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Building an adaptive test model for English reading comprehension in the context of online education

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Abstract

With the ongoing progress of economic globalization, international communication has witnessed a notable surge. Proficiency in English has become indispensable for individuals seeking to comprehend international information and engage in economic, cultural, and scientific exchanges. At the heart of English instruction lies English reading, and using models to test scientific English reading comprehension abilities can significantly enhance the quality and effectiveness of learning. This study aims to augment the quality of adaptive English reading comprehension tests and accurately gage the assessment of college students' English reading abilities. In this research endeavor, the difficulty of objective questions in English reading comprehension was quantified by amalgamating deviance entropy and fuzzy hierarchical analysis. Subsequently, a hybrid test model grounded in Rasch was devised with multiple adaptive algorithms. The test results unveiled that the weighting values of the objective question difficulty model, based on fuzzy hierarchical analysis, changed, ranging from 0.551 to 0.562 for content and from 0.449 to 0.438 for question type. The mean ability values of the 40 students spanned from − 3 to + 3 logits, with the dwell time exhibiting a distribution pattern. Compared with the traditional adaptive test model, the hybrid adaptive test model employed 22.25 questions, resulting in an overlap rate of 0.21. The time taken to assemble the paper was a mere 0.05 s, accompanied by a reduction in difficulty by 0.32, leading to an impressive test efficiency of 98%. The findings of this study underscore that the adaptive model developed herein offers superior ability assessment and effectively enhances students' English reading comprehension learning.

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References

  1. Li M, Geva E, D’Angelo N, Koh PW, Chen X, Gottardo A (2021) Exploring sources of poor reading comprehension in English language learners. Ann Dyslexia 71(2):299–321

    Article  Google Scholar 

  2. Kieffer MJ, Mancilla-Martinez J, Logan JK (2021) Executive functions and English reading comprehension growth in Spanish-English bilingual. J Appl Dev Psychol 73(3):1238–1249

    Google Scholar 

  3. Zhou B (2022) Construction and simulation of online english reading model in wireless surface acoustic wave sensor environment optimized by particle swarm optimization. Discr Dyn Nat Soc 11(2022):1–1

    Google Scholar 

  4. Qian**g M, Lin T (2021) An artificial intelligence based construction and application of english multimodal online reading mode. J Intell Fuzzy Syst 40(2):3721–3730

    Article  Google Scholar 

  5. Tang Q (2021) Analysis of english multiset reading comprehension model based on deep belief neural network. Comput Intell Neurosci 21:809–821

    Google Scholar 

  6. Huang Y, Shu Z (2022) Construction of Dynamic autoparallel foreign language teaching model based on multicore processor. Math Probl Eng 18:876–889

    Google Scholar 

  7. ** T (2021) Design of english diagnostic practice sentence repetition recognition system based on matching tree and edge computing. Wireless Commun Mobile Comput 23(6):796–805

    Google Scholar 

  8. Vermeiren H, Vandendaele A, Brysbaert M (2023) Validated tests for language research with university students whose native language is english: tests of vocabulary, general knowledge, author recognition, and reading comprehension. Behav Res Methods 55(3):1036–1068

    Article  Google Scholar 

  9. Zhang J, Li Z. Design and Implementation of Machine Learning Algorithm in Question Bank System. In: 2023 International Conference on Artificial Intelligence and Education (ICAIE), 2023, 21(1): 35–39.

  10. Agrawal A, Shukla P (2023) Context aware automatic subjective and objective question generation using fast text to text transfer learning. Int J Adv Comput Sci Appl 14(4):40451–40463

    Google Scholar 

  11. Patil Y N, Gandhi S S, Kiwelekar A W, Netak L D. A Small-Scale Ontology to Represent Knowledge About Question Items. In: International Conference on Smart Computing and Communication. Singapore: Springer Nature Singapore, 2023, 46(1): 47–58.

  12. Cheng J, Wang H (2021) Adaptive algorithm recommendation and application of learning resources in english fragmented reading. Complexity 21(11):2534–2545

    Google Scholar 

  13. Liang H, Wan J, Song T, Hou W (2021) Identifying the optimal subsets of test items through adaptive test for cost reduction of ICs. Electronics 10(6):680–698

    Article  Google Scholar 

  14. Sardashti A, Daniali HM, Varedi-Koulaei SM (2022) Geometrical similarity error function-innovative adaptive algorithm methodology in path generation synthesis of the four-bar mechanism using metaheuristic algorithms. Proc Inst Mech Eng, Part C: J Mech Eng Sci 236(3):1550–1570

    Article  Google Scholar 

  15. Tourain C, Piras F, Ollivier A, Hauser D, Poisson JC, Boy F, Thibaut P, Hermozo L, Tison C (2021) Benefits of the adaptive algorithm for retracking altimeter nadir echoes: results from simulations and CFOSAT/SWIM observations. IEEE Transact Geosci Remote Sens 59(12):9927

    Article  Google Scholar 

  16. Yu L, Gui Z (2021) Analysis of enterprise social media intelligence acquisition based on data crawler technology. Entrep Res J 22(11):267–279

    Google Scholar 

  17. Lin CT, Chang SJ, Chen YH (2022) Cognitive learning assessment based on fahp and RSM: a case study of introduction to network course. J Educ Comput Res 59(8):1543–1578

    Article  Google Scholar 

  18. Pham NT, Do AD, Nguyen QV, Ta VL (2021) Xuan heresiarch on knowledge management models at universities using fuzzy analytic hierarchy process (FAHP). Sustainability 13(2):809–821

    Article  Google Scholar 

  19. Kansara S, Modgil S, Kumar R (2023) Structural transformation of fuzzy analytical hierarchy process: a relevant case for Covid-19. Oper Manag Res 16(1):450–465

    Article  Google Scholar 

  20. Xu SL, Yeyao T, Shabaz M (2023) Multi-criteria decision making for determining best teaching method using fuzzy analytical hierarchy process. Soft Comput 27(6):2795–2807

    Article  Google Scholar 

  21. Spiridigliozzi L, Bortolotti M, Dell’Agli G (2023) On the effect of standard deviation of cationic radii on the transition temperature in fluorite-structured entropy-stabilized oxides (F-ESO). Materials 16(6):2219–2230

    Article  Google Scholar 

  22. Bernton E, Ghosal P, Nutz M (2022) Entropic optimal transport: Geometry and large deviations. Duke Math J 171(16):3363–3400

    Article  MathSciNet  Google Scholar 

  23. Chen Y, Wang D, Zhang L, Guo H, Ma J, Gao W (2023) Flood risk assessment of Wuhan, China, using a multi-criteria analysis model with the improved AHP-Entropy method. Environ Sci Pollut Res 30(42):96001–96018

    Article  Google Scholar 

  24. Wang C, Weiss DJ, Su S, Suen KY, Basford J, Andrea LC (2022) Multidimensional computerized adaptive testing: a potential path toward the efficient and precise assessment of applied cognition, daily activity, and mobility for hospitalized patients. Arch Physic Med Rehabilit 103(5):S3-14

    Article  Google Scholar 

  25. Kang H, Han S, Betts J, Muntean W (2022) Computerized adaptive testing for testlet-based innovative items. Br J Math Stat Psychol 75(1):136–157

    Article  Google Scholar 

  26. Cheng SC, Cheng YP, Huang YM (2021) To implement computerized adaptive testing by automatically adjusting item difficulty index on adaptive english learning platform. J Internet Technol 22(7):1599–1608

    Article  Google Scholar 

  27. Lin Z, Chen P, **n T (2021) The block item pocket method for reviewable multidimensional computerized adaptive testing. Appl Psychol Meas 45(1):22–36

    Article  Google Scholar 

  28. Holmes JM, Leske DA, Hercynitic A, Hatt SR, Chandler DL, Li Z, Melia BM, Chen AM, Erzurum SA, Crouch ER (2022) Rasch-calibrated intermittent exotropia questionnaire for children. Symptom questionnaire for children. Opto Vision Sci Offic Publ Am Acad Optomet 99(6):513–520

    Article  Google Scholar 

  29. Baandrup L, Allerup P, Nielsen MØ, Düring SW, Bojesen KB, Leucht S, Galderisi S, Mucci A, Bucci P, Arango C, Díaz-Caneja CM (2022) Scalability of the positive and negative syndrome scale in first-episode schizophrenia assessed by Rasch models. Acta Psych Scand 146(1):21–35

    Article  Google Scholar 

  30. Brann KL, Boone WJ, Splett JW, Bidwell SL (2021) Development of the school mental health self-efficacy teacher survey using rasch analysis. J Psychoeduc Assess 39(2):197–211

    Article  Google Scholar 

  31. Siddique AA, Schnitzer ME, Balakrishnan N, Sotgiu G, Vargas MH, Menzies D, Benedetti A (2024) Two-stage targeted maximum likelihood estimation for mixed aggregate and individual participant data analysis with an application to multidrug resistant tuberculosis. Stat Med 43(2):342–357

    Article  MathSciNet  Google Scholar 

  32. Adak S, Cangi H, Yilmaz AS, Arifoglu U (2023) Development software program for extraction of photovoltaic cell equivalent circuit model parameters based on the Newton-Raphson method. J Comput Electron 22(1):413–422

    Google Scholar 

  33. Yigiter MS, Dogan N (2023) Computerized multistage testing: principles, designs and practices with R. Measure: Interdisc Res Perspect 21(4):254–277

    Google Scholar 

  34. Jiang S, **ao J, Wang C (2023) On-the-fly parameter estimation based on item response theory in item-based adaptive learning systems. Behav Res Methods 55(6):3260–3280

    Article  Google Scholar 

  35. **ao J, Bulut O (2023) Item selection with collaborative filtering in on-the-fly multistage adaptive testing. Appl Psychol Meas 46(8):690–704

    Article  Google Scholar 

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Funding

The research is supported by Research on Digital Protection and Innovative Dissemination Mechanism of Weinan Intangible Cultural Heritage Under Network Media (No. ZDYFJH-35).

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Correspondence to Yufen Wei.

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Wei, Y. Building an adaptive test model for English reading comprehension in the context of online education. SOCA (2024). https://doi.org/10.1007/s11761-024-00395-x

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