At its core, coaching is a form of individual and team development in which the coach helps to bring out the potential of the learner. The coach does this in a manner which supports, encourages, and most importantly, places responsibility for development with the learner (Dembkowski, 2006). There are four core qualities that make up every effective coach, no matter the style by which they lead.

1. Building Rapport and Relationships 
Rapport is the presence of a close and trusting relationship in which the learner and the coach understand each other’s ideas and communicate well. Rapport building involves getting to know one another, understanding where each other come from and what their background is, and most importantly spending time with one another.

2. Asking Questions and Listening
Where are you now, and where do you want to go? Helping learners gain insight through self-evaluation is a key part to coaching. Good coaches listen carefully, are open to learners’ perspectives, and allow learners to vent thoughts and emotions without judgement.

3. Providing Effective Feedback
Coaches that provide effective feedback focus on facts and observed actions, rather than personal reflections of what they think about the learner or team (Dembkowski, 2006). Feedback should be honest, but not judgmental. Good coaches recognize that an important part of their role is to challenge the learner, and giving feedback is a good way to deliver this.

4. Setting Goals and Delivering Results
Effective coaching is about achieving goals. The coach helps the learner set meaningful targets and identify specific behaviors for meeting them. The coach helps to clarify milestones or measures of success and holds the learner accountable for them (Forbes, 2010). Goals are much more likely to be accomplished if they are specific, and clearly defined (Dembkowski, 2006).

A Tale of Two Coaches
Bobby Knight, nicknamed “The General”, was the head men’s basketball coach for the Indiana Hoosiers from 1971-2000, and for Texas Tech from 2001-2008. While at Indiana, Knight let his teams to three NCAA championships and 11 Big Ten Conference championships. He also coached the 1984 USA men’s Olympic team to a gold medal, and has the third most wins in NCAA coaching history. Though we was highly successful, innovative coach, Knight is probably best known for his short temper, angry outbursts, and for throwing a chair across the floor during one of his more famous tirades.

Phil Jackson, nicknamed the “The Zen Master”, was the head basketball coach for the Chicago Bulls from 1987-1998, and for the Los Angeles Lakers from 1999-2004 (and again from 2005-2011). Phil Jackson coached his teams to eleven (11!) NBA championships, an NBA record. Jackson studied human psychology, native American philosophy, and Zen meditation to help him inform coaching strategies. He taught players mindfulness, selflessness, and would lead breathing exercises while burning sage in the locker room.

Technology and Coaching
The importance of coaching is evident, regardless of the style of coaching. However, finding a coach is not exactly an easy task. Until recently, the idea of going to the store and buying a coach for an activity that you are trying to improve on or become an expert in, would have seemed ridiculous. Technology has changed this. There is an endless number of apps on Google Play and Apple’s App Store that boast unique automated coaching experiences. Some of these apps can provide this unique coaching experience through artificial intelligence (AI), which is allowing for a more individualized coaching experience without human intervention. But, how can technology accomplish the four core qualities discussed earlier? Is it possible for AI to achieve features such as rapport building, and asking questions and listening? Even more complex, how does it account for different styles of coaching, that get results in different situations? How does it account for the General vs. Zen Master problem. A big part of this challenge is analyzing behavior based on understanding the learner and the performance environment. In my next installment, I will talk about how technology, and specifically AI, is beginning to overcome this challenge. What are your thoughts?


References
Dembkowski, S. (2006). The seven steps of effective coaching. Thorogood Publishing.
Frankovelgia, C. (2013, June 19). The key to effective coaching. Forbes. https://www.forbes.com/2010/04/28/coaching-talent-development-leadership-managing-ccl.html

Shabnam Mitchell is QIC's new Project Manager! An Agile Certified Practitioner, she has 9 years of experience applying Agile methodologies and frameworks to promote organization efficiency and success. Her experience comes from a wide range of exposures from the banking and financial industries to education and construction managing both large scale and highly critical projects. Shabnam holds an MBA from the University of Phoenix and a B.A. in Economics and Urban Studies from the University of Texas at Austin. Shabnam has led projects from corporate business continuity initiatives and small business change management projects to program and portfolio management through digital disruptions. Shabnam’s professional interests are rooted in simplifying challenging endeavors while engaging and enabling teams to take risks and do their best work, whether in the classroom, at the jobsite, or in the office.

I'll start this out like every blog post in 2020…COVID-19 sucks and has thrown a wrench into everything! From a professional perspective, it has affected us all in different ways, but overall it has forced us to rethink the way we conduct our work. As human factors|UX/UI researchers, this has greatly limited our ability to conduct live field research with human participants. Because of this, methodologies to collect data right now are focused heavily on online or remote approaches. Specifically, web-based surveys may be flooding your inbox. The general use of surveys have been around for quite some time, at a minimum of over a century within the U.S. (Converse, 1987). Web-based methods have been in use since the early days of the internet. Some state it was one of the most significant advances in survey technology within the 20th century (Dillman, 2000). "With great power comes great responsibility" (I'll attribute this quote to the late and great Stan Lee). Okay, so survey writing may not be a superpower, but it is a powerful tool for collecting quantitative and qualitative data when implemented correctly. Actually, if I can understand a person's behavioral patterns based on collected data, then that kind of gives me the power to predict the future. I guess I do have superpowers!

Despite the longevity and broad applications of surveys, I constantly come across poorly designed online surveys and it hurts my human factors brain. This is likely because anyone can create a survey if they want, especially web-based, but this doesn't mean you should. So what should you do? Work with professionals who are trained to extract valuable information from participants or respondents. It may seem like an easy task to do on your own, but being able to generate a valid and effective survey is as much of an art as it is a science. You don't become a great scientist or artist overnight, it takes time and experience to hone those skills. 

Think about it this way, if you're planning to use the data collected from a survey to drive decisions about a product design, an event, or whatever, don't you think it's important to gather the most informative data possible? Especially if these products or events cost hundreds of thousands to millions of dollars to develop. I'll answer for you…YES! So why are there still so many issues with online surveys? I say it's probably because people don't know what they don't know. They aren't aware or trained how to create appropriately worded questions, or organize the flow, or understand the factors that might influence a person's response to a question. And therefore, they end up drawing conclusions that don't accurately reflect respondents' views, behaviors, or beliefs. The end result may be a poorly designed product that no one wants to use.

​This is what the form looks like when you choose an answer. See any other issues with how it's designed? Maybe a color selection issue?

​Rating scales should align with the questions and be consistent. First, the scale should make sense when associated with a question. When Likert-type scales are used to gather responses, the lower end of the scale usually refers to less agreement, frequency, importance, likelihood, or quality. Do you see an issue with the scale below? Second, anchor labels could be used to show the extreme ends of the scale, but should still have an associated value (although it's best to have labels and values for every level of the scale to avoid confusion). Third, a label for the center of the scale should be provided because the middle may be assumed to be a neutral response. Fourth, the scales should be consistent as much as possible throughout the survey. Meaning, if you're using a 10-point scale, the don't make the next question a five- or seven-point as seen in the images below. This just makes answering questions more challenging for the respondents.

Avoid double barreled questions. The questions above are also designed poorly because there are multiple items in the questions that the respondents might not agree with. Asking the respondents if they prefer "workshops, panels, and training" to be online is taking away the ability for respondents to agree with one of those only. It may seem tedious, but these types of questions need to be reworded or broken out into separate questions.

Avoid leading and loaded questions. Do not force respondents to provide answers that don't truly reflect their sentiment. Below it asks respondents to select up to four, but what if respondents only agreed with one or two items? Now you're forcing respondents to provide biased answers that don't truly reflect how they feel or would behave. See a problem with this? Rephrasing the question to say they could choose up to four is different than saying they have to choose them.

Make sure the question wording is clear and well defined. Don't leave the wording of questions open for interpretation (unless that is intentional). Every included word should be chosen with a clear purpose. If you asked different respondents what "long run" meant, you would probably receive many different answers.

These are just some examples to highlight the challenges with creating a valid survey, although there are many other issues that can arise. It may not be obvious until you sit down to analyze the data that you have a complex data set and it's difficult (or impossible) to generate appropriate conclusions for decision-making. Don't waste your time and resources collecting worthless data when you can do it correctly the first time. You'll be happy, your boss will be happy, your customers will be happy, and the respondents will be happy to know their opinions matter. Contact us if you have any questions (no pun intended) or need support with your data collection efforts.

Sports Performance is Cognitively Demanding
Playing any sport requires a high demand of cognitive functioning including, but not limited to, decision making, working memory, visual and perceptual processing, motor functioning, and divided attention. In moments of normal gameplay, the amount of brain processing needed to evaluate, act, and perform optimally for every possible situation is astronomical. Moreover, players frequently encounter high-pressure situations where stress-response regulation can be crucial to performance outcomes (Eysenck and Wilson, 2016). 

With this in mind (no pun intended), take a moment and try to place yourself inside the head of a competitive soccer player; say Lionel Messi for example. At all moments in time on the field, Messi must be aware of his positioning relative to the current and projected state of the ball, his teammates, and the position of his opponents. This is only the start of the mental juggling act needed to be an effective player. Of course, Messi must also act on these thought-processes repeatedly, which requires precise split-second decision-making. In fact, one judgment error or slight hesitation can be a catalyst to a domino effect of miscalculations likely leading to an opponent goal or missed offensive opportunity. So, when it comes to training, the traditional practice paradigm may be enough to improve a player’s physical performance and technical skills, but there’s clearly a mental facet to the game that needs to be considered.

Current CT Applications in the Sports Field
There are several companies producing commercial CT solutions aimed at specifically improving sports performance, such as Axon Sports, FITLIGHT, and NeuroTracker, to name a few. Interestingly, the utilization of CT in the professional sports realm is very present. For instance, NeuroTracker, has an extensive clientele list with teams from the NFL and Premier league bought in, with research supporting their efficacy. One such study used a 3-dimensional multiple object tracking (3D-MOT) task which required participants to track and recall multiple moving objects in a changing visual field (Romeas, Guldner, and Faubert, 2016). The concept behind this task is that it may actually simulate the cognitive processing occurring in Messi’s head when he’s evaluating his surroundings on the field. In the study, the effects of training for 19 male soccer players were examined across the three groups (3D-MOT, passive, and active control). The 3D-MOT group trained twice per week for 5 weeks, whereas the active control watched 3D soccer videos and partook in engaging interviews, and the passive control received no treatment for the evaluations. Interestingly, the 3D-MOT trained group improved by 15% in a measure of on-field passing decision-making. 

However, there were no improvements in shooting or passing accuracy, which underscores the potential lack of transfer effect to similar yet different tasks (Walton et al., 2018). Moreover, another study found that while participants improved significantly in the training task post-training, no evidence was found for near transfer (to another object tracking task) or for a far transfer task (a driving task that required recalling specific locations) (Harris et al., 2020).
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This concept of transferring training to the specific performance task is a major talking-point in terms of the efficacy of CT applications. Transfer of training refers to the generalization of skills attained from training which are then applied to different tasks and domains. One way transfer tasks are categorized is by how near or far they are from the training task, in other words, the closer the transfer task resembles the training task the nearer the transfer. Unsurprisingly, the likelihood of transfer effects is directly related to the degree to which the transfer task resembles the training task, thus near transfer is much more commonly seen (Sala et al., 2019). Sport-based CT is no exception to this phenomenon.

The far transfer of CT on sports performance is especially difficult given that sports are highly variable with many factors that ultimately contribute to the success or demise of a player (e.g., natural performance variances, nutrition, emotional state, sleep deprivation, etc.) (Walton et al., 2018). For example, measuring win/loss ratios (e.g., season performance) may be an appealing metric for CT transfer, but again there are so many factors that play into a team's success it’s difficult to single out CT as the affective variable. 

What Do You Think?
Put yourself in the mind of a coach (yes, I’m sorry, you are officially no longer Messi). Now, consider the following:

Would you pay for a CT product that may or may not transfer successfully to the pitch?
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If it were me, I just might, as it’s a pretty neat concept. That said, I think it’s clear that researchers in the field of CT and developers of these systems should continue to make collaborative efforts to better understand and evaluate the transfer effects of training tasks to real-world performance outcomes. Eventually, with the advent of innovative training designs and accurate ways to determine their effectiveness, I believe CT will not only hold its own in the sports realm, but also in a variety of other settings. 

Although no one from the office directly observed me doing work during the COVID quarantine, someone else was keeping watch. They noted and reported back to me how much time I spent in “deep focus”, on emails and online chats, meeting duration and if these meetings were accompanied by agendas, who my collaborators were, and my level of well-being (i.e., the time I actually “powered down” from work). Yes, Microsoft’s MyAnalytics (see Fig. 1) was my productivity pal while I worked from home. I indulged myself in this new distraction, reading my stats and delving into the research and insights, discovering things like “it can take up to 23 minutes to refocus after checking just one email or chat”, “having long blocks of time to focus without interruptions can help you get challenging work done faster”, and “last-minute invitations are sometimes necessary, but your meetings may be more effective if you give attendees sufficient time to prepare”. 

Applications of data science methods are becoming prevalent in more and more domains of our work and life. Microsoft’s MyAnalytics is just one of many applications (see Fig. 2) that we encounter, whether we know it or not.

A 2018 Forbes article provided a quick overview of some data science tools, which include: (i) data analytics, (ii) predictive modeling, (iii) artificial intelligence and machine learning (Forbes, 2018). As it turns out, these tools closely mirror much of what we do in our everyday interactions with others.

Data analytics merely involves examining and describing past data, such as past usage and activities. There is no prediction or extrapolation done with the data, as is the current state of MyAnalytics.
Human analogy: We perform data analytics when we take a matter-of-fact approach in noting someone’s past behaviors, like when someone was late for all of the Thursday meetings in the past month. We do not use it to infer anything about their patterns of behavior or personality, and do not try to project their future actions.

Predictive analytics takes things a step further. This involves using past data to derive some general pattern or model, which is then used to predict future events. Regression is a common statistical technique for developing such models from past or observed data.
Human analogy: We perform predictive analytics to make sense of the world through patterns. For instance, when we see someone being late for a particular Thursday meeting more than a few times, we start to expect/predict that this person will be late for future meetings. In doing this, we create a “model” of the person’s behavior for meetings.

Artificial Intelligence and Machine Learning (AI/ML) goes even further. In addition to using past data to predict future events, AI/ML involves autonomously learning and adjusting the model without any human intervention as new data comes in. This is what enables Facebook to get better at recognizing your friends by continually learning their unique features from each new picture of them in various poses and lighting conditions.
Human analogy: This type of learning comes closest to what humans actually do with new information. Our understanding and predictions about someone get better the more we see him/her in various contexts and roles. Our knowledge of this person gets richer with each new encounter. For instance, after seeing the same person in other meetings on Thursday, non-work meetings, and meetings with other customers, we realize that s/he is only late for the Thursday meetings that occur with a particular customer.
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So what do you think of apps such as MyAnalytics? Do you think it would help your productivity? What aspects of your life would you not welcome the use of such apps? Leave a comment and tell us what you think!