Abstract
The study addressed the adjustments of academic institutions to online class and modular learning caused by the Covid-19 pandemic. It focused on the development of Learning Management System (LMS) for Data Structures and Algorithms with the main feature that allows students to take modular online learning. The research and development approach includes (i) stages of system development using the Waterfall Method, (ii) level of acceptability of the developed system based on the ISO 25010 standard, (iii) difference in the evaluation of the three groups of respondents, (iv) challenges encountered while using the system, and (v) implementation plan. The respondents chosen through convenience sampling were 60 students, 15 faculty members, and 15 Information Technology (IT) experts. The checklist-format questionnaire was based on the ISO 25010 which determined acceptability using functional suitability, performance efficiency, usability, and reliability criteria. An interview was also conducted to evaluate respondents’ experiences using the system. Based on the respondents’ evaluation, the developed system was ‘acceptable’ as reflected by the obtained weighted means. Further results showed no significant difference in the evaluation of the three groups of respondents in terms of functional suitability, performance efficiency, usability, and reliability.
Keywords: learning management system, online education, data structures and algorithms, ISO 25010, Covid-19 pandemic, chatbot
References
Abdulazeez, A. and Zeebaree, S. (2018). Design and implementation of electronic learning system for Duhok Polytechnic University. Academic Journal of Nawroz University. 7(3), pp. 249-258.
Aguirre, A., Villareal-Freire, A.,, Gil, R., and Collazos, C. (2017). Extending the concept of user satisfaction in e-learning systems from ISO/IEC 25010. Conference Paper in Lecture Notes in Computer Science. pp. 167–179.
Al-Samarraie, H. and Saeed, N. (2018). A systematic review of cloud computing tools for collaborative learning: Opportunities and challenges to the blended-learning environment. Computers & Education. 124, pp. 77–91.
Allison, D.A. (2011). Chatbots in the library: is it time? Faculty Publications, UNL Libraries. https://digitalcommons.unl.edu/libraryscience/280
Alshorman, A. and Bawaneh, A. (2018). Attitudes of faculty members and students towards the use of the learning management system in teaching and learning. The Turkish Online Journal of Educational Technology. 17(3), pp. 1-15.
Andersson, T. (2019). Case study on an implementation of an LMS and its perceived effects on teachers. Master Thesis in Informatics. Linnaeus University.
Arcand, K. (2017). Can chatbots fully replace humans? Not yet. Retrieved from https://www.destinationcrm.com/Articles/Columns-Departments/Customer-Experience/Can-Chatbots-Fully-Replace-Humans-Not-Yet-118196.aspx
Banimahendra, R. and Santoso, H. (2017). Implementation and evaluation of LMS mobile application: SCeLE mobile based on user-centered design. Journal of Physics Conference Series 978.
Barjtya, S., Sharma, A., Rani, U. (2017). A detailed study of Software Development Life Cycle (SDLC) models. International Journal of Engineering And Computer Science. 6(7), pp. 22097-22100
Brown, N., Kölling, M., Crick, T., Jones, S. P., Humphreys, S., & Sentance, S. (2013). Bringing computer science back into schools: Lessons from the UK. Proceedings of the 44th ACM Technical Symposium on Computer Science Education (SIGCSE 2013), pp. 269–274.
Changwong, K., Sukkamart, A., and Sisan, B. (2018). Critical thinking skill development: Analysis of a new learning management model for Thai high schools. Journal of International Studies. 11(2), pp. 37-48.
Clark, D. (2018). The fallacy of “robot” teachers. Donald Clark Plan B. Retrieved from: https://donaldclarkplanb.blogspot.com/search?q=10+uses+for+Chatbots+in+l earning+(with+examples)
Davidovitch, N. and Belichenko, M. (2016). Developmental and implementation challenges of e-learning management systems in higher education. Canadian Center of Science and Education. 6(4), pp. 170-180.
Fadhel, I., Idrus, S., Abdullah, M., Ibrahim, A., Omar, M., and Saad, S. (2019). Nias-Mukalla web based systems success measurement and students satisfaction evaluation based on security factor of systems quality engineering theory (ISO 25010) and other factors. Independent Journal Of Management & Production. 10(6), pp. 2102-2123.
Futurizable (2017). Estado del arte en el desarrollo de chatbots a nivel mundial. Futurizable. Retrieved from: https://futurizable.com/chatbot
Ghilay, Y. (2019). Effectiveness of learning management systems in higher education: Views of lecturers with different levels of activity in LMSs. Journal of Online Higher Education. 3(2). pp 29-50
Gomez, J. (2015). Higher education faculty use of a learning management system in face-to-face classes. California State University.
Holak, B. (2018). Who’s talking? Conversational agent vs. chatbot vs. virtual assistant. TechTarget. Retrieved from: https://searchcio.techtarget.com/feature/Whos-talking-Conversationalagent- vs-chatbot-vs-virtual-assistant
Holmes, K. and Prieto-Rodriguez, E. (2018). Student and staff perceptions of a learning management system for blended learning in teacher education. Australian Journal of Teacher Education, 43(3).
ISO 25000. (2020). ISO/IEC 25010. https://iso25000.com/index.php/en/iso-25000-standards/iso-25010
Karunaratne, T., Zhemchugova, H., Byungura, J., and Olsson, U. (2019). Towards an agile-based process model for effective teacher training on LMS. 18th European Conference on e-Learning.
Kocaleva, M., Stojanovic, I., and Zdravev, Z. (2015). Model of e-learning acceptance and use for teaching staff in higher education institutions. International Journal of Modern Education and Computer Science. 4, pp. 23-31.
Krouska, A., Troussas, C., Virvou, M. (2019). A literature review of social networking- based learning systems using a novel ISO-based framework. Intelligent Decision Technologies, Vol. 13, no. 1, pp. 23-39
Lokare, V., Jadhav, P. (2016). A holistic approach for teaching Data Structure Course in the Department of Information Technology. Journal of Engineering Education Transformations, Volume, No, Month 2016, ISSN 2349-2473, eISSN 2394-1707.
Malik, M., Abid, F., Kalaichelvi, R., and Bhatti, Z. (2018). Challenges of computer science and it in teaching-learning in Saudi Arabia. Sukkur IBA Journal of Computing and Mathematical Sciences. 2 (1), pp. 29-35.
Muhardi, M., Gunawan, S. I., Irawan, Y., & Devis, Y. (2020). Design Of web based LMS (Learning Management System) in SMAN 1 Kampar Kiri Hilir. Journal of Applied Engineering and Technological Science (JAETS), 1(2), 70-76.
Ouadoud, M., Chkouri, M., Nejjari, A. (2018). Learning management system and the underlying learning theories: Towards a new modeling of an LMS. International Journal of Information Science & Technology. 2(1), pp. 25-33.
Pradana, A., Goh, O. S., & Kumar, Y. J. (2018). Intelligent conversational bot for interactive marketing. Journal of Telecommunication, Electronic, and Computer Engineering, 10, pp. 1-7.
Rhode, J., Richter, S., Gowen, P., Miller, T., & Wills, C. (2017). Understanding faculty use of the learning management system. Online Learning, 21(3), pp. 68-86.
Rubin, M. (2013). The effectiveness of live-coding to teach introductory programming. In: Proceeding of the 44th ACM Technical Symposium on Computer Science Education. SIGCSE ’13. New York, NY, USA: ACM. pp. 651-656.
Sentance, S., Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Educ Inf Technol 22:469–495. DOI 10.1007/s10639-016-9482-0
Shukla, V., Verma, A. (2019). Enhancing LMS experience through AIML base and retrieval base chatbot using R language. 2019 International Conference on Automation, Computational and Technology Management (ICACTM).
Tarus, J., Gichoya, D. & Muumbo, A. (2015). Challenges of implementing e-learning in Kenya: A case of Kenyan public universities. International Review of Research in Open and Distributed Learning, 16(1), 120–141.
Thompson, D., & Bell, T. (2013). Adoption of new computer science high school standards by New Zealand teachers. ACM.
Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence. 10(2). pp 1-37.
Zydney, J. M. and Warner, Z. (2016). Mobile apps for science learning: review of research. Computers and Education, vol. 94, pp. 1–17.
