Skip to main content

Advertisement

SpringerLink
Log in

Exploiting the Capabilities of Blockchain and Machine Learning in Education

  • Original Paper
  • Published:
Augmented Human Research Aims and scope Submit manuscript

Abstract

Today, technology has advanced tremendously that it is now being incorporated into the education sector for academic enhancement. Certain technologies like Artificial Intelligence, Machine Learning, Blockchain, Big data, Internet of Things, Augmented Reality, Cloud computing, etcetera changed the conventional education system making it a better platform for the growth of students. In this paper, we dissect the importance of two blooming technologies, Blockchain and Machine Learning, in the education field. Blockchain technology, having data immutability as one of its advantages, has been used in miscellaneous fields for security aspects. It can be used to securely store the degree or other achievement certificates. Such information would be added by the college or university to the blockchain, which can be accessed or shared by the student through the online CV with employers. This approach is secure as there is no need to worry about changes to the institution or the loss of data. Also, Machine learning with its fully capable learning algorithms is the breakthrough technology for future perspectives because it can accurately predict the future based on experience; hence, the incorporation of this technology in the educational field helps the student to make a strategy with the help of various algorithms. By doing such things, better outcomes should be made from present conditions. When the benefits of blockchain are combined with Machine Learning algorithms, we can get certain predictions beforehand and we can securely store the actual results, which is the proposed idea of this study. In this study, the emphasis is made on the impacts created by recent technologies in the educational field and review of various systems proposed by blockchain and machine learning technology and assumption is made for combining two technologies for the betterment of the educational field.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of Data and Material

All relevant data and material are presented in the main paper.

Abbreviations

AI:

Artificial intelligence

AIEd:

Artificial intelligence in education

AODE:

Averaged one-dependence estimators

API:

Application programming interface

AR:

Augmented reality

ARS:

Audience response system

DT:

Decision tree

EDM:

Educational data mining

FFNN:

Feed-forward neural network

GPA:

Grade point average

IBM:

International business machines corporation

IoT:

Internet of things

LGR:

Local and global regression

LRS:

Learning record store

ML:

Machine learning

MLCM:

Multi label consensus classification

MLP:

Multilayer perceptron

MOOC:

Massive open online course

NBT:

Naive Bayes tree

PDF:

Portable document format

SNS:

Social network service

SVM:

Support vector machine

VR:

Virtual reality

References

  1. Shah D, Dixit R, Shah A, Shah P, Shah M (2020) A comprehensive analysis regarding several breakthroughs based on computer intelligence targeting various syndromes. Augment Hum Res 5(1):14

    Article  Google Scholar 

  2. Patel H, Prajapati D, Mahida D, Shah M (2020) Transforming petroleum downstream sector through big data: a holistic review. J Pet Explor Prod Technol 10(6):2601–2611

    Article  Google Scholar 

  3. Ahir K, Govani K, Gajera R, Shah M (2020) Application on virtual reality for enhanced education learning, military training and sports. Augment Hum Res 5:7

    Article  Google Scholar 

  4. Patel D, Shah Y, Thakkar N, Shah K, Shah M (2020) Implementation of artificial intelligence techniques for cancer detection. Augment Hum Res 5(1):6. https://doi.org/10.1007/s41133-019-0024-3

    Article  Google Scholar 

  5. Shah K, Patel H, Sanghvi D, Shah M (2020) A comparative analysis of logistic regression, random forest and KNN Models for the text classification. Augment Hum Res 5:12. https://doi.org/10.1007/s41133-020-00032-0

    Article  Google Scholar 

  6. Patel D, Shah D, Shah M (2020) The intertwine of brain and body: a quantitative analysis on how big data influences the system of sports. Ann Data Sci 7:1–16. https://doi.org/10.1007/s40745-019-00239-y

    Article  Google Scholar 

  7. Talaviya T, Shah D, Patel N, Yagnik H, Shah M (2020) Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif Intell Agric 4:58–73. https://doi.org/10.1016/j.aiia.2020.04.002

    Article  Google Scholar 

  8. Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artif Intell Agric 2:1–12

    Google Scholar 

  9. Kakkad V, Patel M, Shah M (2019) Biometric authentication and image encryption for image security in cloud framework. Multiscale Multidiscip Model, Exp Des 2(4):233–248

    Article  Google Scholar 

  10. Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition for applications in smart agriculture: a comprehensive review. Artif Intell Agric 4:81–95. https://doi.org/10.1016/j.aiia.2020.06.001

    Article  Google Scholar 

  11. Pandya R, Nadiadwala S, Shah R, Shah M (2020) Buildout of methodology for meticulous diagnosis of K-complex in EEG for aiding the detection of Alzheimer’s by artificial intelligence. Augment Hum Res 5(1):3

    Article  Google Scholar 

  12. Marquez J, Villanueva J, Solarte Z, Garcia A (2016) IoT in education: integration of objects with virtual academic communities. Adv Intell Syst Computing. https://doi.org/10.1007/978-3-319-31232-3_19

    Article  Google Scholar 

  13. Aldowah H, Ul Rehman S, Ghazal S, Naufal Umar I (2017) Internet of things in higher education: a study on future learning. J Phys: Conf Ser 892:012017. https://doi.org/10.1088/1742-6596/892/1/012017

    Article  Google Scholar 

  14. Drigas A, Leliopoulos P (2014) The use of big data in education. IJCSI Int J Computer Sci Issues 11:58–63

    Google Scholar 

  15. Popenici SAD, Kerr S (2017) Exploring the impact of artificial intelligence on teaching and learning in higher education. Res Pract Technol Enhanc Learn. https://doi.org/10.1186/s41039-017-0062-8

    Article  Google Scholar 

  16. Tianbo, Zhang. (2012) “The Internet of Things Promoting Higher Education Revolution.” 2012 Fourth international conference on multimedia information networking and security. https://doi.org/10.1109/mines.2012.231.

  17. Memon, M., Bajwa, U. A., Ikhlas, A., Memon, Y., Memon, S., Malani, M. 2018. Blockchain Beyond Bitcoin: block maturity level consensus protocol. 2018 IEEE 5th International conference on engineering technologies and applied sciences (ICETAS). https://doi.org/10.1109/icetas.2018.8629232.

  18. Cheng, Jiin-Chiou, et al. “Blockchain and smart contract for digital certificate.” 2018 IEEE international conference on applied system invention (ICASI), 2018. Crossref, https://doi.org/10.1109/icasi.2018.8394455.

  19. Nakamoto, S., 2008. Bitcoin: A Peer-to-Peer Electronic Cash System, 2008, [online] Available: https://bitcoin.org/bitcoin.pdf.

  20. Nguyen G, Kim K (2018) A survey about consensus algorithms used in blockchain. J Inf Process Syst 14(1):101–128

    Google Scholar 

  21. Kosba, A., Miller, A., Shi, E., Wen, Z., Papamanthou, C. 2016. Hawk: The blockchain model of cryptography and privacy-preserving smart contracts. 2016 IEEE Symposium on security and privacy (SP). https://doi.org/10.1109/sp.2016.55.

  22. Sukhadia A, Upadhyay K, Gundeti M, Shah S, Shah M (2020) Optimization of smart traffic governance system using artificial intelligence. Augment Hum Res 5(1):13

    Article  Google Scholar 

  23. Kundalia K, Shah PY, M, (2020) Multi-label movie genre detection from a movie poster using knowledge transfer learning. Augment Hum Res 5(1):11. https://doi.org/10.1007/s41133-019-0029-y

    Article  Google Scholar 

  24. Jani K, Chaudhuri M, Patel H, Shah M (2020) Machine learning in films: an approach towards automation in film censoring. J Data, Inf Manag 2(1):55–64. https://doi.org/10.1007/s42488-019-00016-9

    Article  Google Scholar 

  25. Parekh V, Shah D, Shah M (2020) Fatigue detection using artificial intelligence framework. Augment Hum Res 5:5

    Article  Google Scholar 

  26. Gandhi M, Kamdar J, Shah M (2020) Preprocessing of non-symmetrical images for edge detection. Augment Hum Res 5:10. https://doi.org/10.1007/s41133-019-0030-5

    Article  Google Scholar 

  27. Parekh P, Patel S, Patel N, Shah M (2020) Systematic review and meta-analysis of augmented reality in medicine, retail, and games. Vis Comput Ind Biomed Art 3:21. https://doi.org/10.1186/s42492-020-00057-7

    Article  Google Scholar 

  28. Panchiwala S, Shah M (2020) A comprehensive study on critical security issues and challenges of the IoT world. J Data, Inf Manag. https://doi.org/10.1007/s42488-020-00030-2

    Article  Google Scholar 

  29. Bacos CA (2019) Machine learning and education in the human age: a review of emerging technologies urban water management for future cities. Springer, Cham, pp 536–543

    Google Scholar 

  30. Bănică L, Burtescu E, Enescu F (2017) The impact of internet-of-things in higher education. Sci Bulletin-Economic Sci 16(1):53–59

    Google Scholar 

  31. Lai JWM, Bower M (2019) How Is the use of technology in education evaluated? a systematic review. Computers Educ 133:27–42. https://doi.org/10.1016/j.compedu.2019.01.010

    Article  Google Scholar 

  32. Richardson ML, Shaffer K, Amini B, Spittler NLJ (2019) Advanced, interactive, image-based education: technology and pedagogy. Curr Probl Diagn Radiol. https://doi.org/10.1067/j.cpradiol.2019.06.003

    Article  Google Scholar 

  33. Williamson B, Pykett J, Nemorin S (2017) Biosocial spaces and neurocomputational governance: brain-based and brain-targeted technologies in education. Discourse: Stud Cultural Politics Educ 39(2):258–275

    Google Scholar 

  34. da Silva MMO et al (2019) Perspectives on how to evaluate augmented reality technology tools for education: a systematic review. J Braz Computer Soc. https://doi.org/10.1186/s13173-019-0084-8

    Article  Google Scholar 

  35. Saritas MT (2015) The emergent technological and theoretical paradigms in education: the interrelations of cloud computing (CC), connectivism and internet of things (IoT). Acta Polytechnica Hungarica 12(6):161–179

    Google Scholar 

  36. Stein S et al (2013) Improving K-12 pedagogy via a cloud designed for education. Int J Inf Manag 33(1):235–241. https://doi.org/10.1016/j.ijinfomgt.2012.07.009

    Article  Google Scholar 

  37. Siemens G (2005) Connectivism: a learning theory for the digital age. Int J Instr Technol Distance Learn. Retrieved from http://www.itdl.org/Journal/Jan_05/article01.htm.

  38. Kosuke M, Hiroyoshi M (2019) Digital university admission application system with study documents using smart contracts on blockchain. Adv Intell Syst Computing. https://doi.org/10.1007/978-3-030-29035-1_17

    Article  Google Scholar 

  39. Gräther, W., Kolvenbach, S., Ruland, R., Schütte, J., Torres, C., Wendland, F., 2018. Blockchain for Education: lifelong learning passport. In: W. Prinz & P. Hoschka (Eds.), Proceedings of the 1st ERCIM blockchain workshop 2018, reports of the european society for socially embedded technologies https://doi.org/10.18420/blockchain2018_07.

  40. Alammary A, Alhazmi S, Almasri M, Gillani S (2019) Blockchain-based applications in education: a systematic review. Appl Sci 9(12):2400

    Article  Google Scholar 

  41. Li R, Wu Y (2020) Blockchain based academic certificate authentication system overview. 1–16

  42. Gopal N, Prakash VV (2018) Survey on blockchain based digital certificate system. Int Res J Eng Technol 5(11):1244–1248

    Google Scholar 

  43. Yumna H, Khan MM, Ikram M, Ilyas S (2019) Use of blockchain in education: a systematic literature review. In: Asian conference on intelligent information and database systems. Springer, Cham, pp 191–202

  44. Ocheja P, Flanagan B, Ueda H, Ogata H (2019) Managing lifelong learning records through blockchain. Res Pract Technol Enhanc Learn. https://doi.org/10.1186/s41039-019-0097-0

    Article  Google Scholar 

  45. Sharples, M., John, D., 2016. The Blockchain and Kudos: A Distributed System for Educational Record, Reputation and Reward. In: Verbert, K.; Sharples, M. and Klobuˇcar, T. eds. Adaptive and adaptable learning: Proceedings of 11th European conference on technology enhanced learning (EC-TEL 2015), Lyon, France, 13 - 16 September 2016. Lecture Notes in Computer Science. Switzerland: Springer, pp. 490–496.

  46. Qazdar A, Er-Raha B, Cherkaoui C, Mammass D (2019) A machine learning algorithm framework for predicting students performance: a case study of baccalaureate students in Morocco. Educ Inf Technol. https://doi.org/10.1007/s10639-019-09946-8

    Article  Google Scholar 

  47. Pandey M, Taruna S (2014) A multi-level classification model pertaining to the student’s academic performance prediction. Int J Adv Eng Technol 7(4):1329–1341

    Google Scholar 

  48. Romero C, Ventura S (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33(1):135–146

    Article  Google Scholar 

  49. Prabha SL, Shanvas ARM (2014) Educational data mining applications. Op Res Appl: Int J 1(1):23–29

    Google Scholar 

  50. Dekker, G.W., Pechenizkiy, M., Vleeshouwers, J.M., 2019. Predicting students drop out: a case study. Proc. Int. Conf. Educ. Data Mining, 41–50.

  51. Anuradha C, Velmurugan T, Anandavally R (2015) Clustering algorithms in educational data mining: a review. Int J Power Control Comput 7(1):47–52

    Google Scholar 

  52. Kucak, Danijel, et al. “Machine learning in education—a survey of current research trends.” Proceedings of the 29th international DAAAM symposium 2018, 2018, 0406–10. https://doi.org/10.2507/29th.daaam.proceedings.059.

  53. Kotsiantis SB (2011) Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artif Intell Rev 37(4):331–344. https://doi.org/10.1007/s10462-011-9234-x

    Article  Google Scholar 

  54. Chui KT, Fung DCL, Lytras MD, Lam TM (2018) Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Comput Hum Behav. https://doi.org/10.1016/j.chb.2018.06.032

    Article  Google Scholar 

  55. Lykourentzou I, Giannoukos I, Nikolopoulos V, Mpardis G, Loumos V (2009) Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput Educ 53(3):950–965

    Article  Google Scholar 

  56. Hu YH, Lo CL, Shih SP (2014) Developing early warning systems to predict students’ online learning performance. Comput Hum Behav 36:469–478

    Article  Google Scholar 

  57. Sati NU (2018) Semi-supervised classification in educational data mining: students’ performance case study. Int J Computer Appl 179(26):13–17

    Google Scholar 

  58. Lizcano D, Lara JA, White B, Aljawarneh S (2019) Blockchain-based approach to create a model of trust in open and ubiquitous higher education. J Computing High Educ. https://doi.org/10.1007/s12528-019-09209-y

    Article  Google Scholar 

  59. Williams P (2018) Does competency-based education with blockchain signal a new mission for universities? J High Educ Policy Manag 41(1):1–14

    Google Scholar 

  60. Hori M, Ono S, Miyashita K, Kobayashi S, Miyahara H, Kita T, Yamada T, Yamaji K (2018) Learning system based on decentralized learning model using blockchain and SNS. In: Proceedings of the 10th international conference on computer supported education - Volume 1: CSEDU. pp 183–190. ISBN 978-989-758-291-2. https://doi.org/10.5220/0006666901830190

  61. Ocheja P, Flanagan B, Ogata H (2018) Connecting decentralized learning records: a blockchain based learning analytics platform. Proceedings of the 8th international conference on learning analytics and knowledge. https://doi.org/10.1145/3170358.3170365

  62. Turkanović M, Hölbl M, Košič K, Heričko M, Kamišalić A (2017) EduCTX: a blockchain-based higher education credit platform. IEEE Access 6:5112–5127

    Article  Google Scholar 

  63. Grech A, Camilleri AF (2017) Blockchain in education. No. JRC108255. Joint Research Centre (Seville site)

  64. Xu Y, Zhao S, Kong L, Zheng Y, Zhang S, Li Q (2017) ECBC: a high performance educational certificate blockchain with efficient query. Springer, Cham, pp 288–304

    Google Scholar 

  65. Sorour SE, Mine T, Goda K, Hirokawa S (2015) A predictive model to evaluate student performance. J Inf Process 23(2):192–201

    Google Scholar 

  66. Zimmerman J, Brodersen KH, Heinimann HR, Buhmann JM (2015) A model-based approach to predicting graduate-level performance using indicators of undergraduate-level performance. J Edu Data Min 7(3):151–176

    Google Scholar 

  67. Pandey M, Taruna S (2016) Towards the integration of multiple classifiers pertaining to the student’s performance prediction. Perspect Sci 8:364–366. https://doi.org/10.1016/j.pisc.2016.04.076

    Article  Google Scholar 

  68. Nieto Y, García-Díaz V, Montenegro C, Crespo RG (2018) Supporting academic decision making at higher educational institutions using machine learning-based algorithms. Soft Comput. https://doi.org/10.1007/s00500-018-3064-6

    Article  Google Scholar 

  69. Kaur A, Umesh N, Singh B (2018) Machine learning approach to predict student academic performance. Int J Res Appl Sci Eng Technol 6, 734–742. https://doi.org/10.22214/ijraset.2018.4125

  70. Ciolacu M, Tehrani AF, Beer R, Popp H (2017) "Education 4.0—Fostering student’s performance with machine learning methods IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME). Constanta 2017:438–443. https://doi.org/10.1109/SIITME.2017.8259941

    Article  Google Scholar 

  71. Xenos M (2004) Prediction and assessment of student behaviour in open and distance education in computers using bayesian networks. Computers Educ 43(4):345–359. https://doi.org/10.1016/j.compedu.2003.09.005

    Article  Google Scholar 

  72. Gupta A, Dengre V, Kheruwala HA, Shah M (2020) Comprehensive review of text-mining applications in finance. Financ Innov. https://doi.org/10.1186/s40854-020-00205-1

    Article  Google Scholar 

  73. Naik B, Mehta A, Shah M (2020) Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease. Vis Comput Ind Biomed Art 3:26. https://doi.org/10.1186/s42492-020-00062-w

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Vishwakarma Government Engineering College and Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University for the permission to publish this research.

Funding

None.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Vishwakarma Government Engineering College, Ahmedabad, Gujarat, India

    Dhruvil Shah, Devarsh Patel, Jainish Adesara & Pruthvi Hingu

  2. Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India

    Manan Shah

Authors
  1. Dhruvil Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Devarsh Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jainish Adesara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pruthvi Hingu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manan Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors make substantial contribution in this manuscript. DS, DP, JA, PH and MS participated in drafting the manuscript. DS, DP, JA, and PH wrote the main manuscript, and all the authors discussed the results and implication on the manuscript at all stages.

Corresponding author

Correspondence to Manan Shah.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical Approval

Not applicable.

Consent for Publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shah, D., Patel, D., Adesara, J. et al. Exploiting the Capabilities of Blockchain and Machine Learning in Education. Augment Hum Res 6, 1 (2021). https://doi.org/10.1007/s41133-020-00039-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41133-020-00039-7

Keywords

Search

Navigation