In the first part of our series exploring the effort and expertise behind effective workforce learning, we delved into instructors’ routine practices that keep them in top teaching form. We will now examine best practices in curriculum design for impactful learning experiences that create long-term value for teams and their organizations.
Skillful instructional design is a multiphase process that incorporates instructional frameworks, rigorous evaluation technologies, and thoughtful user experience elements to create engaging and effective educational offerings. For example, the Data Society instructional design team employs established methodologies that guide curriculum development at all phases, spanning course conception through training impact assessment.
The Successive Approximation Model (SAM) and Analyze, Design, Develop, Implement, and Evaluate (ADDIE) model for curriculum design are among the tools the team uses to customize training that supports clients’ diverse goals. The ADDIE model, comparable to the waterfall project management methodology, is helpful for developing established training modules in subjects, such as Python, that require minimal updates and modifications to make them relevant to different cohorts. However, the SAM model, akin to the agile project management methodology, is ideal for designing bespoke training programs that will serve specific organizational and learner needs and support learning in more dynamic subject areas.
When taking the SAM approach to instructional design, the team embarks upon an iterative, rapid prototyping process with two internal cycles. First, by conducting interviews with SMEs and frequently soliciting client feedback, the team creates preliminary wireframes to shape a tailored training curriculum incrementally. This agile methodology maximizes opportunities for adapting instruction, course pacing, and materials to provide training most relevant to organizational objectives and learners’ domains and functions.
Data Society’s instructional design team issues pre-course and post-course assessments to gauge learning outcomes. In addition, the team follows the Kirkpatrick Model for evaluation to help clients understand how they measure learner outcomes and how these results translate into ROI. A close assessment provides the insights instructional design teams need to measure a course’s impact and implement instructional modifications that keep learning on track.
The Kirkpatrick Model of learning evaluation tracks four levels of training to assess course progress and outcomes by evaluating:
To perform evaluations based on the Kirkpatrick Model, the Data Society team implements the following process:
Level 1 - The team conducts voluntary tests of learners’ feelings about the course and their performance.
Level 2 - To evaluate learning, the team performs periodic knowledge checks—three to four questions each hour—that help them assess the cohort’s understanding of the subject matter, guide instructors, and give the learners a sense of their progress. According to Meghan Cipperley, Data Society’s senior vice-president of learning, this level “...is designed to measure the extent to which training program participants have improved their skills and knowledge as a result of the training.”
Level 3 - In this level, the team gathers insights into the process, impact, and outcomes. It performs a summative evaluation to gauge how effectively the training has helped learners apply their new skills and knowledge.
Level 4 - Finally, design teams ideally maintain a long-term engagement with clients, often filling the role of an academic advisor. This phase helps instructional design teams steer the next cohort of courses, glean insights into how learners use their new skills months after course completion, and assess how their training translates into tangible business results.
These steps are critical to successful educational experiences because they enable instructors and instructional designers to identify problems immediately and take corrective measures when they can be most effective. For example, the Data Society team punctuates the curriculum for more extended programs with stoplights to assess whether course pacing is appropriate.
The way learners engage with instructors also shapes learner experience and outcomes. While the varied instructional formats available today have merits, live, instructor-led training shines in several specific areas. According to Cipperley, live classroom dynamics can engage and drive learners in ways that secluded, asynchronous experiences cannot. For example, she explains, “Students are more likely to want to impress peers, ask questions, and interact with the learning in a way that’s meaningful if they’re given a classroom environment to do so.” Further, facilitating such interactions in virtual settings broadens accessibility and supports organizational budgets by eliminating travel requirements for participation; she continues:
Addressing ongoing debates among L&D departments regarding virtual learning’s value, she points out that virtual training is especially effective when certain conditions are in place. For example, organizations that recognize employee training efforts and achievements—closely following and acknowledging their progress despite the remote nature of their classroom engagement—nurture successful workforce learning outcomes. In addition, tying training to real business problems and solutions through hackathons and capstones reinforces learners’ retention and practical application of new skills, helping organizations realize an immediate return on their investment, Cipperley notes. In summary, for online learning that delivers measurable results, she advises, “Don’t do it in a vacuum.”
In our series exploring the practices that drive effective instructional design and delivery, we’ve seen that successful training is a function of responsive instructional support guided by pedagogical expertise and skillful assessment. From the earliest curriculum design stage to the evaluation of learning outcomes, experienced professionals with up-to-date knowledge create the materials, activities, discussions, and atmosphere that define the learner experience. Ultimately, the quality of these experiences shapes their long-term impact on the workforce and determines the value they bring to organizations and their teams.
Data Society provides customized, industry-tailored data science training solutions—partnering with organizations to educate, equip, and empower their workforce with the skills to achieve their goals and expand their impact.
