1. MMC
  2. All Volumes and Issues
  3. Volume 10, Number 3, 2023
  4. Implementing quality assurance practices in teaching machine learning in higher education

Implementing quality assurance practices in teaching machine learning in higher education

MMC.
2023;
Volume 10, Number 3
: pp. 660–667
https://doi.org/10.23939/mmc2023.03.660
Received: February 16, 2023
Revised: July 08, 2023
Accepted: July 09, 2023

Mathematical Modeling and Computing, Vol. 10, No. 3, pp. 660–667 (2023)

Authors:
  1. Y. Chemlal
  2. M. Azouazi
1
University Hassan II, Faculty of Science, Ben M’Sik, Laboratory LTIM, Casablanca, Morocco
2
University Hassan II, Faculty of Science, Ben M’Sik, Laboratory LTIM, Casablanca, Morocco

The development of machine learning and deep learning (ML/DL) change the skills expected by society and the form of ML/DL teaching in higher education.  This article proposes a formal system to improve ML/DL teaching and, subsequently, the graduates' skills.  Our proposed system is based on the quality assurance (QA) system adapted to teaching and learning ML/DL and implemented on the model suggested by Deming to continuously improve the QA processes.

machine learning
deep learning
assurance quality
higher education
  1. Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton–Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., Teller A.  Artificial intelligence and life in 2030: One hundred year study on artificial intelligence.  Preprint arXiv:2211.06318 (2016).
  2. Shouman O., Fuchs S., Wittges H.  Experiences from Teaching Practical Machine Learning Courses to Master's Students with Mixed Backgrounds.  Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR.  170, 62–67 (2022).
  3. Jannani A., Sael N., Benabbou F.  Machine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators.  Mathematical Modeling and Computing.  10 (2), 534–546 (2023).
  4. Ravi D., Wong C., Deligianni F., Berthelot M., Andreu–Perez J., Lo B., Yang G.  Deep learning for health informatics.  IEEE Journal of Biomedical and Health Informatics.  21 (1), 4–21 (2017).
  5. Suzuki K.  Overview of deep learning in medical imaging.  Radiological physics and technology.  10 (3), 257–273 (2017).
  6. Raghavan M., Barocas S., Kleinberg J., Levy K.  Mitigating bias in algorithmic hiring: Evaluating claims and practices.  FAT* '20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 469–481 (2020).
  7. Heras J.  Deep Learning Projects from a Regional Council: An Experience Report.  Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR.  170, 15–19 (2022).
  8. Sadler D. R.  Academic achievement standards and quality assurance.  Quality in Higher Education.  23 (2),  81–99 (2017).
  9. Luo L.  Reform of Practical Courses in Applied Universities in the Background of Big Data Era-Taking Business Administration as a Pilot.  2019 Asia-Pacific Conference on Advance in Education, Learning and Teaching (ACAELT 2019). 1025–1029 (2019).
  10. Zheng D., Wang Y.  Constructing postgraduate experimental teaching system and cultivating postgraduate innovation ability.  Experimental Technology and Management.  27 (5), 146–147 (2010).
  11. Jiang Y., Li B.  Exploration on the Teaching Reform Measure for Machine Learning Course System of Artificial Intelligence Specialty.  Scientific Programming.  2021, 8971588 (2021).
  12. Steinbach P., Seibold H., Guhr O.  Teaching Machine Learning in 2020.  Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, PMLR.  141, 29–35 (2021).
  13. Guhr O., Kinnaird K. M., Steinbach P.  Teaching Machine Learning in 2021 – An Overview and Introduction.  Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR.  170, 15–19 (2022).
  14. Raschka S.  Deeper Learning By Doing: Integrating Hands-On Research Projects Into A Machine Learning Course.  Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR.  170, 46–50 (2022).
  15. Machumu H. J., Kisanga S. H.  Quality Assurance Practices in Higher Education Institutions: Lesson from Africa.  Journal of Education and Practice.  5 (16), 144–156 (2014).
  16. World Bank. Higher education development for Ethiopia: Pursuing the vision. Washington, World Bank (2003).
  17. Hénard F., Deborah R.  Fostering Quality Teaching in Higher Education: Policies and Practices (2012).
  18. Mårtensson K., Roxå T., Stensaker B.  From quality assurance to quality practices: an investigation of strong microcultures in teaching and learning.  Studies in Higher Education.  39 (4), 534–545 (2014).
  19. Cardoso S., Rosa M. J., Videira P., Amaral A.  Internal quality assurance: A new culture or added bureaucracy?  Assessment & Evaluation in Higher Education.  44 (2), 249–262 (2019).
  20. Oo T. T.  Implementing quality management practices in higher education institutions - the case of technological university.  University Journal of Science Engineering and Research.  01 (02), (2019).
  21. PDCA Cycle – What is the Plan-Do-Check-Act Cycle?  https://asq.org/quality-resources/pdca-cycle.
  22. 2022 Kaggle Machine Learning & Data Science Survey.  https://www.kaggle.com/c/kaggle-survey-2022/data.
  23. Top 30 Machine Learning Skills for Machine Learning Engineer.  https://www.knowledgehut.com/blog/data-science/machine-learning-skills.
  24. Li G., Yuan C., Kamarthi S., Moghaddam M., Jin X.  Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis.  Journal of Manufacturing Systems.  60, 692–706 (2016).