Multiple representations framework in technology acceptance: A structural equation modeling of science educational videos in teaching and learning redox reactions
Open accessAbstract
This study explored the role of the Multiple Representations Framework (MR) within the Technology Acceptance Model (TAM) in understanding pre-service teachers' acceptance of science educational videos in teaching and learning redox reactions. Conducted within the context of an analytical chemistry course, the study employed partial least squares structural equation modeling (PLS-SEM) to analyze the relationships between perceived ease of use (PEU), perceived usefulness (PU), and intention to use (IU), integrating motivation as a key variable. Descriptive statistics revealed positive perspectives on macroscopic, microscopic, and symbolic representations, as well as favorable views on TAM constructs. The measurement model confirmed the reliability and validity of the instruments, with strong outer loadings, internal consistency, and discriminant validity. MR significantly impacted PU and mediated the relationships between PEU, PU, and IU, highlighting its central role in enhancing perceived usefulness. However, MR did not moderate the relationship between PEU and PU, and PEU alone did not directly influence IU. These findings underscore the significance of content quality and representation in influencing technology adoption. The study concludes that integrating MR into educational tools improves conceptual understanding and supports technology adoption. It recommends incorporating MR-based resources into STEM curricula and providing professional development for educators to optimize their design and application.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 57 · Cited by 4
Research works printed but not linked: 65
- Cardellini, L., Chemistry: why the subject is difficult? Educación Quí mica, 2012, 23(2): 305-310. https://doi.org/10.1016/S0187-893X(17)30158-1 · DOI
- Woldeamanuel, M., Ataga, H. and Engida, T., What makes chemistry difficult? African Journal of Chemical Education, 2014, 4(2): 31-43. https://www.ajol.info/in- dex.php/ajce/article/view/104070
- Johnstone, A., Macro-and micro-chemistry, School Science Review, 1982, 64: 377-379.
- Sanchez, J., Translational skills of students in chemistry. Science Education International, 2018, 29(4): 214-219. https://www.icaseonline.net/journal/index.php/sei/article/view/81
- Memiş, E.K., Et, S.Z. and Sönmez, E., Integration of technology into science teaching: a phenomenological study on the experiences of the pre-service teachers. Science Education International, 2023, 34(3): 166-176. https://doi.org/10.33828/sei.v34.i3.1 · DOI
- Opona, A.J.D., Sanchez, J.M.P., and Bondoc, K.P., Use of multiple representations in online general chemistry class: promoting chemical understanding during the Covid-19 pandemic. Kimika, 2022, 33(2): 21-33. https://doi.org/10.26534/kimika.v33i2.21-33 · DOI
- Wang, L., Hodges, G. and Lee, J., Connecting macroscopic, molecular, and symbolic representations with immersive technologies in high school chemistry: the case of redox reactions. Educational Sciences, 2022, 12(7): 428. https://doi.org/10.3390/educsci12070428 · DOI
- Naimah, A., The use of video as a learning media in science learning (a systematic review). Al-Ishlah: Jurnal Pendidikan, 2022, 24(2): 6941-6950. https://doi.org/10.35445/alishlah.v14i4.1565 · DOI
- Erlina, E., Enawaty, E., Melati, H.A. and Lestari, I., Video with multiple representations approach to promote students' conceptual understanding of intermolecular forces. International Journal of Evaluation and Research in Educa andion, 2023, 12(4): 1994-2002. https://doi.org/10.11591/ijere.v12i4.25243 · DOI
- Fatmala, Enawaty, E., Lestari, I., Hairida and Erlina, Development of multiple representation learning videos on hydrocarbon nomenclature. Jurnal IPA dan Pembelajaran IPA, 2024, 8(3): 249-266. https://doi.org/10.24815/jipi.v8i3.39504 · DOI
- Astuti, A.T.D. and Kamaludin, A., Development of animation video learning media loaded contextual on reaction material redox using web apps animaker. Jurnal Pendidikan Sains Indonesia, 2023, 11(3): 574-590. https://dx.doi.org/10.24815/jpsi.v11i3.30434 · DOI
- Shukor, N.A., Abdullah, Z. and Mamad, N., Teachers' perception of using STEM video for teaching and learning. Proceedings of the 26 th International Conference on Computers in Education, 2018. https://v0.apsce.net/icce/icce2018/wp-content/uploads/2018/12/C7-13.pdf
- Guion, J.B.A., Sumalinog, K.P., Taniola, J.V.M., Gomez, S.D., Padilla, K.J., Cortes, S.F., et al., Effectiveness and acceptability of laboratory experiment videos in blended chemistry learning. 2023, 34(1): 36-46. https://doi.org/10.26534/kimika.v34i1.36-46 · DOI
- Sanchez, J.M.P., Integrated macro-micro-symbolic approach in teaching secondary chemistry. Kimika, 28(2): 22-29. https://doi.org/10.26534/kimika.v28i2.238 · DOI
- Luviani, S.D., Mulyani, S. and Widhiyanti, T., A review of three levels of chemical representation until 2020. Journal of Physics: Conference Series, 2021, 1806. https://doi.org/10.1088/1742-6596/1806/1/012206 · DOI
- Lewthwaite, B., Thinking about practical work in chemistry: teachers' considerations of selected practices for the macroscopic experience. Chemistry Education Research and Practice, 2014, 15: 35-46. https://doi.org/10.1039/C3RP00122A · DOI
- Rivera, G.M. and Sanchez, J.M.P., Use of contextualized instructional materials: the case of teaching gas laws in a public uptown high school. Orbital: The Electronic Journal of Chemistry, 2020, 12(4): 276-281. http://dx.doi.org/10.17807/orbital.v12i4.1526 · DOI
- Sanchez, J.M.P., Fernandez, M.J.U., Abgao, J.M.O., Sarona, H.H., Asenjo, S.B.C., Guiroy, B.V., et al., Experimenting on natural acid-base indicators: a home-based chemistry activity during the COVID-19 pandemic as evaluated by teachers. Kimika, 2021, 32(1): 34-45. https://doi.org/10.26534/kimika.v32i1.34-45 · DOI
- Cerna, E., Cortes, S.F., Padilla, K.J., Pepino, C.A., Guion, J.B., Gomez, S.D., et al., Natural acid-base indicators as home-based experiments: feasibility, satisfaction, and teachers' experiences in secondary science blended instruction. Jurnal Pendidikan Progresif, 2023, 13(2): 597-609. https://doi.org/10.23960/jpp.v13.i2.202334 · DOI
- Santos, V.C. and Arroio, A., The representational levels: influences and contributions to research in chemical education. Journal of Turkish Science Education, 2016, 13(1): 3-18. https://doi.org/10.12973/tused.10153a · DOI
- Gumonan, M.D. and Bug-os, M.A.A.C., Development and validation of graphic novel as a supplementary learning material in chemical bonding. American Journal of Educational Research, 2021, 9(10): 654-659. https://doi.org/10.12691/education-9-10-8 · DOI
- Lausin, F. and Kijai, J., The effects of using particulate diagrams on high school students' conceptual understanding of stoichiometry. Human Behavior, Development and Society, 2020, 21(1): 68-77. https://so01.tci-thaijo.org/index.php/hbds/article/view/240003
- Sanchez, J.M.P., Understanding of kinetic molecular theory of gases in three modes of representation among tenth-grade students in chemistry. International Journal of Learning, Teaching and Educational Research, 2021, 20(1): 48-63. https://doi.org/10.26803/ijlter.20.1.3 · DOI
- Lansangan, R.V., Yoma, K.S., Yoma, C.A.F., Sibug, K.P.B., Cabrera, R.M., Gregorio, E.B., et al., CHEMISTORY: integration of creative story writing in understanding chemical elements in online learning. Kimika, 2021, 32(1), 110-128. https://doi.org/10.26534/kimika.v32i1.110-128 · DOI
- Cotiangco, E.N.C., Huraño, N.J.B., Sodoso, E.R.G., Sumagang, M.G.P., Jumao-as, J.J.C., Canoy, J.H., et al., Android-based audio-visual comics in enhancing conceptual understanding and motivation of chemistry concepts. Orbital: The Electronic Journal of Chemistry, 2024, 16(2): 125-135. http://dx.doi.org/10.17807/orbital.v16i2.19953 · DOI
- Liu, Y. and Taber, K.S., Analysing symbolic expressions in secondary school chemistry: their functions and implications for pedagogy. Chemistry Education Research and Practice, 2016, 17: 439-451. https://doi.org/10.1039/C6RP00013D · DOI
- Akesson-Nilsson, G. and Adbo, K., Student translations of the symbolic level of chemistry. Education Sciences, 2024, 14(7): 775. https://doi.org/10.3390/educsci14070775 · DOI
- Jaber, L.Z., and Boujaoude, S., A macro-micro-symbolic teaching to promote relational understanding of chemical reactions. International Journal of Science Education, 2012, 34(7): 973-998. https://doi.org/10.1080/09500693.2011.569959 · DOI
- Lorduy, D.J. and Naranjo, C.P., Teachers' and students' perceptions on the use of the chemical triplet in the teaching-learning process. Revista Cientí fica, 2020, 39(3): 324-340. https://doi.org/10.14483/23448350.16427 · DOI
- Venkatesh, V., Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 2000, 11(4): 342-365. https://doi.org/10.1287/isre.11.4.342.11872 · DOI · Paper page
- He, Y., Chen, Q. and Kitkuakul, S., Regulatory focus and technology acceptance: perceived ease of use and usefulness as efficacy. Cogent Business and Management, 2018, 5(1). https://doi.org/10.1080/23311975.2018.1459006 · DOI
- Bower, M., DeWitt, D. and Lai, J.W.M., Reasons associated with preservice teachers' intention to use immersive virtual reality in education. British Journal of Educational Technology, 2020, 51(6): 2215-2233. https://doi.org/10.1111/bjet.13009 · DOI
- Alejandro, I.M.A., Sanchez, J.M. ., Sumalinog, G.G., Mananay, J.A., Goles, C.E. and Fernandez, C.B., Pre-service teachers' technology acceptance of artificial intelligence (AI) applications in education. STEM Education, 2024, 4(4): 445-465. https://dx.doi.org/10.3934/steme.2024024 · DOI
- Sayaf, A.M., Alamri, M.M., Alqahtani, M.A. and Alrahmi, W.M., Factors influencing university students' adoption of digital learning technology in teaching and learning. Sustainability, 2022, 14(1): 493. https://doi.org/10.3390/su14010493 · DOI
- Widarti, H.R., Permanansari, A. and Mulyani, S., Student misconception on redox titration (a challenge on the course implementation through cognitive dissonance based on the multiple representations). Jurnal Pendidikan IPA Indonesia, 2016, 5(1): 56-62. https://doi.org/10.15294/jpii.v5i1.5790 · DOI
- Li, W.S.S. and Arshad, M.Y., Application of multiple representation levels in redox reactions among tenth grade chemistry teachers. Journal of Turkish Science Education, 2014, 11(3): 35-52. https://doi.org/10.12973/tused.10117a · DOI
- Goes, L.F., Fernandez, C. and Eilks, I., The development of pedagogical content knowledge about teaching redox reactions in German chemistry teacher education. Education Sciences, 2020, 10(7): 170. https://doi.org/10.3390/educsci10070170 · DOI
- Adu-Gyamfi, K., Ampiah, J.G., and Agyei, D.D., Participatory teaching and learning approach: a framework for teaching redox reactions at high school level. International Journal of Education and Practice, 2020, 8(1), 106-120. https://doi.org/10.18488/journal.61.2020.81.106.120 · DOI
- Morales, A., Obaya, A., Montaño, C. and Vargas, Y.M., Exploratory assessment of strategy for learning redox reactions in high school. International Journal of Education, 2020, 8(1), 23-37. https://doi.org/10.5121/ije.2020.8102 · DOI
- Syamsuri, B.S., Anwar, S. and Sumarna, O., Development of teaching material oxidation-reduction reactions through four steps teaching material development (4S TMD). Journal of Physics: Conference Series, 2017, 895. https://doi.org/10.1088/1742-6596/895/1/012111 · DOI
- Ndukwe, U.E., Guided inquiry teaching strategy and students' performance in redox reaction in Rivers State. Faculty of Natural and Applied Sciences Journal of Mathematics, and Science Education, 2023, 4(2): 110-118. https://fnasjournals.com/index.php/FNAS-JMSE/article/view/164
- Mayeem, P.B., Somarid, L.Y. and Abu, R.N., Use of activity-based method to evaluate the teaching and learning of redox reactions among senior high school students. Open Journal of Educational Research, 2023, 3(3): 153-170. https://doi.org/10.31586/ojer.2023.730 · DOI
- Adjei, F., Hanson, R., Sam, A. and Sedegah, S., The use of collaborative approaches on students' performances in redox reactions. Science Education International, 2022, 33(2): 163-170. https://doi.org/10.33828/sei.v33.i2.4 · DOI
- Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M., A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (Second Edition), SAGE Publications, 2017.
- Galatsopoulou, F., Kenterelidou, C., Kotsakis, R. and Matsiola, M., Examining students' perceptions towards video-based and video-assisted active learning scenarios in journalism and communication courses. Education Sources, 2022, 12. https://doi.org/10.3390/educsci12020074 · DOI
- Kohler, S. and Dietrich, T.C., Potentials and limitations of educational videos on YouTube for science communication. Frontiers in Communication, 2021, 6. https://doi.org/10.3389/fcomm.2021.581302 · DOI
- Hair, J.F., Risher, J.J., Sarstedt, M. and Ringle, C.M., When to use and how to report the results of PLS-SEM. European Business Review, 2019, 31(1): 2-24. https://doi.org/10.1108/EBR-11-2018-0203 · DOI · Paper page
- Fornell, C. and Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 1981, 18: 39-50. https://doi.org/10.1177/002224378101800104 · DOI · Paper page
- Kock, N., Using indicator correlation fit indices in PLS-SEM: selecting the algorithm with the best fit. Data Analysis Perspectives Journal, 2020, 1(4): 1-4. https://scriptwarp.com/dapj/2020_DAPJ_1_4/Kock_2020_DAPJ_1_4_XsCorrMatrixIndices.p
- Iacobucci, D., Structural equations modeling: fit indices, sample size, and advanced topics. Journal of Consumer Psychology, 2010, 20(1): 90-98. https://doi.org/10.1016/j.jcps.2009.09.003 · DOI · Paper page
- Basuki, R., Tarigan, Z.J.H., Siagian, H., Limanta, L.S., Setiawan, D. and Mochtar, J., The effects of perceived ease of use, usefulness, enjoyment and intention to use online platforms on behavioral intention in online movie watching during the pandemic era. International Journal of Data and Network Science, 2022, 6: 253-262. https://doi.org/10.5267/j.ijdns.2021.9.003 · DOI
- Kiourexidou, M., Kanavos, A., Klouvidaki, M. and Antonopoulos, N., Exploring the role of user experience and interface design communication in augmented reality for education. Multimodal Technologies and Interaction, 2024, 8(6). https://doi.org/10.3390/mti8060043 · DOI
- Bag, S., Aich, P. and Islam, M.A., Behavioral intention of "digital natives" toward adapting the online education system in higher education. Journal of Applied Research in Higher Education, 2020, 14(1): 16-40. https://doi.org/10.1108/JARHE-08-2020-0278 · DOI
- Gunawan, N. and Rahmawan, S., Chemical learning module based on multiple representations of redox materials. Jurnal Tadris Kimiya, 2023, 8(1): 69-80. https://journal.uinsgd.ac.id/index.php/tadris-kimiya/article/view/23075
- Shernoff, E.S., Von Schalscha, K., Gabbard, J.L., Delmarre, A., Frazier, S.L., Buche, C., et al., Evaluating the usability and instructional design quality of interactive virtual training for teachers (IVT-T). Educational Technology Research and Development, 2020, 68: 3235-3262. https://doi.org/10.1007/s11423-020-09819-9 · DOI
- Pandita, A. and Kiran, R., The technology interface and student engagement are significant stimuli in sustainable student satisfaction. Sustainability, 2023, 15(10). https://doi.org/10.3390/su15107923 · DOI
- Ferná ndez, A.A., López-Torres, M., Ferná ndez, J.J. and Vá zquez-Garcí a, D., Student-generated videos to promote understanding of chemical reactions. Journal of Chemcial Education, 2023, 2: 1039-1046. https://doi.org/10.1021/acs.jchemed.2c00813 · DOI
- Bancoro, J.C., Exploring the influence of perceived usefulness and perceived ease of use on technology engagement of business administration instructors. International Journal of Asian Business and Management, 2024, 3(2): 149-168. https://doi.org/10.55927/ijabm.v3i2.8714 · DOI
- Iyamuremye, A., Mukiza, J., Nsengimana, T., Kampire, E., Sylvain, H. and Nsabayezu, E., Knowledge construction in chemistry through web-based learning strategy: a synthesis of literature. Education and Information Technologies, 2023, 29: 5585-5604. https://doi.org/10.1007/s10639-022-11369-x · DOI
- Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D., User acceptance of information technology: toward a unified view. MIS Quarterly, 2003, 27: 425-478. https://doi.org/10.2307/30036540 · DOI · Paper page
- Ainsworth, S., DeFT: a conceptual framework for considering learning with multiple representations. Learning and Instruction, 2006, 16: 183-198. https://doi.org/10.1016/j.learninstruc.2006.03.001 · DOI · Paper page
- Rau, M.A., Aleven, V. and Rummel, N., Intelligent tutoring systems with multiple representations and self-explanation prompts support learning of fractions. Proceedings of the 14 th International Conference on Artificial Intelligence in Education, 2009, 200: 441-448. http://dx.doi.org/10.3233/978-1-60750-028-5-441 · DOI
- Berthold, K., Eysink, T.H.S. and Renkl, A., Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 2009, 37: 345-363. https://doi.org/10.1007/s11251-008-9051-z · DOI · Paper page
- Barclay, D., Thompson, R. and Higgins, C., The partial least squares (PLS) approach to causal modeling: personal computer use as an illustration. Technology Studies, 1995, 2: 285-309.
- Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M., A primer on partial least squares structural equation modeling, 2017, SAGE Publications, Inc., Los Angeles.
- Authors
- Sanchez, J. M. P., & Science Education Department, C. O. T. E.
- Affiliations
- Cebu Normal University (PH)
- Corresponding author
- Sanchez, Joje Mar P.
- ORCID
- Sanchez, Joje Mar P.
- Venue
- STEM Education
- Published
- 2025
- Volume
- 5
- Issue
- 5
- Pages
- 855-881
- ISSN
- 2767-1925 (ISSN-L)
- OpenAlex
- W4412583442
- Cited by
- 4
- References
- 57
- Citations by year
- 2026: 3 · 2025: 1
- FWCI
- 4.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- STEM Education (publishedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Science Education and Pedagogy
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- technology acceptance model, redox reactions, pre-service chemistry teachers, perceived usefulness, perceived ease of use, intention to use, motivation, analytical chemistry education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review