Crafting the Total Learner Experience: Preventing Data Corruption in Instructional Messaging

Preventing data corruption

Data corruption often causes a barrier to successful implementation of the TLE. Data corruption is a flaw in the design process that cognitively diminishes the transfer of information that specifically promotes learning.

Any of several factors can cause data corruption.

Ambiguous instruction. Avoid including imprecise language such as, “Click the items that the box usually includes.” Note that with sketchy words like “only,” “usually,” “never,” and “nothing,” you risk having learners misinterpret the intent of the learning objective. Help them to succeed by being as clear as possible.

False or unnecessary information. Make sure the content displays the information relevant to the objective. Be careful of including too much information, or information you cannot verify is accurate. Learners are more than willing to question the credibility of your content.

Jargon. Refrain from buzzwords, especially industry specific ones. “Return-on-investment,” “constraints,” “accountability,” “resources,” and “targeting” are examples of jargon. Consider this sentence from a training course I recently reviewed, “Comprehensive, community-oriented involvement naturally leads to a substantial return-on-investment rationale that can be modeled, based on existing practices from specific groups.” Does this sentence really say anything?

Irrelevant visuals. Ask yourself if the visual you are about to design or place near your content will add anything to the instructional objective? Is it just a pretty, decorative graphic, or a sharp-looking photo of a young model? Do not clutter the screen if it adds nothing to the instructional message. Your learners will thank you.

Forced action. Some course material may warrant forcing the learner to travel a prescribed path, but if your course is overly long and tedious, you will frustrate your learner if, on top of requiring them to view 400 screens of content, you force them to view it in the order you dictate.

Limited choice. Try to refrain from limiting the learner’s choices when it comes to the ability to explore your content, unless exploration can diminish the instruction.

Invasive user interface objects. Display only the elements you really need. If your “Help” content is simply a regurgitation of generic information, and is not context sensitive, question whether or not you need the element on the user interface. Do not consume screen real estate with non-essential decoration such as large course title banners or company logos. Reserve as much of the screen for substantive content as possible.

Non-intuitive navigation. The first rule of a good user interface is to not make your user have to think about the user interface. Don’t try to re-invent what many of the largest companies in the world have already figured out: how to build good navigation. Conventions exist, so use them. Do not think you should change or break conventions, especially for a design aesthetic.

Clear, concise, and informative messaging can substantially reduce the risk of data corruption. To aid in preventing data corruption, an effective instructional message should:

• contain credible and verifiable data,
• display constructive visual evidence, and
• show meaningful context and causality.

Evidence and credibility

As the sender of the instructional message, you are the agent of information, and, to successfully influence the learner to action, you must build trust and confidence. In an instructional message, the link between the sender and receiver is prejudiced based on variables such as distance and time. Electronic messaging, by its very definition, is based on one-directional influence. The receiver of the message must be willing to accept the message’s credibility before it can influence her to action. You should craft an instructional message that shows causality at all times — speculation and selective use of data is no substitute for evidence.

You should constantly strive for simplicity and clarity — the very idea of causality is simplicity (Tufte again). You foster the ability to deduce intent without relying on assumption, or too little evidence for the learner, by stripping away unnecessary information, jargon, mismatched meaning, and marketing-speak. Learning improves when the instructional message is verifiable and easily placed in context to the learner’s sense of reality. Your instructional message should contain whatever is necessary to show evidence, and to assist in reasoning.

Display visual evidence

Integrating text and visuals is common in e-Learning courses. The visuals may easily overwhelm the learner if they are irrelevant, segregated from the learning objectives, or do not assist reasoning. Credibility and evidence should be the primary motivating factors behind the integration of a visual into an instructional message. The learner quickly judges the visuals based on quality, the explanatory ability of the visual, and its association with the content. For explanatory visuals, show evidence by annotating, labeling, or highlighting where appropriate. Show credibility by referencing sources as a part of the visual.

A variety of factors determines the learner’s ability to decode the visuals correctly. These factors include:

• Sex — when decoding the meaning of visuals men usually perform slightly better than women do.
• Age — Older adults tend to perform worse than younger adults do.
• Computer skills — Certain learners will perform information retrieval and storage more efficiently than others will. A host of factors, including the ability to manipulate computer interfaces, affects this performance.

Integrating visuals

Although static visuals are usually cheaper than animated visuals, it is not easy to measure the difference between the two in learning pay-off. The main goal of the visual should be to convey a relevant learning message. If the visual strays from the learning objective it will be less relevant, and the learner may experience mental clutter and confusion. Designers often load e-Learning courses with non-relevant visuals meant to heighten emotion, or decorate the user interface. Too many decorative visuals corrupt the learner’s ability to process data, and may diminish the instructional value of the entire message.

To show evidence properly, strive to reveal full details in your visuals. The ability to display complex information in a visual is a design challenge. Before you decide to manipulate the visual “to fit” or to reduce perceived complexity, consider the relationship between the visual, the data surrounding the visual (or included as a part of the visual), and the learner.

Visuals may contain some form of interaction. Sometimes the best way to communicate information is with a visual. Add interactivity to the visual and you may increase the chance the learner will want to interact with the visual. Using visuals (even visuals that contain text) for navigation or interaction is problematic because they don’t necessarily look clickable. Interactive visuals work best when the learner can easily identify what is actually clickable.

Decorative visuals, such as company or department logos, backgrounds, or large course banners, usually do more harm than good, especially when they consume a large area of the interface. They may contribute design flair or a sense of professionalism, but, honestly, do you really need to have a large percentage of the screen devoted to the company logo — especially if the course is only for company employees to view? Do you need to remind them constantly of the organization for which they work?

It is possible for decorative visuals to backfire. Using cartoonish visuals or clip art may frustrate the learner when these images crowd the trigger words, or content. By conducting a thorough audience analysis before your design begins, you can focus on the elements of design that will matter most to your learners, and refrain from losing precious interface real estate to non-relevant decorative visuals.

Screen layouts and templates

Too often templates dictate the placement of data in screen layout designs. Improper use of white space can affect the learner’s cognitive ability, and may even negatively affect learning. Rigid templates can diminish the instructional value and cause learner fatigue and frustration. Don’t be afraid to modify the placement of the data elements — to manipulate the white space — in order to reduce visual noise and clutter.

To improve clarity, I suggest these guidelines for displaying data elements:

• Reduce visual noise.
• Prevent insufficient range of color between similar elements.
• Carefully consider font weights and differences.

Consider sturdy, readable fonts. Arial or Helvetica, often the default font, is rarely strong enough to prevent eye fatigue.

• Use color to enhance spatial dimension.
• Be careful about shading and color usage. Never place shading behind text.
• Remove all unnecessary data.
• Design for harmony between the data and the user interface.
• Focus on the relationship between the visual and the text on the screen, and make sure there is a relationship.
• Avoid thick rules and boxes surrounding text or pictures, especially Microsoft clip art objects. I recommend avoiding clip art completely.
• Layer and separate elements to prevent clutter. It is best to reduce or eliminate decorative visuals.
• Enhance the resolution when possible. Don’t just scale screen shots and allow the data to become distorted.

Displaying complex data

Presenting large amounts of data on a computer increases the risk of data clutter and confusion, which I sometimes refer to as “data fog.” We assume that learners will not “read” too much text on computer displays. How much is too much text? And should we continue to assume learners will not read text on a computer display?

Cognitive load theory is based on information processing research findings about the amount of information a learner can keep in memory. Small segments of “chunked” information facilitate knowledge transfer by enabling the learner to focus attention. Consider how videogame players process information during game play. A game displays large amounts of data, which the player stores and later recalls during moments of heightened emotion. Ace game players learn quickly by doing, recalling key combinations, player moves, shortcuts, goals, and challenges during repetitive play, when the game requires action. With this thought in mind, it may behoove instructional designers to reconsider how they present large amounts of information. Compelling content with strong, relevant visuals chunked appropriately may counteract the possible contamination brought on by the memory overload created by dry, macro-chunked content devoid of a bold visual narrative.

High-resolution displays combined with good instructional design, compelling visual evidence, and readable text can lead the learner to action. Data clutter is more a failure of design than the display of too much information. By presenting credible and verifiable data, you offer full evidence, which may be paramount in helping to achieve the learning objective, and help to prevent data corruption.

Conclusion

Of the many tasks an instructional designer performs, the most important is to ensure the credibility of the instructional message. By involving yourself in the detailed process of analysis, content gathering, evaluation, and construction of the message (including the visual elements), you can guarantee that every step in the process of creating and delivering the Total Learner Experience will be free of data corruption. You can be sure that you are displaying a relevant, cohesive, and accurate message to your learners. Preventing data corruption in instructional messaging is a key component in the larger goal of closing the productivity gap and improving workplace performance.

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