ABSTRACT
This study aims to reveal whether there is a difference in the learning interest (LI) and learning outcomes (LO) of online learning through desktop and mobile applications based on student perceptions. The research occurs at the four Indonesian Information Technology Majoring Vocational High School, Malang City, East Java Province, Indonesia. The research design uses two major stages: the application development stage and the experiment stage under static-group pretest-posttest design. The application development stage uses a predictive product development approach, and an expert audit process is carried out to check the feasibility of the product so that it can be used in the learning process. The group that received a desktop application treatment (Desktop Application Group/ DAG) was set as the control group, and another was appointed as the experimental group, which received a mobile application treatment (Mobile Application Group/ MAG). LI data was collected through questionnaires, while LO data was collected through performance tests. Data was collected before and after the treatment was applied to each group. The data analyzed in the experimental research design were 240 data, of which 120 data were for the desktop application group and 120 for the mobile application group. Data were analyzed using descriptive statistics and independent-sample t-tests. The MAG-LI score (M=85.28; SD=5.89) was higher than the DAG-LI score (M=79.66; SD=0.39). Independent-sample t-test analysis on the DAG-LI and MAG-LI scores yielded t(238)=8.50, p<0.01, and Cohen's d=1.35. The MAG-LI score significantly differs from the DAG-LI score. The MAG-LO score (M=74.44; SD=0.54) was higher than the DAG-LO score (M=72.53; SD=0.44). Independent-sample T-Test analysis on the DAG-LO and MAG-LO scores yielded t(238)=2.66, p<0.01, and Cohen's d=3.88. The MAG-LO score significantly differs from the DAG-LO score.
- K. Komalasari, S. Fitriasari, and D. N. Anggraini, “Living values-based digital learning resources in civic education,” New Educational Review, vol. 63, pp. 85–96, 2021, doi: 10.15804/tner.2021.63.1.07.Google ScholarCross Ref
- M. Leshchenko, Y. Lavrysh, and N. Kononets, “Framework for assessment the quality of digital learning resources for personalized learning intensification,” New Educational Review, vol. 64, pp. 148–159, 2021, doi: 10.15804/tner.2021.64.2.12.Google ScholarCross Ref
- Y. Q. Bai and J. W. Jiang, “Meta-analysis of factors affecting the use of digital learning resources,” Interactive Learning Environments, 2022, doi: 10.1080/10494820.2022.2091608.Google ScholarCross Ref
- E. Abdoh, “Online health information seeking and digital health literacy among information and learning resources undergraduate students,” Journal of Academic Librarianship, vol. 48, no. 6, 2022, doi: 10.1016/j.acalib.2022.102603.Google ScholarCross Ref
- E. Lacka and T. C. Wong, “Examining the impact of digital technologies on students’ higher education outcomes: the case of the virtual learning environment and social media,” Studies in Higher Education, vol. 46, no. 8, pp. 1621–1634, 2021, doi: 10.1080/03075079.2019.1698533.Google ScholarCross Ref
- A. D. Herlambang, S. H. Wijoyo, and A. Rachmadi, “Intelligent Computing System to Predict Vocational High School Student Learning Achievement Using Naïve Bayes Algorithm,” Journal of Information Technology and Computer Science, vol. 4, no. 1, pp. 15–25, 2019, doi: 10.25126/jitecs.20194169.Google ScholarCross Ref
- F. Amalia, T. Afirianto, and A. D. Herlambang, “Weblog Acceptance as an Communication and Alternative Media Learning for Senior High School in Millenium Era,” International Conference on Socio-Political Entrepreneurship, pp. 136–142, 2017.Google Scholar
- K. Kulasegaram, D. Axelrod, and C. Ringsted, “Do One Then See One: Sequencing Discovery Learning and Direct Instruction for Simulation-Based Technical Skills Training,” Academic Medicine, vol. 93, no. 11S, pp. S37–S44, 2018, doi: 10.1097/ACM.0000000000002378.Google ScholarCross Ref
- J. Torrente , “Development of game-like simulations for procedural knowledge in healthcare education,” IEEE Transactions on Learning Technologies, vol. 7, no. 1, pp. 69–82, 2014, doi: 10.1109/TLT.2013.35.Google ScholarCross Ref
- V. Garneli and K. Chorianopoulos, “Programming video games and simulations in science education: exploring computational thinking through code analysis,” Interactive Learning Environments, vol. 26, no. 3, pp. 386–401, 2018, doi: 10.1080/10494820.2017.1337036.Google ScholarCross Ref
- H. J. Dong, R. Abdulla, S. K. Selvaperumal, S. Duraikannan, R. Lakshmanan, and M. K. Abbas, “Interactive on smart classroom system using beacon technology,” International Journal of Electrical and Computer Engineering, vol. 9, no. 5, pp. 4250–4257, 2019, doi: 10.11591/ijece.v9i5.pp4250-4257.Google ScholarCross Ref
- A. Sung, K. Leong, and C. Lee, “A study of learners’ interactive preference on multimedia microlearning,” Journal of Work-Applied Management, vol. 15, no. 1, pp. 96–119, 2022, doi: 10.1108/JWAM-01-2022-0007.Google ScholarCross Ref
- I. Boerma, F. van der Wilt, R. Bouwer, M. van der Schoot, and C. van der Veen, “Mind Mapping during Interactive Book Reading in Early Childhood Classrooms: Does It Support Young Children's Language Competence?,” Early Educ Dev, vol. 33, no. 6, pp. 1077–1093, 2022, doi: 10.1080/10409289.2021.1929686.Google ScholarCross Ref
- E. P. Lousã and M. D. Lousã, “Effect of technological and digital learning resources on students’ soft skills within remote learning: The mediating role of perceived efficacy,” Int J Train Dev, vol. 27, no. 1, pp. 1–17, 2023, doi: 10.1111/ijtd.12280.Google ScholarCross Ref
- S. C. Kong, “Developing information literacy and critical thinking skills through domain knowledge learning in digital classrooms: An experience of practicing flipped classroom strategy,” Comput Educ, vol. 78, pp. 160–173, 2014, doi: 10.1016/j.compedu.2014.05.009.Google ScholarCross Ref
- R. Schmid and D. Petko, “Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students?,” Comput Educ, vol. 136, pp. 75–86, 2019, doi: 10.1016/j.compedu.2019.03.006.Google ScholarDigital Library
- B. Klimova and P. Poulova, “Personalized learning environment—a case study,” Adv Sci Lett, vol. 22, no. 5–6, pp. 1129–1132, 2016, doi: 10.1166/asl.2016.6678.Google ScholarCross Ref
- N. A. Samah, N. Yahaya, and M. B. Ali, “Individual differences in online personalized learning environment,” Educational Research and Reviews, vol. 6, no. 7, pp. 516–521, 2011, [Online]. Available: https://api.elsevier.com/content/abstract/scopus_id/79961058803Google Scholar
- S. Cheng, J. C. Huang, and W. Hebert, “Profiles of vocational college students’ achievement emotions in online learning environments: Antecedents and outcomes,” Comput Human Behav, vol. 138, 2023, doi: 10.1016/j.chb.2022.107452.Google ScholarDigital Library
- W. C. V. Wu, J. S. C. Hsieh, and J. C. Yang, “Creating an online learning community in a flipped classroom to enhance efl learners’ oral proficiency,” Educational Technology and Society, vol. 20, no. 2, pp. 142–157, 2017, [Online]. Available: https://api.elsevier.com/content/abstract/scopus_id/85018761456Google Scholar
- Y. Ariyanto, B. Harijanto, and A. N. Asri, “Analyzing Student's Learning Interests in the Implementation of Blended Learning Using Data Mining,” International journal of online and biomedical engineering, vol. 16, no. 11, pp. 153–160, 2020, doi: 10.3991/ijoe.v16i11.16453.Google ScholarCross Ref
- B. Svoen, S. Dobson, and L. T. Bjørge, “Let's talk and share! Refugees and migrants building social inclusion and wellbeing through digital stories and online learning resources,” International Journal of Inclusive Education, vol. 25, no. 1, pp. 94–107, 2021, doi: 10.1080/13603116.2019.1678802.Google ScholarCross Ref
- C. L. Lai and G. J. Hwang, “A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course,” Comput Educ, vol. 100, pp. 126–140, 2016, doi: 10.1016/j.compedu.2016.05.006.Google ScholarDigital Library
- J. J. Torres-Gordillo and E. A. Herrero-Vázquez, “PLE: Personal learning environment vs. Personalized learning environment,” Revista Espanola de Orientacion y Psicopedagogia, vol. 27, no. 3, pp. 26–42, 2016, doi: 10.5944/reop.vol.27.num.3.2016.18798.Google ScholarCross Ref
- S. M. Syed-Khuzzan and J. S. Goulding, “Personalised learning environments (part 2): A conceptual model for construction,” Industrial and Commercial Training, vol. 41, no. 1, pp. 47–56, 2009, doi: 10.1108/00197850910927769.Google ScholarCross Ref
- F. Sucu and Ü. Çakiroğlu, “ICT teachers’ adaptations to online instruction during Covid-19 pandemic,” International Journal of Information and Learning Technology, vol. 39, no. 3, pp. 209–226, 2022, doi: 10.1108/IJILT-09-2021-0149.Google ScholarCross Ref
- T. Trust and E. Pektas, “Using the ADDIE Model and Universal Design for Learning Principles to Develop an Open Online Course for Teacher Professional Development,” Journal of Digital Learning in Teacher Education, vol. 34, no. 4, pp. 219–233, 2018, doi: 10.1080/21532974.2018.1494521.Google ScholarCross Ref
- A. K. N. Hess and K. Greer, “Designing for engagement: Using the ADDIE model to integrate high-impact practices into an online information literacy course,” Communications in Information Literacy, vol. 10, no. 2, pp. 264–282, 2016, doi: 10.15760/comminfolit.2016.10.2.27.Google ScholarCross Ref
- E. Alepis and M. Virvou, “Mobile versus desktop educational applications,” Intelligent Systems Reference Library, vol. 64, pp. 65–72, 2014, doi: 10.1007/978-3-642-53851-3_6.Google ScholarCross Ref
- M. Lahham, H. Hazimeh, and M. Malli, “A survey on trends on mobile app development and applications,” International Journal of Wireless and Mobile Computing, vol. 23, no. 3–4, pp. 350–360, 2022, doi: 10.1504/ijwmc.2022.127604.Google ScholarDigital Library
- J. M. Fautch, “The flipped classroom for teaching organic chemistry in small classes: Is it effective?,” Chemistry Education Research and Practice, vol. 16, no. 1, pp. 179–186, 2015, doi: 10.1039/c4rp00230j.Google ScholarCross Ref
- I. Leontyeva, N. Pronkin, and M. Tsvetkova, “Visualization of learning and memorization: Is the mind mapping based on mobile platforms learning more effective?,” International Journal of Instruction, vol. 14, no. 4, pp. 173–186, 2021, doi: 10.29333/iji.2021.14411a.Google ScholarCross Ref
- H. Yildiz Durak, “The Effects of Using Different Tools in Programming Teaching of Secondary School Students on Engagement, Computational Thinking and Reflective Thinking Skills for Problem Solving,” Technology, Knowledge and Learning, vol. 25, no. 1, pp. 179–195, 2020, doi: 10.1007/s10758-018-9391-y.Google ScholarCross Ref
- A. D. Herlambang, Y. Tyroni Mursityo, M. C. Saputra, and L. Novianti, “Criteria-Based Evaluation of Academic Information System Usage at Brawijaya University Based On Modified Technology Acceptance Model (TAM),” in 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings, 2018, pp. 272–277. doi: 10.1109/SIET.2018.8693159.Google ScholarCross Ref
- R. A. Karim, N. Idris, I. Ismail, N. H. M. Saad, and A. G. Abu, “The impact of utilizing mobile-assisted mind mapping technique (Mammat) on the development of undergraduate students’ writing performance,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 12 Special Issue, pp. 674–680, 2019, doi: 10.5373/JARDCS/V11SP12/20193264.Google ScholarCross Ref
- D. Novaliendry, A. Huda, LatifahAnnisa, R. R. K. Costa, Yudhistira, and F. Eliza, “The Effectiveness of Web-Based Mobile Learning for Mobile Subjects on Computers and Basic Networks in Vocational High Schools,” International Journal of Interactive Mobile Technologies, vol. 17, no. 9, pp. 20–30, May 2023, doi: 10.3991/ijim.v17i09.39337.Google ScholarCross Ref
- A. D. Herlambang, B. Budiman, and W. S. Wardhono, “Interactive Procedural Knowledge Learning Resources Development in The Context of Competency-Based Training Instructional Approach and Interactive Media Design Subjects for Information Technology Vocational High School,” Elinvo (Electronics, Informatics, and Vocational Education); Vol 7, No 1 (2022): Mei 2022DO - 10.21831/elinvo.v7i1.48215 , Aug. 2022, doi: https://doi.org/10.21831/elinvo.v7i1.48215.Google ScholarCross Ref
- P. Goodyear, “Technology and the articulation of vocational and academic interests: Reflections on time, space and e-learning,” Studies in Continuing Education, vol. 28, no. 2, pp. 83–98, 2006, doi: 10.1080/01580370600750973.Google ScholarCross Ref
- A. Pantoja, “Vocational Interests of Students in an International Network Learning Environment,” REICE. Revista Iberoamericana Sobre Calidad, Eficacia y Cambio en Educacion, vol. 20, no. 3, pp. 185–203, 2022, doi: 10.15366/reice2022.20.3.010.Google ScholarCross Ref
- M. J. Rodriguez-Triana, L. P. Prieto, A. Holzer, and D. Gillet, “Instruction, Student Engagement, and Learning Outcomes: A Case Study Using Anonymous Social Media in a Face-to-Face Classroom,” IEEE Transactions on Learning Technologies, vol. 13, no. 4, pp. 718–733, 2020, doi: 10.1109/TLT.2020.2995557.Google ScholarCross Ref
- Baedhowi , “Effectiveness of school operational support funds to quality of students learning process and outcomes in vocational high school,” Adv Sci Lett, vol. 23, no. 1, pp. 524–527, 2017, doi: 10.1166/asl.2017.7242.Google ScholarCross Ref
- J. R. Fraenkel, N. E. Wallen, and H. H. Hyun, How to Design and Evaluate Research in Education. New York: McGraw Hill LLC, 2022. Accessed: Jun. 27, 2023. [Online]. Available: https://www.mheducation.com/highered/product/how-design-evaluate-research-education-fraenkel-wallen/M9781259913839.htmlGoogle Scholar
- L. R. Aiken, “Three coefficients for analyzing the reliability and validity of ratings,” Educ Psychol Meas, vol. 45, no. 1, pp. 131–142, Mar. 1985, doi: 10.1177/0013164485451012.Google ScholarCross Ref
- J. A. Levin, J. A. Fox, and D. R. Forde, Elementary Statistics in Social Research. USA: Pearson Education, Inc., 2017.Google Scholar
- J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. New York: Lawrence Erlbaum Associates, 1988.Google Scholar
- S. Fox and C. Griffy-Brown, “Technology philosophies, politics and policies in society: Technology in Society Briefing,” Technol Soc, vol. 73, 2023, doi: 10.1016/j.techsoc.2023.102259.Google ScholarCross Ref
- N. A. Farah, A. D. Herlambang, and A. Rachmadi, “Comparison of Indonesian Population Information System Service Performace Based On Information System Functional Scorecard Theory,” Journal of Information Technology and Computer Science, vol. 6, no. 3, pp. 297–307, 2021, doi: 10.25126/jitecs.202163306.Google ScholarCross Ref
- Y. Hanafi, N. M. Murtadho, A. Ikhsan, and T. N. Diyana, “Reinforcing public university student's worship education by developing and implementing mobile-learning management system in the ADDIE instructional design model,” International Journal of Interactive Mobile Technologies, vol. 14, no. 2, pp. 215–241, 2020, doi: 10.3991/ijim.v14i02.11380.Google ScholarCross Ref
- T. Lersilp and S. Lersilp, “Use of information technology for communication and learning in secondary school students with a hearing disability,” Educ Sci (Basel), vol. 9, no. 1, 2019, doi: 10.3390/educsci9010057.Google ScholarCross Ref
- I. Jahnke, Y. M. Lee, M. Pham, H. He, and L. Austin, “Unpacking the Inherent Design Principles of Mobile Microlearning,” Technology, Knowledge and Learning, vol. 25, no. 3, pp. 585–619, 2020, doi: 10.1007/s10758-019-09413-w.Google ScholarCross Ref
- M. Sandesara , “Design and Experience of Mobile Applications: A Pilot Survey,” Mathematics, vol. 10, no. 14. 2022. doi: 10.3390/math10142380.Google ScholarCross Ref
- L. Vorona-Slivinskaya, D. Bokov, and O. Li, “Visualization of Learning and Memorizing Processes Using Mobile Devices: Mind Mapping and Charting,” International Journal of Interactive Mobile Technologies, vol. 14, no. 21, pp. 136–152, 2020, doi: 10.3991/ijim.v14i21.18475.Google ScholarCross Ref
- M. Zhang and X. Li, “Design of Smart Classroom System Based on Internet of Things Technology and Smart Classroom,” Mobile Information Systems, vol. 2021, 2021, doi: 10.1155/2021/5438878.Google ScholarCross Ref
- S. Abulhaija, S. Hattab, A. Abdeen, and W. Etaiwi, “Mobile Applications Rating Performance: A Survey,” International Journal of Interactive Mobile Technologies, vol. 16, no. 19, pp. 133–146, 2022, doi: 10.3991/ijim.v16i19.32051.Google ScholarCross Ref
- T. C. Hsu, W. L. Chen, and G. J. Hwang, “Impacts of interactions between peer assessment and learning styles on students’ mobile learning achievements and motivations in vocational design certification courses,” Interactive Learning Environments, vol. 2020, 2020, doi: 10.1080/10494820.2020.1833351.Google ScholarCross Ref
- Suharno, N. A. Pambudi, and B. Harjanto, “Vocational education in Indonesia: History, development, opportunities, and challenges,” Child Youth Serv Rev, vol. 115, no. 2020, p. 105092, Aug. 2020, doi: 10.1016/j.childyouth.2020.105092.Google ScholarCross Ref
- L. K. J. Baartman and E. De Bruijn, “Integrating knowledge, skills and attitudes: Conceptualising learning processes towards vocational competence,” Educational Research Review, vol. 6, no. 2. pp. 125–134, 2011. doi: 10.1016/j.edurev.2011.03.001.Google ScholarCross Ref
Index Terms
- The Online Learning Interest and Learning Outcomes Through Mobile and Desktop Application Based on the Indonesian Information Technology Majoring Vocational High School Student's Perspective
Recommendations
Project-Based Learning Implementation Effect Comparison on the Students' Cognitive and Psychomotor Learning Outcomes at Indonesian Vocational High School Majoring in Information Technology
SIET '23: Proceedings of the 8th International Conference on Sustainable Information Engineering and TechnologyThis research examines the influence of Project-Based Learning (PjBL) implementation on student learning outcomes in the context of Basic Computer and Networking subjects in Indonesian Vocational High Schools majoring in Information Technology during ...
Integrating Blended Learning into Situational Writing for Vocational High School Students
This study aims to explore vocational high school students' attitudes toward integrating blended learning into situational writing, and the learning effectiveness of that integration. A total of 84 vocational high students were divided into an ...
An analysis of the influence of a mobile learning application on the learning outcomes of higher education students
This study investigated the influence of a mobile learning (M-Learning) application on the learning outcomes of university students. The learning outcomes were assessed in terms of secured score in the Communication Skills course using the App for the ...
Comments