Abstract
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.
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Notes
- 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.
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.
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.
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.
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).
References
Hattie, J.A.C.: Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement. Routledge, Abingdon (2009). https://doi.org/10.1109/ITHET.2015.7218029
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). https://doi.org/10.1109/ITHET.2015.7218029
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). https://doi.org/10.1007/s10758-017-9316-1
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). https://doi.org/10.1145/3303772.3303804
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. https://doi.org/10.1016/j.chb.2018.07.021
Suurtamm, C., et al. (eds.): Assessment in Mathematics Education. ITS, Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32394-7
Hidri, S. (ed.): Revisiting the Assessment of Second Language Abilities: From Theory to Practice. SLLT, Springer, Cham (2018). https://doi.org/10.1007/978-3-319-62884-4
Webb, M.E., et al.: Challenges for IT-enabled formative assessment of complex 21st century skills. Technol. Knowl. Learn. 23, 441–456 (2018). https://doi.org/10.1007/s10758-018-9379-7
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)
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). https://doi.org/10.1016/0022-2836(81)90087-5
Richardson, J.T.E.: Approaches to studying, conceptions of learning and learning styles in higher education. Learn. Individ. Differ. 21(3), 288–293 (2011). https://doi.org/10.1016/j.lindif.2010.11.015
Cassidy, S.: Learning styles: an overview of theories, models, and measures. Educ. Psychol.-UK 24(4), 419–444 (2004). https://doi.org/10.1080/0144341042000228834
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). https://doi.org/10.1080/03075079.2018.1482267
Saldivar, M.G.: A Primer on Survey Response Rate. Learning Systems Institute. Florida State University, Tallahassee (2012)
Gehlbach, H., Barge, S.: Anchoring and adjusting in questionnaire responses. Basic Appl. Soc. Psychol. 34(5), 417–433 (2012). https://doi.org/10.1080/01973533.2012.711691
Guttman, L.: Image theory for the structure of quantitative variates. Psychometrika 21(3), 277–296 (1953). https://doi.org/10.1007/BF02289264
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). https://doi.org/10.1007/978-3-319-41264-1_18
Lind, D.A., Marchal, W.G., Wathen, S.A.: Statistical Techniques in Business & Economics, 15th edn. McGraw-Hill/Irwin, New York (2013)
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). https://doi.org/10.1037/a0024338
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). https://doi.org/10.22237/jmasm/1225512360
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)
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)
Wanous, J.P., Hudy, M.J.: Single-item reliability: a replication and extension. Organ. Res. Methods 4(4), 361–375 (2001). https://doi.org/10.1177/109442810144003
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). http://etheses.dur.ac.uk/13762/
Kaushanskaya, M., Marian, V., Yoo, J.: Gender differences in adult word learning. Acta Psychol. (AMST) 137(1), 24–35 (2011). https://doi.org/10.1016/j.actpsy.2011.02.002
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). https://doi.org/10.1109/ICALT.2017.154
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)
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). https://doi.org/10.1007/s10212-020-00485-2
Truong, H.M.: Integrating learning styles and adaptive e-learning system: current developments, problems and opportunities. Comput. Hum. Behav. 55-B, 1185–1193 (2016). https://doi.org/10.1016/j.chb.2015.02.014
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). https://doi.org/10.1016/j.procs.2018.08.226
Acknowledgement
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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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. https://doi.org/10.1007/978-3-030-97986-7_22
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