Skip to main content
SpringerLink
Log in

Academic Data Analysis and Projection Using Artificial Intelligence

  • Conference paper
  • First Online:
Computer Networks, Big Data and IoT

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 117))

  • 868 Accesses

Abstract

Application of artificial intelligence (AI) in education is an emerging field of research. In a country like India with huge population, education cannot be provided without the participation of private institutions. However, many private institutions are unable to provide sustainable education as they lack the knowledge of the current industrial requirements and the quality of the students being admitted. AI can be used to build a complete road map for improving the performance of students in various directions using their existing academic data and to forecast the ways of improving the performance of the students for sustainable growth of the students and the institution. The proposed research applies AI methods to assist the academic institutes in formulating a necessary framework for making decisions towards sustainable education. The analysis involves different strategies to predict academic performance which comprises, collection of known data, data processing, generating training and testing datasets, building a model, and applying the model to the unknown data. Predictive algorithms are used to identify the most important attributes in academic data to suggest a prediction framework. The internal score and the health conditions are predicted based on the lunch provided to the students. Similarly, the AI model built on the collected data will be applied in the academic databases maintained by the NIC. As a result, more efficient measures can be identified to improve the academic performance of students in higher educational institutions and universities.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Klaise, J., Vacanti, G.: Alibi explain: algorithms for explaining machine learning models. J. Mach. Learn. Res. 22, 1–7 (2021)

    Google Scholar 

  2. Borkar, S., Rajeswari, K.: Attributes selection for predicting students’ academic performance using education data mining and artificial neural network. Int. J. Comput. Appl. 86(10), 25–29 (2014). https://doi.org/10.5120/15022-3310

  3. Kuye, G., Adeyemo, A.B.: Mining students’ academic performance using decision tree algorithms. J. Inf. Technol. Impact 6(3), 161–170 (2006)

    Google Scholar 

  4. Altabrawee, H., Ali, O.A.J., Ajmi, S.Q.: Predicting students’ performance using machine learning techniques. J. Univ. BABYLON Pure Appl. Sci. 27(1), 194–205 (2019). https://doi.org/10.29196/jubpas.v27i1.2108

    Article  Google Scholar 

  5. Alloghani, M., Al-Jumeily, D., Hussain, A., Aljaaf, A.J., Mustafina, J., Petrov, E.: Application of machine learning on student data for the appraisal of academic performance. In: Proceedings—International Conference on Developments in eSystems Engineering, DeSE, vol. 2018-Septe, no. September, pp. 157–162, 2019. https://doi.org/10.1109/DeSE.2018.00038

  6. Ofori, F., Maina, E., Gitonga, R.: Using machine learning algorithms to predict students ’ performance and improve learning outcome: a literature based review Francis Ofori, Dr. Elizaphan Maina and Dr. Rhoda Gitonga. J. Inf. Technol. 4(1), 33–55 (2020). ISSN: 2617-3573

    Google Scholar 

  7. Ahmad, Z., Shahzadi, E.: Prediction of students’ academic performance using artificial neural network. Eric 40(3), 157–164 (2018)

    Google Scholar 

  8. Ahmad, F., Ismail, N.H., Aziz, A.A.: The prediction of students. Acad. Perform. Using Classif. Data Min. Tech. 9(129), 6415–6426 (2015)

    Google Scholar 

  9. Almarabeh, H.: Analysis of students’ performance by using different data mining classifiers. Int. J. Mod. Educ. Comput. Sci. 9(8), 9–15 (2017)

    Article  Google Scholar 

  10. Baker, R.Y.A.N.S.J.D., Blum, A.L., Langley, P.: The state of educational data mining in 2009: a review and future visions. J. Educ. Data Min. 5(8), 3–16 (2009)

    Google Scholar 

  11. Bragança, R., Portela, F., Santos, M., Bramer, M.: A regression data mining approach in lean production. Concurr. Comput. Pract. Exp. 31(22), 4449 (2019)

    Article  Google Scholar 

  12. Bunce, D.M., Hutchinson, K.D.: The use of the GALT (group assessment of logical thinking) as a predictor of academic success in college chemistry. J. Chem. Educ. 70(3), 183 (2009)

    Article  Google Scholar 

  13. Parker, J.D., Hogan, M.J., Eastabrook, J.M., Oke, A., Wood, L.M.: Emotional intelligence and student retention: predicting the successful transition from high school to university. Pers. Individ. Dif., 41(7),1329–1336 (2006)

    Google Scholar 

  14. Peng, Y., Kou, G., Shi, Y., Chen, Z.: A descriptive framework for the field of data mining and knowledge discovery. Int. J. Inf. Technol. Decis. Mak. 7(4), 639–682 (2008)

    Article  Google Scholar 

  15. Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man, Cybern. Part C (Appl. Rev.) 40(6), 601–618 (2010)

    Google Scholar 

  16. Shahiri, A.M., Husain, W., Rashid, N.A.: A review on predicting Student’s performance using data mining techniques. Procedia Comput. Sci. 72, 414–422 (2015)

    Article  Google Scholar 

  17. Willems, J., Coertjens, L., Tambuyzer, B., Donche, V.: Identifying science students at risk in the first year of higher education: the incremental value of non-cognitive variables in predicting early academic achievement. Eur. J. Psychol. Educ. 34(4), 847–872 (2019)

    Article  Google Scholar 

  18. Yassein, N.A., Helali, R.G.M., Mohomad, S.B.: Information technology & software engineering predicting student academic performance in KSA using data mining techniques. J. Inf. Technol. Softw. Eng. 7(5), 1–5 (2017)

    Google Scholar 

  19. Haoxiang, W., Smys, S.: Big Data analysis and perturbation using data mining algorithm. J. Soft Comput. Paradig. 1(3), 18–28 (2021)

    Google Scholar 

  20. Smys, S., Jennifer, S.R.: Analysis of deep learning techniques for early detection of depression on social media network-a comparative study. J. trends Comput. Sci. Smart Technol. 1(3), 24–39 (2021)

    Google Scholar 

  21. Mugunthan, S.R., Vijayakumar, T.: Design of improved version of sigmoidal function with biases for classification task in ELM domain. J. Soft Comput. Paradig. 2(3), 70–82 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MEPCO Schlenk Engineering College, Sivakasi, India

    K. Kanagaraj & K. Ramya Barathi

  2. National Informatics Centre, Chennai, India

    Joyce R. Amirtharaj

Authors
  1. K. Kanagaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joyce R. Amirtharaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Ramya Barathi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Kanagaraj .

Editor information

Editors and Affiliations

  1. Dean (R&D), CARE College of Engineering, Trichy, India

    A. Pasumpon Pandian

  2. Department of Electrical and Computer Engineering, Ryerson Communications Lab, Toronto, ON, Canada

    Xavier Fernando

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Wang Haoxiang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Kanagaraj, K., Amirtharaj, J.R., Ramya Barathi, K. (2022). Academic Data Analysis and Projection Using Artificial Intelligence. In: Pandian, A.P., Fernando, X., Haoxiang, W. (eds) Computer Networks, Big Data and IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 117. Springer, Singapore. https://doi.org/10.1007/978-981-19-0898-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-0898-9_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0897-2

  • Online ISBN: 978-981-19-0898-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation