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. 2024 May 23;24(1):564.
doi: 10.1186/s12909-024-05555-3.

Providing insights into health data science education through artificial intelligence

Narjes Rohani  1 , Kobi Gal  2   3 , Michael Gallagher  4 , Areti Manataki  5
Affiliations

Providing insights into health data science education through artificial intelligence

Narjes Rohani et al. BMC Med Educ. .

Abstract

Background: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students' learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs.

Methods: We applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students' engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics.

Results: We found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning.

Conclusions: We applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.

Keywords: Artificial intelligence; Educational data mining; Health data science; Health informatics; Learning analytics; Learning engagement; Learning strategy; Learning tactic; Medical education.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The schema of the methodology employed to find learning tactics and strategies employed by students (RQ3).
Fig. 2
Fig. 2
Relative no. visits of parent pages of each type of educational resource. The figure shows log of the relative number of visits for each educational resources by each group of students. LP: low performance students, MP: moderate performance students, HP: high performance students.
Fig. 3
Fig. 3
The relative average watching time of each video lecture for students. The relative video watch was calculated by dividing the average watched time by the total length of each video lecture.
Fig. 4
Fig. 4
Average frequency of using each video action per student during watching each topic. “end” indicates watching a video until the end. “pause” means the student paused the video. “seek” means going forwards or backwards in a video. “play” indicates replaying a video after a pause. “start” means starting a video from the beginning. The topics in the x-axis are listed based on the order of teaching in the course (with the exception of Case Studies, which are spread across the course duration).
Fig. 5
Fig. 5
Frequency plot of each learning tactic, showing how many times each learning action was used in that tactic
Fig. 6
Fig. 6
Average frequency of using each learning tactic by low, moderate, and high engagement learners (5b). as well as averaged final grade for each strategy group (5a)
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References

    1. Goldsmith J et al. The emergence and future of public health data science. Public Health Rev, 2021: p. 4. - PMC - PubMed
    1. Subrahmanya SVG, et al. The role of data science in healthcare advancements: applications, benefits, and future prospects. Ir J Med Sci (1971-) 2022;191(4):1473–83. doi: 10.1007/s11845-021-02730-z. - DOI - PMC - PubMed
    1. Muni Kumar N, Manjula R. Role of big data analytics in rural health care-A step towards svasth bharath. Int J Comput Sci Inform Technol. 2014;5(6):7172–8.
    1. Burghard C. Big data and analytics key to accountable care success. IDC Health Insights. 2012;1:1–9.
    1. Habehh H, Gohel S. Machine learning in healthcare. Curr Genom. 2021;22(4):291. doi: 10.2174/1389202922666210705124359. - DOI - PMC - PubMed

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