Intelligent Course Recommender Chatbot Using Natural Language Processing

Tan Chia Wei (1), Mohd Hanafi Ahmad Hijazi (2), Suraya Alias (3), Ag Asri Ag Ibrahim (4), Mohd Fairuz Iskandar Othman (5)
(1) Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
(2) Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
(3) Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
(4) Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
(5) Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Fulltext View | Download
How to cite (IJASEIT) :
Wei, Tan Chia, et al. “Intelligent Course Recommender Chatbot Using Natural Language Processing”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 5, Sept. 2022, pp. 1915-20, doi:10.18517/ijaseit.12.5.14798.
Selecting elective courses in university is challenging for students as they do not know if the courses fit their interests and provide relevant knowledge or skills for their future professions. In some cases, students may register for courses without truly understanding the courses, eventually leading to course selection mistakes. A system that can recommend courses based on student's preferences is deemed necessary to address this problem. This paper proposed an intelligent course recommender system that helps students find suitable courses based on their strengths and interests. It consists of two phases. First, an intelligent course matching engine is designed and developed. The student's input is processed using natural language processing. A convolutional neural network is used to perform Part-of-Speech tagging. Keywords are identified from the processed input, and keyword matching is performed between the student's input and the courses' keywords. The most relevant courses are identified. Second, a chatbot is developed to implement the developed intelligent course matching engine. The chatbot captured student's preferences using a human-like conversation and recommended the identified most relevant courses to the students. The system is evaluated by a group of students in Universiti Malaysia Sabah. The evaluation of the usability and functionality results shows the acceptability of the proposed system, although some future work is needed based on the feedback received.

L. Movchan and I. Zarishniak, "The Role of Elective Courses in Students”² Professional Development: Foreign Experience," Comp. Prof. Pedagog., vol. 7, no. 2, pp. 20-26, Jun. 2017, doi: 10.1515/rpp-2017-0018.

G. Hancerliogullari Koksalmis, "Factors Affecting Selection of Elective Courses: The Use of Multi-Criteria Decision Making Model," J. Bus. Adm. Res., vol. 1, no. 1, Jan. 2019, doi: 10.30564/jbar.v1i1.205.

V. Roy and C. Parsad, "Efficacy of MBA: on the role of network effects in influencing the selection of elective courses," Int. J. Educ. Manag., vol. 32, no. 1, pp. 84-95, Jan. 2018, doi: 10.1108/IJEM-01-2017-0005.

W. Khan et al., "Part of Speech Tagging in Urdu: Comparison of Machine and Deep Learning Approaches," IEEE Access, vol. 7, pp. 38918-38936, 2019, doi: 10.1109/ACCESS.2019.2897327.

D. Kumawat and V. Jain, "POS Tagging Approaches: A Comparison," Int. J. Comput. Appl., vol. 118, no. 6, pp. 32-38, May 2015, doi: 10.5120/20752-3148.

D. W. Otter, J. R. Medina, and J. K. Kalita, "A Survey of the Usages of Deep Learning for Natural Language Processing," IEEE Trans. Neural Networks Learn. Syst., vol. 32, no. 2, pp. 604-624, Feb. 2021, doi: 10.1109/TNNLS.2020.2979670.

S. Sun, C. Luo, and J. Chen, "A review of natural language processing techniques for opinion mining systems," Inf. Fusion, vol. 36, pp. 10-25, Jul. 2017, doi: 10.1016/j.inffus.2016.10.004.

D. Jurafsky and J. H. Martin, Speech and Language Processing, 2nd ed. Prentice Hall, 2008.

R. E. Salah and L. Qadri binti Zakaria, "A Comparative Review of Machine Learning for Arabic Named Entity Recognition," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 2, p. 511, Apr. 2017, doi: 10.18517/ijaseit.7.2.1810.

R. Sukthanker, S. Poria, E. Cambria, and R. Thirunavukarasu, "Anaphora and coreference resolution: A review," Inf. Fusion, vol. 59, pp. 139-162, Jul. 2020, doi: 10.1016/j.inffus.2020.01.010.

K. O'Shea and R. Nash, "An Introduction to Convolutional Neural Networks," CoRR, vol. abs/1511.0, Nov. 2015.

M. A. Parwez, M. Abulaish, and Jahiruddin, "Multi-Label Classification of Microblogging Texts Using Convolution Neural Network," IEEE Access, vol. 7, pp. 68678-68691, 2019, doi: 10.1109/ACCESS.2019.2919494.

Y. Kim, "Convolutional Neural Networks for Sentence Classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1746-1751, doi: 10.3115/v1/D14-1181.

A. Hassan and A. Mahmood, "Convolutional Recurrent Deep Learning Model for Sentence Classification," IEEE Access, vol. 6, pp. 13949-13957, 2018, doi: 10.1109/ACCESS.2018.2814818.

M. Skjuve, A. Fí¸lstad, K. I. Fostervold, and P. B. Brandtzaeg, "My Chatbot Companion - a Study of Human-Chatbot Relationships," Int. J. Hum. Comput. Stud., vol. 149, p. 102601, May 2021, doi: 10.1016/j.ijhcs.2021.102601.

S. Ayanouz, B. A. Abdelhakim, and M. Benhmed, "A Smart Chatbot Architecture based NLP and Machine Learning for Health Care Assistance," in Proceedings of the 3rd International Conference on Networking, Information Systems & Security, 2020, pp. 1-6, doi: 10.1145/3386723.3387897.

T. Araujo, "Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions," Comput. Human Behav., vol. 85, pp. 183-189, Aug. 2018, doi: 10.1016/j.chb.2018.03.051.

S. Provoost, H. M. Lau, J. Ruwaard, and H. Riper, "Embodied Conversational Agents in Clinical Psychology: A Scoping Review," J. Med. Internet Res., vol. 19, no. 5, p. e151, May 2017, doi: 10.2196/jmir.6553.

W. Villegas-Ch, A. Arias-Navarrete, and X. Palacios-Pacheco, "Proposal of an Architecture for the Integration of a Chatbot with Artificial Intelligence in a Smart Campus for the Improvement of Learning," Sustainability, vol. 12, no. 4, p. 1500, Feb. 2020, doi: 10.3390/su12041500.

L. Keston and W. Goodridge, "AdviseMe: An Intelligent Web-Based Application for Academic Advising," Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 8, 2015, doi: 10.14569/IJACSA.2015.060831.

C. Chun Ho, H. L. Lee, W. K. Lo, and K. F. A. Lui, "Developing a Chatbot for College Student Programme Advisement," in 2018 International Symposium on Educational Technology (ISET), 2018, pp. 52-56, doi: 10.1109/ISET.2018.00021.

S. Meftah, N. Semmar, and F. Sadat, "A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts," in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 2018, pp. 2821-2828.

S.-W. Kim and J.-M. Gil, "Research paper classification systems based on TF-IDF and LDA schemes," Human-centric Comput. Inf. Sci., vol. 9, no. 1, p. 30, Dec. 2019, doi: 10.1186/s13673-019-0192-7.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).