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By implementing a prescriptive learning approach, organisations can provide a set of diverse resources that
align with individual learning styles. This approach ensures that there is:
● customised learning journeys tailored according to different people( for example, different educational
backgrounds) based on individual needs and preferences.
● a variety of learning materials that cater to different learning styles and help in easier grasping of concepts.
● enough leverage or advantage to the learners to progress at their own pace, accommodating their schedules
and learning speeds.
● create an environment that makes learners feel comfortable and gain new skills in an environment to
supports continuous learning and encourages self-directed exploration.
● each participant can choose the materials and methods that work best for them, leading to more effective
learning and greater improvement in data literacy skills over time.
Evaluate
Designing an evaluation metric for the data literacy program involves creating
a structured framework to assess participants’ progress and the effectiveness
of the program overall. It helps to:
● improve participants’ overall data literacy skills.
● establish clear criteria to measure the success of the data literacy program
and individual participant growth.
● establish a schedule for assessing participant progress to monitor their development over time.
Data Literacy Framework—An Iterative Process
This means the development and enhancement of data literacy skills are not static or one-time event. Instead,
they evolve through continuous cycles of learning, application, and refinement.
• Learning
✶ Learning is the initial stage where individuals acquire new knowledge and skills related to data literacy.
✶ Individuals engage in various learning activities, such as formal training sessions, online courses, reading
materials, and hands-on workshops to gain insights into data concepts, tools, and methodologies.
• Application
✶ Application involves putting acquired knowledge and skills into practice in real-world contexts.
✶ Individuals apply what they have learned to analyse real datasets, solve data-related problems, and make
informed decisions.
✶ They are engaged in data projects, experiments, or simulations to gain practical experience and develop a
deeper understanding of data concepts.
• Refinement
✶ Refinement focuses on reflecting the past experiences, identifying areas for improvement, and enhancing
data literacy skills over time.
✶ Feedback from peers, mentors, supervisors, and outcomes of data-related activities informs the refinement
process, guiding individuals to adjust their practices accordingly.
Data Security and Privacy
The terms data security and data privacy are often used interchangeably, but they mean different things. Data privacy
determines who can access the data, while data security involves tools and policies to restrict access to the data.
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