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A Case Study on Students’ Opinions About Adaptive and Classical Tests

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Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference (MIS4TEL 2022)

Abstract

Assessment in education is an important task. Albeit being relatively old techniques, the adoption of adaptive (CAT) instead of classical testing (FIT) still poses several questions. According to the literature, a CAT test should be faster to complete, more precise in estimating the student’s ability, and more engaging to take than FIT. On the other hand, a FIT test should be easier to prepare and complete, and less prone to anxiety than CAT. In this context, the paper initially reports on a study conducted with students (that used CAT and FIT) about different aspects of their assessments’ experiences. The results show that students seem to prefer CAT, considered fairer, more practical and faster than FIT, independently of the different attitudes towards technology. Furthermore, students did not report more anxiety or more workload with CAT. Moreover, we also evaluate different methods to convert the ability measured with CAT into a grade, so to verify if the adopted method could have influenced the results of the questionnaire.

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Notes

  1. 1.

    For completeness, the range for \(\theta \) that nullify the average difference between grades calculated through CTT and the proposed linear conversion is \([-4.15,4.15]\).

References

  1. Alrifai, M., Gennari, R., Vittorini, P.: Adapting with evidence: the adaptive model and the stimulation plan of TERENCE. In: Vittorini, P., Gennari, R., Marenzi, I., de la Prieta, F., Rodríguez, J. (eds.) International Workshop on Evidence-Based Technology Enhanced Learning. AISC, vol. 152, pp. 75–82. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28801-2_9

    Chapter  Google Scholar 

  2. Angelone, A.M., Vittorini, P.: A report on the application of adaptive testing in a first year university course. In: Uden, L., Liberona, D., Sanchez, G., Rodríguez-González, S. (eds.) LTEC 2019. CCIS, vol. 1011, pp. 439–449. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20798-4_38

    Chapter  Google Scholar 

  3. Athaya, H., Dwi, R., Nadir, A., Sensuse, D.I., Suryono, R.R.: Moodle implementation for e-learning: a systematic review. In: 6th International Conference on Sustainable Information Engineering and Technology 2021, vol. 14 (2021). https://doi.org/10.1145/3479645.3479646

  4. Bernardi, A., et al.: On the design and development of an assessment system with adaptive capabilities. In: Di Mascio, T., et al. (eds.) MIS4TEL 2018. AISC, vol. 804, pp. 190–199. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98872-6_23

    Chapter  Google Scholar 

  5. Birnbaum, A.: Some latent trait models and their use in inferring an examinee’s ability. In: Statistical Theories of Mental Test Scores, pp. 395–479 (1968)

    Google Scholar 

  6. Brown, G.T.L., Abdulnabi, H.H.A.: Evaluating the quality of higher education instructor-constructed multiple-choice tests: impact on student grades. Front. Educ. 2, 24 (2017). https://doi.org/10.3389/feduc.2017.00024

    Article  Google Scholar 

  7. Chalmers, R.P.: mirt: a multidimensional item response theory package for the R environment. J. Stat. Softw. 48(6), 1–29 (2012). https://doi.org/10.18637/jss.v048.i06

    Article  Google Scholar 

  8. Colwell, N.M.: Test anxiety, computer -adaptive testing, and the common core. J. Educ. Train. Stud. 1(2), 50–60 (2013). https://doi.org/10.11114/JETS.V1I2.101

    Article  Google Scholar 

  9. Conejo, R., Guzmán, E., Trella, M.: The SIETTE automatic assessment environment. Int. J. Artif. Intell. Educ. 26(1), 270–292 (2016). https://doi.org/10.1007/s40593-015-0078-4

    Article  Google Scholar 

  10. De Champlain, A.F.: A primer on classical test theory and item response theory for assessments in medical education. Med. Educ. 44(1), 109–117 (2010). https://doi.org/10.1111/j.1365-2923.2009.03425.x

    Article  Google Scholar 

  11. DeVellis, R.F.: Classical test theory. Med. Care 44(11), S50–S59 (2006). https://doi.org/10.2307/41219505

    Article  Google Scholar 

  12. Di Mascio, T., Gennari, R., Melonio, A., Vittorini, P.: The user classes building process in a TEL project. In: Vittorini, P., Gennari, R., Marenzi, I., de la Prieta, F., Rodríguez, J. (eds.) International Workshop on Evidence-Based Technology Enhanced Learning. AISC, vol. 152, pp. 107–114. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28801-2_13

    Chapter  Google Scholar 

  13. Dunn, L., Morgan, C., O’Reilly, M., Parry, S.: The Student Assessment Handbook. Routledge, Abingdon (2003). https://doi.org/10.4324/9780203416518

    Book  Google Scholar 

  14. Eleje, L.I., Onah, F.E., Abanobi, C.C.: Comparative study of classical test theory and item response theory using diagnostic quantitative economics skill test item analysis results. Eur. J. Educ. Soc. Sci. 3(1), 57–75 (2018)

    Google Scholar 

  15. Embretson, S.E., Reise, S.P.: Item Response Theory for Psychologists. Lawrence Erlbaum Associates, Mahwah (2000)

    Google Scholar 

  16. Erdt, M., Fernández, A., Rensing, C.: Evaluating recommender systems for technology enhanced learning: a quantitative survey. IEEE Trans. Learn. Technol. 8(4), 326–344 (2015). https://doi.org/10.1109/TLT.2015.2438867

    Article  Google Scholar 

  17. Fritts, B.E., Marszalek, J.M.: Computerized adaptive testing, anxiety levels, and gender differences. Soc. Psychol. Educ. 13(3), 441–458 (2010). https://doi.org/10.1007/S11218-010-9113-3

    Article  Google Scholar 

  18. Galassi, A., Vittorini, P.: Automated feedback to students in data science assignments: improved implementation and results. In: 14th Biannual Conference of the Italian SIGCHI Chapter (CHItaly 2021), New York, NY, USA. ACM, Bolzano (2021). https://doi.org/10.1145/3464385.3464387

  19. Gikandi, J., Morrow, D., Davis, N.: Online formative assessment in higher education: a review of the literature. Comput. Educ. 57(4), 2333–2351 (2011). https://doi.org/10.1016/J.COMPEDU.2011.06.004

    Article  Google Scholar 

  20. Hambleton, R.K., Jones, R.W.: Comparison of classical test theory and item response theory and their applications to test development. Educ. Meas. Issues Pract. 12(3), 38–47 (1993). https://doi.org/10.1111/j.1745-3992.1993.tb00543.x

    Article  Google Scholar 

  21. Ling, G., Attali, Y., Finn, B., Stone, E.A.: Is a computerized adaptive test more motivating than a fixed-item test? Appl. Psychol. Meas. 41(7), 495–511 (2017). https://doi.org/10.1177/0146621617707556

    Article  Google Scholar 

  22. Magis, D., Barrada, J.R.: Computerized adaptive testing with R: recent updates of the package catR. J. Stat. Softw. 76(Code Snippet 1), 1–19 (2017). https://doi.org/10.18637/jss.v076.c01

    Article  Google Scholar 

  23. Mulwa, C., Lawless, S., Sharp, M., Arnedillo-Sanchez, I., Wade, V.: Adaptive educational hypermedia systems in technology enhanced learning: a literature review. In: Proceedings of the 2010 ACM Conference on Information Technology Education - SIGITE 2010 (2010). https://doi.org/10.1145/1867651

  24. Qasem, M.A.N.: A comparative study of classical theory (CT) and item response theory (IRT) in relation to various approaches of evaluating the validity and reliability of research tools. IOSR J. Res. Method Educ. (IOSR-JRME) 3(5), 77–81 (2013)

    Article  Google Scholar 

  25. R Core Team: R: A Language and Environment for Statistical Computing (2018). https://www.R-project.org/

  26. Riffenburgh, R.H.: Statistics in Medicine. Elsevier/Academic Press, Cambridge (2012)

    MATH  Google Scholar 

  27. Rindskopf, D.: Reliability: measurement. Int. Encycl. Soc. Behav. Sci. 13023–13028 (2001). https://doi.org/10.1016/B0-08-043076-7/00722-1

  28. Scalise, K., Allen, D.D.: Use of open-source software for adaptive measurement: concerto as an R-based computer adaptive development and delivery platform. Br. J. Math. Stat. Psychol. 68(3), 478–496 (2015). https://doi.org/10.1111/BMSP.12057

    Article  Google Scholar 

  29. Tonidanel, S., Quinones, M.A.: Psychological reactions to adaptive testing. Int. J. Sel. Assess. 8(1), 7–15 (2000). https://doi.org/10.1111/1468-2389.00126

    Article  Google Scholar 

  30. Vispoel, W.P., Rocklin, T.R., Wang, T.: Individual differences and test administration procedures: a comparison of fixed-item, computerized-adaptive, and self-adapted testing. Appl. Measur. Educ. 7(1), 53–79 (1994). https://doi.org/10.1207/s15324818ame0701_5

    Article  Google Scholar 

  31. Vittorini, P., Menini, S., Tonelli, S.: An AI-based system for formative and summative assessment in data science courses. Int. J. Artif. Intell. Educ. 1–27 (2020). https://doi.org/10.1007/s40593-020-00230-2

  32. Wainer, H., et al.: Computerized Adaptive Testing. Routledge, Abingdon (2000). https://doi.org/10.4324/9781410605931

    Book  Google Scholar 

  33. Weiss, D.J., Kingsbury, G.G.: Application of computerized adaptive testing to educational problems. J. Educ. Measur. 21(4), 361–375 (1984). https://doi.org/10.1111/j.1745-3984.1984.tb01040.x

    Article  Google Scholar 

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Correspondence to Pierpaolo Vittorini .

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Questionnaire

Questionnaire

 

1

2

3

4

5

Personal attitude

\(Q_{1.1}\)

My attitude towards technology is good

     

Computerised Adaptive Test (CAT)

\(Q_{2.1}\)

I am in favour of using CAT

     

\(Q_{2.2}\)

I perceived CAT as fair

     

\(Q_{2.3}\)

CAT is practical and fast

     

\(Q_{2.4}\)

I think that CAT needs more preparation than FIT

     

Fixed-Item Test (FIT)

\(Q_{3.1}\)

I am in favour of using FIT

     

\(Q_{3.2}\)

I perceived FIT as fair

     

\(Q_{3.3}\)

FIT is practical and fast

     

\(Q_{3.4}\)

I think that FIT needs more preparation than CAT

     

General

\(Q_{4.1}\)

The sequential way of asking questions allowed me

     
 

to focus more on the right answer

\(Q_{4.2}\)

Regardless of the subject, taking an exam with

     
 

CAT, rather than FIT, made me feel more anxious

Each question is answered with the following 5-points Likert scale:

“1 = Completely disagree”, “2 = Disagree”, “3 = Unsure”, “4 = Agree”, “5 = Completely agree”.

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Angelone, A.M., Vittorini, P. (2023). A Case Study on Students’ Opinions About Adaptive and Classical Tests. In: Temperini, M., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference. MIS4TEL 2022. Lecture Notes in Networks and Systems, vol 580. Springer, Cham. https://doi.org/10.1007/978-3-031-20617-7_5

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