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Quality evaluation for multimedia contents of e-learning systems using the ANP approach on high speed network

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Abstract

E-learning systems have played an important role in the education field and have been widely employed in many educational institutions. Although the need to evaluate the quality of e-learning systems is emerging, there is currently no appropriate evaluation method due to the complicated correlations between quality attributes. This study develops a quality evaluation model that calculates the priority weights of each quality attribute while accounting for their correlations and evaluates the overall quality of a learning system with numerical results. First, the study constructs a quality attribute network that reflects the correlations between 4 main quality clusters and 19 sub-attributes. Second, it calculates the priority weights of the attributes using the Analytic Network Process (ANP). Finally, using the quality network and weights, this study evaluates three types of e-learning systems employed by Kyunghee Cyber University. The results indicate that the proposed evaluation method provides a mechanism for objectively analyzing and comparing the qualities of various kinds of learning systems and suggests guidelines for constructors and managers of learning systems.

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References

  1. Adibi S (2010) A remote interactive non-repudiation multimedia-based m-learning system. Telematics Inform 27(4):377–393

    Article  Google Scholar 

  2. Aldholay AH, Isaac O, Abdullah Z, Ramayah T (2018) The role of transformational leadership as a mediating variable in DeLone and McLean information system success model: The context of online learning usage in Yemen. Telematics Inform 35(5):1421–1437

    Article  Google Scholar 

  3. Alkhattabi M, Neagu D, Cullen A (2011) Assessing information quality of e-learning systems: a web mining approach. Comput Hum Behav 27(2):862–873

    Article  Google Scholar 

  4. Aragonés-Beltrán P, Chaparro-González F, Pastor-Ferrando JP, Rodríguez-Pozo F (2010) An ANP-based approach for the selection of photovoltaic solar power plant investment projects. Renew Sust Energ Rev 14(1):249–264

    Article  Google Scholar 

  5. Aragonés-Beltrán P, García-Melón M, Montesinos-Valera J (2017) How to assess stakeholders' influence in project management? A proposal based on the Analytic Network Process. Int J Proj Manag 35(3):451–462

    Article  Google Scholar 

  6. Carchiolo V, Longheu A, Malgeri M (2010) Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment. Inf Sci 180(10):1893–1907

    Article  Google Scholar 

  7. Chang C-W, Wu C-R, Lin C-T, Lin H-L (2007) Evaluating digital video recorder systems using analytic hierarchy and analytic network processes. Inf Sci 177(16):3383–3396

    Article  Google Scholar 

  8. Chao R-J, Chen Y-H (2009) Evaluation of the criteria and effectiveness of distance e-learning with consistent fuzzy preference relations. Expert Syst Appl 36:10657–10662

    Article  Google Scholar 

  9. Chiu C-M, Sun S-Y, Sun P-C, Ju TL (2007) An empirical analysis of the antecedents of web-based learning continuance. Comput Educ 49:1224–1245

    Article  Google Scholar 

  10. Chuang SC, Tsai CC (2005) Preferences toward the constructivist internet-based learning environments among high school students in Taiwan. Comput Hum Behav 21(1):255–272

    Article  Google Scholar 

  11. Chung SH, Lee AHL, Pearn WL (2006) Analytic Network Process (ANP) Approach for Product Mix Planning in Semiconductor Fabricator. Int J Prod Econ 96:15–36

    Article  Google Scholar 

  12. Davis FD, Bogozzi RP, Warshaw PR (1989) User acceptance of computer technology – A comparison of two theoretical models. Manag Sci 35(8):982–1003

    Article  Google Scholar 

  13. DeLone WH, McLean ER (1992) Information systems success: the quest for the dependent variable. Inf Syst Res 3(1):60–95

    Article  Google Scholar 

  14. DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: A ten year update. J Manag Inf Syst 19(4):9–30

    Article  Google Scholar 

  15. Georgieva ES, Smrikarov AS, Georgiev TS (2011) Evaluation of mobile learning system. Procedia Computer Science 3:632–637

    Article  Google Scholar 

  16. Gunasekaran A, McNeil RD, Shaul D (2002) E-learning: Research and applications. Ind Commer Train 34(2):44–53

    Article  Google Scholar 

  17. Halonen R, Acton T, Golden W, Conboy K (2009) DeLone & McLean success model as a descriptive tool in evaluating the use of a virtual learning environment, International Conference on Organizational Learning, Knowledge and Capabilities (OLKC 2009). Netherlands, Amsterdam, pp 1–16

    Google Scholar 

  18. Halonen R, Thomander H (2008) Measuring Knowledge Transfer Success by D&M. Working Papers on Information Systems 8(41):1–12

    Google Scholar 

  19. Herrera-Viedma E, Pasi G, Lopez-Herrera AG, Porcel C (2006) Evaluating the Information Quality of Web Sites: A Methodology Based on Fuzzy Computing with Words. J Am Soc Inf Sci Technol 57(4):538–549

    Article  Google Scholar 

  20. Ho-Won J, Seung-Gweon K, Chung C-S (2004) Measuring Software Product Quality: A Survey of ISO/IEC 9126. IEEE Softw 21(5):88–92

    Article  Google Scholar 

  21. Huang Y-M, Huang Y-M, Huang S-H, Lin Y-T (2012) A ubiquitous English vocabulary learning system: Evidence of active/passive attitudes vs. usefulness/ease-of-use. Comput Educ 58(1):273–282

    Article  Google Scholar 

  22. Jiang X, Fan H, Zhang Y, Yuan Z (2018) Using interpretive structural modeling and fuzzy analytic network process to identify and allocate risks in Arctic shipping strategic alliance. Polar Science 17:83–93

    Article  Google Scholar 

  23. Jung U, Seo DW (2010) An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria. Decis Support Syst 49(3):335–342

    Article  Google Scholar 

  24. Lee-Post A (2009) E-Learning Success Model: an Information Systems Perspective. Electronic Journal of e-Learning 7(1):61–70

    Google Scholar 

  25. Lin H-F (2010) An application of fuzzy AHP for evaluating course website quality. Comput Educ 54:877–888

    Article  Google Scholar 

  26. Meade LM, Sarkis J (1999) Analyzing Organizational Project Alternatives for Agile Manufacturing Processes: An Analytical Network Approach. Int J Prod Res 37:241–261

    Article  MATH  Google Scholar 

  27. Michael R, Iris V (2005) Linking Theory and Practice: Performing a Reality Check on a Model of IS Success, ECIS 2005 Information Systems in a Rapidly Changing Economy, pp. 26-28

  28. Ngai EWT, Poon JKL, Chan YHC (2007) Empirical examination of the adoption of WebCT using TAM. Comput Educ 48(2):250–267

    Article  Google Scholar 

  29. Pituch KA, Lee YK (2006) The influence of system characteristics on e-learning use. Comput Educ 47(2):222–244

    Article  Google Scholar 

  30. Rai A, Lang SS, Welker RB (2002) Assessing the validity of IS success model: An empirical test and theoretical analysis. Inf Syst Res 13(1):50–69

    Article  Google Scholar 

  31. Ramayah T, Ahmad NH, Lo M-C (2010) The role of quality factors in intention to continue using an e-learning system in Malaysia. Procedia Soc Behav Sci 2(2):5422–5426

    Article  Google Scholar 

  32. Ranganathan C, Ganapathy S (2002) Key Dimensions of Business to Consumer Web Sites. Inf Manag 39:457–465

    Article  Google Scholar 

  33. Roky H, Al Meriouh Y (2015) Evaluation by Users of an Industrial Information System (XPPS) Based on the DeLone and McLean Model for IS Success. Procedia Economics and Finance 26:903–913

    Article  Google Scholar 

  34. Saaty TL (1980) The analytic hierarchy process, planning, priority setting and resource allocation. McGraw-Hill, New York

    MATH  Google Scholar 

  35. Saaty TL (1996) Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburgh

    Google Scholar 

  36. Samarasinghe SM, Tretiakov A (2009) A multi-dimensional measure of e-learning systems success, Proceedings ascilite Auckland, pp. 908-910

  37. Srinivasan A (1985) Alternative Measures of System Effectiveness: Associations and Implications. MIS Q 9(3):243–253

    Article  Google Scholar 

  38. Su J-H, Wang B-W, Hsiao C-Y, Tseng VS (2010) Personalized rough-set-based recommendation by integrating multiple contents and collaborative information. Inf Sci 180(1):113–131

    Article  Google Scholar 

  39. Villalba MT, Fernandez-Sanz L, Martinez JJ (2010) Empirical support for the generation of domain-oriented quality models. IET Softw 4(1):1–14

    Article  Google Scholar 

  40. Wang W-T, Wang C-C (2009) An empirical study of instructor adoption of web-based learning systems. Comput Educ 53:761–774

    Article  Google Scholar 

  41. Wang Y-S, Wang H-Y, Shee DY (2007) Measuring e-learning systems success in an organizational context: Scale development and validation. Comput Hum Behav 23:1792–1808

    Article  Google Scholar 

  42. Wang S-L, Wu C-Y (2011) Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Syst Appl 38(9):10831–10838

    Article  Google Scholar 

  43. Wixom BH, Todd PA (2005) A theoretical integration of user satisfaction and technology acceptance. Inf Syst Res 16(1):85–102

    Article  Google Scholar 

  44. Wu T-T, Huang Y-M, Chao H-C, Park JH (2011) Personlized English reading sequencing based on learning portfolio analysis. Inf Sci, Available online 21:1–16

    Google Scholar 

  45. Yüksel İ, Dagˇdeviren M (2007) Using the analytic network process (ANP) in a SWOT analysis – A case study for a textile firm. Inf Sci 177(16):3364–3382

    Article  MATH  Google Scholar 

  46. Zhang L, Wen H, Li D, Fu Z, Cui S (2010) E-learning adoption intention and its key influence factors based on innovation adoption theory. Math Comput Model 51:1428–1432

    Article  Google Scholar 

  47. Zhao Y (2007) Social studies teachers’ perspectives of technology integration. J Technol Teach Educ 15(3):311–333

    Google Scholar 

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Author information

Authors and Affiliations

  1. Humanitas College, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul, 130-701, Republic of Korea

    Cheol-Rim Choi & Hwa-Young Jeong

Authors
  1. Cheol-Rim Choi
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  2. Hwa-Young Jeong
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Corresponding author

Correspondence to Hwa-Young Jeong.

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Appendices

Appendix 1. Un-weighted supermatrix

 

SYSQ

INFQ

SERQ

ATTR

AB

RT

ST

UF

EU

AC

CO

CU

FM

RE

AV

NA

RS

EM

MC

CD

LA

WD

EN

S

Y

S

Q

AB

0.0558

0.0000

0.0000

0.9677

0.2969

0.0000

0.0000

0.0000

0.0000

0.1673

0.4745

0.2518

0.1473

0.0852

0.0000

0.0000

0.0000

0.0000

0.0000

RT

0.1407

0.0323

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.1150

0.0407

0.1259

0.5628

0.0558

0.0000

0.0000

0.0000

0.0000

0.0000

ST

0.0867

0.9677

1.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.5277

0.2592

0.1479

0.1238

0.0683

0.0000

0.0000

0.0000

0.0000

0.0000

UF

0.2264

0.0000

0.0000

0.0323

0.5396

0.0000

0.0000

0.0000

0.0000

0.0950

0.0770

0.2518

0.0905

0.2865

0.0000

0.0000

0.0000

0.0000

0.0000

EU

0.4904

0.0000

0.0000

0.0000

0.1635

0.0000

0.0000

0.0000

0.0000

0.0950

0.1486

0.2226

0.0756

0.5042

0.0000

0.0000

0.0000

0.0000

0.0000

I

N

F

Q

AC

0.0000

0.0000

0.0000

0.0000

0.0000

0.0793

0.5416

0.0000

0.2385

0.1793

0.2833

0.2699

0.1584

0.2869

0.1716

0.5538

0.5637

0.2879

0.1750

CO

0.0000

0.0000

0.0000

0.0000

0.0000

0.5008

0.0772

0.0000

0.6250

0.5727

0.5048

0.5476

0.1258

0.0829

0.2426

0.2420

0.2576

0.2046

0.2462

CU

0.0000

0.0000

0.0000

0.0000

0.0000

0.1400

0.1344

0.0323

0.0000

0.1410

0.1494

0.0559

0.6147

0.5553

0.2426

0.0719

0.1095

0.1692

0.2894

FM

0.0000

0.0000

0.0000

0.0000

0.0000

0.2799

0.2468

0.9677

0.1365

0.1070

0.0625

0.1266

0.1011

0.0749

0.3432

0.1323

0.0692

0.3383

0.2894

S

E

R

Q

RE

0.1649

0.1678

0.4645

0.1250

0.1429

0.0000

0.0000

0.0000

0.0000

1.0000

0.0867

0.0000

0.0000

0.0000

0.2222

0.1044

0.0514

0.0780

0.0525

AV

0.5058

0.1367

0.1895

0.2500

0.1429

0.0000

0.0000

0.0000

0.0000

0.0000

0.0558

0.0000

0.9677

0.6250

0.2559

0.1234

0.0646

0.1521

0.1678

NA

0.1001

0.0725

0.0871

0.2500

0.2857

0.0000

0.0000

0.0000

0.0000

0.0000

0.2264

1.0000

0.0000

0.2385

0.1717

0.5664

0.4970

0.1609

0.2758

RS

0.1556

0.5078

0.1534

0.1250

0.1429

0.0000

0.0000

0.0000

0.0000

0.0000

0.1407

0.0000

0.0323

0.0000

0.1280

0.0768

0.1410

0.0805

0.0619

EM

0.0726

0.1152

0.1055

0.2500

0.2856

0.0000

0.0000

0.0000

0.0000

0.0000

0.4904

0.0000

0.0000

0.1365

0.2222

0.1290

0.2460

0.5285

0.4420

A

T

T

R

MC

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

1.0000

0.2969

0.0000

0.2385

0.4146

CD

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.1634

0.5396

0.6250

0.1748

LA

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.1634

0.0000

0.0899

WD

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.5397

0.0000

0.1365

0.2598

EN

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.2670

0.0000

0.0609

Appendix 2. Weighted supermatrix

 

SYSQ

INFQ

SERQ

ATTR

AB

RT

ST

UF

EU

AC

CO

CU

FM

RE

AV

NA

RS

EM

MC

CD

LA

WD

EN

S

Y

S

Q

AB

0.0419

0.0000

0.0000

0.7258

0.2227

0.0000

0.0000

0.0000

0.0000

0.1046

0.2966

0.1574

0.0921

0.0533

0.0000

0.0000

0.0000

0.0000

0.0000

RT

0.1055

0.0242

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0719

0.0254

0.0787

0.3518

0.0349

0.0000

0.0000

0.0000

0.0000

0.0000

ST

0.0650

0.7258

0.7500

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.3298

0.1620

0.0924

0.0774

0.0427

0.0000

0.0000

0.0000

0.0000

0.0000

UF

0.1698

0.0000

0.0000

0.0242

0.4047

0.0000

0.0000

0.0000

0.0000

0.0594

0.0481

0.1574

0.0566

0.1791

0.0000

0.0000

0.0000

0.0000

0.0000

EU

0.3678

0.0000

0.0000

0.0000

0.1226

0.0000

0.0000

0.0000

0.0000

0.0594

0.0929

0.1391

0.0473

0.3151

0.0000

0.0000

0.0000

0.0000

0.0000

I

N

F

Q

AC

0.0000

0.0000

0.0000

0.0000

0.0000

0.0793

0.5416

0.0000

0.2385

0.0245

0.0387

0.0368

0.0216

0.0392

0.1054

0.3403

0.3463

0.1769

0.1075

CO

0.0000

0.0000

0.0000

0.0000

0.0000

0.5008

0.0772

0.0000

0.6250

0.0782

0.0689

0.0747

0.0172

0.0113

0.1491

0.1487

0.1583

0.1257

0.1513

CU

0.0000

0.0000

0.0000

0.0000

0.0000

0.1400

0.1344

0.0323

0.0000

0.0192

0.0204

0.0076

0.0839

0.0758

0.1491

0.0442

0.0673

0.1040

0.1778

FM

0.0000

0.0000

0.0000

0.0000

0.0000

0.2799

0.2468

0.9677

0.1365

0.0146

0.0085

0.0173

0.0138

0.0102

0.2109

0.0813

0.0425

0.2079

0.1778

S

E

R

Q

RE

0.0412

0.0420

0.1161

0.0313

0.0357

0.0000

0.0000

0.0000

0.0000

0.2385

0.0207

0.0000

0.0000

0.0000

0.0260

0.0122

0.0060

0.0091

0.0062

AV

0.1265

0.0342

0.0474

0.0625

0.0357

0.0000

0.0000

0.0000

0.0000

0.0000

0.0133

0.0000

0.2308

0.1491

0.0300

0.0145

0.0076

0.0178

0.0197

NA

0.0250

0.0181

0.0218

0.0625

0.0714

0.0000

0.0000

0.0000

0.0000

0.0000

0.0540

0.2385

0.0000

0.0569

0.0201

0.0664

0.0582

0.0189

0.0323

RS

0.0392

0.1270

0.0384

0.0313

0.0357

0.0000

0.0000

0.0000

0.0000

0.0000

0.0336

0.0000

0.0077

0.0000

0.0150

0.0090

0.0165

0.0094

0.0073

EM

0.0182

0.0288

0.0264

0.0625

0.0714

0.0000

0.0000

0.0000

0.0000

0.0000

0.1170

0.0000

0.0000

0.0326

0.0260

0.0151

0.0288

0.0619

0.0518

A

T

T

R

MC

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.2684

0.0797

0.0000

0.0640

0.1113

CD

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0439

0.1448

0.1678

0.0469

LA

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0439

0.0000

0.0241

WD

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.1449

0.0000

0.0366

0.0697

EN

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0797

0.0000

0.0163

Appendix 3. Limited supermatrix (Normalized)

 

SYSQ

INFQ

SERQ

ATTR

AB

RT

ST

UF

EU

AC

CO

CU

FM

RE

AV

NA

RS

EM

MC

CD

LA

WD

EN

S

Y

S

Q

AB

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

0.0892

RT

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

0.0364

ST

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

0.1182

UF

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

0.0579

EU

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

0.0602

I

N

F

Q

AC

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

0.1103

CO

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

0.1151

CU

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

0.0556

FM

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

0.1271

S

E

R

Q

RE

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

0.0308

AV

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

0.0415

NA

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

0.0392

RS

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

0.0195

EM

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

0.0284

A

T

T

R

MC

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

0.0275

CD

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

0.0212

LA

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

0.0036

WD

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

0.0132

EN

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

0.0051

Appendix 4. Questionnaires for evaluating web-based learning systems (WBLS)

Criteria

Question/Statement

Reference

SYSQ

AB. I can easily access the WBLS anytime I want to use it.

Pituch and Lee [29]

RT. The waiting time for loading learning materials is reasonable.

H.F. Lin [25]

EU. It is easy for me to understand how to study using the WBLS.

Davis et al. [12]

ST. The WBLS is consistently stable while I study without system errors.

UF. The supporting tools, processes and communications provided by the WBLS are friendly to use.

 

INFQ

AC. The WBLS can provide me with accurate and precise information to do my study.

CU. Learning materials from the WBLS are always up to date.

Rai et al. [30]

CO. The WBLS provides me with a complete set of learning materials without construction errors in the learning content.

H.F. Lin [25]

FM. The content of learning materials (such as range, depth and structure) are clearly presented on the web-page.

 

SERQ

RE. The WBLS provides the right solution to my requests.

H.F. Lin [25]

RS. I can receive a quick response from the WBLS when I encounter technical problems or require communication.

AV. The WBLS is present and ready for my immediate use at any time.

 

NA. The WBLS has easy navigation for finding learning materials.

H.F. Lin [25]

EM. According to the learner’s background, the WBLS provides individual attention to the learner.

H.F. Lin [25]

ATTR

MC. The WBLS fully uses multimedia features to increase learning efficiency.

 

WD. The webpage design of the WBLS is well-organized.

H.F. Lin [25]

CD. The WBLS provides appropriate learning scenarios to facilitate communications.

EN. Using the WBLS provides learners with enjoyment.

LA. Using the WBLS is helpful for attaining a maximal level of learning performance.

 

Appendix 5. Correlations between sub-attributes in each quality cluster

figure c

1.1 Appendix 6. Statistical analysis for score values of users

Attribute

VoD

On-screen

Animation

Min.

Max.

Avg.

Std.

Min.

Max.

Avg.

Std.

Min.

Max.

Avg.

Std.

System quality

 Accessibility

 Response time

 Stability

 User friendly

 Easy-to-use

0.80

0.80

0.80

0.80

0.80

1.00

1.00

1.00

1.00

1.00

0.92

0.88

0.90

0.84

0.90

0.0982

0.0977

0.0994

0.0804

0.1006

0.80

0.80

0.80

0.80

0.80

1.00

1.00

1.00

1.00

1.00

0.88

0.86

0.88

0.82

0.82

0.0985

0.0922

0.0931

0.0603

0.0632

0.80

0.80

0.80

0.80

0.80

1.00

1.00

1.00

1.00

1.00

0.90

0.88

0.88

0.86

0.84

0.1006

0.0985

0.0990

0.0922

0.0804

Information quality

 Accuracy

 Completeness

 Currency

 Format

0.60

0.80

0.40

0.80

1.00

1.00

0.60

1.00

0.82

0.88

0.54

0.82

0.1083

0.0985

0.0922

0.0603

0.80

0.80

0.60

0.60

1.00

1.00

1.00

1.00

0.90

0.90

0.82

0.80

0.1006

0.1005

0.1671

0.1272

0.80

0.80

0.60

0.80

1.00

1.00

0.80

1.00

0.90

0.92

0.64

0.84

0.1006

0.0980

0.0804

0.0821

Service quality

 Reliability

 Availability

 Navigability

 Responsiveness

 Empathy

0.80

0.80

0.80

0.80

0.80

1.00

1.00

1.00

1.00

1.00

0.88

0.92

0.94

0.92

0.90

0.0985

0.0990

0.0922

0.0990

0.1005

0.80

0.80

0.80

0.80

0.60

1.00

1.00

1.00

1.00

1.00

0.86

0.88

0.88

0.88

0.88

0.0922

0.0985

0.0990

0.0993

0.1334

0.80

0.80

0.80

0.60

0.80

1.00

1.00

1.00

1.00

1.00

0.84

0.90

0.90

0.90

0.94

0.0836

0.1006

0.1005

0.1046

0.0922

Attractiveness

 Multimedia capability

 Course design

 Learnability

 Webpage design

 Enjoyment

0.80

0.80

0.80

0.60

0.60

1.00

1.00

1.00

1.00

1.00

0.88

0.88

0.88

0.80

0.82

0.0980

0.0993

0.0975

0.0899

0.1408

0.80

0.60

0.60

0.60

0.60

1.00

1.00

1.00

1.00

1.00

0.88

0.86

0.82

0.78

0.78

0.0980

0.1288

0.1083

0.1099

0.1124

0.60

0.60

0.60

0.60

0.60

1.00

1.00

1.00

1.00

1.00

0.86

0.86

0.84

0.80

0.78

0.0945

0.0960

0.1003

0.1272

0.1083

  1. (Number of respondents = 150)

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Choi, CR., Jeong, HY. Quality evaluation for multimedia contents of e-learning systems using the ANP approach on high speed network. Multimed Tools Appl 78, 28853–28875 (2019). https://doi.org/10.1007/s11042-019-7351-8

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