Effects of Digital Learning Materials on nursing students’ mathematics learning, self-efficacy, and task value in vocational education

Nurse Education in Practice

Volumn 44, Issue , 2020, Pages

  Zwart, Diana P ^a   Noroozi, Omid ^b   Van Luit, Johannes E H ^c   Goei, Sui Lin ^a,d   Nieuwenhuis, Arjen ^e  

^a WINDESHEIM UNIVERSITY OF APPLIED SCIENCES   (Netherlands)

^b WAGENINGEN UNIVERSITY   (Netherlands)

^c UTRECHT UNIVERSITY   (Netherlands)

^d VU UNIVERSITY AMSTERDAM   (Netherlands)

^e Alfa College   (Netherlands)

Author keywords

Digital learning materials (DLMs); Nursing mathematics; Self efficacy; Task value

Indexed keywords

ADOLESCENT; EDUCATION; FEMALE; HUMAN; MATHEMATICS; NURSING STUDENT; PEER GROUP; PROBLEM SOLVING; SELF CONCEPT; VOCATIONAL EDUCATION;

ADOLESCENT; EDUCATION, DISTANCE; EDUCATIONAL MEASUREMENT; FEMALE; HUMANS; MATHEMATICS; PEER GROUP; PROBLEM SOLVING; SELF EFFICACY; STUDENTS, NURSING; VOCATIONAL EDUCATION;

EID: 85081691233     PISSN: 14715953     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.nepr.2020.102755     Document Type: Article

References (51)

1. Adhikari, R., Tocher, J., Smith, P., Corcoran, J., MacArthur, J., A multi-disciplinary approach to medication safety and the implication for nursing education and practice. Nurse Educ. Today 34 (2014), 185–190, 10.1016/j.nedt.2013.10.008.
2. Actiz Health Organization, Veilige principes in de medicatieketen [Safety principles in chain of medication]. https://www.zorgvoorbeter.nl/docs/PVZ/vindplaats/medicatieveiligheid/veilige-principes-in-de-medicatieketen.pdf, 2012.
3. Alexander, P.A., Judy, J.E., The interaction of domain-specific and strategic knowledge in academic performance. Rev. Educ. Res. 58 (1988), 375–404.
4. Alloway, T.P., Alloway, R.G., Investigating the predictive roles of working memory and IQ in academic attainment. J. Exp. Child Psychol. 106 (2010), 20–29, 10.1016/j.jecp.2009.11.003.
5. Alloway, T.P., Bibile, V., Lau, G., Computerized working memory training: can it lead to gains in cognitive skills in students?. Comput. Hum. Behav. 29 (2013), 632–638, 10.1016/j.chb.2012.10.023.
6. Anderson, J.R., Schunn, C.D., Implications of the ACT-R learning theory: No magic bullets. Glaser, R., (eds.) Advances in Instructional Psychology, Vol. 5, Educational Design and Cognitive Science, 2000, Lawrence Erlbaum, Mahwah, New Jersey, 1–33.
7. Andrews, S.E., Aikens, M.L., Life science majors' math-biology task values relate to student characteristics and predict the likelihood of taking quantitative biology courses. J. Microbiol. Biol. Educ. 19 (2019), 1–10, 10.1128/jmbe.v19i2.1589.
8. Ashcraft, M.H., Krause, J.A., Working memory, math performance, and math anxiety. Psychon. Bull. Rev. 14 (2007), 243–248, 10.3758/BF03194059.
9. Baartman, L.K.J., De Bruijn, E., Integrating knowledge, skills, and attitudes: conceptualising learning processes towards vocational competence. Educ. Res. Rev. 6 (2011), 125–134, 10.1016/j.edurev.2011.03.001.
10. Baddeley, A.D., Working Memory. 1986, Oxford University Press, New York.
11. Baldwin, A.L., Theories of Child Development. 1967, John Wiley and Sons, New York.
12. Bandura, A., Social Foundations of Thought and Action: A Social Cognitive Theory. 1986, Prentice-Hall, Englewood Cliffs, NJ.
13. Chen, J.-L., The effects of education compatibility and technological expectancy on e-learning acceptance. Comput. Educ. 57 (2011), 1501–1511, 10.1016/j.compedu.2011.02.009.
14. Compton, L.K.L., Preparing language teachers to teach language online: a look at skills, roles, and responsibilities. Comput. Assist. Lang. Learn. 22 (2009), 73–99, 10.1080/09588220802613831.
15. Corno, L., On teaching adaptively. Educ. Psychol. 43 (2008), 161–173, 10.1080/00461520802178466.
16. Daubert, E.N., Ramani, G.B., Math and memory in bilingual preschoolers: the relations between bilingualism, working memory, and numerical knowledge. J. Cognit. Dev. 20 (2019), 314–333, 10.1080/15248372.2019.1565536.
17. De Mooij, S.M.M., Kirkham, N.Z., Raijmakers, M.E.J., Van der Maas, H.L.J., Dumontheil, I., Should online math learning environments be tailored to individuals' cognitive profiles?. J. Exp. Child Psychol. 191 (2020), 1–15, 10.1016/j.jecp.2019.104730.
18. Denissen, J.J.A., Zarrett, N.R., Eccles, J.S., I like to do it, I'm able, and I know I am: longitudinal couplings between domain-specific achievement, self-concept, and interest. Child Dev. 78 (2007), 430–437, 10.1111/j.1467-8624.2007.01007.x.
19. Gray, B., Informal learning in an online community of practice. J. Distance Educ. 19 (2004), 20–35.
20. Hadwin, A.F., Järvelä, S., Miller, M., Self-regulated, co-regulated, and socially shared regulation of learning. Zimmerman, B.J., Schunk, D.H., (eds.) Handbook of Self-Regulation of Learning and Performance, 2011, Routledge, New York, 65–84.
21. Huang, C., Discriminant and incremental validity of self-concept and academic self-efficacy: a meta-analysis. Educ. Psychol. 32 (2012), 777–805, 10.1080/01443410.2012.732386.
22. Joo, Y.J., Lim, K.Y., Kim, J., Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Comput. Educ. 62 (2013), 149–158, 10.1016/j.compedu.2012.10.027.
23. Kalyuga, S., Liu, T.C., Guest editorial: managing cognitive load in technology-based learning environments. Educ. Technol. Soc. 18:4 (2015), 1–8 http://www.jstor.org/stable/jeductechsoci.18.4.1.
24. Kollar, I., Ufer, S., Reichersdorfer, E., Vogel, F., Fischer, F., Reiss, K., Effects of collaboration scripts and heuristic worked examples on the acquisition of mathematical argumentation skills of teacher students with different levels of prior achievement. Learn. InStruct. 32 (2014), 22–36, 10.1016/j.learninstruc.2014.01.003.
25. Lagendijk, R., Schaap, K., De Wit, P., Kramer, M., Lugten, I., Wolters, S., Kluiters, C., Van Abswoude, J., Folkertsma, J., Wynia, R., Startrekenen [Starting Mathematics]. 2013, Deviant, Amersfoort, The Netherlands.
26. Lee, L.-T., Hung, J.C., Effects of blended e-learning: a case-study in higher education tax learning setting. Human-centric Comput. Inf. Sci. 5 (2015), 1–15, 10.1186/s13673-015-0024-3.
27. Leutner, D., Motivation and emotion as mediators in multimedia learning. Learn. InStruct. 29 (2014), 174–175, 10.1016/j.learninstruc.2013.05.004.
28. Lovett, A., Forbus, K., Modeling visual problem solving as analogical reasoning. Psychol. Rev. 124 (2017), 60–90, 10.1037/rev0000039.
29. Mayer, R.E., Incorporating motivational learning into multimedia learning. Learn. InStruct. 29 (2014), 171–173, 10.1016/j.learninstruc.2013.04.003.
30. Moreno, R., Does the modality principle hold for different media? A test of the method-affects-learning hypothesis. J. Comput. Assist. Learn. 22 (2006), e149–e158, 10.1111/j.1365-2729.2006.00170.x.
31. Noroozi, O., Biemans, H.J.A., Weinberger, A., Mulder, M., Chizari, M., Scripting for construction of a transactive memory system in multidisciplinary CSCL environments. Learn. InStruct. 25 (2013), 1–12, 10.1016/j.learninstruc.2012.10.002.
32. Noroozi, O., Busstra, M.C., Mulder, M., Biemans, H.J.A., Tobi, H., Geelen, M.M.E.E., Van ‘t Veer, P., Chizari, M., Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment. Educ. Technol. Res. Dev. 60 (2012), 193–221, 10.1007/s11423-011-9217-2.
33. Passey, D., Goodison, R., Machell, J., McHugh, G., The Motivational Effect of ICT on Pupils. 2004, Lancaster University, Department of Educational Research http://downloads01.smarttech.com/media/research/international_research/uk/lancaster_report.pdf.
34. Pekrun, R., The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ. Psychol. Rev. 18:4 (2006), 315–341, 10.1007/s10648-006.
35. Peng, P., Namkung, J., Barnes, M., Sun, C., A meta-analysis of mathematics and working memory: moderating effects of working memory domain, type of mathematics skill, and sample characteristics. J. Educ. Psychol. 108 (2016), 455–473, 10.1037/edu0000079.
36. Prast, E.J., van de Weijer, E., Miocevic, M., Kroesbergen, E.H., Van Luit, J.E.H., Relations between mathematics achievement and motivation in students of diverse achievement levels. Contemp. Educ. Psychol. 55 (2018), 84–96, 10.1016/j.cedpsych.2018.08.002.
37. Raven, J.C., Court, J.H., Raven, J., Standard Progressive Matrices. 1992 edition, 1992, Oxford Psychologists Press, Oxford, UK.
38. Reichwein Zientek, L., Fong, C.J., Phelps, J.M., Sources of self-efficacy of community college students enrolled in developmental mathematics. J. Furth. High. Educ. 43 (2019), 183–200, 10.1080/0309877X.2017.1357071.
39. Stelzer, F., Richard's, M.M., Andrés, M.L., Vernucci, S., Introzzi, I., Cognitive and maths-specific predictors of fraction conceptual knowledge. Educ. Psychol. 111 (2019), 446–458, 10.1080/01443410.2019.1693508.
40. Sweller, J., Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ. Psychol. Rev. 22 (2010), 123–138, 10.1007/s10648-010-9128-5.
41. Tricot, A., Sweller, J., Domain-specific knowledge and why teaching generic skills does not work. Educ. Psychol. Rev. 26 (2014), 265–283, 10.1007/s10648-013-9243-1.
42. Van der Veen, I., Peetsma, T., The development in self-regulated learning behavior of first-year students in the lowest level of secondary school in The Netherlands. Learn. Indiv Differ 19 (2009), 34–46, 10.1016/j.lindif.2008.03.001.
43. Van Merriënboer, J.J.G., Kirschner, P.A., Ten Steps to Complex Learning. third ed., 2017, Routledge, New York, NY.
44. Weeks, K.W., Hutton, B.M., Young, S., Coben, D., Clochesy, J.M., Pontin, D., Safety in numbers 2: competency modelling and diagnostic error assessment in medication dosage calculation problem-solving. Nurse Educ. Pract. 13 (2013), e23–e32, 10.1016/j.nepr.2012.10.013.
45. Weeks, K.W., Hutton, B.M., Young, S., Coben, D., Clochesy, J.M., Pontin, D., Safety in numbers 3: authenticity, Building knowledge & skills and Competency development & assessment: the ABC of safe medication dosage calculation problem-solving pedagogy. Nurse Educ. Pract. 13 (2013), e23–e26 http://dx.doi.org/10.1016/j.nepr.2012.10.013.
46. Weeks, K.W., Lyne, P., Torrance, C., Written drug dosage errors made by students: the threat to clinical effectiveness and the need for a new approach. Clin. Effect Nurs. 4:1 (2000), e20–e29, 10.1054/cein.2000.0101.
47. Wei, W., Yuan, H., Chen, C., Zhou, X., Cognitive correlates of performance in advanced mathematics. Br. J. Educ. Psychol. 82 (2012), 157–181, 10.1111/j.2044-8279.2011.02049.x.
48. Wigfield, A., Eccles, J.S., Expectancy-value theory of achievement motivation. Contemp. Educ. Psychol. 25 (2000), 68–81, 10.1006/ceps.1999.1015.
49. Wu, J.-H., Tennyson, R.D., Hsia, T.-L., A study of student satisfaction in a blended e-learning system environment. Comput. Educ. 55 (2010), 155–164, 10.1016/j.compedu.2009.12.012.
50. Xinhao, X., Fengfeng, K., Designing a virtual-reality-based, gamelike math learning environment. Am. J. Dist. Educ. 30 (2016), 27–38, 10.1080/08923647.2016.1119621.
51. Zwart, D.P., Van Luit, J.E.H., Noroozi, O., Goei, S.L., The effects of digital learning material on students' mathematics learning in vocational education. Cogent Educ., 4, 2017, 1313581, 10.1080/2331186X.2017.1313581.