The Relationships Among ICT-Related Psychological Factors,School Contextual Factors and Secondary Students’ Reading Performance: A Multilevel Analysis Across 47 Economies

Abstract

Evolving information and communication technology (ICT) reshapes people’s reading activities by popularizing digital reading. Past studies focused on the relationships between students’ frequency of ICT use and reading performance but neglected the possible interrelationships between students’ ICT-related psychological factors, school contextual factors, and reading performance. This study applied the Self-determination Theory (SDT) to explore the relationships between students’ ICT-related psychological factors (perceived interest, autonomy, competence, and social interaction in using ICT) and reading performance. The possible moderation effects of schools’ contextual variables (schools’ support in ICT devices and schools’ support in teachers’ capacity to integrate technology in teaching) were also investigated. Data from 222,293 secondary students from 10,103 schools in 47 economies who participated in the Program for International Student Assessment (PISA) 2018 were analyzed using three-level hierarchical linear modeling. The results provided support for the SDT by confirming the positive correlations between students’ ICT-related interest, autonomy and competence and reading performance, and the accentuating moderation effect of schools’ contextual factors. However, students’ perceived social interaction in ICT use was negatively correlated with reading performance, and this negative correlation worsened with increasing schools’ support in ICT devices while buffered with increasing schools’ support in teachers’ capacity to use technology.

Keywords

reading performance ICT-related psychological factors school contextual factors hierarchical linear modeling PISA 2018

Get full access to this article

View all access options for this article.

References

Albion

P. R.

Tondeur

Forkosh-Baruch

Peeraer

(2015). Teachers’ professional development for ICT integration: Towards a reciprocal relationship between research and practice. Education and Information Technologies, 20(4), 655–673. https://doi.org/10.1007/s10639-015-9401-9.

Alshaya

Oyaid

(2017). Designing and publication of interactive e-book for students of princess Nourah Bint Abdulrahman University: An empirical study. Journal of Education & Practice, 8(8), 41–57. https://files.eric.ed.gov/fulltext/EJ1138827.pdf.

Anderson

S. E.

Putman

R. S.

(2020). Special education teachers’ experience, confidence, beliefs, and knowledge about integrating technology. Journal of Special Education Technology, 35(1), 37–50. https://doi-org.eproxy.lib.hku.hk/10.1177/0162643419836409

Baker

Wigfield

(1999). Dimensions of children’s motivation for reading and their relations to reading activity and reading achievement. Reading Research Quarterly, 34(4), 452–477. https://doi.org/10.1598/rrq.34.4.4

Bhattacharjee

Deb

(2016). Role of ICT in 21st century’s teacher education. International Journal of Education and Information Studies, 6(24741), 1–6. https://doi.org/10.1038/srep24741.

Bowerman

B. L.

O’Connell

R. T.

(1990). Linear statistical models: An applied approach. Duxbury.

Cohen

(1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155.

Cohen

(1983). Applied multiple regression/correlation analyses for the behavioral sciences (2nd ed.). Erlbaunm.

Dawson

J. F.

(2014). Moderation in management research: What, why, when, and how. Journal of Business & Psychology, 29(1), 1–19. https://doi-org.10.1007/s10869-013-9308-7.

10.

De Naeghel

Valcke

De Meyer

Warlop

Van Braak

Van Keer

(2014). The role of teacher behavior in adolescents’ intrinsic reading motivation. Reading and Writing, 27(9), 1547–1565. https://doi-org.eproxy.lib.hku.hk/10.1007/s11145-014-9506-3.

11.

Deci

E. L.

Ryan

R. M.

(1985). Intrinsic motivation and self-determination in human behavior. Plenum.

12.

Deci

E. L.

Ryan

R. M.

(2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi-org.10.1007/s10869-013-9308-7.

13.

Deci

E. L.

Ryan

R. M.

(2012). Motivation, personality, and development within embedded social contexts: An overview of self-determination theory. In Ryan

R. M.

(Ed.), The Oxford handbook of human motivation (pp. 85–107). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195399820.013.0006.

14.

Deci

E. L.

Ryan

R. M.

(2015). Self-determination theory. In James

(Ed.), International encyclopedia of the social & behavioral sciences (2nd ed., pp. 486–491). Elsevier Ltd. https://doi.org/10.1016/b978-0-08-097086-8.26036-4.

15.

Dillon

(1994). Designing usable electronic text ergonomics aspects of human information usage. Taylor & Francis.

16.

Dogan

N. A.

Celik

(2021). Teachers’ skills to integrate technology in education: Two path models explaining instructional and application software use. Education and Information Technologies, 26(1), 1311–1332. https://doi-org.10.1007/s10639-020-10310-4.

17.

Eryilmaz

Rivera-Gutiérrez

Sandoval-Hernandez

Sakay

M. J. A.

(2020). Should different countries participating in PISA interpret socioeconomic background in the same way? A measurement invariance approach. Revista Iberoamericana de Educación, 84(1), 109–133. https://doi.org/10.35362/rie8413981.

18.

Field

(2013). Discovering statistics using IBM SPSS statistics. SAGE Publications Ltd.

19.

Gan

Menkhoff

Smith

(2015). Enhancing students’ learning process through interactive digital media: New opportunities for collaborative learning. Computers in Human Behavior, 51(part B), 652–663. https://doi.org/10.1016/j.chb.2014.12.048.

20.

Gumus

Atalmis

E. H.

(2011). Exploring the relationship between purpose of computer usage and reading skills of Turkish students: Evidence from PISA 2006. TOJET - Turkish Online Journal of Educational Technology, 10(3), 129–140. http://eproxy.lib.hku.hk/login?url=https://www-proquest-com.eproxy.lib.hku.hk/scholarly-journals/exploring-relationship-between-purpose-computer/docview/1288354512/se-2?accountid=14548.

21.

Guthrie

J. T.

Cox

K. E.

(2001). Classroom conditions for motivation and engagement in reading. Educational Psychology Review, 13(3), 283-302. https://doi.org/10.1023/A:1016627907001.

22.

Guthrie

J. T.

Hoa

L. W.

Wigfield

Tonks

S. M.

Perencevich

K. C.

(2005). From spark to fire: Can situational reading interest lead to long‐term reading motivation? Reading Research & Instruction, 45(2), 91–117. https://doi.org/10.1080/19388070609558444.

23.

Hamari

Shernof

D. J.

Rowe

Coller

Asbell-Clarke

Edwards

(2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179. https://doi.org/10.1016/j.chb.2015.07.045.

24.

Hofmann

D. A.

(1997). An overview of the logic and rationale of hierarchical linear models. Journal of Management, 23(6), 723–744. https://doi.org/10.1016/S0149-2063(97)90026-X.

25.

Hopfenbeck

T. N.

Lenkeit

El Masri

Y. E.

Cantrell

Ryan

Baird

J. A.

(2018). Lessons learned from PISA: A systematic review of peer-reviewed articles on the programme for international student assessment. Scandinavian Journal of Educational Research, 62(3), 333–353. https://doi.org/10.1080/00313831.2016.1258726.

26.

Hox

J. J.

Moerbeek

Van de Schoot

(2017). Multilevel analysis: Techniques and applications (3rd ed.). Routledge.

27.

Gong

Lai

Leung

F. K. S.

(2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13. https://doi.org/10.1016/j.compedu.2018.05.021.

28.

Hyman

J. A.

Moser

M. T.

Segala

L. N.

(2014). Electronic reading and digital library technologies: Understanding learner expectation and usage intent for mobile learning. Educational Technology Research & Development, 62(1), 35–52. https://doi.org/10.1007/s11423-013-9330-5.

29.

Jack

Higgins

(2019). What is educational technology and how is it being used to support teaching and learning in the early years? International Journal of Early Years Education, 27(3), 222–237. https://doi.org/10.1080/09669760.2018.1504754.

30.

Juhaňák

Zounek

Záleská

Bárta

Vlčková

(2019). The relationship between students’ ICT use and their school performance: Evidence from PISA 2015 in the Czech Republic. Orbis Scholae, 12(2), 37–64. https://karolinum.cz/data/clanek/6483/OS_12_2_0037.pdf.

31.

Jutarat

(2016). Students’ motivation and learning and teachers’ motivational strategies in English classrooms in Thailand. English Language Teaching, 9(4), 64–75. http://dx.doi.org/10.5539/elt.v9n4p64.

32.

Kitisak

Piriya

(2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25(4), 2933–2970. https://doi.org/10.1007/s10639-019-10089-z.

33.

Kozlowski

S. W. J.

Klein

K. J.

(2000). A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes. In Klein

K. J.

Kozlowski

S. W. J.

(Eds.), Multilevel theory, research and methods in organizations: Foundations, extensions, and new directions (pp. 3–90). Jossey-Bass.

34.

Lee

Y. H.

J. Y.

(2012). The effect of individual differences in the inner and outer states of ICT on engagement in online reading activities and PISA 2009 reading literacy: Exploring the relationship between the old and new reading literacy. Learning and Individual Differences, 22(3), 336–342. https://doi-org.eproxy.lib.hku.hk/10.1016/j.lindif.2012.01.007.

35.

Lorah

(2018). Effect size measures for multilevel models: Definition, interpretation, and TIMSS example. Large-scale Assessments in Education, 6(1), 1–11. https://doi.org/10.1186/s40536-018-0061-2.

36.

Bradley

K. D.

(2008). Using multilevel modeling to investigate school effects. In O’Connell

A. A.

Maccoach

D. B.

(Eds.), Multilevel modeling of educational data (pp. 59–110). Information Age Publishing.

37.

McClanahan

Nottingham

(2019). A suite of strategies for navigating graphic novels: A dual coding approach. The Reading Teacher, 73(1), 39–50. https://doi.org/10.1002/trtr.1797.

38.

Menard

(1995). Applied logistic regression analysis. Sage.

39.

Mlay

S. V.

Sabi

H. M.

Tsuma

C. K.

Langmia

(2015). Uncovering reading habits of university students in Uganda: Does ICT matter? International Journal of Education and Development Using Information and Communication Technology, 11(2), 38–50. https://www-proquest-com.eproxy.lib.hku.hk/docview/1714103888?pq-origsite=primo&accountid=14548.

40.

Mullis

I. V. S.

Martin

M. O.

(Eds.). (2019). PIRLS 2021 assessment frameworks. TIMSS & PIRLS International Study Center. https://timssandpirls.bc.edu/pirls2021/frameworks/.

41.

Nastasa

Golu

Buruiana

Oprea

(2021). Teachers’ work-home interaction and satisfaction with life: The moderating role of core self-evaluations. Educational Psychology, 41(6), 806–820. https://doi.org/10.1080/01443410.2020.1852182.

42.

Naumann

Sälzer

(2017). Digital reading proficiency in German 15-year-olds: Evidence from PISA 2012. Z Erziehungswiss, 20(4), 585–603. https://doi-org.eproxy.lib.hku.hk/10.1007/s11618-017-0758-y.

43.

Nelson

M. J.

Hawk

N. A.

(2020). The impact of field experiences on prospective preservice teachers’ technology integration beliefs and intentions. Teaching and Teacher Education, 89, 1–11. https://doi.org/10.1016/j.tate.2019.103006.

44.

Nikolopoulou

Akriotou

Gialamas

(2019). Early reading skills in English as a foreign language via ICT in Greece: Early childhood student teachers’ perceptions. Early Childhood Education Journal, 47(5), 597–606. https://doi.org/10.1007/s10643-019-00950-8.

45.

OECD (2010). PISA 2009 results: What students know and can do: Student performance in reading, mathematics and science (Volume I). OECD Publishing.

46.

OECD (2017). PISA 2015 technical report. OECD Publishing.

47.

OECD (2019). PISA 2018 assessment and analytical framework. OECD Publishing.

48.

OECD (2020). PISA 2018 technical report. OECD Publishing. https://www.oecd.org/pisa/data/pisa2018technicalreport/.

49.

Petko

Cantieni

Prasse

(2017). Perceived quality of educational technology matters: A secondary analysis of students’ ICT use, ICT-related attitudes, and PISA 2012 test scores. Journal of Educational Computing Research, 54(8), 1070–1091. https://doi-org.eproxy.lib.hku.hk/10.1177/0735633116649373.

50.

Prestridge

Tondeur

(2015). Exploring elements that support teachers’ engagement in online professional development. Education Sciences, 5(3), 199–219. https://doi.org/10.3390/educsci5030199.

51.

Radia

(2019). Approaching a reading course via moodle-based blended learning: EFL learners’ insights. Modern Journal of Language Teaching Methods, 9(11), 1–12. https://doi.org/10.26655/mjltm.2019.11.1.

52.

Raudenbush

S.W.

Bryk

A. S.

(1992). Hierarchical linear models: Applications and data analysis methods (advanced quantitative techniques in the social sciences). Sage Publication Inc.

53.

Razak

N. A.

Jalil

H. A.

Krauss

S. E.

Ahmad

N. A.

(2018). Successful implementation of information and communication technology integration in Malaysian public schools: An activity systems analysis approach. Studies in Educational Evaluation, 58(2018), 17–29. https://doi.org/10.1016/j.stueduc.2018.05.003.

54.

Reeve

Jang

(2006). What teachers say and do to support students’ autonomy during a learning activity. Journal of Educational Psychology, 98(1), 209–218. https://doi.org/10.1037/0022-0663.98.1.209.

55.

Rouet

Levonen

(1996). Studying and learning with hypertext: Empirical studies and their implications. In Rouet

J. F.

(Ed.), Hypertext and cognition (pp. 9–23). Lawrence Erlbaum.

56.

Scherer

Siddiq

Tondeur

(2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009.

57.

Schiefele

(1991). Interest, learning, and motivation. Educational Psychologist, 26(3–4), 299–323. https://doi.org/10.1080/00461520.1991.9653136.

58.

Skryabin

Zhang

Liu

Zhang

(2015). How the ICT development level and usage influence student achievement in reading, mathematics, and science. Computers and Education, 85, 49–58. https://doi.org/10.1016/j.compedu.2015.02.004.

59.

Studenmund

A. H.

Cassidy

H. J.

(1987). Using econometrics: A practical guide. Little.

60.

Ungar

O. A.

Baruch

A. F.

(2016). Perceptions of teacher educators regarding ICT implementation. Interdisciplinary Journal of E-Skills and Life Long Learning, 12, 279–296. https://doi.org/10.28945/3606. http://www.informingscience.org/Publications/3606.

61.

Vázquez-Cano

Gómez-Galán

Infante-Moro

López-Meneses

(2020). Incidence of a non-sustainability use of technology on students’ reading performance in PISA. Sustainability, 12(2), 749. https://doi.org/10.3390/su12020749.

62.

Vlachopoulos

S. P.

Michailidou

(2006). Development and initial validation of a measure of autonomy, competence, and relatedness in exercise: The basic psychological needs in exercise scale. Measurement in Physical Education and Exercise Science, 10(4), 179–201. https://doi.org/10.1207/s15327841mpee1003_4.

63.

Wang

J. C. K.

B. L. L.

Liu

W. C.

Ryan

R. M.

(2016). Can being autonomy-supportive in teaching improve students’ self-regulation and performance? In Liu

W. C.

Wang

J. C. K.

Ryan

R. M.

(Eds.), Building autonomous learners (pp. 227–243). Springer Singapore. https://doi.org/10.1007/978-981-287-630-0_12.

64.

Wang

M. C.

Peverly

S. T.

(1986). The self-instructive process in classroom learning contexts. Contemporary Educational Psychology, 11(4), 370–404. https://doi.org/10.1016/0361-476X(86)90031-7.

65.

Wigfield

Guthrie

J. T.

(1997). Relations of children’s motivation for reading to the amount and breadth of their reading. Journal of Educational Psychology, 89(3), 420–432. https://doi.org/10.1037/0022-0663.89.3.420.

66.

Xiao

Liu

(2019). Regression analysis of ICT impact factors on early adolescents’ reading proficiency in five high-performing countries. Frontiers in Psychology. 16(10), 1646. https://doi.org/10.3389/fpsyg.2019.01646.

67.

Zhang

D. H.

Liu

L. M.

(2016). How does ICT use influence students’ achievements in math and science over time? Evidence from PISA 2000 to 2012. Eurasia Journal of Mathematics Science & Technology Education, 12(9), 2431–2449. https://doi.org/10.12973/eurasia.2016.1297a.

68.

Zylka

Christoph

Kroehne

Hartig

Goldhammer

(2015). Moving beyond cognitive elements of ICT literacy: First evidence on the structure of ICT engagement. Computers in Human Behavior, 53, 149–160. https://doi.org/10.1016/j.chb.2015.07.008.