Ecampus's Role in Supporting Learning in Higher Education
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10.58578/alsystech.v1i1.1371
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Abstract
Technology plays an important role in supporting an education that can develop students' understanding. One platform that can be used as a learning medium is the E-Campus. This web application is often used by students and lecturers. In line with that the research is intended to find out the use of E-Campus as a learning medium for universities in the current era. This research was conducted using a quantitative method by conducting online surveys and in-depth interviews with students at various universities. The results of this study are the use of the ecampus web application in the learning process at various universities today, which is beneficial for a number of students and lecturers at tertiary institutions. To evaluate and understand ecampus in the learning process requires a deep understanding for students so they can use it properly. Therefore the limitation of this research is that the researcher did not make a design for using the e-campus in the study. The researcher hopes that further researchers can conduct research to build and develop this ecampus web application.
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References
Aloqaily, M., Otoum, S., Ridhawi, I. A., & Jararweh, Y. (2019). An intrusion detection system for connected vehicles in smart cities. Ad Hoc Networks, 90, 101842. https://doi.org/10.1016/j.adhoc.2019.02.001
Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020). COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10), 249. https://doi.org/10.3390/a13100249
Arranz, N., Arroyabe, M., Li, J., & Fernandez de Arroyabe, J. C. (2020). Innovation as a driver of eco‐innovation in the firm: An approach from the dynamic capabilities theory. Business Strategy and the Environment, 29(3), 1494–1503. https://doi.org/10.1002/bse.2448
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2018). The Moral Machine experiment. Nature, 563(7729), 59–64. https://doi.org/10.1038/s41586-018-0637-6
Backes, C., Abdelkader, A. M., Alonso, C., Andrieux-Ledier, A., Arenal, R., Azpeitia, J., Balakrishnan, N., Banszerus, L., Barjon, J., Bartali, R., Bellani, S., Berger, C., Berger, R., Ortega, M. M. B., Bernard, C., Beton, P. H., Beyer, A., Bianco, A., Bøggild, P., … Garcia-Hernandez, M. (2020). Production and processing of graphene and related materials. 2D Materials, 7(2), 022001. https://doi.org/10.1088/2053-1583/ab1e0a
Badrinathan, S. (2021). Educative Interventions to Combat Misinformation: Evidence from a Field Experiment in India. American Political Science Review, 115(4), 1325–1341. https://doi.org/10.1017/S0003055421000459
Bottazzi, L., & Lusardi, A. (2021). Stereotypes in financial literacy: Evidence from PISA. Journal of Corporate Finance, 71, 101831. https://doi.org/10.1016/j.jcorpfin.2020.101831
Calixto, J. B. (2019). The role of natural products in modern drug discovery. Anais Da Academia Brasileira de Ciências, 91(suppl 3), e20190105. https://doi.org/10.1590/0001-3765201920190105
Castro, M. R. H., Calthorpe, L. M., Fogh, S. E., McAllister, S., Johnson, C. L., Isaacs, E. D., Ishizaki, A., Kozas, A., Lo, D., Rennke, S., Davis, J., & Chang, A. (2021). Lessons From Learners: Adapting Medical Student Education During and Post COVID-19. Academic Medicine, 96(12), 1671–1679. https://doi.org/10.1097/ACM.0000000000004148
Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020). Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing, 97, 106754. https://doi.org/10.1016/j.asoc.2020.106754
Chen, S., Li, R., Li, X., & Xie, J. (2018). Electrospinning: An enabling nanotechnology platform for drug delivery and regenerative medicine. Advanced Drug Delivery Reviews, 132, 188–213. https://doi.org/10.1016/j.addr.2018.05.001
Chen, S.-H., Chen, T.-Y., Chang, Y.-H., Wei, H.-W., & Shih, W.-K. (2018). UnistorFS: A Union Storage File System Design for Resource Sharing between Memory and Storage on Persistent RAM-Based Systems. ACM Transactions on Storage, 14(1), 1–22. https://doi.org/10.1145/3177918
Corroon, J., & Phillips, J. A. (2018). A Cross-Sectional Study of Cannabidiol Users. Cannabis and Cannabinoid Research, 3(1), 152–161. https://doi.org/10.1089/can.2018.0006
Deldjoo, Y., Schedl, M., Cremonesi, P., & Pasi, G. (2021). Recommender Systems Leveraging Multimedia Content. ACM Computing Surveys, 53(5), 1–38. https://doi.org/10.1145/3407190
Du, X., Heumueller, T., Gruber, W., Almora, O., Classen, A., Qu, J., He, F., Unruh, T., Li, N., & Brabec, C. J. (2020). Unraveling the Microstructure‐Related Device Stability for Polymer Solar Cells Based on Nonfullerene Small‐Molecular Acceptors. Advanced Materials, 32(16), 1908305. https://doi.org/10.1002/adma.201908305
Elsalem, L., Al-Azzam, N., Jum’ah, A. A., Obeidat, N., Sindiani, A. M., & Kheirallah, K. A. (2020). Stress and behavioral changes with remote E-exams during the Covid-19 pandemic: A cross-sectional study among undergraduates of medical sciences. Annals of Medicine and Surgery, 60, 271–279. https://doi.org/10.1016/j.amsu.2020.10.058
Forrest, K., Mac Kinnon, M., Tarroja, B., & Samuelsen, S. (2020). Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sectors in California. Applied Energy, 276, 115439. https://doi.org/10.1016/j.apenergy.2020.115439
Gao, W., Ota, H., Kiriya, D., Takei, K., & Javey, A. (2019). Flexible Electronics toward Wearable Sensing. Accounts of Chemical Research, 52(3), 523–533. https://doi.org/10.1021/acs.accounts.8b00500
Gariazzo, C., Stafoggia, M., Bruzzone, S., Pelliccioni, A., & Forastiere, F. (2018). Association between mobile phone traffic volume and road crash fatalities: A population-based case-crossover study. Accident Analysis & Prevention, 115, 25–33. https://doi.org/10.1016/j.aap.2018.03.008
Ghani, N. A., Hamid, S., Targio Hashem, I. A., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417–428. https://doi.org/10.1016/j.chb.2018.08.039
Hassell, L. A., Peterson, J., & Pantanowitz, L. (2021). Pushed Across the Digital Divide: COVID-19 Accelerated Pathology Training onto a New Digital Learning Curve. Academic Pathology, 8, 2374289521994240. https://doi.org/10.1177/2374289521994240
Higgins, D., Hahn, C., Xiang, C., Jaramillo, T. F., & Weber, A. Z. (2019). Gas-Diffusion Electrodes for Carbon Dioxide Reduction: A New Paradigm. ACS Energy Letters, 4(1), 317–324. https://doi.org/10.1021/acsenergylett.8b02035
Hopkinson, P., Zils, M., Hawkins, P., & Roper, S. (2018). Managing a Complex Global Circular Economy Business Model: Opportunities and Challenges. California Management Review, 60(3), 71–94. https://doi.org/10.1177/0008125618764692
Hu, S.-W., Sung, P.-J., Nguyen, T. P., Sheng, Y.-J., & Tsao, H.-K. (2020). UV-Resistant Self-Healing Emulsion Glass as a New Liquid-like Solid Material for 3D Printing. ACS Applied Materials & Interfaces, 12(21), 24450–24457. https://doi.org/10.1021/acsami.0c04121
Idrees, M., Ahmed, S., Mohammed, Z., Korivi, N. S., & Rangari, V. (2020). 3D printed supercapacitor using porous carbon derived from packaging waste. Additive Manufacturing, 36, 101525. https://doi.org/10.1016/j.addma.2020.101525
Ilyas, A., Shahzad, K., & Kamran Malik, M. (2023). Emotion Detection in Code-Mixed Roman Urdu—English Text. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1–28. https://doi.org/10.1145/3552515
Jabreel, M., & Moreno, A. (2019). A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets. Applied Sciences, 9(6), 1123. https://doi.org/10.3390/app9061123
Junker, R. G., Azar, A. G., Lopes, R. A., Lindberg, K. B., Reynders, G., Relan, R., & Madsen, H. (2018). Characterizing the energy flexibility of buildings and districts. Applied Energy, 225, 175–182. https://doi.org/10.1016/j.apenergy.2018.05.037
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Khalid Khan, S., Shiwakoti, N., & Stasinopoulos, P. (2022). A conceptual system dynamics model for cybersecurity assessment of connected and autonomous vehicles. Accident Analysis & Prevention, 165, 106515. https://doi.org/10.1016/j.aap.2021.106515
Kraiem, N., AlKhanjari, Z., Jamoussi, Y., & Al Abri, A. (2020). PerLCol: A Framework for Personalized e-Learning with Social Collaboration Support. International Journal of Computing and Digital Systems, 9(3), 483–493. https://doi.org/10.12785/ijcds/090312
Lakitan, B., Lindiana, L., Widuri, L. I., Kartika, K., Siaga, E., Meihana, M., & Wijaya, A. (2019). Inclusive and Ecologically-Sound Food Crop Cultivation at Tropical Non-Tidal Wetlands in Indonesia. AGRIVITA Journal of Agricultural Science, 41(1). https://doi.org/10.17503/agrivita.v40i0.1717
Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., Azman, A. S., Reich, N. G., & Lessler, J. (2020). The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine, 172(9), 577–582. https://doi.org/10.7326/M20-0504
Lee, J. A., Heberlein, E., Pyle, E., Caughlan, T., Rahaman, D., Sabin, M., & Kaar, J. L. (2021). Evaluation of a Resiliency Focused Health Coaching Intervention for Middle School Students: Building Resilience for Healthy Kids Program. American Journal of Health Promotion, 35(3), 344–351. https://doi.org/10.1177/0890117120959152
Li, F., Fan, S., Wang, Y., & Lu, J. (2021). Chinese University Students’ Experience of WeChat-Based English-Language Vocabulary Learning. Education Sciences, 11(9), 554. https://doi.org/10.3390/educsci11090554
Lin, P.-S., Ozkul, S., Guo, R., & Chen, C. (2018). Assessment of countermeasure effectiveness and informativeness in mitigating wrong-way entries onto limited-access facilities. Accident Analysis & Prevention, 116, 79–93. https://doi.org/10.1016/j.aap.2017.11.027
Lindner, C., Rienow, A., & Jürgens, C. (2019). Augmented Reality applications as digital experiments for education – An example in the Earth-Moon System. Acta Astronautica, 161, 66–74. https://doi.org/10.1016/j.actaastro.2019.05.025
Liu, M., Fang, S., Dong, H., & Xu, C. (2021). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346–361. https://doi.org/10.1016/j.jmsy.2020.06.017
Ma, W., Cheng, F., & Liu, Y. (2018). Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials. ACS Nano, 12(6), 6326–6334. https://doi.org/10.1021/acsnano.8b03569
Makki, A., & Bali, A. O. (2021). The Use of Social Media as a Platform in Education: Ramifications of COVID-19 in Iraq. Academic Journal of Interdisciplinary Studies, 10(3), 394. https://doi.org/10.36941/ajis-2021-0093
Marbin, J., Hutchinson, Y.-V., & Schaeffer, S. (2021). Avoiding the Virtual Pitfall: Identifying and Mitigating Biases in Graduate Medical Education Videoconference Interviews. Academic Medicine, 96(8), 1120–1124. https://doi.org/10.1097/ACM.0000000000003914
Marimuthu, R., Gupta, S., Stapleton, L., Duncan, D., & Pasik-Duncan, B. (2022). Challenging the Digital Divide: Factors Affecting the Availability, Adoption, and Acceptance of Future Technology in Elderly User Communities. Computer, 55(7), 56–66. https://doi.org/10.1109/MC.2022.3172026
Mayer, R. E. (2019). Thirty years of research on online learning. Applied Cognitive Psychology, 33(2), 152–159. https://doi.org/10.1002/acp.3482
McDonald, C. K., MacLeod, N. D., Lisson, S., & Corfield, J. P. (2019). The Integrated Analysis Tool (IAT) – A model for the evaluation of crop-livestock and socio-economic interventions in smallholder farming systems. Agricultural Systems, 176, 102659. https://doi.org/10.1016/j.agsy.2019.102659
Mehta, S., & Zhang, J. (2021). Biochemical Activity Architectures Visualized–Using Genetically Encoded Fluorescent Biosensors to Map the Spatial Boundaries of Signaling Compartments. Accounts of Chemical Research, 54(10), 2409–2420. https://doi.org/10.1021/acs.accounts.1c00056
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine, 52(6), 446–462. https://doi.org/10.1007/s12160-016-9830-8
Nikiphorou, E., Santos, E. J. F., Marques, A., Böhm, P., Bijlsma, J. W., Daien, C. I., Esbensen, B. A., Ferreira, R. J. O., Fragoulis, G. E., Holmes, P., McBain, H., Metsios, G. S., Moe, R. H., Stamm, T. A., de Thurah, A., Zabalan, C., Carmona, L., & Bosworth, A. (2021). 2021 EULAR recommendations for the implementation of self-management strategies in patients with inflammatory arthritis. Annals of the Rheumatic Diseases, 80(10), 1278–1285. https://doi.org/10.1136/annrheumdis-2021-220249
Panis, I. C., Setyosari, P., Kuswandi, D., & Yuliati, L. (2020). Design Gamification Models in Higher Education: A Study in Indonesia. International Journal of Emerging Technologies in Learning (IJET), 15(12), 244. https://doi.org/10.3991/ijet.v15i12.13965
Posever, N., Sehdev, M., Sylla, M., Mashar, R., Mashar, M., & Abioye, A. (2021). Addressing Equity in Global Medical Education During the COVID-19 Pandemic: The Global Medical Education Collaborative. Academic Medicine, 96(11), 1574–1579. https://doi.org/10.1097/ACM.0000000000004230
Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778. https://doi.org/10.1016/j.compedu.2019.103778
Rahman, M. S., & Abdel-Aty, M. (2018). Longitudinal safety evaluation of connected vehicles’ platooning on expressways. Accident Analysis & Prevention, 117, 381–391. https://doi.org/10.1016/j.aap.2017.12.012
Rajeh, M. T., Aboalshamat, K. T., Nassar, A. A., Sembawa, S. N., Al Hebshi, S. A., & Badri, M. K. (2020). Insights on Using Social Media in Dental Education: A Cross-Sectional Study in Saudi Arabia. The Open Dentistry Journal, 14(1), 717–723. https://doi.org/10.2174/1874210602014010717
Ray, J. M., Wong, A. H., Yang, T. J., Buck, S., Joseph, M., Bonz, J. W., Auerbach, M. A., Couturier, K., Tomassoni, A. J., Schwartz, M. L., & Evans, L. V. (2021). Virtual Telesimulation for Medical Students During the COVID-19 Pandemic. Academic Medicine, 96(10), 1431–1435. https://doi.org/10.1097/ACM.0000000000004129
Roberts, D. (2019). Higher education lectures: From passive to active learning via imagery? Active Learning in Higher Education, 20(1), 63–77. https://doi.org/10.1177/1469787417731198
Roy, S., Cherevotan, A., & Peter, S. C. (2018). Thermochemical CO 2 Hydrogenation to Single Carbon Products: Scientific and Technological Challenges. ACS Energy Letters, 3(8), 1938–1966. https://doi.org/10.1021/acsenergylett.8b00740
Shevchik, S. A., Kenel, C., Leinenbach, C., & Wasmer, K. (2018). Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Additive Manufacturing, 21, 598–604. https://doi.org/10.1016/j.addma.2017.11.012
Sun, N., Wei, L., Shi, S., Jiao, D., Song, R., Ma, L., Wang, H., Wang, C., Wang, Z., You, Y., Liu, S., & Wang, H. (2020). A qualitative study on the psychological experience of caregivers of COVID-19 patients. American Journal of Infection Control, 48(6), 592–598. https://doi.org/10.1016/j.ajic.2020.03.018
Sushama, S., Murkute, P., Ghadi, H., & Chakrabarti, S. (2019). Enhancing Acceptor-Based Optical Behavior in Phosphorus-Doped ZnO Thin Films Using Boron as Compensating Species. ACS Applied Electronic Materials, 1(3), 325–339. https://doi.org/10.1021/acsaelm.8b00082
Tan, T. F., Li, Y., Lim, J. S., Gunasekeran, D. V., Teo, Z. L., Ng, W. Y., & Ting, D. SW. (2022). Metaverse and Virtual Health Care in Ophthalmology: Opportunities and Challenges. Asia-Pacific Journal of Ophthalmology, 11(3), 237–246. https://doi.org/10.1097/APO.0000000000000537
Tong, Y., Chen, P., Zhang, M., Zhou, T., Zhang, L., Chu, W., Wu, C., & Xie, Y. (2018). Oxygen Vacancies Confined in Nickel Molybdenum Oxide Porous Nanosheets for Promoted Electrocatalytic Urea Oxidation. ACS Catalysis, 8(1), 1–7. https://doi.org/10.1021/acscatal.7b03177
Vidanaralage, J., Dharmaratne, A., & Haque, S. (2022). Schema and emotion in memory retrieval following video-based learning: An artificial intelligence study. Australasian Journal of Educational Technology, 109–132. https://doi.org/10.14742/ajet.7473
Wainman, B., Pukas, G., Wolak, L., Mohanraj, S., Lamb, J., & Norman, G. R. (2020). The Critical Role of Stereopsis in Virtual and Mixed Reality Learning Environments. Anatomical Sciences Education, 13(3), 401–412. https://doi.org/10.1002/ase.1928
Yu, S., Wang, Y., Jiang, L.-P., Bi, S., & Zhu, J.-J. (2018). Cascade Amplification-Mediated In Situ Hot-Spot Assembly for MicroRNA Detection and Molecular Logic Gate Operations. Analytical Chemistry, 90(7), 4544–4551. https://doi.org/10.1021/acs.analchem.7b04930
Zawacki-Richter, O., & Latchem, C. (2018). Exploring four decades of research in Computers & Education. Computers & Education, 122, 136–152. https://doi.org/10.1016/j.compedu.2018.04.001
Zhang, Z., Yang, M., Yan, X., Guo, X., Li, J., Yang, Y., Wei, D., Liu, L., Xie, J., Liu, Y., Liang, L., & Yao, J. (2020). The Antibody-Free Recognition of Cancer Cells Using Plasmonic Biosensor Platforms with the Anisotropic Resonant Metasurfaces. ACS Applied Materials & Interfaces, 12(10), 11388–11396. https://doi.org/10.1021/acsami.0c00095
Zhao, S., Wu, R., Tao, J., Qu, M., Zhao, M., Fan, C., & Zhao, H. (2023). perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online Games. ACM Transactions on Information Systems, 41(1), 1–29. https://doi.org/10.1145/3530012
