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The Mechanism of Influence Between ICT and Students’ Science Literacy: a Hierarchical and Structural Equation Modelling Study

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Abstract

Information and communication technology (ICT) is key to educational development. This study explores the mechanism influencing the use of ICT on students’ science literacy. We utilized two-level hierarchical linear models and structural equation models to analyze data collected from the 2015 Program for International Student Assessment (PISA) in China. Results indicate that student-level and school-level ICT factors, in particular ICT interest, autonomy in using ICT, and ICT availability at school positively impact the development of students’ science literacy. Further, we found some notable interactions between school-level factors and student-level ICT variables. Moreover, there are structural relationships among ICT availability, ICT emotional perception, ICT learning usage behaviors, science self-efficacy, and science literacy. We also found that teacher-delivered science instruction has a negative moderating effect on ICT learning usage and science self-efficacy. These findings have important implications of how to integrate ICT in future science teaching practices.

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Funding

Joint Fund of Astronomy, U1731243, CuiLan Qiao

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  1. CuiLan Qiao

    Present address: School of Physical Science and Technology, Central China Normal University, NO.152 Luoyu Road, 430079, Wuhan, Hubei, People’s Republic of China

Authors and Affiliations

  1. School of Physical Science and Technology, Central China Normal University, NO.152 Luoyu Road, 430079, Wuhan, Hubei, People’s Republic of China

    Qing Guo

  2. Bahrain Teachers College, University of Bahrain, 1054, Zallaq, Bahrain

    Bashirah Ibrahim

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Appendices

Appendix 1: A summary of student- and school-level variables

Description

Label

Source in the questionnaire

Student-level

Demographic background

Students’ gender (female:0; male:1)

GENDER

ST004D01T (NEW)

Parents' highest level of education

HISCED

HISCED in PISA database (CAL)

Index of family economic, social and cultural status

ESCS

ESCS in PISA database (WLE)

ICT factors

Age of first use of electronics

AGEDIG

IC002Q01NA (NEW)

Age of first use of the Internet

AGEINT

IC004Q01TA (NEW)

ICT availability (number of ICT) at home

ICTHOME

ICTHOME in PISA database (CAL)

ICT usage for learning at home

HOMESCH

HOMESCH in PISA database (WLE)

ICT usage for recreation outside of school

ENTUSE

ENTUSE in PISA database (WLE)

ICT usage for science learning outside of school

SCIACT

ST146Q(01\03\06\07\08\09) (NEW)

Interest in ICT

INTICT

INTICT in PISA database (WLE)

ICT competence perception

COMPICT

COMPICT in PISA database (WLE)

Autonomy in using ICT

AUTICT

AUTICT in PISA database (WLE)

Social interaction around ICT

SOIAICT

SOIAICT in PISA database (WLE)

Science performance-related variables

Performance on the PISA science literacy test

SCILIT

PV1SCIE-PV10SCIE in PISA database

Science self-efficacy

SCIEFF

SCIEEFF in PISA database (WLE)

Teacher lecture-based instruction

TDTEACH

TDTEACH in PISA database (WLE)

School-level

ICT factors

ICT availability (number of ICT) at school

ICTSCH

ICTSCH in PISA database (CAL)

ICT usage for learning at school

USESCH

USESCH in PISA database (WLE)

Student-computer ratio

RATCMP

RATCMP in PISA database (CAL)

School context factors

School location (rural:0; city:1)

SCHLOCAT

SC001Q01TA (NEW)

School type (private:0; public:1)

SCHTYPE

SC013Q01TA (NEW)

Student–teacher ratio

STRATIO

STRATIO in PISA database (CAL)

School size (total number of students and teachers)

SCHSIZE

SCHSIZE in PISA database (CAL)

Class size (total number of students in the class)

CLSIZE

CLSIZE in PISA database (CAL)

School educational facility shortage

EDUSHORT

EDUSHORT in PISA database (WLE)

School teacher resource shortage

STAFFSHO

STAFFSHORT in PISA database (WLE)

School science education resource shortage

SCEIERES

SC059Q01NA-SC059Q08NA (NEW)

Appendix 2: Correlation coefficient matrices between the variables at student-level

 

GENDER

HISCED

ESCS

AGEDIG

AGEINT

ICTHOME

ENTUSE

HOMESCH

INTICT

COMPICT

AUTICT

SOIAICT

GENDER

1.000

           

HISCED

.007

1.000

          

ESCS

.001

.847

1.000

         

AGEDIG

−.104

−.308

−.427

1.000

        

AGEINT

−.066

−.353

−.489

.672

1.000

       

ICTHOME

.064

.406

.598

−.360

−.425

1.000

      

ENTUSE

.145

.096

.174

−.188

−.206

.311

1.000

     

HOMESCH

−.002

.155

.225

−.134

−.181

.329

.473

1.000

    

INTICT

.114

.171

.241

−.266

−.281

.227

.362

.235

1.000

   

COMPICT

.196

.174

.248

−.240

−.268

.302

.347

.256

.511

1.000

  

AUTICT

.186

.195

.272

−.270

−.299

.283

.351

.258

.494

.621

1.000

 

SOIAICT

.213

.070

.120

−.129

−.154

.230

.349

.317

.378

.520

.494

1.000

Appendix 3: Correlation coefficient matrices between the variables at school-level

 

ICTSCH

USESCH

SCHLOCAT

SCHTYPE

STRATIO

SCHSIZE

RATCMP

CLSIZE

EDUSHORT

STAFFSHOR

SCIERES

ICTSCH

1.000

          

USESCH

.058

1.000

         

SCHLOCAT

.143

.192

1.000

        

SCHTYPE

.015

−.100

−.215

1.000

       

STRATIO

−.270

.124

.134

−.415

1.000

      

SCHSIZE

−.221

.221

.144

−.143

.415

1.000

     

RATCMP

.186

.268

.150

.047

−.075

−.264

1.000

    

CLSIZE

−.369

.066

−.009

−.122

.304

.457

−.400

1.000

   

EDUSHORT

−.299

.177

−.102

−.108

.093

.067

.080

.102

1.000

  

STAFFSHOR

−.243

.127

−.148

−.005

.087

−.001

.077

.053

.690

1.000

 

SCIERES

.403

.153

.275

.021

−.130

−.011

.200

−.170

−.265

−.328

1.000

Appendix 4: Internal reliability of each construct

Construct

Observation

Estimate

Factor loading

CR

AVE

Unstd

S.E

t(sig)

Std

SMC

AUTICT

AUT1

1.000

  

.804

.646

.896

.633

 

AUT2

.985

.013

74.689***

.793

.629

  
 

AUT3

.878

.012

71.270***

.764

.584

  
 

AUT4

.997

.013

74.973***

.796

.634

  
 

AUT5

.898

.012

77.962***

.821

.674

  

COMPICT

ICO1

1.000

  

.632

.399

.839

.640

 

ICO2

1.376

.024

56.564***

.850

.723

  
 

ICO3

1.493

.027

55.777***

.894

.799

  

HOMESCH

HMS1

1.000

  

.550

.303

.833

.504

 

HMS2

1.368

.033

42.069***

.667

.445

  
 

HMS3

1.558

.033

47.298***

.849

.721

  
 

HMS4

1.268

.028

45.420***

.766

.587

  
 

HMS5

1.175

.028

42.682***

.683

.466

  

SCIACT

SAT1

1.000

  

.627

.393

.898

.693

 

SAT2

1.513

.023

65.369***

.945

.893

  
 

SAT3

1.532

.023

65.714***

.959

.920

  
 

SAT4

1.185

.021

55.701***

.751

.564

  

SCIEFF

SEF1

1.000

  

.686

.471

.867

.522

 

SEF2

1.194

.021

56.182***

.735

.540

  
 

SEF3

1.274

.022

58.717***

.775

.601

  
 

SEF4

1.273

.022

58.516***

.771

.594

  
 

SEF5

1.027

.020

50.172***

.647

.419

  
 

SEF6

1.254

.023

54.724***

.713

.508

  

Appendix 5: Discriminant validity between constructs

 

AVE

SCIACT

SCIEFF

HOMSCH

COMICT

AUTICT

SCIACT

.693

.832

    

SCIEFF

.522

.360

.722

   

HOMSCH

.504

.382

.217

.710

  

COMICT

.640

.207

.215

.297

.800

 

AUTICT

.633

.152

.222

.275

.669

.796

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Guo, Q., Qiao, C. & Ibrahim, B. The Mechanism of Influence Between ICT and Students’ Science Literacy: a Hierarchical and Structural Equation Modelling Study. J Sci Educ Technol 31, 272–288 (2022). https://doi.org/10.1007/s10956-021-09954-9

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