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Interactive Advising with Bots: Improving Academic Excellence in Educational Establishments
American Journal of Operations Management and Information Systems
Volume 3, Issue 1, March 2018, Pages: 6-21
Received: Feb. 15, 2018; Accepted: Mar. 1, 2018; Published: Mar. 20, 2018
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Wilson Nwankwo, Department of Computer Science & Information Technology, Wellspring University, Benin, Nigeria
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Student advising services are often regarded as the mainstream vehicle for promoting relationships, understanding, and performance in academic institutions especially at the tertiary level. However, it is often fraught with challenges in developing countries in respect of insufficient supporting manpower and attendant high cost of running effective services. In institutions where the services exist, not all students benefit from it as a result of some factors such as: the sub-optimal performance of the advising personnel; negative psychological complex in students arising from unusual egocentrism (especially in those students who are regarded as “low performers” and would prefer not to be openly confronted); handicapped students especially those students with visible handicaps e.g. speech problems, etc. This paper is the first part of a study aimed at creating a balance in the foregoing situations by presenting a design of a faceless automated “AdvisorBot” based on the bot framework. The design reflects a virtual support system model which could be adopted to enhance student support and course advising efficiency. Analysis of the existing system in most tertiary institutions in Nigeria reveals that student support services actually exist though not efficient in the sense that there are seldom specialized units/departments dedicated to this function in majority of the Institutions especially the public institutions where student advising is the work of academic staff in the various departments. The design follows a mix of the agent and object-oriented approaches and produces an implementation-ready specification whose full implementation would effectively support students during their studies. The system facilitates the process of advising by providing quick and easy access to valuable information, and giving important feedback on several issues involved in student advisement, which otherwise would take considerable time.
Academic Advising, Intelligent Bot, Bot, ChatBot, DSS, Tertiary Education, Nigeria
To cite this article
Wilson Nwankwo, Interactive Advising with Bots: Improving Academic Excellence in Educational Establishments, American Journal of Operations Management and Information Systems. Vol. 3, No. 1, 2018, pp. 6-21. doi: 10.11648/j.ajomis.20180301.12
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Freeman, L. C. (2017) “Establishing Effective Advising Practices to Influence Student Learning and Success”[online]. Available at: research/periodicals/establishing-effective-advising-practices-influence-student [Accessed 20 January 2017].
NACADA (2017) “The Power to Transform Higher Education Through Excellence in Academic Advising” [online]. Available at: [Accessed 25 January 2018].
Rob, P., & Coronel, C.(2012). Database systems: Design, implementation, and management (10th ed.). Boston: Thompson.
Holsapple, C. W., and Whinston, A. B.(1996). Decision support systems: A knowledge-based approach. New York: West Publishing company.
Werghi, N. and Kamoun, F. (2010) “A decision-tree-based system for student academic advising and planning in information systems programmes”, International Journal of Business Information Systems, Vol. 5, No. 1, pp. 1–18. Available from: [Accessed Jan 19 2018].
Pizzolato, J. E. (2006). Achieving college student possible selves: Navigating the space between commitment and achievement of long-term identity goals. Cultural Diversity and Ethnic Minority Psychology, 12(1), 57-69.
Dinkel, J. J., Mote, J., & Venkataramanan, M. A. (1989) “An Efficient Decision Support System For Academic Course Scheduling”, Operations Research, Vol. 37, No. 6, pp. 853-864, INFORMS.
Wehrs, W. E.(1992) "Using an Expert System to Support Academic Advising", Journal of Research on Computing in Education, Vol. 2, No. 4.
Deniz, D. Z., and Ersan, I.(2002). An Academic Decision Support System Based on Academic Performance Evaluation for Student and Program Assessment, The Int. Journal of Eng. Education, Vol. 18, No. 2, pp. 236-244, Expanded SCI journal.
Isa, K., Mohamad, S. and Tukiran, Z. (2007) ‘Development of intelligent planning system (INPLANS): an analysis of student’s performance using fuzzy systems’, Proc. IASTED Int. Multi-Conference: Artificial Intelligence & Applications, Innsbruck, Austria, pp. 567–572.
McDonald, K. and Prosser, P. (2002) ‘A student advisory system: a configuration problem for constraint programming’, ECAI Workshop W4 on Configuration, pp. 20–22.
Sandvig, J.J. and Burke, R. (2005) ‘AACORN: a CBR recommender for academic advising’, Technical Report, TR05-015, DePaul University.
Pokrajac, D. and Rasamny, M. (2006) ‘Interactive virtual expert system for advising’, Frontiers in Education Conference, pp. 18–23.
Olawande, D., Onyeka, E., Ibukun, A., Charles, A.(2014) “Implementation of an Intelligent Course Advisory Expert System”, International Journal of Advanced Research in Artificial Intelligence, Vol. 5, No. 4.
Griffiths, N. & Lim Choi Keung, S. (n.d.) “Agent-Based Systems: An Introduction to Agents and an Overview of Recent/Current Research” [online]. Available at: [Accessed 10 February 2018].
Wooldridge and Jennings, Intelligent Agents. KER 10(2), 1995].
Jennings, N. R.(2000) “On agent-based software engineering“ Artificial Intelligence, Vol. 17, pp. 277-296.
Luck, M., McBurney, P., Shehory, O. and Willmott, S. (2005),”Agent Technology: Computing as Interaction A Roadmap for Agent Based Computing”[online], AgentLink II. Available at: [Accessed 5 February 2018].
Giorgini, P. & Henderson-Sellers(2005) "Agent-Oriented Methodologies:An Introduction"[online]. Available at: [Accessed 10 January 2018].
Garoui, M. Mazigh, B. El Ayeb, B. and Koukam, A.(2014) Towards To An Agent-Oriented Modeling And Evaluating Approach For Vehicular Systems Security, International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1. Available at: [Accessed 21 January 2018].
Kazma, J.(1998) "Intelligent agents: Getting others to do your work for you". IEEE Potentials, Vol. 17, No. 2, pp. 16-19.
Maes, P.(1994) "Agents that Reduce Work and Information Load", Communication of the ACM, Vol. 37, No. 7, pp. 31-41.
Mladenic, D. (1999) "Text-Learning and Related Intelligent Agents: A Survey", Intelligent Systems, Vol. 14, No. 4, pp. 44-54.
[Subramanian, K. R., Shiang, T. K., Lee, S., and Sue, G. B. (1999) "Intelligent Agent Platform for Procurement" In IEEE International Conference on Systems, Man, and Cybernetics, Tokyo, Japan.
Lee, R.S.T., and Liu, J. N. K.(2004) "iJADE Web-miner: an intelligent agent framework for Internet shopping", IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 4, pp. 461-473.
Zhang, B., Chen, X., Liu, G., and Cai, Q. (2000) "Agent Architecture: A Survey on RoboCup Simulator Teams", In 3rd World Congress on Intelligent Control and Automation.
Ito, N., Shinoda, K., Nakagaw, K., Iwata, K., Du, X., and Ishii, N.(1999) “Action Decision with Incomplete Information: A Temporal Database Approach": In IEEE International Conference on Systems, Man and Cybernetics, Tokyo, Japan.
Kraus, S., and Lehmann, D.(1988) "DIPLOMAT, an Agent in a Multi-Agent Environment: An Overview": In Seventh Annual International Phoenix Conference on Computers and Communications.
Elharrar, D.(2017) "7 Types of Bots: Different ways to deliver value"[online]. Available at: [Accessed 2 February 2018].
Pratt, E. (2017) "Artificial Intelligence and Chatbots in Technical Communication – A Primer" [online]. Available at: [Accessed 2 February 2018].
Surmenok, P.(2016) "Chatbot Architecture"[online]. Available at: [Accessed 5 February 2018].
Persiyanov, D.(2017) "Chatbots with Machine Learning: Building Neural Conversational Agents"[online]. Available at: [Accessed 4 February 2018].
Sansonnet, J. P., Leray, D., & Martin, J. C. (2006). Architecture of a Framework for Generic Assisting Conversational Agents. Intelligent Virtual Agents Lecture Notes in Computer Science, 145–156.
Gupta J., Forgionne G. A., Mora M. T.(2006) ‖IntelligentDecision-making Support Systems. Foundations, Applications and Challenges, Springer-Verlag.
Zambonelli, F.(2010) Agent-oriented software engineering [online]. Available at: [Accessed 31 January 2018].
Butterfield, A. and Ngondi, G. E. (2016) A Dictionary of Computer Science (7th ed.), Oxford University press.
Artale, A.(2008) "FORMAL METHODS"[online]. Available at: [Accessed 12 February 2018].
Dam, M.(2015) "Introduction to Temporal Logic"[online]. Available at: [Accessed 12 February 2018].
De Boer, F. S. (2004) “Methodology for Agent-Oriented Software Design”[online]. Available at: [Accessed 31 January 2018].
Giorgini, P. & Henderson-Sellers(2005) "Agent-Oriented Methodologies:An Introduction"[online]. Available at: [Accessed 10 February 2018].
Burmeister, B.(1996). Models and methodology for agent-oriented analysis and design: In K Fischer (ed.), Working Notes of the KI’96 Workshop on Agent-Oriented Programming and Distributed Systems.
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