This article will show you how to systematically assess and select components for blended learning programs. Blended learning shouldn’t be a hit-or-miss proposition. Rather, it should follow a calculated approach that provides appropriate solutions to your business needs.
The central tenet of blended learning theory is that you apply the learning delivery strategy that best suits the type of training you’re trying to accomplish. For example, instructor-led training is more effective at teaching skills that involve person-to-person contact, while Web-based training (WBT) is an efficient means to deliver concise systems training to a widely distributed audience. You can “blend” by selecting the single best approach for a given course or by applying different learning strategies within a course.
To ensure that your learning resources are deployed in the most efficient manner to maximize learning (no matter what the level of blending), focus on a given program’s purpose and goal for the company. Working from the program’s operational and instructional goals, you match the appropriate learning strategies and techniques to the program. In practical terms, you are looking to select the right ingredients to make the best blend that satisfies your goal.
The first phase of creating a successful blend for a program is to understand the program’s instructional nature. This means identifying the general learning elements that define the program’s instructional intent and events. Virtually all learning programs boil down to some combination of these learning elements:
- Introduction: Overviews the topics and sets job-related context.
- Relevance: Clarifies the need for or importance of the topics relative to the learners.
- Content: Presents the topics, subjects, facts, concepts and issues.
- Tools: Introduce and provide instruction for using job- or task-related tools.
- Performance Support: Identifies and locates relevant information available from references and explains how it is to be applied.
- Customization: Provides clarification of considerations and issues related to the learners’ location and/or workgroup.
- Review: Discusses the preceding elements and restates how they are used on the job.
- Practice: Performs activity to work with or apply the knowledge and skills acquired in the preceding elements.
- Assessment: Evaluates the learners’ ability to use or perform with the knowledge and skills obtained.
- Job Performance: Monitors/follows up learners on the job to further refine desired performance.
- Feedback: Provides learners with meaningful information relative to their understanding, knowledge, skill or performance. (Note: From this point, it is assumed that feedback is consistently integrated with each of the other learning elements; thus it will not appear as an independent learning element.)
For example, imagine that you are rolling out a new ordering system in your company. It is your responsibility to design, develop and implement the associated training program. Considering the learning elements above, you would determine that the program should include: introduction, relevance, tools (the system), performance support, review, practice and assessment.
Blended Learning Components
The second phase of creating an effective blend is picking the appropriate ingredients that will meet the program’s goals and make it rich and satisfying to learners. These ingredients are the learning components that make up the “blend” in blended learning. The components are the means and methods of learning, such as classroom instruction, WBT, PSTs and simulations. These are but a few of the dozens of basic components that can go into a program’s blend.
To manage the vast catalog of available learning components, you can first identify the relevant component categories. Categories group blended learning components based on their application—how the components best lend themselves to accomplishing instructional goals. Learning component categories for blended learning programs include:
- Instruction: Presents content and related discussion. Includes group instruction, which is facilitated or synchronous, and self-instruction, which is self-directed and asynchronous.
- Performance support: Provides support for the performance of a task. Includes reference (self-directed) or aid (assisted by others).
- Tools: Items used for performance on the job.
- Collaboration: Allows for communication or networking among peers.
- Practice: Allows learners to perform realistic tasks in a non-operational environment.
- Evaluation: Assesses knowledge, skill and performance with feedback to learner.
The critical and defining step in blended learning design is selecting the most appropriate components based on a given program’s learning elements and business drivers. You begin this process by identifying the component categories that apply to each of the program’s learning elements, as presented in Table 1. You can either assess a program’s needs collectively, combining all categories across the program for a course-level blend, or selectively, assessing the categories applicable to each distinct learning element for blending within a course.
For example, imagine that you want to selectively identify categories for the portion of ordering system training that addresses the system’s performance support tools. Using Table 1, you find the “Performance Support” learning element in the first column, then read across to identify the relevant component categories: instruction, tools and performance support.
Now you come to the actual blended learning component selection. Using the categories derived from Table 1, you identify the corresponding individual learning components from Table 2. These components form a program’s pantry, if you will—the collection of appropriate ingredients from which you select to make the program’s specific mix.
The final selection of components is driven by the program’s business and operational requirements. It is important to consider issues such as the training culture, technology infrastructure, evaluation requirements, audience characteristics, content characteristics and available development time and budget. These issues guide the final component selection by ruling out some components and by illuminating others that are more appropriate for the program.
For example, considering the performance support portion of our hypothetical ordering-system training program, we might find that our business and operational requirements call for a low-budget, self-instructional approach. This guides the use of the self-instruction category in Table 2. For the other categories we identified in Table 1, the tool in our case is the ordering system, and the performance support piece could be an electronic performance support system (EPSS) linked to the ordering system.
Now that we have clearly defined the appropriate component categories based on Table 1 and our requirements analysis (giving us the self-instruction, tool and performance support reference categories), we can review the corresponding lists in Table 2 to identify component recommendations. Next you begin the process of ruling out items that are not apropos to the program’s implementation. A final analysis of the remaining components in relation to the program’s requirements enables you to hone in on the best components for the program’s design. In this case, you would determine that the best blended design for teaching the ordering system’s performance support capabilities could consist of a self-paced slide presentation (low-budget WBT) that involves a demonstration of the EPSS’ features and capabilities. Now you know how to make the blended learning program work.
Blended learning isn’t rocket science, but it does require proper analysis and design to make it effective. It’s not just a matter of throwing together all of the available resources and calling it training. Blended learning takes vision, discipline and the right ingredients to make the best blend. By following a systematic analysis and design approach, you can enjoy the satisfying taste of success.
Jim Elsenheimer is a training and performance specialist with Pearson Performance Solutions. Jim has more than 18 years of experience working with Fortune 500 companies and has had articles on performance support and knowledge management systems published by ISPI and ASTD. His recent work developing the Blended Learning Analysis and Design Expediter (BLADE) is the basis for this article. E-mail Jim at firstname.lastname@example.org.
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