Critical Thinking Concepts

Select 2 articles of different disciplines from HBR or MIT Sloan Management Review or Academy of Management Perspective. Provide article name, author(s) name,year, journal, vol, issue, # of pages. For instance (Panicker, A., Agrawal, R.K. and Khandelwal, U. (2018), “Inclusive workplace and organizational citizenship behavior”, Equality, Diversity and Inclusion: An International Journal, Vol.37 No.6, pp.530-550)

Write 4-5 page report:

Summarize both the articles

Identify how critical thinking is applied. Is there any variance for different discipline?

Identify which stage of critical thinking the article/author is. Provide your justification for the same

Which structure/form  is more relevant for the selected article?

What are the main arguments in each article? What are the assumptions behind each argument?Evaluate the assumptions (Strong/Weak)

Provide Recommendations & conclusion

Four Skills Tomorrow’s Innovation Workforce Will Need

Frontiers

Essay

 January 30, 2020  Reading Time: 18 min 

Tucker J. Marion, Sebastian K. Fixson, and Greg Brown

Frontiers
, 

Innovation

, 

Leadership

, 

Innovation Strategy

, 

Leading Change

, 

Talent Management

MIT SMR FRONTIERS

This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management.

Image courtesy of Jim Frazier/theispot.com

Throughout history, new technologies have demanded step shifts in the skills that companies need. Like the First Industrial Revolution’s steam-powered factories, the Second Industrial Revolution’s mass-production tools and techniques, and the Third Industrial Revolution’s internet-based technologies, the Fourth Industrial Revolution — currently being driven by the convergence of new digital, biological, and physical technologies — is changing the nature of work as we know it. Now the challenge is to hire and develop the next generation of workers who will use artificial intelligence, robotics, quantum computing, genetic engineering, 3D printing, virtual reality, and the like in their jobs.

The problem, strangely enough, appears to be two-sided. People at all levels complain bitterly about being either underqualified or overqualified for the jobs that companies advertise. In addition, local and regional imbalances among the kinds of people companies want and the skills available in labor pools are resulting in unfilled vacancies, slowing down the adoption of new technologies.

Before organizations can rethink how to design jobs, organize work, and compete for talent in a digital age, they must systematically identify the capabilities they need now, and over the next decade, to innovate and survive. For more than 10 years, we’ve been studying the impact of digital design and product development tools on organizations, their people, and their projects.

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 We’ve found that the competencies companies need most are business-oriented rather than technical. That’s true even for brick-and-mortar companies that are trying to become more digital.

Top of Form

Bottom of Form

And most companies are beginning to realize that they can’t just hire all-new workforces; there aren’t enough qualified recruits, and the expense would be enormous. Instead, they need to retrain and redeploy existing employees and other members of their communities, in addition to hiring and contracting new ones to fill their needs. However, rapid technological change has rendered skill cycles shorter than ever; key competencies of even a decade ago are passé today, and most of tomorrow’s jobs remain unknown.

Waiting for the fog to clear isn’t an option. Companies must identify and develop the core skills their employees will need going forward. Our interviews, surveys, and case studies have revealed that most companies focus on refining the skills their people already possess, which doesn’t prepare existing employees or new hires for the business challenges they’ll face when using emerging technologies in their jobs. We’ve also found that young digerati, many of whom come into the workforce from narrow academic streams, are typically more captivated by digital technologies than they are by business problems. And yet, given the sweeping changes that the new technologies are likely to bring about, companies would do well to cultivate four broad business-oriented competencies in tomorrow’s innovators.

1. Omniscience

To know it all may be a godlike, even insufferable, goal. But tomorrow’s talent must aspire to understand everything — or at least much more than they currently do — about their businesses. Employees must grasp key connections: links between physical machines and digital systems, between each step of the value chain, between the company’s current and future business models.

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 And they must know their customers’ businesses — how and when their customers’ products and services are used, how their customers’ organizational processes work, and the related challenges and opportunities. That’s the only way companies will be able to evolve from selling products and services to delivering outcomes — a process that will likely change the very businesses they’re in.

For instance, a major medical device manufacturer we studied has moved from developing R&D-driven solutions to delivering patient outcomes, which has become possible because of new technologies and big data. The company needed to quickly employ more people with a systemic understanding of everything it does, including patient care and rehabilitation and treatment efficacy. To move the needle on patient outcomes, it’s critical to understand all those aspects of the system and the associated variables. Thus, the business will demand that existing and new employees have a broader understanding about the underlying science, the delivery technologies, and the industry than almost all of them, other than top management, currently possess. Breadth of knowledge cannot substitute for depth, either; employees must also be able to make deep dives into the vertical aspects of the business when necessary.

Let’s consider another example: The Canadian company Dental Wings is using recent advancements in digital design, digital imaging, and additive manufacturing, as well as a collaboration platform, to rethink its dental implant business. From the dentist’s initial assessment to patient recovery, the company has started adopting new technologies to improve its processes and provide better care. For instance, all-new imaging capabilities provide more accurate pictures of the dental site that can be used not only to create digital models for implants, but also to develop tools to help surgeons define the optimal surgical paths. That reduces exploration of the implant site, which helps reduce recovery time and lowers the risk of infection. To innovate at each step, Dental Wings’ employees need to understand how the new processes and systems connect and work together.

The need to know more holds true for people in every function, but especially so in R&D and product design. In the not-too-distant future, product designers who are designing new earth-moving equipment will have to use AI and internet of things (IoT) sensor data to model, analyze, develop, and modify features in near real time. Once in the field, each prototype and its digital twin will operate simultaneously so that the designers will have access to data 24-7. They must be trained to use it to develop improvements for the current model on the fly as well as to better design the next generation of equipment.

In almost every brick-and-mortar company, dozens of digital platforms will have to be coordinated, the data mined, and the insights used in a harmonized effort between the human team and AI systems. Orchestrating all that data, whether from design outcomes or field performance, will require people who understand the value of each data point and how all the pieces fit together. It will also require knowledge across myriad disciplines, such as mechanical and electrical engineering, computer sciences, and product development, because the variables in a complex system interact in many ways. For instance, the location of a sensor on a suspension lever (a mechanical issue) will affect the data that the sensor electrically measures, which will in turn affect the mathematical algorithms that determine the lever’s accuracy. Companies whose employees can manage and navigate complex data-based systems will be best equipped to improve the performance of their products, reduce maintenance costs, and attract and retain customers.

A Perfect Storm of Megatrends

Businesses tend to overlook the fact that the Fourth Industrial Revolution is gaining ground just as two other major shifts are exacerbating the skills shortage.

See more

2. Entrepreneurial Mindset

Although it may sound obvious, innovation teams will need to become more enterprising to succeed. They must become boundary pushers in terms of not just the products they wish to develop, but also the processes they use. The two are closely linked.

In large businesses, R&D and product development teams are organized like most other functions. They must follow the company’s guidelines about sourcing hardware, materials, and technologies to do their work and can use only IT-approved tools. R&D must adhere to time-tested procedures and rules for sharing information about or testing prototypes and product designs. And traditional R&D teams usually work in a centralized way, relatively insulated from the outside.

All that works well when business is as usual, but these are extraordinary times. R&D is meant to push technical boundaries, so R&D teams must learn to redraw organizational boundaries to keep pace with technological change. Essentially, they must become digital intrapreneurs, using the latest tools or, if necessary, creating them. That involves experimenting with new software and systems outside those recommended by IT, and even
developing some solutions in-house.

For incumbents, that can be a shock to the system — most people are used to working on proprietary systems and tools, getting things “right” before launch, and offering better products over time. Moving toward open systems, beta versions, and constant iteration can feel like a clash of civilizations in established companies, but they need to do so to innovate for today, as well as tomorrow. Collaboration is central to this effort. One study of 152 managers found that companies that used digital tools for collaboration improved performance — as measured by the number of concepts and prototypes developed — during the early stages of innovation. And another study of 400 companies showed that more-innovative organizations, measured by similar yardsticks, used such tools more frequently than less-innovative ones. Since better collaboration leads to more innovation, the collaborative tools and processes that organizations use are critical. Figuring those out requires an entrepreneurial mindset as well.

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For example, at a large company outside Boston, a new digital group is working on completely changing the way the organization designs products. This small team has asked for, and been given, the freedom to use any tools it wants, wherever they may originate. So the team has created a new system from scratch that allows it to test design structures in real time. The group also uses several digital platforms, most developed by unknown startups, to communicate and collaborate both internally and externally. It’s unlikely that IT approves or is even aware of what’s happening, but top management realizes that the company’s digital transformation will never occur if teams like this one are confined by rigid boundaries.

There’s a reason entrepreneurs in high-tech startups are risk-tolerant, and it’s time that intrapreneurs, or innovators in established companies, followed in their footsteps. Look at Proto Labs, which manufactures injection molds and machined parts and offers additive manufacturing services. To accelerate the time it takes to develop the first tooling cuts for its clients, the R&D group quickly developed some software on its own. The program could identify possible manufacturing problems in the digital-parts files sent by clients.

Through its automated platform, Proto Labs R&D communicates any possible glitches it detects directly to clients so that they can rectify those well before production starts. If such revisions were made after test production had begun (as they were in the old days, before the homegrown software existed), the process would have been deemed client-unfriendly and would have cost both the client and the company time and money. Proto Labs has also added downloadable tools and other materials to help clients design better parts, ensuring that everyone in the ecosystem benefits from the process improvements. These offers are the outcome of entrepreneurial actions of Proto Labs employees.

3. Bottom-Line Focus

In a data-driven world, employees need to be just as skilled at thinking about business models as they are at designing and implementing systems. Thanks to IoT and other technologies, companies’ value-capture strategies can be shaped not just by the marketing, sales, and business development functions, but also by R&D and product development. IDEO’s Tom Kelley describes people who look for business opportunities, beyond the current challenges, as cross-pollinators. Fostering that capability will be key.

Product engineers, for instance, must consider what kinds of sensors should be used, their placement, and the data types captured in light of possible revenue streams and cost savings. After all, big data poses as many challenges as opportunities. All hands must be on deck. The number of IoT-connected devices, estimated at around 2 billion in 2006, soared to 11 billion by 2019, and, according to Statista, is projected to touch 75 billion by 2025. Companies are capturing an enormous amount of data: IoT-generated data, estimated in 2016 at around 22 zettabytes (1 zettabyte equals 1 trillion gigabytes), reached 52 zettabytes by 2019 and is projected to hit 85 zettabytes by 2021.

While a company’s digital people may appear to be on the front lines of the data explosion, they also need to be able to figure out what all that data means for the business and how it can be monetized. They must go beyond checking where the data originated, how dependable it is, where it is stored, and whether it has a coherent sequence. All that is useful but has become mere hygiene.

In focusing on business relevance, data technicians should be trained to ask some key questions: Can the data be used to monitor our products’ performance and be offered as a service? Can that be done in real time? How else can the data be analyzed to generate insights about customers and their needs? For instance, can it be used to change the way customers schedule preventive maintenance for our products?

The need to be business-focused throughout the organization can lead to dramatically different customer-facing roles. One fast-growing company we studied develops sensor-based modules for the aerospace, automotive, and medical industries. It recently combined the roles of the product development manager and the product manager in all its lines of business — a radical step that immediately helped speed up cycle times.

To have a product position that is both inward- and customer-facing is unusual even today. Traditionally, the product manager would assess market trends and customer needs while developing working relationships with the company’s clients. He or she would then feed the R&D team — led by a product development manager — the information to develop new products, systems, and solutions, or improve old ones. Once the company combined the two roles, the speed with which new technical solutions were matched with prospects, and vice versa, rose dramatically.

Combing the two roles also created avenues for the cocreation of nontraditional solutions. For instance, by drawing on data from IoT sensors, the company was able to develop several new applications that reduced operating costs in areas that could not be assessed earlier, because the product development/product manager could now understand clients’ pain points as well as all the solutions the company’s technologies could provide.

4. Ethical Intelligence

Machines, overseen by smart humans, will make many design decisions. Though they are innately logical, they lack empathy. That will have consequences for companies, consumers, and society. Doing the right thing will become only more challenging as digital systems become increasingly complex.

People must examine machines’ choices through an ethical lens — and weigh in. Companies will have to figure out how design decisions and digital systems affect each stakeholder and factor in the likely unintended consequences. In industries such as aerospace, automotive, and medical device development, traditional engineering processes like risk analysis and failure mode and effects analysis (FMEA) should also be deployed during the development of digital platforms and products. For instance, when Twitter’s founders created the platform, they didn’t imagine it could be used to influence elections with the use of fake accounts and bots. However, a coder putting the platform through a design FMEA would have identified the possibility well before people caught a glimpse of the platform’s dark side.

Given AI’s potential, every company needs to consciously decide what good judgment looks like. Take the case of Boeing’s 737 Max 8, where, according to recent reports, pilots complained about an issue with the aircraft software while testing it years before 346 people died in two crashes.

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 However, those concerns never made it to the Federal Aviation Administration — a tragic failure of ethics at all levels of the company. The countermeasures lie beyond the scope of this article but must include new codes of conduct, fresh corporate responsibility norms, KPIs that reinforce personal accountability, and specialized training.

To embed a watchdog mentality in the culture, companies should provide ethics training — and clearly define what ethical means in their specific context. Moreover, agility may be the norm, but companies still need to be disciplined in terms of process. That means a heightened emphasis on developing tools that improve quality and stop bad design from hurting people. Making processes more digital must not take away from the inherent value of techniques such as control plans and independent testing, whose importance should be engrained in tomorrow’s talent.

As ecosystems develop, companies must use ethical intelligence to consider implications for all their stakeholders. At one open innovation platform, we found ethical breeches by the participants as well as the platform’s management. The lapses affected the quality of ideas and input from the community as well as the trust among stakeholders. Companies must build guardrails into their platforms if they want to keep the faith of society, which already views corporations and intelligent machines with distrust. That could include more visibility into management processes and decisions, a clearer articulation of privacy policies, and better identification and reporting of anomalies in the system. Think of the impact on Facebook’s image if it had reported the issues it experienced with foreign bots in 2016 in real time.

Why Structure Matters

Traditional companies will have to experiment with new organizational structures to get the best out of their people. Otherwise, tensions between well-entrenched managers and digital talent may thwart transformation, and the digital folks may walk out the door.

In their restructuring, it’s important for companies to signal that digital transformation is critical for their futures. One radical approach is to replace the central R&D unit with a digital product design group. A well-known shoe company recently did this. The new group oversees the development of a new approach to product design, testing, and analysis, which will include customized generative design and analysis tools. Top management views this group as spearheading the company’s future product development process.

Another option is to form a digital group that floats from project to project across the organization, as one leading consumer electronics company has done. There, digital experts hover over projects in various businesses and countries, providing input whenever asked or needed. The flexibility reduces the number of digital experts the company needs, even as it helps retain them, because they enjoy the variety of opportunities and challenges the arrangement provides.

Some companies, like Apple, have internal venture teams to develop new products. Others are now doing so with a generational twist by creating new venture teams made up entirely of millennials and centennials to come up with new products and processes. A large pharmaceutical manufacturer we studied invited its youngest employees to conceptualize and implement a new way to connect patients, doctors, and the company during clinical trials for its products. Those employees used their native expertise in mobile technologies and social media to keep all stakeholders informed and involved. Top management let them run the show, without allowing the rest of the organization to interfere. Funded by an internal venture capital panel, the project was tested, and eventually the company rolled it out to a wider audience. All too often, such projects are killed after their conceptualization, but companies that institutionalize entrepreneurial ecosystems can substantially improve their ability to innovative.

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To be sure, the goal isn’t to have a bifurcated talent pool in a company but rather an organization in which all the talent works together in a continuum, from hardware-focused experts to digital natives, from baby boomers to centennials. That’s how many design and innovation companies now function, with older designers using sketches and hand-formed foam prototypes while recent graduates go right to CAD software. Interestingly, the approaches can be effective if used together. At one design company we studied, the older designers, who preferred traditional methods, learned over time how the younger designers worked, and the younger ones gained a deeper sense of what they were doing from their older colleagues. It wasn’t long before all the designers, regardless of age, were using digital tools for project management, communication, and collaboration.

It isn’t easy for companies to change, especially from within. Kodak’s middle management was skeptical of digital technology, for instance, and internal inertia was one of the key reasons it failed to make the transition from physical film.

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 However, identifying and bringing in the skills needed to move forward with innovation can help kick-start the transformation process. Indeed, doing so may make all the difference between success and failure.

How Companies Are Using Intelligent Automation to Be More Innovative

December 05, 2019

In their embrace of more digitized ways of working, many organizations have adopted robotics to automate repetitive processes. Now those organizations are seeking to scale these solutions with artificial intelligence to go beyond the routine to the innovative.

In May 2019, Deloitte surveyed 523 executives in a range of industries in 26 countries across Africa, the Americas, Asia, and Europe about their intelligent automation strategies and the impact on their workforces.

Our analysis found that these companies are not only continuing to use robotic process automation (RPA) but are moving beyond it by increasing deployment of intelligent automation. Fifty-eight percent of surveyed executives report they have started their automation journey. Of these, 38% are piloting (1-10 automations), 12% are implementing (11-50 automations), and 8% are automating at scale (51+ automations) – twice as many as in 2018.

Organizations believe they can now transform their business processes, achieving higher speed and accuracy by automating decisions on the basis of structured and unstructured inputs. They expect an average payback period of 15 months – and in the scaling phase, just nine months.

Process fragmentation – the way processes are managed in a wide range of methods – is seen by 36% of survey respondents as the main barrier to the adoption of intelligent automation. IT readiness is considered the main barrier by 17% of organizations.

Success Factors

Analysis of the survey data reveals that companies adopting intelligent automation at scale have six distinguishing characteristics:

· An enterprisewide strategy for intelligent automation, which helps to generate higher returns in workforce capacity, cost reduction, and revenues

· Combining RPA and artificial intelligence (AI), leading to an average increase in revenue of 9% as opposed to 3% in those that do not combine the technologies

· Technology, infrastructure, and cybersecurity in place, enabling a 21% reduction in costs compared with 13% among organizations that lack these functions

· Mature process definitions, standards, and processes, which produce an average increase in back-office workforce capacity of 19% compared with 12% among organizations that do not have these in place

· Clear understanding of how to capture value, leading to an average cost reduction of 21% versus 15% in firms with less understanding

· Radical simplification driven by a need for cost reduction, which is present in 73% of scaling organizations compared with only 61% in piloting ones

Innovative Applications

The strength of intelligent automation comes to the fore when RPA combines with AI to enable applications that go beyond the routine to the innovative: from collecting and processing data to analyzing and making contextual decisions. However, a significant number of survey respondents (48%) admit to neither thinking about nor implementing an intelligent automation strategy that includes AI. Another 36% include AI in their strategy but not at scale. Only 11% of companies are currently scaling solutions that include AI.

Preparing the Workforce

AI increases the productive capacity of the human workforce. Over 90% of organizations expect AI to increase their workforce capacity. On average, they expect a 26% increase in back-office capacity over the next three years and a 17% increase in capacity in their core business operations. Despite the opportunity presented by intelligent automation to increase productivity, 44% of organizations have not yet calculated how their workforce’s roles and tasks, and the way tasks are performed, will change.

Moreover, almost two-thirds have not considered what proportion of their workforce needs to retrain as a result of automation. Even organizations that have automated at scale (51+ automations) are not yet thinking about this, with 53% stating that they have not yet explored whether their workforce needs to reskill as a result of their automation strategy.

Reskilling based on how the human workforce will interact with machines, including changes to role definitions, should be baked into organizations’ plans for intelligent automation adoption in order to leverage the expected capacity enhancement. But 38% of organizations are not yet retraining employees whose roles have changed.

Redefining Work

The new possibilities created by intelligent automation mean work should be redefined by:

· The outputs and problems the workforce solves, not the activities and tasks executed

· The teams and relationships people engage and motivate, not the subordinates they supervise

· The tools and technologies that both automate work and augment the workforce to increase productivity and enhance value to customers

· The integration of development, learning, and new experiences into the day-to-day (often real-time) flow of work

Finding Talent

The talent needed to automate is hard to find: Fifty-nine percent of those piloting automation believe they lack the workforce capacity and skills required.

Demographic trends are shrinking the pool of available talent. By 2028, there will be up to 8 million fewer workers in Europe than there are today.

But in recent years, the relationship between workers and many organizations has changed, allowing for full-time, part-time, contract, freelance, and gig employment. Organizations should better utilize the “alternative workforce” that offers short-term access to highly skilled workers during the implementation and scaling of automation.

A Supportive Workforce

There is a widespread perception that automation may eliminate jobs. But 74% of survey respondents believe their workforce is either supportive or highly supportive of their intelligent automation strategy. The perceived level of stakeholder support tends to grow significantly as organizations move further along their automation journey. Thirty-two percent of executives whose companies are piloting said their workforce is unsupportive, compared with just 12% in companies that are implementing or scaling.

The year 2020 looks to be a breakout year for intelligent automation. Firms have targeted low-value opportunities for task-based automation and will increasingly seek to incorporate more advanced analytical and AI technologies as part of their solutions.

IMPORTANCE OF CRTICAL THINKING

Critical thinking for Managers (01/13/2020)

INTRODUCTION
Critical thinking means ‘making clear, reasoned judgments’ – Beyer (1995).
Critical thinking is the art of analyzing and evaluating thinking with a view to improving it.
Critical thinking is based on the substantive approach developed by Dr. Richard Paul and his colleagues at the Center and Foundation for Critical Thinking over multiple decades.
It entails five essential dimensions of critical thinking:
1. The analysis of thought.
2. The assessment of thought.
3. The dispositions of thought.
4. The skills and abilities of thought.
5. The obstacles or barriers to critical thought.

ESSENTIAL DIMENSIONS OF CRITICAL THINKING

BECOMING A CRITIC OF YOUR THINKING
HOW SKILLED IS YOUR THINKING?
WHAT QUESTIONS DO YOU HAVE IN YOUR MIND?

STEPS IN CRITICAL THINKING
Responding successfully to the questions in your mind is the art of thinking.
For successful thinking, the foll: steps are followed
1. Identify the problem/question
2. Gather data, opinion, & arguments
3. Analyze & evaluate the data
4. Identify assumptions
5. Establish significance
6. Make a decision/Reach a conclusion

HOW TO MAXIMIZE QUALITY OF THINKING?
Make learning about thinking a priority
Ask Unusual questions

GOOD VS. BAD THINKING
ARE THERE WAYS TO DISCOVER GOOD & BAD THINKING?

VARIETIES/STRUCTURE/TYPE OF CRITICAL THINKING
12 Forms of Critical Thinking:
1. Global critical thinking – multidimensional, interdisciplinary, transdisciplinary, generalizable
2. Specialized Critical thinking – intradisciplinary, non global, partial
3. Socratic critical thinking – fair-minded, ethical, strong sense critical thinking
4. Sophistic critical thinking – unethical, selfish narrow-minded critical thinking
5. Explicit critical thinking – conscious awareness to improve skills & develop strategies for that purpose
6. Implicit critical thinking – without conscious awareness

VARIETIES/STRUCTURE/TYPE OF CRITICAL THINKING
7. Systematic critical thinking (integrated) use all concepts & principles
8. Episodic critical thinking (occasionally, not systematically, consistently, unintegrated critical thought)
9. Emancipatory critical thinking (flexible, not lock into rigid set of assumptions)
10. Constrained critical thinking (trapped, not entertain other possible viewpoints)
11. Critical thinking based in natural languages (ordinary rather than specialized language)
12. Critical thinking based in technical languages ( extensive vocabulary of terms & concepts)

HIGHLY RECOMMENDED TYPES OF CRITICAL THINKING
Global (any discipline or domain)
Socratic (fairness in reasoning, thinking)
Explicit (identify problems in his/her reasoning)
Systematic (approach complex problems in a systematic/integrated way)
Emancipated (minimize bias, prejudices)
Natural Languages ( using natural language to analyze/assess)

TOP CRITICAL THINKING SKILLS
1. Analysis – ability to collect & process information & knowledge
2. Interpretation – concluding the processed information
3. Inference – assess the knowledge is reliable & sufficient
4. Evaluation – ability to make decision on available information
5. Explanation – communicate your findings & reasoning clearly
6. Self Regulation – constantly monitor and correct your ways of thinking
7. Open mindedness – taking into account other possibilities & points of view
8. Problem Solving – ability to tackle unexpected problems & resolve conflicts

TEST YOUR KNOWLEDGE (SITUATIONAL ANALYSIS – CLASS EXERCISE)
Identify which critical thinking skills (from the above slide) applies to each situation:
1. Describe a situation where you challenged the way you and your colleagues did their jobs?
2. Describe a situation where you saw a problem & took steps to fix it.
3. Tell about a time you had to persuade to see your side of things.

STAGES OF DEVELOPMENT IN CRITICAL THINKING
SELF MANAGEMENT
PARTS OF THINKING

CHAPTERS 2, 3, 4

STAGES OF CRITICAL THINKING

STAGES of Critical Thinking
1. Unreflective thinker Features:
Make assumptions
Unaware of intellectual traits
Create illusions
Egocentric/Self-centered
Stereotype others
Prefer not to change the behavior as it’s comfortable.
2. Challenged thinker Features:
Individuals realize normal thinkers often think poorly move into the second stage
Aware about role thinking plays in their lives
Understand the basic elements of reasoning (concepts, assumptions, questions at issue, purpose, point of view, information, implications and consequences, etc.)
Apply standards for the assessment of thinking (clarity, accuracy, relevance, etc.)
But have only a superficial understanding of these concepts

STAGES OF CRITICAL THINKING
3. Beginning thinker Features:
Control their thinking process
Realize it’s common to experience difficulty in reasoning/problem solving – take deliberate measures to monitor and improve thinking.
Efforts are hit and miss.
Understand egocentric situations
Encourage critic of self thinking
Understand the role of self-monitoring, but sporadic at the same.
4. Practicing thinker Features:
Understand how thinking flaws sometimes
Understand the importance of self-monitoring
Challenge self thinking otherwise become egocentric
Understand human minds are self-deceptive, hence critic their own conclusions, beliefs, & opinions
Limited insight into deeper level of thoughts

STAGES OF CRITICAL THINKING
5. Advanced Thinker Features:
Actively analyze, assess, & critique own thinking in the significant areas of lives.
Have insight and understanding of problems at deeper levels of thought.
Well- developed sense of their own egocentric nature, strive to be fair-minded.
If identified bias/double standard, quickly correct the thinking to be fair.
Develop understanding of the relationships between thoughts, desires, emotional needs, and feelings.
Able to control the extent of egocentrism through careful monitoring of thoughts.
6. Accomplished Thinker Features:
Establish a systematic plan to assess & correct their own thinking.
Continuous critiquing self thinking for improvement
Extensively practiced critical thinking traits and skills, able to develop new insight into deeper levels of thought
Fair-minded, regularly recognize and control their own egocentric nature.
Recognize relationships between thoughts, desires, feelings, and emotional needs, and correct their thinking when motivated by irrelevant emotions.

SELF UNDERSTANDING
“ If you’re actively working on increasing your self-awareness then you’re familiar with critical thinking”
The difference between an individual who doesn’t think critically and one who does:
Person 1
Someone says something to this person that scares her. She can’t figure out what to do and doesn’t know how to assess what’s true or false about what she’s being told. Because she doesn’t understand the topic at hand, she draws conclusions based on visceral feelings, suppositions, or hunches rather than facts. Unable to ascertain what’s really going on, she remains uninformed and fearful.
Person 2
This person has been told the same thing, initially feels scared, but has the presence of mind to evaluate the topic. She does some research to determine what is true or false about what she’s been told based on demonstrable and verifiable facts. She is able to view the issue in context and asses its likely impact on her life. She reacts appropriately based on the information she’s collected.

EGOCENTRIC THINKING
Our family, country, region, religion, feelings, & values are specially privileged in our egocentric mind.
Egocentric thinkers not consider:
The rights & needs of others
Do not appreciate others point of view
Do not identify self limitations
The most commonly used psychological standards in human thinking:
It’s true because I believe it
It’s true because we believe it
It’s true because I want to believe it
It’s true because I always have believed it
It is true because it is in my selfish interest to believe it

FUNDAMENTAL MOTIVES BEHIND EGOCENTRIC THINKING

RECOGNIZING THE MIND’S THREE DISTINCTIVE FUNCTIONS

RECOGNIZING THE MIND’S THREE DISTINCTIVE FUNCTIONS
Thinking – create meaning
Feeling – Monitor & evaluate meaning created by thinking function
Wanting – allocates energy

RELATIONSHIP TO YOUR MIND
WHAT ARE THE CONSEQUENCES OF POSITIVE & NEGATIVE THOUGHT IN MIND?

EMOTIONAL INTELLIGENCE (EI)
Emotional intelligence is a simple concept: It’s the ability to make emotions work
for,
instead of against you
.
Emotional intelligence comes in all different packages, shapes, and sizes
Our emotions influence practically everything about our lives.
Emotions determine how we choose our leaders and how our leaders choose us.
Emotional intelligence manifests itself in various ways
The best way to protect yourself from harmful uses of emotional intelligence is by striving to increase your own.
Emotions are beautiful. They make us human. Enjoy them. Love them. Embrace them. But never underestimate their power, and their potential to do harm.

CRITICAL THINKERS DISTINGUISH BETWEEN INFERENCES & ASSUMPTIONS
Assumption: It is an unstated premise, cannot be logically derived from any existing information.
It cannot stand on its own.
Assumptions are generally given to present some new information. These can also be part of some beliefs.
Inference: It is that piece of information which can be logically deducted from the one or more statements.
Combination of an assumption (valid/true) and a fact results in an inference (correct/valid).
Assumption + Fact → Inference

INFERENCES
Important part of critical thinking – bringing what is subconscious in our thought to the level of conscious realization.
This includes – experiences are shaped by the inferences we make during those experiences.
We separate our experiences into two categories: the raw data & interpretation to our data.
We make inferences based on the people & situation
Different people make different inferences because they bring to situations different viewpoints & see data differently.
Examples:
Person One Situation:
A man is lying in the gutter.
Inference: That man’s a bum.
Assumption: Only bums lie in gutters.
Person Two Situation:
A man is lying in the gutter.
Inference: That man is in need of help.
Assumption: Anyone lying in the gutter is in need of help

SITUATIONAL ANALYSIS
1. Consider the features & characteristics of each stage of critical thinking development. What stage describes how you reasoned when you were in early stage of your career? Share specific experiences from your life which reflect the stage of thinking
2. Did you begin to change/develop as a critical thinker in growth stage of your career? How? Share specific experiences which demonstrate these changes?
3. Assess your own recent development as a critical thinker. Do you think you have advanced through any of these stages? Why or why not?
4. If you have not, what are some barriers to advancing & making through these stages? If you have, what has enabled you to advance in or develop your skills a a critical thinker?
5. What are your personal goals for yourself in the area of critical thinking? Be specific.