Multimedia Learning

Multimedia learning describes learning through the use of pictures and words. Examples of multimedia learning include watching a PowerPoint presentation, watching a pre-recorded lecture or reading a physics textbook.

The multimedia principle serves as the foundation for Multimedia Design Theory. This principle asserts that deeper learning occurs from words and pictures than from just words. Simply adding images or graphics to words does not assure a deeper level of learning, however. Multimedia instructional content is more likely to create a meaningful learning experience if the content is developed with the following assumptions from cognitive science in mind:

• Active processes assumption
  Active learning entails carrying out a coordinated set of cognitive processes during learning.
• Dual-channel assumption
  Dual channels, one for visual/pictorial and one for auditory/verbal processing.
• Limited-capacity assumption
  Each channel has limited capacity for processes.

Working memory is the part of memory that consciously processes information. Working memory is severely limited (see Memory and Learning). Because much of the instructional content presented to students is novel, faculty must remember the limitations of working memory when they design instructional materials. Lessons developed with consideration for the limitations of students working memory are more likely to be effective than lessons developed without. For example, if you provide students with written instructions for small-group activities, instead of simply stating the instructions one time, students will not need to remember the instructions as they work.

One problem that can arise when words and pictures are presented together is a situation called cognitive overload. In this scenario, the processing demands associated with the learning task exceed the learner’s cognitive processing capacity. There are three types of cognitive load: extraneous, intrinsic and germane. Poor instructional design can increase each of these.

• Extraneous cognitive load
  This type of cognitive load results when students are asked to use working memory for tasks other than the primary learning objective. Such designs fail to steer working memory resources towards schema construction and automation. From the example above, students must use working memory to remember the instructions for the small-group activity, instead of focusing on the key concepts that the faculty just taught.
• Intrinsic cognitive load
  This type of cognitive load result from the inherent complexity of the information that must be processed. For example, understanding a complex equation that includes Greek symbols means the student must be able to remember and keep track of the mathematical meaning of each symbol. Instructional design can’t eliminate intrinsic load, but faculty should realize that they have automated many skills and concepts that students must still use working memory to understand and process.
• Germane cognitive load
  This type of cognitive load results from effortful learning, leading to schema production and automation. This is different from intrinsic load which is the inherent work involved in the task, while germane cognitive load is the work involved in learning from the task. For example, a multiplication problem has the same intrinsic load for a fifth grade student and a teacher, but higher germane cognitive load for the young student who is learning more from the task.

When presenting multimedia content to students, faculty can take certain steps to reduce cognitive load and to help ensure an effective transmission of the material. Mayer & Moreno (2003) outline nine specific strategies to reduce the cognitive load of multimedia presentations:

• Off-loading
  Move some essential processing from the visual channel to the auditory channel, or vice versa if there is too much verbal explanation given. Learning is more effective when information is presented as audio rather than as text on the screen.
• Segmenting
  Take time to pause between small content segments to allow students time to process information. Learning is more effective when a lesson is presented in small pieces rather than as a continuous entity.
• Pre-training
  Include relevant names and characteristics of system components. Learning is better when students are aware of names and behaviors of various system components.
• Weeding
  Eliminate extraneous, albeit interesting, material. Learning is more effective without the inclusion of extraneous information. At least one study has shown, however, that up to 50% additional extraneous material did not harm learner performance if it was interesting or motivating.
• Signaling
  Include cues for how to process material to avoid processing extraneous material. Learning is more effective when signals are included. For example, add directions for how to move through a system diagram that does not have a clear linear path.
• Aligning
  Place written words near corresponding graphics to reduce the need for visual scanning. Learning is more effective when words are placed near corresponding image parts.
• Eliminate redundancy
  Don’t present identical streams of spoken or written words. Learning is more effective when information is presented as audio as opposed to as audio and on-screen text. For example, don’t read your PowerPoint slides to students.
• Synchronizing
  Present audio and corresponding images simultaneously. Learning is more effective when images and narration are presented simultaneously as opposed to successively.
• Individualizing
  Assure that students possess skill for holding mental representations.