisy data[J].Measurement,2024,226114171-. Search in Google Scholar

[25] KalayB,DemiralpM.Fundamental elements of vector enhanced multivariance product representation[J]. AIP Conference Proceedings,2012,1479(1):1998. Search in Google Scholar

[26] Luo R,Huang S,Chen H, et al.Code Confusion in White Box Crowdsourced Software Testing[J]. International Journal of Performability Engineering, 2021, 17(3): 276-288. Search in Google Scholar

[27] Wolfgang M.From black box to clear box: A hypothesis testing framework for scalar regression problems using deep artificial neural networks[J].Applied Soft Computing,2023,146. Search in Google Scholar