carousel-Home

October 3, 2024Press ReleaseService Design,Organizational Transformation,Federal,Health

Radiant Digital Awarded Contract by ACL to Enhance, Maintain, and Support the Older Americans Act Performance System

[Vienna, VA, October 2023] — Radiant Digital is pleased to announce that it has been awarded a 5-year contract by the Department of Health and Human Services (HHS) Administration for Community Living (ACL). Radiant will be providing maintenance, enhancement, support, and technical assistance for the Older Americans Act Performance System (OAAPS). This is a mission-critical platform that collects essential data on programs under Titles III, VI, and VII of the Older Americans Act (OAA), including information on participants, services, and expenditures.
The Radiant Digital team consisting of strategists, UX/CX designers, developers, social scientists, and change managers, is committed to enhancing and supporting OAAPS to better serve all stakeholders. The team will apply a structured management approach that incorporates a human-centered agile methodology, promoting a strong grantee experience. Radiant Digital will deploy the right people, tools, and processes to deliver rigorous engineering and high-quality data at a high velocity, with transparency, repeatability, and continuous improvement.
"Radiant Digital is very grateful to ACL for trusting us to assist them in their mission to serve Older Americans. Our team is dedicated to the mission and is committed to leveraging our expertise and resources to enhance the Older Americans Act Performance System. With a strong focus on applications, data and infrastructure, digital experience, and organizational transformation, we are well-prepared to support ACL’s mission.” said Dr. Shankar Rachakonda, Chief Executive Officer of Radiant Digital.
About Radiant Digital: Radiant Digital is a global team of experts in digital transformation dedicated to helping organizations excel in the digital age. Radiant Digital empowers businesses to thrive in an increasingly complex and interconnected world.
For more information about Radiant Digital, please visit:
www.radiant.digital
For media inquiries, please contact:
Josh Dupont
Marketing Manager
info@radiant.digital

July 29, 2022InsightsHealth,Service Design,Commercial,Education,Customer Experience

Empathy That Goes Beyond User Experience Research

UXEmpathy is an important human attribute to have in your personal and professional lives. But how does empathy work? And why is empathy a vital part of any user experience research?

In this blog, we explore what it means to incorporate empathy into your business life and how you can use empathy to become a better User Experience Researcher.

What is Empathy?

In practice, empathy can take many forms. The Merriam-Webster Dictionary states that empathy is the “action of understanding, being aware of, being sensitive to, and vicariously experiencing the feelings, thoughts, and experience of another of either the past or present without having the feelings, thoughts, and experience fully communicated in an objectively explicit manner.”

Empathy is about understanding the people around you and responding with emotional intelligence. It is something that you can practice every day and never truly master because empathy will differ with every person you interact with. While it is important to bring empathy to your personal life, it is equally important to understand the role empathy can play in business environments.

Empathy in User Experience Research is All About

Empathy is a big part of User Experience Research because it is about seeing the problems and experiences through the eyes of the users’. It is not easy to accomplish, but if done correctly, this approach can produce extremely valuable information about your users. This data can then be used to help design teams make decisions that are informed by their product users’ needs, likes and dislikes.

When you operate with empathy at the forefront of your mind, you can dig deeper, learn more, and derive more valuable insights. The idea is not to simply solve a need; empathy in user experience is about fully enhancing user lives by taking away unnecessary barriers.

For example, you are building a website, and a quarter of your users are students who are dyslexic. How should you approach your UX design? Instead of designing a standard website and adding an extra font to cater to people with dyslexia, you should design with accessibility in mind right from the beginning.

Consider a range of options like the text to speech, speech recognition, and spell checker. Use your empathy skills to see from the perspectives of dyslexic people and see how your website can be improved to accommodate everyone.

How Can Empathy Enhance Our Daily Lives?

Empathy has tremendous power to enhance our daily lives. With empathy, we can connect to others at a deeper level and understand a variety of perspectives. We can relate to each other with more honesty and learn to be less judgemental. Some people learn empathy from an early age, and it becomes more than just a character trait; it is an integral part of who they are.

Empathy is powerful, and therefore it needs to be treated with caution. As with most things, it is all about balance. You need to have a healthy amount of empathy for those around you, but you also need to take care of yourself, or you will suffer from empathy burnout and fatigue.

If empathy doesn't come naturally to you, there are plenty of ways to develop your empathetic side. In an article by Clair Cain Miller in the New York Times, Miller suggested a number of ways people can improve their empathy:

• Talk to New People: Instead of staring at your phone, start conversations with strangers while waiting in line, while on a train, or at the grocery store. Fully and actively listen. Be curious about people with different backgrounds than you, and don’t be afraid to ask questions.

• Get Involved With a Shared Cause: We are more alike than different. Volunteer and get involved with something that is close to your heart. In doing so, you will not only help yourself, but you will also learn about yourself and your capacity for empathy. Learn about all the different people involved in making a difference and join in.

• Admit Your Biases: We all have biases. They are an innate aspect of our human nature. Acknowledge your biases and move forward with curiosity while actively working on avoiding making conclusions about people, places, and things around you through mental shortcuts.

Takeaways

You can learn plenty from making empathy a significant part of your daily business life. A team aware of the power of empathy will more often than not be able to work better together and create products that address real consumer issues.

Who knows? If you want to have a long and fruitful career in User Experience Research, you have to work on your empathy skills all the time actively. If empathy doesn’t come naturally, you can always test Claire Cain Miller’s suggestions and see what happens.

Ultimately, working on empathy beyond user experience research will make you more aware and appreciative of others. This will improve the way you live your life and the way you do your work. Why not give it a try?

To learn more about how empathy informs user experience research, please contact our UX experts.

January 6, 2022InsightsHealth,Digital Strategy & Experience,Education,Training And Development

Does Fidelity Matter in Simulation-based Learning?

Simulation

One of the earliest renditions of simulation-based learning is credited to Edwin Link. He created the modern flight simulator predecessor between 1929-1931 in response to World War I, where more pilot and plane losses were attributed to accidents rather than combat (Hays & Singer, 1989). Since then, simulation usage and design have progressively improved, with simulation-based practices currently being utilized across several industries with a powerful presence in the military, aviation, and health care industry as a means to conduct training, evaluation, and research. Simulations are defined as “approximations to the reality that require trainees to react to problems or conditions as they would under genuine circumstances” (Tekian, McGuire, & McGaghie, n.d.). Examples of commonly used simulation devices include VR Head Mounted Displays (HMDs), computer-operated life-size medical mannequins, full-motion flight simulators, and various computer-based simulation programs (e.g., driving simulator, industrial processes simulator, machinery operator simulator, etc.). A few of the benefits offered by simulations over traditional learning methods include long-term cost reduction, the experience of alternative conditions and courses of action, provides a realistic job preview, a more effortless transfer of training to the operational environment, provides a practice setting without risk of harm nor negative consequences. It produces a lower carbon footprint (Myers, Starr, & Mullins, 2018).

The Fidelity Question

As technological advancements continue to enhance the capabilities of simulation design, the question of simulation fidelity (the degree to which a simulation device can replicate the actual environment (Gross et al. (1999); Alessi (1988)) and its relationship to learning effectiveness have become a highly debated topic among instructional design and training professionals. Framed as the fidelity question researchers are asking – how similar to the actual task situation must a training situation be to provide practical training? And further, does maximum fidelity equal maximum transfer of training? To answer these questions, we first need to understand the construct of fidelity better.

Simulator-based training is often categorized as either low-fidelity simulations (LFS) or high-fidelity simulations (HFS). In general, high-fidelity refers to simulations that more closely replicate the actual environment or feel more ‘real.’ In contrast, low-fidelity refers to simulations that are typically simpler in design and functionality and may only replicate certain aspects of the environment. It should also be noted that there are no standardized parameters to distinguish low from high fidelity simulation, nor a universal definition of fidelity, as the meaning and definition of each may vary depending on the industry and the designated environment being simulated. This lack of construct validity is one of the main challenges observed in infidelity research.

When considering the best method for measuring simulation fidelity, it would be inaccurate to look at simulation fidelity as a singular variable, as it is by definition the culmination of the experience that occurs when all seven fidelity types merge. As shown in Table 1 below, there are eight distinct definitions of fidelity, including simulation fidelity's combined factor. These fidelity types serve a different function within the simulated environment and should be taken into consideration both as a whole and individually in the calculation of total simulation fidelity.

Table 1: Fidelity Definitions (Hancock et al., 2019)

Fidelity type	References	Definition
Simulation fidelity	Gross et al. (1999); Alessi (1988)	The degree to which the device can replicate the actual environment, or how “real” the simulation appears and feels.
Physical fidelity	Allen (1986)	The degree to which the device looks, sounds, and feels like the actual environment.
Visual-audio fidelity	Rinalducci (1996)	Replication of visual and auditory stimulus.
Equipment fidelity	Zhang (1993)	Replication of actual equipment hardware and software.
Motion fidelity	Kaiser and Schroeder (2003)	Replication of motion cues felt in the actual environment.
Psychological fidelity	Kaiser and Schroeder (2003)	The degree to which the device replicates psychological and cognitive factors (i.e., communication, situational awareness).
Task fidelity	Zhang (1993); Roza (2000); Hughes and Rolek (2003)	Replication of tasks and maneuvers executed by the user.
Functional fidelity	Allen (1986)	How the device functions, works, and provides actual stimuli as the actual environment.

 

Table 1 lists several definitions of fidelity as defined in research. Simulation fidelity provides the fundamental purpose of fidelity in simulation experience, describing how “real” the simulation appears and feels. Meanwhile, the other definitions of fidelity can, for the most part, be broken into two main categories: those that describe the physical experience and those that represent the psychological or cognitive experience (Hancock et al., 2019). The most commonly discussed fidelity category is physical fidelity which encompasses visual-audio fidelity, equipment fidelity, and motion fidelity. These combine to simulate the look, sound, feel, and occasionally smell of the environment. While the second category of psychological-cognitive fidelity goes beyond the look and feel of the simulation to describe the degree to which the user is psychologically and cognitively engaged in the same manner when compared to the degree to which the actual environment would engage the user (examples: simulated stress and workload). The remaining fidelity types of task fidelity and functional fidelity are concerned with how the user interacts with the simulated environment including the degree to which the simulator replicates the tasks of the environment and the degree to which the simulator reacts to performed tasks as they are executed by the user.

Fidelity and Transfer of Training

The main goal of a training simulator is to promote the development of a skill, ability, or area of knowledge that is required for the successful completion of a target task. The effectiveness of the simulator thus depends on the extent to which the acquired knowledge/skills/abilities through practicing the simulated task can be transferred to the target task.

Intuitively, a positive correlation between the degree of realism of a simulator and the effect on transfer of training would be assumed, especially as it is supported by a number of theories including the theory of identical elements (Thorndike, 1913) which states that the most effective transfer of skills occurs between simulator and the operational environment when both share common elements. But, despite this assumption and theory, numerous studies have found no distinct advantage of High Fidelity (HF) compared to Low Fidelity (LF) simulation with regards to improvement of knowledge or skills, with several studies even reporting increased declarative knowledge of participants in the LF simulation groups. These results reveal that there are likely several other important factors that need to be considered in the transfer of simulation-based training apart from simulation fidelity.

One of the models discussed fairly regularly in the articles which failed to prove the high-fidelity advantage was the “Alessi Hypothesis,” which provides several theorized explanations for the failed transfer of training. The first theorized explanation states that there is a certain point at which adding too much fidelity results in negative learning experiences as high fidelity equals high complexity, which requires more cognitive skills thus increasing trainee workload, which in turn impedes participant learning (Alessi, 1988). The second theorized explanation discusses the connection between fidelity and learning describing it as a nonlinear relationship largely dependent on other factors such as the trainees' experience level and stage of instruction. Meaning, to experience optimized training effectiveness, the degree of fidelity in a simulation should attempt to match the level of difficulty expressed by the learning objective as well as the training stage of the learner (Alessi, 1988). Depicted in Figure 1 is Alessi’s model of the relationship between the degree of fidelity and learning for novice, experienced learners, and expert learners.

Figure 1: Degree of fidelity and stage of learner (Alessi, 1988)

This strategy of defining the level of capability and training objectives first followed by the degree of fidelity not only makes practical sense but would also contribute to cost reduction in the overall use of simulation-based education. In another article written on maritime training facilities, they describe their strategy of keeping training costs low while maximizing training effect by employing a similar strategy of utilizing LF simulators in the initial stages of learning to familiarize and train basic skills, while developing HF simulators to train advanced technical and non-technical skills (Renganayagalu, et. al., 2019).

Relevant Theories

Cognitive Load Theory (CLT)

In addition to the model above, a number of developed theories were also discussed in the research, both to explain possible reasons for lack of transfer in HF simulation, as well as to guide future simulation development. One of the most commonly referenced theories in the explanation of the failure to transfer includes the cognitive load theory (CLT), an instructional theory that describes learning and problem solving within the context of how information is processed and addresses the limitations of working memory. While long-term memory has a limitless capacity, working memory is limited to five to nine informational elements at any given time, with many of those elements forgotten within 20 seconds, unless rehearsed or practiced (van Merrienboer & Sweller, 2010). Therefore, if the cognitive load is too high, learning and performance will be affected as the learner is not able to properly process and retain the content being delivered. In the case of high-fidelity simulations which involves completing tasks with a high level of intractability and often through the manipulation of multiple elements at once, increased cognitive load is highly possible, especially in the case of novice learners. To combat this, one study, in particular, held an introductory course covering the fundamental basics of the training to decrease the initial level of cognitive load trainees would experience.

NLN Jeffries Simulation Theory

In addition to the cognitive load theory there was one other theory mentioned throughout the research, but in relation to future recommendations for simulation, development to ensure that transfer of training occurs. This theory known as the NLN Jeffries Simulation Theory (2005, 2007, 2012) is a theoretical framework that has received extensive empirical support and is recommended as a guide in the development of simulated experiences. The framework is composed of seven aspects, beginning with the background and design aspects which should be considered before the simulation experience and define simulation goals and resource allocation, the four aspects involved in the conducting of the simulation experience including the facilitator, the participant, the educational strategies, and the dynamic relationship between each of them, and finally, the aspect which defines the outcomes of the simulation including participant reaction, learning development, and behavioral transfer. As seen in the model below this process is intended to aid instructional designers in the implementation of an effective simulation design, from developing objectives, to evaluating effectiveness.

Figure 2: NLN Jeffries Simulation Theory

Additional Participant Outcomes Related to HFS

Even though the main body of this article is meant to examine the relationship between fidelity and learning effectiveness, through the review of the literature there were several other participant outcomes demonstrated to have a relationship with high-fidelity simulations. These outcomes include heightened levels of participant self-efficacy, stress, and self-confidence in skill deployment.

Increased Self-Efficacy

Increased self-efficacy was one of the most commonly discussed factors in relation to high-fidelity simulations. Perceived self-efficacy concerns an individual’s perception of self-confidence to successfully complete a task (Bandura, 1977) and is believed to be influential on the student’s level of performance, choice of tasks, and the amount of effort put into performing those tasks. Self-efficacy which has been acquired before or during training leads to an increased motivation to learn and better learning outcomes (Salas et al., 2012). One article for example utilized a measure of self-efficacy which was given to participants at the beginning, middle, and end of their designated simulation training, and found that those who participated in the high-fidelity simulation showed statistically significant improvement in self-efficacy following each completion of the survey, as compared to the control group who only showed improvement once during the survey completion. Additionally, another study that examined high-fidelity and self-efficacy in law enforcement officers found that high fidelity increased self-efficacy, emotional arousal, and led to positive training transfer from the lessons learned in the simulator scenarios.

Increased Stress

Another factor observed to be unique to high-fidelity simulations was the increased level of stress experienced by participants as compared to those in low-fidelity simulation. Some of the possible explanations given for this increased stress response included the amplified level of external audio and visual stimuli, as well as the hyper-realistic form factor of the simulated patient who in this specific simulation was bleeding and outwardly experiencing pain. Even though stress from an outside consideration may be considered a negative experience, the authors of this article argued that induced stress during high-fidelity simulation may be beneficial for the participants as they may be able to develop their stress management skills within this simulated environment to be carried over into actual clinical practice.

Increased Self-Confidence

One article, in particular, attempted to assess the level of confidence participants felt in employing skills learned during the simulation-based training, after having found inflated levels of self-confidence in participants of previously conducted studies using high-fidelity simulation. In this article, participants were randomly assigned to two groups of LF and HF simulation during curricular advanced life support (ALS) training courses. Before the course, 69% of participants in the HF group were assumed to have a significant advantage over the LF group in skill development, which did not significantly decrease over the training, with 53% of the participants still reporting assumed advantage at completion. Additionally, 41% of students in the HF group considered themselves better performers in handling resuscitation despite having no knowledge of the LD group's training process. Even though confidence of skill development would typically be considered a positive outcome, in this case, the authors of this article wanted to specifically highlight the dangers associated with overly elevated levels of confidence after the completion of an HFS as there is a positive link between overconfidence and risk-taking behaviors.

Additional Distinctions Between Effective & Non-Effective HFS

Since this article has refuted the notion that high-fidelity alone can predict simulation effectiveness, it may be helpful for future applications to recognize the simulation features, when combined with high-fidelity simulation, to help to produce better results (Issenberg et. al., 2005).

1. Providing feedback - 47% of journal articles report educational feedback is the most important feature of high-fidelity simulation-based education
2. Repetitive practice - 39% of journal articles identified repetitive practice as a key feature involving the use of high-fidelity simulations in education
3. Curriculum integration - 25% of journal articles cited integration of simulation-based exercises into the educational curriculum as an essential feature of their effective use
4. Range of difficulty level - 14% of journal articles address the importance of the range of task difficulty level as an important variable in simulation-based education
5. Multiple learning strategies - 10% of journal articles identified the adaptability of high-fidelity simulations to multiple learning strategies as an important factor in their educational effectiveness
6. Capture clinical variation - 10% of journal articles cited simulators that capture a wide variety of clinical conditions as more useful than those with a narrow range
7. Controlled environment - 9% of journal articles emphasized the importance of using high-fidelity simulations in a controlled environment where learners can make, detect, and correct errors without adverse consequences
8. Individualized learning - 9% of journal articles highlighted the importance of having reproducible, standardized educational experiences where learners are active participants, not passive bystanders

At Radiant Digital we have the ability to create training that best meets your organizational needs. Our team of instructional designers and content creators will guide you in creating training that is both engaging and effective, contact us today to learn more.

 

References

Alessi, S. M. (1988). Fidelity in the design of instructional simulations. Journal of Computer-Based Instruction, 15(2), 40–47.

Cognitive theory of multimedia learning (Mayer). Learning Theories. (2020, March 5). Retrieved December 8, 2021, from https://www.learning-theories.com/cognitive-theory-of-multimedia-learning-mayer.html.

Hancock, P. A., Vincenzi, D. A., Wise, J. A., & Mouloua, M. (2019). Human factors in simulation and training. CRC Press.

Hays, R. T., & Singer, M. J. (1989). Simulation fidelity in training system design: Bridging the gap between reality and training. Springer-Verlag Publishing. https://doi.org/10.1007/978-1-4612-3564-4

Issenberg, S. B., McGaghie, W. C., Petrusa, E. R., Lee Gordon, D., & Scalese, R. J. (2005). Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Medical teacher, 27(1), 10–28. https://doi.org/10.1080/01421590500046924

Jeffries, P. R., Rodgers, B., & Adamson, K. (2015). NLN Jeffries Simulation Theory: Brief Narrative Description. Nursing education perspectives, 36(5), 292–293. https://doi.org/10.5480/1536-5026-36.5.292

Makransky, G., Andreasen, N. K., Baceviciute, S., & Mayer, R. E. (2021). Immersive virtual reality increases liking but not learning with a science simulation and generative learning strategies promote learning in immersive virtual reality. Journal of Educational Psychology, 113(4), 719–735. https://doi.org/10.1037/edu0000473

Massoth, C., Röder, H., Ohlenburg, H., Hessler, M., Zarbock, A., Pöpping, D. M., & Wenk, M. (2019). High-fidelity is not superior to low-fidelity simulation but leads to overconfidence in medical students. BMC medical education, 19(1), 29. https://doi.org/10.1186/s12909-019-1464-7

Myers, P. L., Starr, A. W., & Mullins, K. (2018). Flight simulator fidelity, training transfer, and the role of instructors in optimizing learning. International Journal of Aviation, Aeronautics, and Aerospace, 5(1). https://doi.org/10.15394/ijaaa.2018.1203

Nicolaides, Marios & Theodorou, Efthymia & Emin, Elif & Theodoulou, Iakovos & Andersen, Nikolai & Lymperopoulos, Nikolaos & Odejinmi, Jimi & Kitapcioglu, Dilek & Aksoy, Mehmet & Papalois, Apostolos & Sideris, Michail. (2020). Team performance training for medical students: Low vs high fidelity simulation. Annals of Medicine and Surgery. 55. 10.1016/j.amsu.2020.05.042.

Renganayagalu, Sathiya Kumar & Mallam, Steven & Nazir, Salman & Ernstsen, Jørgen & Hogström, Per. (2019). Impact of Simulation Fidelity on Student Self-efficacy and Perceived Skill Development in Maritime Training. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation. 13. 663-669. 10.12716/1001.13.03.25.

Roza, Z & Gross, D & Harmon, Scott. (2000). Report Out of the Fidelity Experimentation ISG.

Tekian A, McGuire C, McGaghie W. Innovative simulations for assessing professional competence: from paper-and-pencil to virtual reality. Univ of Illinois at Chicago Dept.

van Merriënboer, J. J., & Sweller, J. (2010). Cognitive load theory in health professional education: design principles and strategies. Medical education, 44(1), 85–93. https://doi.org/10.1111/j.1365-2923.2009.03498.x

 

August 6, 2021InsightsTraining And Development,Health,Education,Change Management

Leading Digital Transformation & Change Management

Digital transformation is no longer a buzzword; instead, it reshapes how organizations do business, train employees, and interact with customers. The digital transformation took flight at the onset of the global COVID-19 pandemic and continues to take heightened importance as organizations accelerate digital solutions to meet the needs of flexible work schedules, hybrid teams, learning development trends, and how customers receive products or services. The McKinsey Global Survey suggests that the adoption of digital technologies has sped up by three to seven years in a span of months. The results below show this acceleration occurring across key areas of the business model and provide a reason to prepare the workforce for digital transformation.

Consider digital transformation as a catchall term for describing the implementation of new technologies, talent, and processes to improve business operations and customer satisfaction. Although digital transformation replaces traditional workflows with new technologies, it is not all about digitizing the business; instead, it is about employees and leading them through change. At the core of digital transformation is the organizations’ ability to effectively upskill and motivate employees and managers to adapt to new technologies. So, whether your organization is staying above the curve, lagging, or barely keeping up with digital transformational solutions, the secret for successful implementation is focusing on employee training and strategies that prepare the workforce for the transformation.

This article will explore why digital transformation fails, how to avoid pitfalls, and effective change management strategies to execute the transformation.

Why employees play a pivotal role in your digital transformation

Whether you are implementing a new HR system, customer management system, or a cloud-based solution, the reality is that such software provides frequent incremental releases, and to keep up with ever-changing software, change, and learning leaders will be tasked with creating innovative communications and learning programs that inform employees what the new technology means for their role and responsibilities. McKinsey & Company also estimates that 70% of transformations fail due to lack of support from employees and effective communication from leadership, so it is imperative to engage employees in the transformation from conception through implementation.

Obstacles to digital transformation and how to avoid pitfalls

As previously stated, lack of communication from leadership and the organization is one of the biggest deterrents to digital transformation and is the reason such initiatives fail. To limit communication missteps, Widen Director of Customer Success Michael Shattuck recommends forming a “digital transformation committee” made up of team members from different levels within the business, and I could not agree more. The committee's purpose is to identify barriers across the company’s culture, technology, and process. The development of the committee will aid in the organization or team-specific communication needed to facilitate the transformation.

In addition to managing communication, missteps consider the following when developing your organization's transformation strategy.

1. Start from the top: Change that starts at the top reflects a committed, invested, and unified leadership. During mergers, research around leadership has found that the leader's presence, guidance, and support alleviated employee fears, reduced anxiety, and helped employees feel more confident about the transformation.
2. Develop a suite of learning resources: One of the most overlooked segments of the office ecosystem when discussing digital transformations is the employees themselves, both new hires and ongoing professionals. If an organization fails to keep up with its employees’ training and development needs, it will fail in its digital transformation efforts. The goal is to make employees active learners in the transformation process. Blended learning solutions such as microlearning, virtual led training, or online webinars are effective ways to train employees on the tools and platforms they will work with daily. Radiant Digital can assess your training needs and develop learning programs that have longevity if you seek to learn solutions that support you during digital transformation initiatives.
3. Minimize disruption: Changing existing processes within an organization can be a headache; however, mitigating the effects of those changes on employees is vital. Although leadership may see the introduction of automation into core business functions to save time and money, employees who were previously tasked with these roles may feel replaced, threatened with obsolescence, or lacking direction.

In addition, digital transformation initiatives requiring organizational restructuring may cause employees who are moved to another position to feel indignant, confused or wonder what was wrong with the previous structure. To minimize this disruption and associated resistance among the workforce, the following may be considered:

• Plan for some disruption/resistance and create awareness around the transformation early.
• Fostering a culture that supports change or transformation.
• Empowering champions such as project managers or team leaders to provide clarity and context for changes.

Here at Radiant Digital, we are ready to support your overall digital transformation strategy by guiding you through the key phases of change management. Reach out to our team to learn more about our learning and change solutions.

June 10, 2021InsightsHealth,Education,Motivation Strategies,Training

Beyond Adult Learning Theory: Motivations of Adult Learners

While training may be necessary to employers, the employees will often balk. So, you have to ask, “How do I motivate the reluctant learner?" Like all things instructional design, the answer to this question is, “It depends.”

First, we assume that the employee is otherwise engaged in their job. How to motivate an employee to work, much less actively engage in training, is beyond the scope of this article.

Second, let’s get a working definition of motivation. Richard Clark (2019) defined it as “the willingness to get the job done by starting rather than procrastinating, persisting in the face of distractions, and investing enough mental effort to succeed.”

There are multiple reasons an employee may not be motivated, but let’s address the obvious ones – the ones I didn’t find in the academic literature – first. Don’t be the company that sends employees to safety training one week and have them violate all that they learned in the next week. You’ve not only negated that training, but you’ve also set up the mindset that training doesn’t matter and that employees will ignore anything they are told in training. A related tactic is to send an employee to training but still expect a whole week’s work.  With mixed messages and limited resources, the employee can’t do it all and will fail in one area or the other.

That brings us to self-efficacy, a person’s belief in their ability to succeed in a given task. Bandura’s (1982, 1997) Social Cognitive Theory posits that self-efficacy drives the exertion of mental effort (the cognitive resources used and allocating for learning). In other words, why bother if you won’t succeed? If you want someone to engage in their training, set them up to succeed. Because past successes can increase self-efficacy, make sure your training embeds small victories early on. But you have to balance it – an over-inflated self-efficacy can result in exerting too little effort, probably because they underestimate the amount of mental effort – the cognitive resources used and allocated for learning – required to complete the task. The anticipated level of mental effort is essential. Its graph looks like an inverted “u.” If someone expects too much mental effort (the task is too challenging), they won’t try. However, if they anticipate the task to take minimal cognitive action, they again will minimize how much they put into it. In practicality, this means you should be straightforward about the time and difficulty level of a class. An accurate description allows your learners to plan accordingly and avoid suffering the crisis of confidence that can come when they don’t succeed in something they expected to be easy. Additionally, well-designed training, especially those based on Cognitive Load Theory, can break even complex constructs into manageable sections.

This brings to mind attribution errors, one of Clark’s (2019) four reasons employees lose motivation. Attribution errors come about when a learner is trying to figure out why something negative and unexpected happened. Those who place the blame on something outside their control (e.g., I’m too stupid; the trainer’s test was too hard) are likely to quit trying. You can help motivate this employee by assisting them in concluding that the task is doable, but they hadn’t put in enough effort.

Disruptive emotions such as anxiety, depression, and anger can also impede learning (Clark, 2019). If there is test anxiety, a discussion with the employee is in order. Explain the purpose of the training is to teach and not to fail. Describe the testing process (pen and paper, computer-based, or hands-on) and assure the employee that passing is possible if they pay attention and invest effort. If the course is quite tricky and failing is a possibility, then addressing this will generally fall outside the realm of the training department, along with other types of anxiety, anger, and depression. Your organization probably has a process in place that perhaps involves the supervisor, human resources, and maybe an employee assistance program.

Along with lack of self-efficacy, attribution errors, and disruptive emotions, Clark (2019) discusses a values mismatch – when an employee doesn’t care enough to learn.  Assuming they are otherwise engaged in their job, there are several ways to address this. First, find a way to get the employee interested – perhaps as a challenge or in some way linked to the tasks the employee likes to do.  Another tact is to emphasize the importance and utility of the training, its impact on the employee, the group, or the company.

Most educational theories of motivation involve the constructs of self-efficacy, persistence, and mental effort. Well-designed training can assist in all these. Good training with a balanced cognitive load is doable, even if it takes time and effort. Radiant Digital is well-equipped to help you with your training needs. Reach out to us and see how we can improve your employee’s training.

 

References

Clark, R. E., & Saxberg, B. (2019). 4 Reasons Good Employees Lose Their Motivation. Harvard Business Review.

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Macmillan.

April 22, 2021White PaperTraining and Development (eLearning and Instructor Led Course development),Education,Health,Digital Strategy & Experience

Using Complex Learning and Instructional Design when things get complicated

Complex learning involves the integration of qualitatively different knowledge and skills along with their relationships and interaction rules. All the following tasks are complex:

• Psychotherapy
• Selling
• Troubleshooting
• Hardware design
• Selecting the most appropriate statistical test for a given set of circumstances
• Balancing competing priorities, such as prioritizing worker safety while maximizing return on investment and minimizing costs

 

As part of the “real world,” complex tasks frequently have novel variations and uncertainty. Every customer is a new challenge; almost no business models factored in a pandemic. These skills require knowledge transfer: taking what you know, making adaptions, and applying it in different situations.

Especially when dealing with uncertainty, complex learning is not well-suited for a linear instructional design model. Unfortunately, education and traditional training do just that: They teach a string of tasks sequentially and then place the onus of mastering the portion on the learner. Because these tasks and interactions are merely too much to learn at one time, they overload learners’ cognitive processes. The result is wasted training time, increased employee stress, burnout, and turnover while yielding less-than-optimal results.

When faced with complex tasks, I base my training on the Four-Component Instructional Design Model (4C/ID). Explicitly designed to reduce overall cognitive load and courage transfer, this nonlinear model developed by van Merriënboer and his colleagues breaks training down into components: (1) learning tasks, (2) supportive information, (3) procedural information, and (4) part-task practice.

Learning tasks

When using 4C/ID, I start with the two fundamental questions, “What do the learners need to do?” and “What do they need to know to do this?”

Learning tasks include projects, problems, case studies, etc. You would start with fully formed, authentic tasks. These learning tasks should start as straightforward as possible while still being faithful to avoid overload. As the learner masters the simple cases, add complexity.

Say, for example, you are trying to train a cohort of spokespersons for a large corporation. The first learning task could be telling a friendly press corps that the Company is adding 1,000 jobs to a new area. It would involve receiving the information, facing the press, giving the news, and responding to questions. To truly master the role, the spokesperson must also prepare their script from primary sources and raw documents. This should be the second learning task. The third task could be going on a financial radio show and discussing missed financial milestones.  These are all authentic tasks, and, for most jobs, they should be practiced in as realistic a setting as possible. Notice, too, that they are hugely divergent. This is by design: the variability in the tasks encourages knowledge transfer.

Not all complex tasks are as nebulous as the spokesperson. Teaching hardware design would have a different approach to whole task practice. In this case, the learner is guided through Worked Examples. These examples would highlight the complexities and interactivity of the information. The next step would be to provide similar examples, except with small portions of the solution removed. The learner would complete the highly scaffolded problems, and progress with each iteration require more significant input from the learners. For transfer, the difficulties should still vary.

Supportive Information

The next component is Supportive Information. This information is provided to help with the less common variants of the task. This isn’t a simple blurb on a computer screen. This type of information offers cognitive strategies: how to approach a situation, the way a given set of circumstances fits into the overall knowledge base that the task or job requires. You work on the best way to begin and the best way to do it into their mental model. One of the more difficult instructional design tasks is working with true experts to get the best mental model.

Procedural Information

The third component is procedural information, which supports the ordinary, routine tasks of the job. This information is provided just in time. In other words, you don’t present the learners with formula until they need the procedure. Doing so ahead of time adds noise, or more accurate, extraneous cognitive load. This type of information can be just a blurb on a pop-up window. It explains a basic how-to, with the facts, principles, and rules associated. For the spokesperson, this can be how to gather the press for a press conference or announcement.

For our hardware designer, the procedural information could be formulas or standard bits of circuitry repeatedly.

Part-Task Practice

Finally, you have part-task practice. Part-task practice usually involves some particularly tedious or difficult tasks and an enormous amount of repetition. For physicians, it could be tying sutures on a vein. For our spokespersons, this is most likely dealing with hostile questions while thinking on their feet. They will spend a lot of time being peppered with aggressive or leading questions, irrelevant details, and erroneous leaps of logic.

Standard differential equations and integrations are the most likely candidates for part-task practice. After mastering math, the designer can automate large portions of the design workload.

Although 4C/ID is an excellent model, it’s still just a model and isn’t suitable for every situation. Instructional design is complex, like the tasks we have been discussing. To get the training your organization deserves, you need instructional designers who can blend models, adapt models, or let the content drive the learning.

Radiant Digital can design and build the training your organization needs, all the way from specific compliance training to consequence-critical complex learning.

 

 

 

 

February 10, 2021White PaperOrganizational Transformation,Training and Development (eLearning and Instructor Led Course development),Knowledge Management,Education,Health

The Problem isn’t the Training; it’s Effective Knowledge Transfer

One of the biggest challenges for organizations is what happens after the training. Typically, training is seen as an isolated event. Afterward, many learning development professionals and supervisors find themselves asking, “why is the employee not using the information from training” or “why hasn’t the employee’s performance increased following training.” Questions such as this suggest that knowledge transfer did not occur after the employee left the classroom or virtual training session.

This article will explore the design of the training and execution phase of training in driving effective knowledge transfer in the workplace.

What is Knowledge Transfer?

For training to be practical, both learning and transfer of training need to occur. Knowledge transfer can be defined as a learner's ability to apply the behavior, knowledge successfully, and skills acquired in a learning event to the job, resulting in improved job performance. Trainees can fail to apply training content to their jobs incorrectly, either because the training was not conducive to learning. The work environment provides them with the opportunity to use the training content or supports its correct use. So, how does an organization avoid this as much as possible? First, consider knowledge transfer during the design or purchase of training, and second develop an execution plan to reinforce the expected learning outcomes of training.  Often, knowledge transfer is considered after training has already occurred. However, the trainees’ perception of the work environment and its support for training has influenced their motivation to learn.

Design of Training

As a first step in improving knowledge transfer, let us consider the design of training. Training design includes evaluating how to create a learning environment to help the trainee acquire the learning outcomes. Boring lectures, lack of meaningful content in e-learning, and training that doesn’t allow employees to practice and receive feedback-all methods demotivate trainees and make it difficult for them to learn and use what they have learned. However, many companies are using innovative instructional strategies to make training more exciting and to help trainees apply it to their work.

Technique 1: Incorporate Different Training Methods.

Mirror the workplace: The similarity of the tasks and materials in the learning event and the learners’ work environment affects the knowledge transfer rate. Machin and Fogarty, two researchers, found that learning transfer increases when the physical characteristics of the tasks and the learning environment match the performance environment.

• This instructor-led training can be combined with leader-led discussions with interactive assignments that participants complete in groups in virtual breakout rooms. The benefit of this approach is it offers trainees the opportunity to engage with the learning material and immediately apply it to their daily work. The sooner the trainee can apply the knowledge, the better for knowledge transfer.

• Include polling scenarios within webinars that reflect common instances in which the trainee will use the information taught. This keeps learners engaged and ready to apply knowledge after training.

Model the way: Modeling is a technique shown to increase learning transfer, as it provides a demonstration of how to apply learning on the job. This can quickly be done through virtual reality training, which according to LinkedIn, education continues to be on the rise. Modeling will apply learning in training and allow learners to practice will learn in training and increase learning transfer by as much as 37 percent, according to Michael Limbach, who has researched several approaches to enhancing learning effectiveness and transfer.

Space it Out: Learning transfer is impossible if learners forget what they learned in training. Use spaced repetition in your training program to minimize the forgetting curve. For example, send snapshots of learning content before the main training event, and follow up with detailed summaries of key topics after training. Will Thalheimer, in his 2006 work, “Spacing Learning Events Over Time: What the Research Says,” identifies that although learning and memory are vital during a training event, knowledge is rapidly forgotten afterward.

He also points out that spacing reinforcement, which is spaced repetition or interval reinforcement on the job after training, enhances how much trainees will retain and apply to their work. Thalheimer and many other researchers identify are that the closer in time learning is delivered to the situations when it is needed, the less forgetting will be a factor. Applying spaced repetition for learning transfer can occur immediately after training and is built into your training plan's execution phase. Again, remember learning transfer should be part of the organization's training strategy before training is implemented.

Technique 2: Ask your Target Audience

In designing training programs, a small focus group comprised of the training target audience can help a learning designer assess what trainees need or desire from the training. Gathering such feedback could enhance the training program, thus improving knowledge transfer to occur following training.

After the Training Event

As mentioned previously, interval reinforcement can be implemented to help curve forgetting after initial training. Organizations can apply this approach by delivering the training information via daily emails, hosting lunch and learns, or using virtual reality tools to amplify employee engagement in the learning journey.

By implementing a consistent post-training interval reinforcement program of learning, organizations can ensure the learning process continues and can be applied on the job. Here at Radiant Digital, we can help you map an effective training strategy, starting with a need assessment through effective knowledge transfer methods to occur in your organization.

January 7, 2021InsightsOrganizational Transformation,Knowledge Management,Education,Health

[Webinar] Cognitive Task Analysis in Practice: Exploring Methods

The conclusion to our CTA webinar series. Dr. Sheila Mitchell explores methods of cognitive task analysis in practice. With standard interview techniques, experts omit up to 70% of their decision points. She explains how an expert’s knowledge can be more fully captured, allowing organizations to maintain their knowledge base and provide better quality training.

Our guest speaker Dr. Sheila Mitchell is a Senior Instructional Designer at Radiant. After earning her doctorate at the University of Virginia, she has worked in diverse fields such as biosecurity, safety, and the energy sector. Sheila’s goal is to create efficient training firmly rooted in Cognitive Load Theory.

December 1, 2020InsightsHealth,Product Development and Integration,Education,Information Communication Technology (ICT),Agile Methodologies,Customer Experience

The Power of Product Ownership

As more organizations dip their toes into the ‘agile pool’, it’s important to continuously analyze and assess the customer’s needs to maintain a competitive or market advantage.

In organizations that still rely on more traditional approaches for project and program delivery, this should be happening already. Business analysts (or comparable) are typically assigned to assess and document many functional requirements from customers before working with technical subject matter experts to translate. While useful, this approach doesn’t enable agility and impedes an organization’s ability to identify and respond to changing customer and market needs.

In some cases, it’s also possible that organizations stand up ‘customer experience’ teams who, in part, dedicate their time to better understanding a customer’s journey to identify new, better, or more desirable products and services. This, too, can be effective, but depending on the timing, frequency, and level of expertise involved, it’s possible that data captured may not articulate the real-time and nuanced perspectives of the actual customer.

In these cases, to empower and enable a better understanding of what a customer truly wants and expects, there is one role in ruling them all—the Product Owner.

What exactly is a Product Owner?

As the name implies, the Product Owner represents the product vision and voice of the customer. In essence, they “own” the customer’s requirements.

While Product Owners can be anyone from anywhere, the most successful Product Owners have a clear understanding of what customers want and expect. Product Owner roles typically align with business-facing departments like Marketing, as they position to identify and articulate the customer’s point of view.

From a more tactical perspective, the Product Owner is responsible for translating the needs, wants, and desires of target customers into user stories that prioritize and shared with the development teams responsible for bringing target capabilities and products to life.

These user stories are then refined and provided to Scrum teams who trace the functionality and translate into technical requirements to meet the criteria. A Product Owner’s value shines when we further define acceptance criteria, or a “definition of done”, to ultimately ensure that the software and delivery teams truly grasp and deliver the right value at the right time.

In traditional Scrum environments, the Product Owner is one of the primary trinity roles that help ensure that teams are clear on what the customer expects. By partnering with the Scrum Master, mature teams can help define requirements and translate them into consistent value delivered to the customer. By collaborating with Solution or Technical Leads, Product Owners help ensure that they are aligned on the big picture and tuned in to the right offerings for the customers and markets they represent.

The Value of Product Ownership

Organizations that support fully dedicated Product Owners stand to benefit the most, but even part-time Product Owners can help organizations maintain a critical link to customer needs and expectations. Product Owners have a direct line to the downstream users and consumers who benefit from the software or capabilities develop, and they can maintain a laser focus on sourcing and communicating this business value to the supporting teams. This provides an immediate boost to backend software and IT teams because they can trust the requirements; they give and focus on the technical solution. In turn, Product Owners can help clarify what to expect when functional capabilities or desired outcomes are not exact.

Additionally, while IT shops worldwide continue to evolve, it is scarce to have software developers that have a deep understanding of the house's business side. While IT shops excel at developing the code behind the scenes to make things happen, we must create and craft products and experiences innately and intrinsically in line with the customers’ wants and desires.

So, What Makes a Good Product Owner?

Like any role, Product Owners can draw significantly on their personal experience and expertise to live into the role. But in general, some essential characteristics will help ensure success, especially for folks that are new to the role:

Be Empathetic - Just Listen!

Successful Product Owners embrace the role by understanding the needs of the customers or market segments they represent. It’s not uncommon for Product Owners to actively participate in customer discovery activities, user experience workshops, and other customer-focused exercises to walk in the shoes of their customers. This helps build a stake in the game and ensure they truly understand their customer’s perspectives.

Own the Vision (and the Backlog)

For several reasons, software and delivery teams can become confused about what is desired or expected. This is the Product Owner’s time to shine; By concentrating on the business value opportunities and staying plugged into the organization’s strategic drivers, Product Owners formulate the vision for realizing the business value and translate that vision into achievable outcomes. Delivery teams benefit by having an exact, prioritized list of requirements that encompasses the value target.

Sponsor and Accept

While the Scrum Master protects and supports the team, the Product Owner protects and supports the business value. When delivery teams are ready to demo working software, code, or solutions, it is the Product Owner’s responsibility to accept their work if it meets the customer’s requirements. If target outcomes or deliverables don’t meet, the Product Owner helps steer the delivery teams and provide feedback to get them back on track for the next iteration.

Risk vs. Reward

Organizations that do not have dedicated Product Owners risk inconsistent value delivery. Inconsistency can not only impact an organization’s bottom line but the happiness of its customers as well.

While it’s possible for some backend support teams to function without having dedicated Product Ownership, Scrum teams and organizations may lose focus on the real value proposition when delivering software or deploying solutions. Additionally, while organizations can achieve some level of success without formally observing the role, Product Owners are critical to a business's success and organizational agility. Without dedicated Product Owners owning the product visions and representing the customer’s ever-changing needs, fully realizing the benefits of business agility stifle.

Organizations that commit to dedicated Product Owners are drawing a line in the sand. For the most valuable software, services, and capabilities to be delivered, Product Owners must represent and understand the customer’s needs. By maintaining a pulse on what customers value the most, organizations help ensure that they are creating products and experiences that customers want, trickling down into more significant revenue, market penetration, and customer satisfaction.

Is your organization tapping into the Power of Product Ownership? Radiant Digital has personnel and expertise to help your organization deliver great products and customer experiences. For help scaling and maturing your product delivery pipeline, contact Radiant Digital at [info@radiant.digital].

by Frank Cannistra, Radiant Digital

All rights reserved. © 2020 Radiant Digital Solutions

November 1, 2020WebinarsHealth,Knowledge Management,Education,Training And Development

[Webinar] Apply Cognitive Task Analysis to improve training

 

This webinar series explains how an expert’s knowledge can be more fully captured, allowing organizations to maintain their knowledge base and provide better quality training.

Our guest speaker Dr. Sheila Mitchell is a Senior Instructional Designer at Radiant. After earning her doctorate at the University of Virginia, she has worked in diverse fields such as biosecurity, safety, and the energy sector. Sheila's goal is to create efficient training firmly rooted in Cognitive Load Theory.