Skip to main content
SpringerLink
Log in

Design and Functionality of a University Academic Advisor Chatbot as an Early Intervention to Improve Students’ Academic Performance

  • Conference paper
  • First Online:
Computational Science and Technology

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 724))

Abstract

This paper introduces the design and functionality of a university academic advisor chatbot, which leverages on the result of a prediction model to predict students’ academic performance, to do early intervention to assist students who may need academic guidance. The prediction model is based on students’ attendance and scores of formative assessments to predict the score of the final summative assessment using a suitable machine learning algorithm. Scikit-learn library using Python will be used in this research to run the machine learning algorithms. The chatbot will be developed using Dialogflow which is integrated with one of the text messaging apps and established connection to a database. The database stores students’ attendance, scores of formative assessments, scores of final summative assessments and the status of students whom the chatbot has reached out to. This research aims to reduce the workload of lecturers to reach out to every student who is predicted to have problems in their academic studies and at the same time, be able to assist students using a chatbot.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.whatsapp.com/.

  2. 2.

    https://telegram.org/.

  3. 3.

    https://hangouts.google.com/.

  4. 4.

    https://scikit-learn.org/.

  5. 5.

    https://dialogflow.com/.

References

  1. Pellegrini G, Segafredo C (2015) Keeping pace: educational choice motivations and first-year experiences in the words of Italian students. In: Understanding student participation and choice in science and technology education. Springer, Netherlands, pp 259–273. https://doi.org/10.1007/978-94-007-7793-4_16

  2. Shehata S, Arnold KE (2015) Measuring student success using predictive engine. In: Proceedings of the fifth international conference on learning analytics and knowledge—LAK 2015. ACM Press, New York, pp 416–417. https://doi.org/10.1145/2723576.2723661

  3. Sudipa IGI, Wijaya INSW, Radhitya ML, Mahawan IMA, Arsana INA (2020) An android-based application to predict student with extraordinary academic achievement. In: journal of physics: conference series, vol 1469. Institute of Physics Publishing, Bali. https://doi.org/10.1088/1742-6596/1469/1/012043

  4. Gray CC, Perkins D (2019) Utilizing early engagement and machine learning to predict student outcomes. Comput Educ 131:22–32. https://doi.org/10.1016/j.compedu.2018.12.006

  5. Keller B, Baleis J, Starke C, Marcinkowski F (2019) Machine learning and artificial intelligence in higher education: a state-of-the-art report on the German University landscape. https://www.phil-fak.uni-duesseldorf.de/fileadmin/Redaktion/Institute/Sozialwissenschaften/Kommunikations-_und_Medienwissenschaft/KMW_I/Working_Paper/Keller_et_al.__2019__-_AI_in_Higher_Education.pdf

  6. Tempelaar DT, Rienties B, Giesbers B (2015) In search for the most informative data for feedback generation: learning analytics in a data-rich context. Comput Hum Behav 47:157–167. https://doi.org/10.1016/j.chb.2014.05.038

  7. Sohail S, Khanum A, Alvi A (2019) Hybrid fuzzy-statistical system for learning analytics. In: Proceedings of 2018 IEEE international conference on teaching, assessment, and learning for engineering, TALE 2018, pp 989–994. https://doi.org/10.1109/TALE.2018.8615182

  8. Gkontzis AF, Kotsiantis S, Tsoni R, Verykios VS (2018) An effective LA approach to predict student achievement. In: ACM international conference proceeding series, pp 76–81. https://doi.org/10.1145/3291533.3291551

  9. Juliane C, Arman AA, Sastramihardja HS, Supriana I (2017) Predicting the presence of learning motivation in electronic learning: a new rules to predict. TELKOMNIKA (Telecommun Comput Electron Control) 15(3):1223–1229. https://doi.org/10.12928/telkomnika.v15i3.4286

    Article  Google Scholar 

  10. Hussain M, Zhu W, Zhang W, Abidi SMR (2018) Student engagement predictions in an e-learning system and their impact on student course assessment scores. Comput Intell Neurosci 2018:1–21. https://doi.org/10.1155/2018/6347186

    Article  Google Scholar 

  11. Budiman E et al (2018) Performance of decision tree C4.5 algorithm in student academic evaluation. In: Alfred R, Iida H, Ibrahim A, Lim Y (eds) Computational science and technology. ICCST 2017. Lecture notes in electrical engineering, vol 488. Springer, Singapore, pp 380–389. https://doi.org/10.1007/978-981-10-8276-4_36

  12. Yu C (2018) SPOC-MFLP: a multi-feature learning prediction model for SPOC students using machine learning. J Appl Sci Eng 21(2):279–290. https://doi.org/10.6180/jase.201806_21(2).0016

    Article  Google Scholar 

  13. Solis M, Moreira T, Gonzalez R, Fernandez T, Hernandez M (2018) Perspectives to predict dropout in university students with machine learning. In: 2018 IEEE international work conference on bioinspired intelligence (IWOBI). IEEE. https://doi.org/10.1109/IWOBI.2018.8464191

  14. Ortigosa A et al (2019) From lab to production: lessons learnt and real-life challenges of an early student-dropout prevention system. IEEE Trans Learn Technol 12(2):264–277. https://doi.org/10.1109/TLT.2019.2911608

    Article  Google Scholar 

  15. Almeda MV et al (2018) Comparing the factors that predict completion and grades among for-credit and open/MOOC students in online learning. Online Learn J 22(1):1–18. https://doi.org/10.24059/olj.v22i1.1060

    Article  Google Scholar 

  16. Abou Gamie E, Abou El-Seoud S, Salama M, Hussein W (2019) Multi-dimensional analysis to predict students’ grades in higher education. Int J Emerg Technol Learn (iJET) 14(02):4. https://doi.org/10.3991/ijet.v14i02.9905

    Article  Google Scholar 

  17. Kubiatko M, Hsieh M-Y, Ersozlu ZN, Usak M (2018) The motivation toward learning among Czech high school students and influence of selected variables on motivation. Revista de Cercetare si Interventie Sociala 60:79–93

    Google Scholar 

  18. Al-Shabandar R, Hussain A, Laws A, Keight R, Lunn J (2017) Towards the differentiation of initial and final retention in massive open online courses. In: 13th international conference on intelligent computing, ICIC 2017, vol 10361. Springer, Liverpool, United Kingdom, pp 26–36. https://doi.org/10.1007/978-3-319-63309-1_3

  19. Schumacher C, Ifenthaler D (2018) The importance of students’ motivational dispositions for designing learning analytics. J Comput High Educ 30(3):599–619. https://doi.org/10.1007/s12528-018-9188-y

    Article  Google Scholar 

  20. Amigud A, Arnedo-Moreno J, Daradoumis T, Guerrero-Roldan A-E (2017) Using learning analytics for preserving academic integrity. Int Rev Res Open Distrib Learn 18(5):192–210. https://doi.org/10.19173/irrodl.v18i5.3103

    Article  Google Scholar 

  21. Azcona D, Hsiao I-H, Smeaton AF (2019) Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Model User-Adap Inter 29(4):759–788. https://doi.org/10.1007/s11257-019-09234-7

    Article  Google Scholar 

  22. Gitinabard N, Xu Y, Heckman S, Barnes T, Lynch CF (2019) How widely can prediction models be generalized? Performance prediction in blended courses. IEEE Trans Learn Technol 12(2):184–197. https://doi.org/10.1109/TLT.2019.2911832

    Article  Google Scholar 

  23. Hew KF, Qiao C, Tang Y (2018) Understanding student engagement in large-scale open online courses: a machine learning facilitated analysis of student’s reflections in 18 highly rated MOOCs. Int Rev Res Open Distrib Learn 19(3):69–93. https://doi.org/10.19173/irrodl.v19i3.3596

    Article  Google Scholar 

  24. Hlosta M, Zdrahal Z, Zendulka J (2017) Ouroboros. In: Proceedings of the seventh international learning analytics & knowledge conference. ACM, Vancouver, pp 6–15. https://doi.org/10.1145/3027385.3027449

  25. Doleck T, Lemay DJ, Basnet RB, Bazelais P (2020) Predictive analytics in education: a comparison of deep learning frameworks. Educ Inf Technol 25(3):1951–1963. https://doi.org/10.1007/s10639-019-10068-4

    Article  Google Scholar 

  26. Wan S, Niu Z (2020) A hybrid e-learning recommendation approach based on learners’ influence propagation. IEEE Trans Knowl Data Eng 32(5):827–840. https://doi.org/10.1109/TKDE.2019.2895033

    Article  Google Scholar 

  27. Shawar BA, Atwell E (2015) ALICE chatbot: trials and outputs. Computación y Sistemas 19(4):625–632. https://doi.org/10.13053/cys-19-4-2326

  28. Fadhil A, Villafiorita A (2017) An adaptive learning with gamification & conversational UIs: the rise of CiboPoliBot. In: Adjunct publication of the 25th conference on user modeling, adaptation and personalization. ACM, Bratislava, pp 408–412. https://doi.org/10.1145/3099023.3099112

  29. Agus Santoso H et al (2018) Dinus intelligent assistance (DINA) chatbot for university admission services. In: 2018 international seminar on application for technology of information and communication. IEEE, Semarang, pp 417–423. https://doi.org/10.1109/ISEMANTIC.2018.8549797

  30. Chun Ho C, Lee HL, Lo WK, Lui KFA (2018) Developing a chatbot for college student programme advisement. In: Wang FL, Iwasaki C, Konno T, Au O, Li C (eds) 2018 international symposium on educational technology (ISET). IEEE, Osaka, pp 52–56. https://doi.org/10.1109/ISET.2018.00021

  31. Carayannopoulos S (2018) Using chatbots to aid transition. Int J Inf Learn Technol 35(2):118–129. https://doi.org/10.1108/IJILT-10-2017-0097

    Article  Google Scholar 

  32. Singh J, Joesph MH, Jabbar KBA (2019) Rule-based chabot for student enquiries. J Phys Conf Ser 1228(1). https://doi.org/10.1088/1742-6596/1228/1/012060

  33. Khin NN, Soe KM (2020) University chatbot using artificial intelligence markup language. In: 2020 IEEE conference on computer applications (ICCA). IEEE, Yangon, Myanmar. https://doi.org/10.1109/ICCA49400.2020.9022814

  34. Patel NP, Parikh DR, Patel DA, Patel RR (2019) AI and web-based human-like interactive university chatbot (UNIBOT). In: 2019 3rd international conference on electronics, communication and aerospace technology (ICECA). IEEE, Coimbatore, pp 148–150. https://doi.org/10.1109/ICECA.2019.8822176

  35. Verleger M, Pembridge J (2018) A pilot study integrating an AI-driven chatbot in an introductory programming course. In: 2018 IEEE frontiers in education conference (FIE), vol 2018. IEEE, San Jose, pp 1–4. https://doi.org/10.1109/FIE.2018.8659282

  36. Mekni M, Baani Z, Sulieman D (2020) A smart virtual assistant for students. In: Petkov N, Strisciuglio N, Travieso-Gonzalez CM (eds) Proceedings of the 3rd international conference on applications of intelligent systems. ACM, Las Palmas de Gran Canaria, Spain. https://doi.org/10.1145/3378184.3378199

  37. Vahdat M, Oneto L, Anguita D, Funk M, Rauterberg M (2015) Educational process mining (EPM): a learning analytics data set data set. https://archive.ics.uci.edu/ml/datasets/Educational+Process+Mining+%28EPM%29%3A+A+Learning+Analytics+Data+Set

  38. Chung JY, Lee S (2019) Dropout early warning systems for high school students using machine learning. Child Youth Serv Rev 96:346–353. https://doi.org/10.1016/j.childyouth.2018.11.030

  39. Limsathitwong K, Tiwatthanont K, Yatsungnoen T (2018) Dropout prediction system to reduce discontinue study rate of information technology students. In: 2018 5th international conference on business and industrial research (ICBIR). IEEE, Bangkok, pp 110–114. https://doi.org/10.1109/ICBIR.2018.8391176

Download references

Author information

Authors and Affiliations

  1. Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Mei Shyan Lim, Sin-Ban Ho & Ian Chai

  2. Faculty of Computing and Information Technology, Tunku Abdul Rahman University College, 53300, Kuala Lumpur, Wilayah Persekutuan, Malaysia

    Mei Shyan Lim

Authors
  1. Mei Shyan Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sin-Ban Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ian Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mei Shyan Lim .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia

    Rayner Alfred

  2. Research Center for Entertainment Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Hiroyuki Iida

  3. Faculty of Computer Science and Information Technology, Department of Informatics, Universitas Mulawarman, Samarinda, Kalimantan Timur, Indonesia

    Haviluddin Haviluddin

  4. Department of Environmental Management, Faculty of Environment, Society and Design, Lincoln University, Christchurch, New Zealand

    Patricia Anthony

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lim, M.S., Ho, SB., Chai, I. (2021). Design and Functionality of a University Academic Advisor Chatbot as an Early Intervention to Improve Students’ Academic Performance. In: Alfred, R., Iida, H., Haviluddin, H., Anthony, P. (eds) Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 724. Springer, Singapore. https://doi.org/10.1007/978-981-33-4069-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-33-4069-5_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4068-8

  • Online ISBN: 978-981-33-4069-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation