Practice-Oriented Training on DevOps Methodology
Practice-Oriented Training on DevOps Methodology
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In just six weeks, students with experience in Data Science were able to master real skills of DevOps engineering.
Responsibility: Instructional Design
Target Audience: Students studying Big Data Engineering in a long-term program
Tools Used: EdX, GitHub, Figma Jam, Google Sheets, Docs, Forms, Zoom
Year: 2021
Problem
Problem
Students of the Big Data Engineering program were required to master DevOps skills as part of their long-term training. By this point, they had already learned programming, machine learning, and data engineering skills.
The training aimed to teach students to automate assembly processes, configure software, and deploy software. This needed to be mastered quickly, despite the complex requirements.
Moreover, training is needed to considerably enhance the contentment of the learners and elevate the educational quality in contrast to preceding units of the curriculum.
Process
Process
Firstly, we formulated requirements and conducted an SME casting based on a brief course description from curriculum creators.
Goal Setting
Goal Setting
As the course has limited time to teach real commercial skills, we have decided to use authentic problems as the basis for instructional design. The study tasks were special because they allowed students to use their own projects to practice skills in DevOps. It was assumed that this would potentially increase motivation and bring learning even closer to reality.
In collaboration with SME, we outlined the learning outcome and broke down it into authentic problems.
Design Learning Strategy
Design Learning Strategy
After in-depth SME interviews and brainstorming with the team, I developed an educational solution strategy that best suited our goal, students' personal characteristics, and contextual constraints.
Content Development
Content Development
After I got approval for the idea, I began creating the content as per the outline.
Development stage included:
creation of content for self-study (texts, videos, tests);
writing group session plans;
detailed formulation of project tasks and evaluation criteria.
During development, I often used an iterative approach and returned to the principle of constructive alignment.
Collaboration with SME
Collaboration with SME
In this case, we took an approach to working with SMEs that greatly contributed to the final result. We created an environment that allowed the expert to feel like a part of the team, rather than just a hired specialist.
We utilized agile methodologies, including setting sprints and holding retrospective meetings. The sprint goals were set with the expert's motivation in mind, so we could encourage their participation in the project. I also utilized coaching techniques to grow the expert as a speaker and mentor. This gave the SME no monetary benefits. In this way, their high level of involvement ultimately contributed to the course's excellent quality.
Results
Results
More than half of the students took all opportunities and completed the course within the recommended timeframe. The final completion rate was nearly 90%.
The average mastering score was 79%. We calculated it as the percentage of total points earned for all project assignments out of the maximum possible.
Students praised the quality of education and their satisfaction. The customer satisfaction score was 80%, which is several times higher than in previous modules of the training program.
While a quantitative analysis of stakeholder satisfaction was not conducted, it is worth noting that management recognized and rewarded work to improve the quality of training.
TAKEAWAYS
TAKEAWAYS
This project has contributed to my experience in full-cycle course development. I learned that extraordinary solutions aren't always needed, and simple proven methods can be the best.
Furthermore, this experience has emphasized the importance of establishing strong and trusting working relationships with subject matter experts.
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