KM in Education

Sharing instruction through curriculum mapping

While speaking with a charter school start-up today, I learned of their recent selection of a curriculum mapping tool to enable collaboration and sharing of instruction planning and assessment among teachers and administration. I decided to learn more about curriculum mapping and have summarized below what I found.

Curriculum mapping appears to have grown out of the pioneering ideas in Heidi Hayes-Jacobs' book, Mapping the Big Picture: Integrating Curriculum and Assessment K–12. In her first chaper, she writes:

With so little real-time data available, we find two polarized tendencies. One is to become rigid and lock step with curriculum guides, giving the impression that all is under control. The second is to become so loose and vague that no one seems to have a clue as to what is going on.

To make sense of our students' experiences over time, we need two lenses: a zoom lens into this year's curriculum for a particular grade and a wide-angle lens to see the K–12 perspective. The classroom (or micro) level is dependent on the site and district level (a macro view)....We need to change the process used in making curriculum decisions because most curriculum committees are ineffective at actually producing work that directly affects student performance.

What Dr. Jacobs proposes is a method that transforms curriculum planning into a vehicle for communication, collaboration and ongoing dynamic review of instructional planning. She also introduces the novel approach of taking a student's perspective in looking at the curriculum holistically and longitudinally to identify redundancies or gaps.

The first two chapters of Dr. Jacobs book provide a good overview of the process and reasons behind curriculum mapping. For a good primer on curriculum maps and different models, consult Least Tern's presentation.

The market for curriculum mapping is fast evolving, as many sites list outdated names and links. The major players all make reference to Dr. Jacobs' methodology as the basis for their design.

Among the more recognized curriculum mapping tools, I found:
AEL's Curriculum Creator - Curriculum Mapping & Design Tool
Atlas Curriculum Management System
Techpaths
WestJam's Curriculum Mapper

Additional players include:
Scantron's Curriculum Designer
Schoolution's MyLessonBuilder and CurrMapper

Beyond these vendors, many organizations such as NCREL and individuals offer consulting services for curriculum mapping.

Dr. Jacobs' site for curriculum design offers further resources.

Embedding knowledge

Even with the capacity and ability to organize its knowledge, an organization does not ensure its ability to effectively leverage and apply its knowledge.

As I have been helping an Oakland middle school redesign its afterschool program, it has become clear that communication is a key element to be improved. Teachers and afterschool tutors both hold critical knowledge about students, yet this knowledge is not effectively shared between the two groups. By using different tutoring approaches and knowledge capture strategies, tutors can organize knowledge so it is more accessible for teachers. Yet it still remains useless unless teachers take the time to access it and use the knowledge.

We recognize that it is unlikely that busy teachers are going to proactively check for new knowledge, whether with the tutor (tacit) or from documentation (explicit). It would become one more task in their already bulging days. However, teachers do always check their mail or in-boxes, and it is a relatively simple matter to embed critical afterschool tutor knowledge in that daily check-in. Accessing this new knowledge is now a natural extension of a teachers' habits, and the teacher is much more likely to act upon the knowledge s/he has received.

Organizing knowledge - Part III

Taxonomies and meta data are simply tools for organizing both tacit and explicit knowledge to make them more accessible.

A Rolodex represents a low-tech version of accessing tacit knowledge. An online example would be the Faculty Index at Stanford's School of Education. Research Interest has been used as one type of meta data, and all the different Research Interests make up the basic taxonomy. Because the Stanford faculty agreed on a common term for each Research Interest, anyone who wants to locate all the Stanford faculty associated with a specific interest can do so very easily given this knowledge organization method.

Examples of explicit knowledge organization abound. However, organizational approaches still remain rather simple. The GEM website uses basic metadata such as subject, type, level, and keywords. Clicking on Browse in the navigation box allows users to view the simple taxonomy associated with the meta data.

By establishing base metadata and taxonomies, organizations reap the benefits of managing knowledge. Internal employees can both share and locate relevant knowledge easily. Sharing across organizations, however, becomes more challenging as the knowledge organization tools are often defined differently from one to another. In the education sector, it is the definition of standards by organizations such as GEM, that will enable greater sharing of knowledge among educators, schools and districts.

Organizing knowledge - Part II

Meta data, as defined in Part I, are essentially the building blocks for knowledge organization. To use meta data effectively, organizations must create a knowledge map to ensure everyone is using the same terminology.

A knowledge map is often represented by a taxonomy. The word taxonomy has Greek origins, deriving from 'taxis' meaning arrangement and 'onoma' meaning name. Taxonomies facilitate the structuring and labeling of meta data. Returning to our organizational meta data example in the previous posting, an organizational taxonomy would define the Cook County School District, both by its hierarchial structure, as well as its names. The taxonomy would define the Human Resources Department as well as the Classroom Services Division and the six other divisions it oversees.

Establishing common taxonomies in existing organizations can frequently become messy political battles. Implicit relationships and process ownership become explicit in the definition of taxonomies. For political reasons, what may often make sense logically, that professional development is planned by the Human Resources Department, may sometimes not be the case. For this reason, many organizations define two taxonomies - a hierarchical taxonomy based on departmental structure of the organization and a functional taxonomy defining the key functions of the organization.

Organizing knowledge - Part I

Whether knowledge is explicit or implicit, to easily access it, we must be able to identify where it is located. To effectively organize their internal knowledge, institutions are increasingly relying on meta data, that is, information that describes knowledge. Meta data helps organizations classify and understand their knowledge, so they can make better decisions.

Meta data spans many types, of which we will explore three in this posting.

Organizational Meta Data
This meta data captures the function or part of an organization under which knowledge is created. For example, if for Cook County School District defines a new operating procedure, the applicable organizational meta data might describe the relevant function as Human Resources, the sub-function as Hiring, and the relevant locations as High Schools. This meta data defines the relationship of the knowledge to the organization, enabling easier searching and retrieving based on the organizational function. We inherently do this with organizational charts, enabling easier identification of people and implicitly, their tacit knowledge.

Knowledge Accountability Meta Data
The people associated with knowledge creation and revision are captured with this type of meta data. For the same example of a hiring procedure, a Knowledge Owner would perhaps be the HR Director, since she would be accountable for any changes. The Knowledge Approver could be an HR manager who has to review the procedure before it is distributed. Finally, the Knowledge Creator and Updater may be an HR analyst who both originates and maintains the procedure. Additionally, dates associated with each person may also describe the procedure. For example, a hiring procedure may have dates recording its creation, its last review, and its last update. Knowing all the people who 'touch' a piece of knowledge and the associated dates helps organizations relate their explicit and implicit knowledge to each other, to easily find one using another, and keep track of the recency of their knowledge.

Knowledge Characteristics Meta Data
Every piece of knowledge can be qualitatively described by its unique characteristics. For example, the hiring knowledge from above would have a name, Hiring College Guidance Counselors. It also represents a type of knowledge, in this case, a Procedure. Other knowledge types might include Forms, Lesson Plans, or Programs. The format (e.g., Word Document), purpose, audience and key words would be additional possible knowledge characterics to be used as meta data. This meta data is perhaps one of the most commonly used, because the qualitative characteristics of knowledge often serve as the base for online searches typically yielding poor results. Without specific relationships tied to the organization's people and functions or processes, knowledge is more easily lost within the organization.

Defining knowledge - Part II

With a better understanding of what knowledge is, we can now define the different forms of knowledge.

Explicit knowledge, is often found in a tangible format. It is what we capture in books, newspapers, lecture notes, and electronic files. A researcher records his interpretation of a lab experiment into a notebook, transforming it into explicit knowledge. Organizations use filing systems to organize and search mechanisms like Google to then locate this type of knowledge. Many organizations, especially education organizations, face the constant challenge of effectively categorizing and updating this form of knowledge, so the latest versions are always easily accessible.

Implicit, also known as tacit, knowledge resides in people's heads and is not easily written down. It is what we learn through cultural immersion, storytelling, and personal experience. Teachers often draw the most valuable lessons in training programs from real-life classroom stories of peers or mentors. Accessing these experiences becomes more difficult outside of the school context as few formal networks and processes successfully tap into tacit knowledge.

Defining knowledge - Part I

Knowledge is a term with which we are all familiar. Yet how easily can we differentiate knowledge from data, information, understanding or wisdom?

Merriam-Webster provides one definition of knowledge as:

the fact or condition of knowing something with familiarity gained through experience or association.

That roughly corresponds with the definition proposed by the renowned systems thinker. Dr. Russell Ackoff, who is credited with defining knowledge within a hierarchy. Below, I explain the hierarchical terms within an educational context.

Data - raw quantitative or qualitative facts with no inherent meaning. Imagine opening a document listing all of a school's student test scores. Data is composed of the many numbers, codes, and names that have no immediate relevance.

Information - data that has been filtered or processed to represent a relationship. For instance, taking the same example of a document of student test scores, we can narrow the field of vision to the math scores of a specific student, Mark. We have now gathered information, essentially test scores that we find meaningful because of its relationship to Mark and math.

Knowledge - information that is given context based on patterns and/or experience. From Mark's math test scores, his math teacher may observe that his math performance significantly improves during the first week of every month. The math teacher 's knowledge resulted from placing Mark's test information in the context of a pattern.

Understanding - synthesizing knowledge to arrive at the underlying reason for a pattern. Mark's mother, after a parent-teacher conference, realizes that Mark's math performance improvements are timed with his grandfather's monthly visits when he tutors Mark in math. By linking her knowledge of the visits with Mark's testing pattern, Mark's mother has gained an understanding for Mark's monthly math improvement spurts.

Wisdom - evaluating understanding to determine underlying principles or processes. Understanding why Mark's grandfather's visits help Mark's math performance requires wisdom to discern whether Mark is influenced by the additional adult attention, his grandfather's presence, or even the books that Mark's grandfather brings along with him. Wisdom enables decisions for the future. In Mark's case, wisdom would enable his mother or teacher to make better decisions to help him consistently improve his math.

Data-driven decision-making

The amount of data that U.S. businesses collect on each customer has grown enormously over the last decade. It probably helps explain the continued consumer spending, as smart businesses target the most promising customer segment with attractive products and services based on their accumulated knowledge of their customers' life events, daily activities, income, and household habits.

The same trend is in its early stages in U.S. education. School districts are heavily investing in the technological capabilities to aggregate student data. With more data on students' backgrounds, academic performance, and personal development, as well as curriculum and teacher effectiveness, schools will be better equipped to make more informed decisions about how to best help their students, teachers and staff. One day, schools should be able to improve their performance simply because their increased knowledge capabilities will help them identify where and how they can most help each individual student.

On feedback

Depsite the controversy surrounding the No Child Left Behind Act, it should be applauded for creating a feedback loop that did not exist before. Evalution of overall student performance inherently provides feedback on the quality of a school's curriculum and the effectiveness of its teachers, enabling schools to improve their performance by better understanding the strengths and weaknesses in their offerings

It seems that in different areas of the education, feedback has often been neglected. Whether it is education schools surveying their graduates on the relative effectiveness of lessons applied in the field or principals evaluated by their staff and parents, the lack of feedback makes continuous improvement much more difficult. Closing that loop in every process involving teachers, schools, and students will help the education system as a whole make greater and more effective improvements.

A KM Field of Dreams - If we build it, will they come?

KM is essentially about getting the right knowledge to the right people in the right place at the right time.

Unfortunately, organizations frequently launch KM initiatives because 'if we build it, they will come.' Successful initiatives, however, embed knowledge-sharing directly into their processes and culture; they do not rely simply on repositories of information. One of the best KM success stories took place at British Petroleum where critical technical knowledge was quickly accessed by remote teams to rapidly resolve costly problems. BP invested heavily in changing their processes, people's behavior and technologies to ensure that the right knowledge could get to these teams in remote locations when they needed it. Without any of these elements, they would not have achieved the same tremendous performance and cost improvements.

In education, KM addresses the same challenge of getting the right knowledge to the right people in the right place at the right time. However, the rewards of success are far more compelling. Imagine how many children would be positively impacted by a new teacher receiving the critical knowledge to help him overcome a specific classroom problem. Consider how many kids would benefit from a school or district better managing its finances, so its administrators and educators can focus less on dollars and jobs and more on the quality of instruction. The responsibility of KM practitioners in education is not about simply building repositories of best practices, but to work with educators and schools to identify where and when they need critical knowledge and how best to get that knowledge to them in the easiest, fastest, and probably cheapest way possible.