The world is getting flatter – Why isnt our educational system

Blog post
edited by
Matt Tricomi

Problem: The world is getting flatter – Why isn`t our educational system?

There are massive functional and communication inefficiencies inherent in our current public educational system at the elementary and secondary levels.  The problems are:

  • Lesson preparation and delivery work activity is highly redundant and costly.
  • Effective communication between student, teacher and parent is woefully inadequate and compromises student potential

Using simple architecture analysis techniques looking at the enterprise and specifically dissecting the education segment, these inefficiencies have been identified, modeled and evaluated and mapped to emerging educational solutions that will redress them.  The educational solutions presented in this paper have been developing organically within the education community but do not have a cohesive adoption strategy that will optimize their full potential.

What is insanity? 

According to Einstein “it is doing the same thing over and over again and expecting different results”.  What does this quote have to do with our approach to education?  This is what we do; year after year after year.  During a typical school year, our 3,335,100 public school teachers working in 98,817(1) elementary, middle and high schools, prepare lesson plans, deliver lessons and assess student’s progress that to a large degree are redundant (See Figure 1).  This is performed at the cost of approximately $63 billion dollars a year.

 

Is this really the ideal way to run an educational system?  Is it the best use of skilled professional’s valuable time?  Is it a good use of a tremendously valuable and costly public resource? Here is the size of the resource pool that currently works in the U.S. primary and secondary education system:

  • ES and K Teachers 1,655,800 – Median Salary $50,150
  • MS Teachers 641,700 – Median Salary $50,770
  • HS Teachers 1,037,600 – Median Salary $52,200 (2)

To illustrate the point, every year tens of thousands of elementary school math teachers prepare and deliver a lesson on adding and subtracting negative numbers to their 25 or 30 children.  

Each teacher would have invested varying levels of rigor, dedication, creativity, design or customization to their approach.  Once done, the average teacher could probably expect a third of their students not be challenged, one third in need some moderate assistance and the rest to be struggling or in the process of giving up.

It is way too easy to blame the teachers. It is convenient and perhaps fashionable, to say it is the teacher’s shortcomings and fallback on sound-byte-based thinking:

  • They are not well enough prepared,
  • We don’t get the “best and the brightest”
  • Old fashioned, technically illiterate,
  • Unions, tenured and lazy,
  • Out of touch with current pedagogical approaches.

The vast majority of classroom teachers face the daunting reality that students learn in many different ways, at different rates, have different states of cognitive, emotional, and social readiness.  Not an uncommon phenomenon in most classrooms.  Imagine an educational model where the availability of a highly qualified teacher, their devotion or the amount time they have to solve a student’s learning barrier is no longer tied to a classroom clock.

Recent research strongly suggests that a highly effective teacher, given three years, can impact student achievement by 50 percentage points (2) when results are compared against other teachers and standardized test scores.  

 Regardless of one’s opinion of the value of standardized testing, these teachers are doing something very right.  They have “it”.  The data implies there are inherent benefits to be realized in high quality lesson preparation and delivery based on these individual success stories.  If we assume that only 5% of the teaching profession possesses this impact quality, it would leave us with a serious question: Can we significantly raise the performance of our teacher to a point that we could fundamentally change the existing educational model’s performance?  Let’s face it; teachers are no different than many other professionals.  They are subject to the same bell curve as are lawyers and doctors and engineers. We have a few great ones, a number of good ones, some in the middle amongst others. Enough said.

Are we betting our future on great teachers, a skill that seems to be pretty rare and should be highly cherished? 

Do we think we can train or re-craft even half our teachers to be that impactful?  No company of 1/10,000 the size of our education system would dream of trying such an approach.  Even if the best and the brightest wanted to commit to education, would we have enough money to pay for the quality and retain them? What would a business do?  They would analyze their value chain and the supporting processes and technical capacities to see what can be done to improve overall performance of the system.  They would not focus on just one high value component.  Companies would research and develop cost effective alternatives that drive systematic improvement.  The investment would be designed to compensate for the inadequacies of the system and attempt to improve the overall performance. 

In this case, how can we systematically improve the quality of lesson preparation and delivery to ensure greater student achievement? How do we tap some of those intangible qualities of impactful teachers and make them available to more students?  How do we increase the number of students who can demonstrate they  “learn” the lesson and improve their educational foundation? In essence, how do we create greater value from those rare teachers and their proven techniques?

Within education today, there are a number progressive grass root movements that are beginning to show the way to do just that. 

Many people have heard of the Khan Academy, an evolving multiple discipline digital video curriculum that enables students to interact with a lesson at their own pace, both outside or inside the classroom.   Another fascinating development is the flipped classroom. Here, creative teachers, once again, using digital video technology and the Internet, have developed video based lesson plans that allow the student to educate themselves, at their pace, within the confines of a coordinated schedule.  The student accesses the lesson online while they are at home or at a friend’s house.  The understanding of the lesson is assessed during the classroom hours.

While the students are learning the lesson at home, the teachers will have transformed the classroom time into working sessions.  In this milieu, the students get help with the application of the lesson via “homework” exercises, group discussions, collaborative projects and ultimately ensuring the qualitative understanding of the lesson.  Those that can learn faster learn more.  Those that need help are given assistance. These approaches allow students to absorb the lesson and push themselves towards personal growth instead of being subjected to the fundamental constraining parameter – the amount of teaching time in the class room.  A classroom of 25 to 30 students with differing skills and modes of learning are now free to spend the “appropriate” time to absorb the lesson in much more effective and personalized ways.  This can level the playing field and address the socio-economic achievement gaps within the system.

For more on the recommendation, continue reading to the next blog post: 


Data Science Research Areas Punch List

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added by
Wiki Admin

In Launching this Data Science Research service area, the following are areas of data science research we are actively pursuing.

Solutions 

  • Tactical Industry and Trend Context Reports: Data Visualizations
  • Implementable Changes in Industrial Engineering Practices
  • Linking Research & Commercial Industries
  • Crowd and Commercial Effectivity
  • Place-based and Geospatial business cases and impact levels by data theme, product, and service types
  • Semantic vs. machine learning applications for integrating large Corporate or Government common datasets
  • Real-Time National or World Scale Topological Big Data Modeling and Decision Support 
  • Proving existing major corporate and government datasets, social information data quality and semantic readiness, and existing or new platforms and applications to support Smart Cities in simulation environment such as urban planning, decision making, and policy/rules-based intelligence (aka Real-world SimCity and Civilization models)
  • Remote Sensing Integration with BigData Sources and Analytics
  • New Energy Model Research & Development Repository and Social Network Enhancement
  • Information Patterns & Historical Analysis
  • Integrating Computer and Library Science Techniques
  • Blending Machine Learning and Semantic Web
  • Historical Timeline Visualizations – knowledge, technical evolution
  • Roadmap Prediction Visualizations
  • AI/Robotic Integration with Decision Making
  • Data Supply Chain models analysis in support of creating data ecosystem flow for major static and real-time datasets.
  • Impacts of Next Generation or Internet2 architectures on existing content and dataset

Management

Data Science and Architecture Management Research

  • Integrating Academic GeoScience Communities using Architecture Methods 
  • Investigating how Bill/Policy Motives align with Federal Portfolio
  • Leveraging Architecture concepts to advise and improve bills
  • Real-World Enterprise Architecture analysis
  • Federal readiness for architecture and change management maturity by agency using 
  • Performance Measurement analysis for management and budget policies
  • Reduction and impact evaluation of burden on government agencies for data calls – 
  • Value-measurement on policies and metadata
  • Strategic progression of maturing datasets (i.e. What dataset to build next and butterfly effect?)
  • Realistic blending of private sector and public sector best of breed techniques
  • Historical context analysis for current information management policy and bills for future decisions
  • Analyze policy shaping techniques (i.e. market-driven policy, policy reformation, protectionism policy, new value transition or adoption) diversity by industry.
  • Improving Product Management Subjectivity
  • Agile Project Management
  • Architecture Methodology
  • Data Supply Chain Management efficiency patterns
  • Integrating Geospatial Architectures into Industry
  • Industry Acceleration & Stabilization Evaluations
  • Gaming theory application readiness for Corporate and Government policy and increasing energy and quality output (i.e. MMORPG, Social Network, Strategy games, incentive models, talent/skill development, state of integration such as Mechanical Turk models).

Data Science Research Concepts

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added by
Wiki Admin

Our corporate goal is two-fold:

  • Analyze and assure forward-thinking concepts are applicable
  • Increase our expert staff knowledge base

We first need to assure our concepts and consultants are current, relevant to our partners, clients and industry, and forward thinking; second, allowing for us to excite, retain and increase our talent knowledge base for longer period of time than typical consulting-only firms which allows us to lower personnel costs and maintain lower overhead costs. 

We are actively working and establishing new research partnership academia (i.e. major university research hubs, local STEM programs), government (i.e. Federal Programs, State, Local), municipal services (i.e. water utilities), and engineering/scientific service companies.

In considering the types of innovative data science research that Xentity is seeking to make a transformative impact upon Data Science Solutions Research and Data Science and Architecture Management Research

How do we stay on top of the evolving world of data science

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edited by
Wiki Admin

Our goal is stay involved in understanding the newer approaches, patterns and concepts in business, technology, policy and governance that could help enhance our customers capabilities in our core services based on readiness, maturity, and industry need in data science. 

In Research, we continually re-invest in new models, patterns, and constructs that go back into our designs. We are investigating new data solution architectures and methodologies to keep up with massive, and rapid change.

Our research areas will be in testing early architecture concepts and patterns that are new to client program and organizational adoption as well as research enhancements in management constructs. This could result in adding credibility to newer architectural patterns or analyzing or creating methods, approaches, and maturity for readiness for client adoption in corporate and government environments.

How can integrating data enhance products, management, applications, remote sensing, knowledge building, and culture impacts (positive and negative)? 

We are participating in forward looking academic research looking at the next wave of architecture and management in regards to Data Science

  • Can place-based and geospatial data themes, products and services be demonstrated in business cases to truly impact all areas of business – for earth-based issues to enterprise resources to social science? 

  • What is the right level of progression for metadata programs to enter into improved discovery. What is the investment balance of moving into semantic web models or interpretative signals and machine learning?
  • Do we understand national and industry datasets butterfly effect to guide strategic national or corporate investment in maturing data strategically?
  • How can we accelerate the induction of supply chain management concepts proven for decades in capital investment and industrial engineering into data science and data management?
  • How to take crowdsourcing to next maturity of game theory to not only enhance data, but increase the quality output of data and reducing the energy?

These are some of the topics we are actively and seeking to engage in the study of data science. Our research areas seek to test early architecture concepts, patterns, and management constructs  that are new to client programs and organizational adoption. 

What topics are we interested in?

 

We are actively working and establishing new research partnership academia (i.e. major university research hubs, local STEM pograms), government (i.e. Federal Programs, State, Local), municipal services (i.e. water utilities), and engineering/scientific service companies.

 

In considering the types of innovative data science research that Xentity is seeking to make a transformative impact upon the following captures some of the directions Xentity is investigating. The following are example architecture solution and management research topics and areas for information integration, information service, and analyis and synthesis research include

 

Data Science Solutions Research

 

  • Tactical Industry and Trend Context Reports: Data Visualizations
  • Implementable Changes in Industrial Engineering Practices
  • Linking Research & Commercial Industries
  • Crowd and Commercial Effectivity
  • Place-based and Geospatial business cases and impact levels by data theme, product, and service types
  • Semantic vs. machine learning applications for integrating large Corporate or Government common datasets
  • Proving existing major corporate and government datasets, social information data quality and semantic readiness, and existing or new platforms and applications to support Smart Cities in simulation environment such as urban planning, decision making, and policy/rules-based intelligence (aka Real-world SimCity and Civilization models)
  • Remote Sensing Integration with BigData Sources and Analytics
  • New Energy Model Research & Development Repository and Social Network Enhancement
  • Information Patterns & Historical Analysis
  • Integrating Computer and Library Science Techniques
  • Blending Machine Learning and Semantic Web
  • Historical Timeline Visualizations – knowledge, technical evolution
  • Roadmap Prediction Visualizations
  • AI/Robotic Integration with Decision Making
  • Data Supply Chain models analysis in support of creating data ecosystem flow for major static and real-time datasets.
  • Impacts of Next Generation or Internet2 architectures on existing content and dataset

 

Data Science and Architecture Management Research

 

  • Investigating how Bill/Policy Motives align with Federal Portfolio
  • Leveraging Architecture concepts to advise and improve bills
  • Real-World Enterprise Architecture analysis
  • Federal readiness for architecture and change management maturity by agency using 
  • Performance Measurement analysis for management and budget policies
  • Reduction and impact evaluation of burden on government agencies for data calls – 
  • Value-measurement on policies and metadata
  • Strategic progression of maturing datasets (i.e. What dataset to build next and butterfly effect?)
  • Realistic blending of private sector and public sector best of breed techniques
  • Historical context analysis for current information management policy and bills for future decisions
  • Analyze policy shaping techniques (i.e. market-driven policy, policy reformation, protectionism policy, new value transition or adoption) diversity by industry.
  • Improving Product Management Subjectivity
  • Agile Project Management
  • Architecture Methodology
  • Data Supply Chain Management efficiency patterns
  • Integrating Geospatial Architectures into Industry
  • Industry Acceleration & Stabilization Evaluations
  • Gaming theory application readiness for Corporate and Government policy and increasing energy and quality output (i.e. MMORPG, Social Network, Strategy games, incentive models, talent/skill development, state of integration such as Mechanical Turk models).



Xentity Awarded GSA Schedule

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edited by
Wiki Admin

Today (Sept. 12, 2012), Xentity received a multi-year GSA Federal Supply Schedule contract. Under the GSA Consolidate Schedule 00CORP), Xentity has been awarded Special Item Numbers (SINs) for both IT Schedule 70 and MOBIS.

ScheduleSIN(s)Description
IT Schedule 70 C132-51, C132-51RCIT Professional Services
MOBISC874-1, C874-1RCMOBIS Consulting Services

This will enable Xentity to continue its progression towards providing best available pricing for its Government customers as this helps simplify acquisition even further on top of the 8(a) business development program acquisition tools. The following is a brief description of services. Links to the schedules can be found on the Schedules page.

Description of Services

SIN C132-51 and C132-51RC – IT Professional Services

Xentity’s services below focus on enterprise architecture, management and communication lifecycle challenges for large organizations for private and public sector. We operate in extending the current leadership and management capabilities to offer out innovative architecture, design, and change approaches and solutions that help accelerate and increase quality and relevance of the project, program, management, governance, and implementation.  Xentity’s IT Professional Services include the following:

  • Mission or resource system solution architecture design
  • Physical component/server architecture model design
  • Physical and logical data modeling
  • As-is architecture pattern analysis
  • System inventories
  • Technology inventory analysis
  • Segment, enterprise, or technology analysis of business or mission areas
  • Analysis of technical business models as-is, to-be, and transition stages for maturity, common trends, precedence and potential pain points
SIN C874-1 and C874-1RC – MOBIS Consulting Services

In addition to Xentity’s enterprise services, our consultants have subject matter expertise in applying these services to issues critical to large world transformation such as energy, climate, geospatial, natural resources, travel, and education. We also invest in researching and piloting our patterns to allow us to prove our concepts and strengthen the specific business cases. This allows us to provide re-usable approaches, innovative concepts and solution patterns applicable to customer specific industries.  Xentity’s MOBIS services include the following:

  • Business case financial, performance, and technology scenarios modeling
  • Analysis of requirements, governance, business process, service portfolio, and stakeholder input
  • Target architecture, new business models, and governance maturity transformation efforts Analysis and design of financial, performance, and organizational models
  • Organizational readiness assessments
  • Findings, recommendations, alternative, cost, risk, impact, and performance analysis using structured approved methods
  • Project path proposal and definition including providing frameworks for jobs, teams, functions and groups of functions