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What Kind of E-assessment Feedback Is Important to Students? An Empirical Study

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Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 642)


E-assessment needs to provide detailed feedback that students would use. To this end, the content of feedback should include assessment information that is important to students. By using a convenient sample of twenty second-year undergraduate students, this study explored the extent to which different kinds of assessment feedback were important to students. Therefore, an online questionnaire was administered which, apart from asking values of some background variables including academic achievement, listed a number of different feedback techniques supporting activities applied in deep and strategic approaches to learning and studying. For each technique, students had to indicate the extent of the importance they assigned to it. It was found that: (1) although feedback techniques supporting a deep approach were as important to students as those supporting a strategic approach, the importance of the former was positively related to that of the latter; (2) feedback techniques supporting a deep approach were more important to females than males; (3) when the importance of all these feedback techniques together was considered in a specific way, their relevance to an increase in students’ knowledge, skills and motivation could be demonstrated. Although this study used a small sample that might characterise it as preliminary research, it revealed valuable findings, which due to their considerable effect size, make the sample size less questionable. Suggestions for further research are included.


  • E-assessment
  • Feedback
  • Language learning
  • Learning approach
  • Undergraduate students

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  1. 1.

    A learning object is a specifically designed, modular unit of a learning resource – content items, practice items, and assessment items that are combined based on a single learning objective.

  2. 2.

    GMFTs importance and Gender correlated (−0.471, df = 18, p = 0.036). Relevant partial correlations concerning GMFTs importance controlling for Gender were: 0.637, df = 17, p = 0.003 (with Course achievement); 0.444, df = 17, p = 0.057 (with Academic achievement); 0.637, df = 17, p = 0.003 (with E-assessment participation).

  3. 3.

    The mean of absolute difference between the responses to these item-pairs was calculated for each participant (e.g., [15]), and the mean and standard deviation of this measure were 1.86 and 1.37, respectively. Furthermore, the measure positively correlated with Course achievement (0.574, df = 18, p = 0.008), meaning that participants with lower course achievement responded to adjacent item-pairs in a more similar way [22].

  4. 4.

    The medians of D5 importance, DAFTs4 importance (the remaining four DAFTs), FTs9 importance (the remaining nine FTs) were 6.66, 7.58, and 7.42, respectively. Their differences in question were significant: D5 importance – DAFTs4 importance: z = –3.286, p = 0.000; D5 importance – FTs9 importance: z = –2.688, p = 0.005. D5 importance was of high reliability by applying correction for attenuation (e.g., [23]).

  5. 5.

    As expected, E-assessment participation was positively related to achievement (see Table 2; χ2 = 5.051, df = 1, p = 0.025 for both achievement variables).


  1. Hattie, J.A.C.: Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement. Routledge, Abingdon (2009).

  2. Vozniuk, A., Rodrguez-Triana, M.J., Holzer, A., Govaerts, S., Sandoz, D., Gillet, D.: Contextual learning analytics apps to create awareness in blended inquiry learning. In: Proceedings of 2015 International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, New York (2015).

  3. Roberts, L.D., Howell, J.A., Seaman, K.: Give me a customizable dashboard: personalized learning analytics dashboards in higher education. Technol. Knowl. Learn. 22, 317–333 (2017).

    CrossRef  Google Scholar 

  4. Lim, L., Dawson, S., Joksimović, S., Gasevic, D.: Exploring students’ sensemaking of learning analytics dashboards: does frame of reference make a difference? In: LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pp. 250–259. ACM, New York (2019).

  5. Howell, J.A., Roberts, L.D., Mancini,V.O.: Learning analytics messages: impact of grade, sender, comparative information and message style on student affect and academic resilience. Comput. Hum. Behav. 89, 8–15.

  6. Suurtamm, C., et al. (eds.): Assessment in Mathematics Education. ITS, Springer, Cham (2016).

    CrossRef  Google Scholar 

  7. Hidri, S. (ed.): Revisiting the Assessment of Second Language Abilities: From Theory to Practice. SLLT, Springer, Cham (2018).

    CrossRef  Google Scholar 

  8. Webb, M.E., et al.: Challenges for IT-enabled formative assessment of complex 21st century skills. Technol. Knowl. Learn. 23, 441–456 (2018).

    CrossRef  Google Scholar 

  9. Kadijevich, D.M., Ljubojevic, D.: E-assessment feedback: Students’ opinions on what to include. In: Jovanović, S., Trebinjac, B. (eds.) Proceedings of the 11th Conference on E-learning, pp. 82–85. Metropolitan University, Belgrade (2020)

    Google Scholar 

  10. Entwistle, N.: Motivational factors in students approaches to learning. In: Schmeck, R.R. (ed.), Learning Strategies and Learning Styles, pp. 21–51. Prenum Press, New York (1988).

  11. Richardson, J.T.E.: Approaches to studying, conceptions of learning and learning styles in higher education. Learn. Individ. Differ. 21(3), 288–293 (2011).

    CrossRef  Google Scholar 

  12. Cassidy, S.: Learning styles: an overview of theories, models, and measures. Educ. Psychol.-UK 24(4), 419–444 (2004).

    CrossRef  Google Scholar 

  13. Lindblom-Ylnne, S., Parpala, A., Postareff, L.: What constitutes the surface approach to learning in the light of new empirical evidence? Stud. High. Educ. 44(12), 2183–2195 (2019).

    CrossRef  Google Scholar 

  14. Saldivar, M.G.: A Primer on Survey Response Rate. Learning Systems Institute. Florida State University, Tallahassee (2012)

    Google Scholar 

  15. Gehlbach, H., Barge, S.: Anchoring and adjusting in questionnaire responses. Basic Appl. Soc. Psychol. 34(5), 417–433 (2012).

    CrossRef  Google Scholar 

  16. Guttman, L.: Image theory for the structure of quantitative variates. Psychometrika 21(3), 277–296 (1953).

    MathSciNet  CrossRef  MATH  Google Scholar 

  17. Kadijevich, D., Odovic, G., Maslikovic, D.: Using ICT and quality of life: comparing persons with and without disabilities. In: Miesenberger, K., Bühler, C., Penaz, P. (eds.) ICCHP 2016, Part I. LNCS, vol. 9758, pp. 129–133. Springer, Cham (2016).

    CrossRef  Google Scholar 

  18. Lind, D.A., Marchal, W.G., Wathen, S.A.: Statistical Techniques in Business & Economics, 15th edn. McGraw-Hill/Irwin, New York (2013)

    Google Scholar 

  19. Fritz, C.O., Morris, P.E., Richler, J.J.: Effect size estimates: current use, calculations, and interpretation. J. Exp. Psychol. Gen. 141(1), 2–18 (2012).

    CrossRef  Google Scholar 

  20. Ruscio, J.: Constructing confidence intervals for Spearman’s rank correlation with ordinal data: a simulation study comparing analytic and bootstrap methods. J. Mod. Appl. Stat. Methods 7(2), 416–434 (2008).

    CrossRef  Google Scholar 

  21. Burton, L., Nelson, L.: The relationships between personality, approaches to learning and academic success in first-year psychology distance education students. In: Critical Visions, Proceedings of the 29th HERDSA Annual Conference, Western Australia, 10–12 July 2006, pp. 64–72. Higher Education Research and Development Society of Australasia, Milperra, Australia (2006)

    Google Scholar 

  22. Kadijevich, D.M., Ljubojevic, D., Gutvajn, N.: Anchoring and adjusting in students’ responses to a questionnaire about the importance of given e-feedback techniques. In: Domazet, B., Raspopović Milić, M. (eds.) Proceedings of the 12th conference on e-learning, pp. 122–124. Metropolitan University, Belgrade (2021)

    Google Scholar 

  23. Wanous, J.P., Hudy, M.J.: Single-item reliability: a replication and extension. Organ. Res. Methods 4(4), 361–375 (2001).

    CrossRef  Google Scholar 

  24. Okk Saengsawang, P.P.: The use of blended learning to support vocabulary learning and knowledge retention in Thai tertiary EFL classrooms, Durham theses, Durham University, Durham, UK (2020).

  25. Kaushanskaya, M., Marian, V., Yoo, J.: Gender differences in adult word learning. Acta Psychol. (AMST) 137(1), 24–35 (2011).

    CrossRef  Google Scholar 

  26. Jeria, H., Villalon, J.: Incorporating open education resources into computer supported marking tool to enhance formative feedback creation. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pp. 256–260. (2017).

  27. Kadijevich, D.M., Gutvajn, N.: Feedback supporting deep and strategic approaches to learning and studying: a case study on production cost. In: Domazet, B., Raspopović Milić, M. (eds.) Proceedings of the 12th Conference on E-learning, pp. 119–121. Metropolitan University, Belgrade (2021)

    Google Scholar 

  28. Papadatou-Pastou, M., Touloumakos, A.K., Koutouveli, C., Barrableet, A.: The learning styles neuromyth: when the same term means different things to different teachers. Eur. J. Psychol. Educ. 36, 511–531 (2021).

    CrossRef  Google Scholar 

  29. Truong, H.M.: Integrating learning styles and adaptive e-learning system: current developments, problems and opportunities. Comput. Hum. Behav. 55-B, 1185–1193 (2016).

  30. Kolekar, S.V., Pai, R.M., Pai, M.M.M.: Adaptive user interface for Moodle based e-learning system using learning styles. Procedia Comput. Sci. 135, 606–615 (2018).

    CrossRef  Google Scholar 

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The authors wish to thank all students who participated in this study. The research done by the first and third authors was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. 451-03-68/2022-14/200018).

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Correspondence to Djordje M. Kadijevich .

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Kadijevich, D.M., Ljubojevic, D., Gutvajn, N. (2022). What Kind of E-assessment Feedback Is Important to Students? An Empirical Study. In: Passey, D., Leahy, D., Williams, L., Holvikivi, J., Ruohonen, M. (eds) Digital Transformation of Education and Learning - Past, Present and Future. OCCE 2021. IFIP Advances in Information and Communication Technology, vol 642. Springer, Cham.

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