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Students’ Preference Analysis on Online Learning Attributes in Industrial Engineering Education during the COVID-19 Pandemic: A Conjoint Analysis Approach for Sustainable Industrial Engineers

Author

Listed:
  • Ardvin Kester S. Ong

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Yogi Tri Prasetyo

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Michael Nayat Young

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • John Francis T. Diaz

    (Department of Finance and Accounting, Asian Institute of Management, 123 Paseo de Roxas, Legazpi Village, Makati, Metro Manila 1229, Philippines)

  • Thanatorn Chuenyindee

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    Logistics and Supply Chain Management Program, Nakhon Pathom Rajabhat University, Nakhon Pathom 73000, Thailand)

  • Poonyawat Kusonwattana

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Nattakit Yuduang

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    Institute of Research and Development, Valaya Alongkorn Rajabhat University, Phahonyothim Rd., Khlong Nueng, Khlong Luang District, Pathum Thani 13180, Thailand)

  • Reny Nadlifatin

    (Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia)

  • Anak Agung Ngurah Perwira Redi

    (Industrial Engineering Department, BINUS Graduate Program—Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia)

Abstract

The decline of enrollees for industrial engineering during the COVID-19 pandemic and the increasing demand for professional industrial engineers should be explored. The purpose of this study was to determine the preference of industrial engineering students of different educational levels on online learning during the COVID-19 pandemic. Specifically, this study utilized conjoint analysis with orthogonal design considering seven attributes: delivery type, layout, term style, final requirements, Coursera requirements, seatwork and practice sets, and platforms. Among the attributes, 20 stimuli were created through SPSS and were answered voluntarily by 126 respondents utilizing a 7-point Likert Scale. The respondents were comprised of 79 undergraduate, 30 fully online master’s degree, and 17 master’s and doctorate degree students collected through purposive sampling. One university from the two available universities that offer all educational levels of IE in the Philippines was considered. The results showed that undergraduate students considered the final requirements with multiple-choice as the highest preference, followed by non-modular term style, and no seatwork and practice sets. In addition, fully online master’s degree students considered delivery type with the mix as the highest preference, followed by layout, and no seatwork and practice sets. Finally, master’s and doctorate degree students considered final requirements with publication as the highest preference, followed by no seatwork and practice sets, and mix delivery type. The students are technologically inclined, want to learn at their own pace, know where and how to get additional online learning materials, but still need the guidance of teachers/professors. The results would help contribute to the theoretical foundation for further students’ preference segmentation, specifically on online learning during the COVID-19 pandemic worldwide. Moreover, the design created could be utilized for other courses in measuring students’ preference for online learning even after the COVID-19 pandemic.

Suggested Citation

  • Ardvin Kester S. Ong & Yogi Tri Prasetyo & Michael Nayat Young & John Francis T. Diaz & Thanatorn Chuenyindee & Poonyawat Kusonwattana & Nattakit Yuduang & Reny Nadlifatin & Anak Agung Ngurah Perwira , 2021. "Students’ Preference Analysis on Online Learning Attributes in Industrial Engineering Education during the COVID-19 Pandemic: A Conjoint Analysis Approach for Sustainable Industrial Engineers," Sustainability, MDPI, vol. 13(15), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8339-:d:601829
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    References listed on IDEAS

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    Cited by:

    1. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Kerr Lorenzo Picazo & Kim Aaron Salvador & Bobby Ardiansyah Miraja & Yoshiki B. Kurata & Thanatorn Chuenyindee & Reny Nadlifatin & Anak Agung Ngurah Perwira , 2021. "Gym-Goers Preference Analysis of Fitness Centers during the COVID-19 Pandemic: A Conjoint Analysis Approach for Business Sustainability," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    2. Jenalyn Shigella G. Yandug & Erika Mae D. Costales & Ardvin Kester S. Ong, 2023. "A Conjoint Analysis Approach, Implications, and Mitigation Plans in Analyzing Students’ Preferences for Online Learning Delivery Types during the COVID-19 Pandemic for Engineering Students: A Case Stu," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    3. S. Ong, Ardvin Kester & Prasetyo, Yogi Tri & Chuenyindee, Thanatorn & Young, Michael Nayat & Doma, Bonifacio T. & Caballes, Dennis G. & Centeno, Raffy S. & Morfe, Anthony S. & Bautista, Christine S., 2022. "Preference analysis on the online learning attributes among senior high school students during the COVID-19 pandemic: A conjoint analysis approach," Evaluation and Program Planning, Elsevier, vol. 92(C).
    4. Ardvin Kester S. Ong & Jelline C. Cuales & Jose Pablo F. Custodio & Eisley Yuanne J. Gumasing & Paula Norlene A. Pascual & Ma. Janice J. Gumasing, 2023. "Investigating Preceding Determinants Affecting Primary School Students Online Learning Experience Utilizing Deep Learning Neural Network," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    5. Ma. Janice J. Gumasing & Ardvin Kester S. Ong & Maria Angelica D. Bare, 2022. "User Preference Analysis of a Sustainable Workstation Design for Online Classes: A Conjoint Analysis Approach," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    6. Jeya Amantha Kumar & Sharifah Osman & Mageswaran Sanmugam & Rasammal Rasappan, 2022. "Mobile Learning Acceptance Post Pandemic: A Behavioural Shift among Engineering Undergraduates," Sustainability, MDPI, vol. 14(6), pages 1-13, March.

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