Development E-Portoqu: A Validated Digital Portfolio that Improves Vocational Teachers’ Performance

Didik Iskandar; Aceng Hasani; Hidayatullah Hidayatullah

doi:10.55927/jeda.v4i3.391

Authors

Didik Iskandar Sultan Ageng Tirtayasa University
Aceng Hasani Sultan Ageng Tirtayasa University
Hidayatullah Hidayatullah State Islamic University of Sultan Maulana Hasanuddin

DOI:

https://doi.org/10.55927/jeda.v4i3.391

Keywords:

Digital Portfolio, Vocational Education, Teacher Appraisal, ADDIE Model

Abstract

This study develops and validates ePortoqu, a digital portfolio for vocational teacher appraisal in Indonesia, using the ADDIE-based Research and Development (R&D) model. A quasi-experimental design was conducted across 10 vocational schools involving 120 teachers and 12 experts. Validation included content validity (Aiken’s V = 0.85–0.93), usability testing (SUS mean = 79.2; α = 0.91), and effectiveness evaluation through pre- and post-tests. Results showed significant improvement in teacher performance (Mpre = 72.4 vs. Mpost = 84.9; t(119) = 9.27, p < 0.001, d = 1.04), particularly in reflective practice (+15.2 points). Usage analytics indicated consistent artefact uploads (median = 15/teacher) and a rapid 48-hour feedback cycle. The study contributes by demonstrating tri-validation (content, usability, and effectiveness) of digital portfolios in vocational contexts and offering design principles for scalable teacher appraisal systems in developing countries. ePortoqu shifts appraisal from administrative routine to reflective, feedback-driven practice with implications for policy and AI-based integration in vocational education.

References

Al-Khasawneh, A., & Jaradat, R. (2021). Technology acceptance and adoption of digital tools among teachers: Evidence from vocational education. Education and Information Technologies, 26(3), 3215–3232. https://doi.org/10.1007/s10639-020-10413-8

Bicen, H., & Cavus, N. (2022). The impact of digital portfolios on teacher professional development and reflective practices. Computers & Education, 185, 104520. https://doi.org/10.1016/j.compedu.2022.104520

Brooke, J. (2021). SUS: A retrospective. International Journal of Human-Computer Interaction, 37(3), 226–233. https://doi.org/10.1080/10447318.2020.1860510

Çelik, İ., & Aydin, M. (2021). Evaluating teacher technology integration using the TPACK framework. Journal of Educational Computing Research, 59(5), 940–963. https://doi.org/10.1177/0735633120978224

Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). Sage.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Field, A., Miles, J., & Field, Z. (2022). Discovering statistics using IBM SPSS statistics (6th ed.). Sage.

Gefen, D., Rigdon, E. E., & Straub, D. (2011). An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii–xiv. https://doi.org/10.2307/23044042

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Jisc. (2020). Student digital experience insights survey 2020: Findings from students in UK further and higher education. Jisc. https://doi.org/10.5281/zenodo.4309230

Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press.

Koo, A., & Tam, C. (2023). Digital appraisal and formative assessment: Emerging practices in vocational training. Journal of Vocational Education and Training, 75(4), 587–605. https://doi.org/10.1080/13636820.2022.2109817

Lai, J. W. M., & Bower, M. (2020). How is the use of technology in education evaluated? A systematic review. Computers & Education, 151, 103819. https://doi.org/10.1016/j.compedu.2020.103819

Lee, M., & Tsai, C. C. (2021). Teachers’ use of digital portfolios for assessment and professional growth. Australasian Journal of Educational Technology, 37(5), 88–103. https://doi.org/10.14742/ajet.7045

Miles, M. B., Huberman, A. M., & Saldaña, J. (2020). Qualitative data analysis: A methods sourcebook (4th ed.). Sage.

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

Moon, T., & Lim, C. (2022). Exploring the role of feedback in online portfolio-based learning. Interactive Learning Environments, 30(6), 1055–1072. https://doi.org/10.1080/10494820.2020.1769681

OECD. (2021). Teachers and leaders in vocational education and training. OECD Publishing. https://doi.org/10.1787/59f4d0a9-en

Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539–569. https://doi.org/10.1146/annurev-psych-120710-100452

Revelle, W., & Condon, D. M. (2019). Reliability from α to ω: A tutorial. Psychological Assessment, 31(12), 1395–1411. https://doi.org/10.1037/pas0000754

Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). Sage.

Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2022). Motivation in education: Theory, research, and practice (5th ed.). Pearson.

Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14. https://doi.org/10.3102/0013189X015002004

Susanto, H., & Wibowo, A. (2024). Teacher reflection as a performance metric: Evidence from Indonesian vocational schools. Asia Pacific Journal of Education, 44(1), 78–95. https://doi.org/10.1080/02188791.2023.2175026

Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.

Teo, T., Huang, F., & Hoi, C. K. W. (2018). Expanding the technology acceptance model to examine teacher acceptance of online learning technology. Interactive Learning Environments, 26(7), 897–910. https://doi.org/10.1080/10494820.2017.1421560

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540