Performance Analysis (PA)

http://www.josseybass.com/legacy/rossett/rossett/what_is_pa.htm

What is Performance Analysis?

A serious definition goes like this:

Performance analysis involves gathering formal and informal data to help customers and sponsors define and achieve their goals. Performance analysis uncovers several perspectives on a problem or opportunity, determining any and all drivers towards or barriers to successful performance, and proposing a solution system based on what is discovered.

A lighter definition is:

Performance analysis is the front end of the front end. It's what we do to figure out what to do. Some synonyms are planning, scoping, auditing, and diagnostics.

What does a performance analyst do?

Here's a list of some of the things you maybe doing as part of a performance analysis:

Interviewing a sponsor

Reading the annual report

Chatting at lunch with a group of customer service representatives

Reading the organization's policy on customer service, focusing particularly on the recognition and incentive aspects

Listening to audiotapes associates with customer service complaints

Leading a focus group with supervisors

Interviewing some randomly drawn representatives

Reviewing the call log

Reading an article in a professional journal on the subject of customer service performance improvement

Chatting at the supermarket with somebody who is a customer, who wants to tell you about her experience with customer service

A performance analysis identifies where we are, where we want to go, the reasons why we aren't there yet, and recommends ways to get there.

According to Wendy Clash an Educational Technologist at San Diego State University
Department of Educational Technology offers this information.

After determining the cause(s) of a performance problem, you recommend solution systems.

Cause:	Solution:
Motivation	information, documentation, coaching.
Environment Support	flexibility with schedule and job duties, availability of tools, reorganization of workplace.
Organization Support	incentive programs and appraisal process, change in policy
Knowledge and Skills	instruction, coaching, job aids, and electronic support systems.

Solution systems are easy to implement when they require minor changes to company policy and/or the working environment.

When delivery of information or more significant changes are needed, it's time to call in the experts.

Knowledge Engineering

http://www.epistemics.co.uk/Notes/61-0-0.htm

Knowledge engineering is a field within artificial intelligence that develops knowledge-based systems. Such systems are computer programs that contain large amounts of knowledge, rules and reasoning mechanisms to provide solutions to real-world problems.

A major form of knowledge-based system is an expert system, one designed to emulate the reasoning processes of an expert practitioner (i.e. one having performed in a professional role for very many years). Typical examples of expert systems include diagnosis of bacterial infections, advice on mineral exploration and assessment of electronic circuit designs.
Importance of Knowledge Acquisition

The early years of knowledge engineering were dogged by problems. Knowledge engineers found that acquiring enough high-quality knowledge to build a robust and useful system was a very long and expensive activity. As such, knowledge acquisition was identified as the bottleneck in building an expert system. This led to knowledge acquisition becoming a major research field within knowledge engineering.

The aim of knowledge acquisition is to develop methods and tools that make the arduous task of capturing and validating an expert’s knowledge as efficient and effective as possible. Experts tend to be important and busy people; hence it is vital that the methods used minimize the time each expert spends off the job taking part in knowledge acquisition sessions.
Knowledge Engineering Principles

Since the mid-1980s, knowledge engineers have developed a number of principles, methods and tools that have considerably improved the process of knowledge acquisition. Some of the key principles are summarized as follows:

* Knowledge engineers acknowledge that there are different types of knowledge, and that the right approach and technique should be used for the knowledge required.
* Knowledge engineers acknowledge that there are different types of experts and expertise, such that methods should be chosen appropriately.
* Knowledge engineers recognize that there are different ways of representing knowledge, which can aid the acquisition, validation and re-use of knowledge.
* Knowledge engineers recognize that there are different ways of using knowledge, so that the acquisition process can be guided by the project aims.
* Knowledge engineers use structured methods to increase the efficiency of the acquisition process


Knowledge engineering (KE) was defined in 1983 by Edward Feigenbaum, and Pamela McCorduck as follows:

KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.[1]

At present, it refers to the building, maintaining and development of knowledge-based systems.[2] It has a great deal in common with software engineering, and is used in many computer science domains such as artificial intelligence [3], [4], including databases, data mining, expert systems, decision support systems and geographic information systems. Knowledge engineering is also related to mathematical logic, as well as strongly involved in cognitive science and socio-cognitive engineering where the knowledge is produced by socio-cognitive aggregates (mainly humans) and is structured according to our understanding of how human reasoning and logic works.

Various activities of KE specific for the development of a knowledge-based system:

* Assessment of the problem
* Development of a knowledge-based system shell/structure
* Acquisition and structuring of the related information, knowledge and specific preferences (IPK model)
* Implementation of the structured knowledge into knowledge bases
* Testing and validation of the inserted knowledge
* Integration and maintenance of the system
* Revision and evaluation of the system.

Being still more art than engineering, KE is not as neat as the above list in practice. The phases overlap, the process might be iterative, and many challenges could appear. Recently, emerges meta-knowledge engineering[5] as a new formal systemic approach to the development of a unified knowledge and intelligence theory.


http://en.wikipedia.org/wiki/Knowledge_engineers


Knowledge engineers are computer systems experts who are trained in the field of expert systems. Receiving information from domain experts, the knowledge engineers interpret the presented information and relay it to computer programmers who code the information in to systems databases to be accessed by end-users. Knowledge engineers are used primarily in the construction process of computer systems (Bultman, Kuipers & van Harmelen 2000).

Using information relayed by the domain experts, knowledge engineers are experts at constructing meaningful, useful, and simplistic Knowledge-Based Systems (KBS). Often knowledge engineers are employed to break down the information passed on by domain experts into more simplistic terms which cannot be easily communicated by the highly technalized domain expert (ESDG 2000).

SCORM-compliant courseware

What is SCORM?
Wikipedia has this definition. Sharable Content Object Reference Model (SCORM) is a collection of standards and specifications for web-based e-learning. It defines communications between client side content and a host system called the run-time environment (commonly a function of a learning management system). SCORM also defines how content may be packaged into a transferable ZIP file.

SCORM is a specification of the Advanced Distributed Learning (ADL) Initiative, which comes out of the Office of the United States Secretary of Defense.

SCORM 2004 introduces a complex idea called sequencing, which is a set of rules that specifies the order in which a learner may experience content objects. In simple terms, they constrain a learner to a fixed set of paths through the training material, permit the learner to "bookmark" their progress when taking breaks, and assure the acceptability of test scores achieved by the learner. The standard uses XML, and it is based on the results of work done by AICC, IMS Global, IEEE, and Ariadn.

I like to example given by Rustici Software on the website scorm.com. They explain it is terms of DVD's. Regardless to the type of player the you own, you expect for any of the movies that work in that player despite who made it. This is true because to standards that established in that industry.

SCORM stands for “Sharable Content Object Reference Model”.
“Sharable Content Object” indicates that SCORM is all about creating units of online training material that can be shared across systems. SCORM defines how to create “sharable content objects” or “SCOs” that can be reused in different systems and contexts.

“Reference Model” reflects the fact that SCORM isn’t actually a standard. ADL didn’t write SCORM from the ground up. Instead, they noticed that the industry already had many standards that solved part of the problem. SCORM simply references these existing standards and tells developers how to properly use them together.


SCORM is produced by ADL, a research group sponsored by the United States Department of Defense (DoD).

For information on SCORM, check out this sites.
www.toolbook.com/community_scorm.php
www.scorm.com/scorm_explained

Electronic Performance Support Systems

The Encyclopedia of Educational Technology defines EPSS ase an extension and further development of the workplace job aid. As described by Rossett & Gautier-Downes (1991), job aids support work and activity, are external to the individual and have three discrete functions, which are:

1. providing information,
2. supporting procedures, and
3. guiding decision making.

When these three functions of job aids interconnect within an integrated technology-based system, a “highly sophisticated technological job aid” called an electronic performance support system results (Van Tiem, Moseley, & Dessinger, 2004, p. 71). Characteristic advantages of an EPSS over a traditional non-technology based job aid include:

* user ability to quickly access large quantities of information,
* support for multiple users anywhere at any time the delivery technology is available, and
* user ability to receive interactive coaching and guidance.

Electronic Performance Support Systems

Gloria J. Gery, a consultant in the fields of business learning and electronic performance support, is the author of "Electronic Performance Support Systems" (Gery Performance Press, 1991), the seminal book on EPSS.

Gloria Gery published a book in 1991 that defines electronic performance support systems (EPSS) as
"an integrated electronic environment that is available to and easily accessible by each employee and is structured to provide immediate, individualized on-line access to the full range of information, software, guidance, advice and assistance, data, images, tools, and assessment and monitoring systems to permit job performance with minimal support and intervention by others."

Every article that I researched start with Gery's definition. She is concerned the expert of EPSS.

Also in 1991, Barry Raybould gave a shorter definition:

a computer-based system that improves worker productivity by providing on-the-job access to integrated information, advice, and learning experiences.

According to an article that I found entitled, Types of Electronic Performance Support Systems:Their Characteristics and Range of Designs by Deborah Alpert Sleight in 1993 who is an Educational Psychologist at Michigan State University, types are some common uses for EPSS.

An electronic performance support system (EPSS) displays some or all of the following characteristics.

Computer-based: EPSSs are computer-based, which is what the "electronic" in their name indicates. There have been older attempts at performance support systems, such as a series of manuals, job aids, and other paper material. But it wasn't until the advent of powerful multimedia computers that optimal performance support could be made possible. Optimal support includes quick and easy access to the information needed at the time the task is being performed.

Access during task: EPSSs provide access to the discrete, specific information needed to perform a task at the time the task is to be performed. This is a two-part characteristic: 1) access to the specific information needed to perform a task, and 2) access to the information at the time the task is to be performed. If one part of this characteristic does not exist, then the characteristic changes and is no longer a performance support characteristic. The discrete, specific information provided may be:

* data: the type of data may be textual or numeric, such as prices, locations, and names. Or they may be visual, such as photographs and motion video footage. Or they may be audio, such as conversations, speeches, and music.
* instruction: the instruction may be a list of steps to take, a motion video showing a procedure, or a simulation of a task that allows the user to practice.
* advice: the advice may be an expert system that asks the user questions, then suggests the most appropriate procedure or step to do next.
* tools: the software tools may be a spreadsheet, a statistical analysis package, and a program that controls industrial robots.

This availability of information, instruction, advice and tools makes much prior training unnecessary.

This makes EPSS versatile and user adaptive.

She states that the benefits of EPSS are:
Used on the job: An EPSS provides information to people at their workstation on the job, or in simulations or other practice of the job. The information is provided at the worker's workstation as the worker sees a need for it. The EPSS can be used in simulations or other practice of the job, so that the worker learns both the information he or she will probably need when doing the job, and how to use the EPSS itself.

Controlled by the worker: The worker decides when and what information is needed. There is no need for a teacher, as the worker is guided by the needs of the task. The motivation is provided by the worker's desire to accomplish the task.

Reduce the need for prior training: The easy availability of the information needed to perform a task reduces the need for much (but probably not all) prior training in order to accomplish the task.

Easily updated: The very nature of an EPSS, that it provides the information needed to perform a task, requires that it be easily updatable, in order to keep the information that it provides current. The computerized nature of an EPSS makes updating faster and easier in some ways than in other media, such as print, video, or audio.

Fast access to information: The user must be able to access the needed information quickly when it is needed on the job. Otherwise the EPSS is no better than a set of manuals, which probably contain the information, but the information is difficult to find when needed.

Irrelevant information not included: The user is able to access only the specific, discrete information needed at that instant, instead of having to wade through loads of irrelevant information to find the few details needed. This is one of the problems with instruction that is not specific to a task; it forces the user to sift through it looking for the details needed. This sifting not only slows the user down, but can result in confusion.

Allow for different levels of knowledge in users: In order to speed up information access and understanding, an EPSS can provide minimal information for those who do not want details, yet, through the hypertext links in the databases and through optional tutorials, provide detail for those who do want more.

Allow for different learning styles: Through multimedia, an EPSS can accommodate users with varied learning styles, thus providing more optimal learning. The same information can be presented in visual, textual, and audio formats, with the user selecting the format.

Integrate information, advice, and learning experiences: An EPSS can integrate information, advice, and learning experiences for the user. For example, a database entry might describe a procedure. The user may not know if the procedure is the proper one to use, so he or she might turn to the advisor to find out. The advisor would ask the user some questions about what he or she needs to accomplish, then would suggest which procedure to use. The user might then access a tutorial on using the procedure, and practice it through a simulation, before actually performing the procedure.

Artificial intelligence: Artificial intelligence is an essential characteristic of EPSSs, according to Carr (Carr, 1992), but not according to Gery. I think that at this early stage of performance support system design and use, AI is not essential, but that eventually it will be one of the defining characteristics of EPSS. This will happen when research on EPSS and on AI has progressed further.

An EPSS is not an absolute system that contains all these characteristics. Rather, different systems will fall on a continuum of these characteristics. An EPSS displaying all these characteristics would be the ideal. Since performance support systems are still young, it is more likely that many will display only the key characteristics.

Learning management System

Being new to all of the terms, I goggled LMS. Wikipedia defines LMS as is software for delivering, tracking and managing training/education. LMSs range from systems for managing training/educational records to software for distributing courses over the Internet and offering features for online collaboration. In many instances, corporate training departments purchase LMSs to automate record-keeping as well as the registration of employees for classroom and online courses.
I found in my searching the BlackBoard is a LMS offered by the Dell corporation. WOW! Something that I could visibly see.

This is Dell description of BlackBoard on Dell.com. "Blackboard is software for delivering, tracking and managing training/education. LMSs range from systems for managing training/educational records to software for distributing courses over the Internet and offering features for online collaboration. In many instances, corporate training departments purchase LMSs to automate record-keeping as well as the registration of employees for classroom and online courses".

LMS allow educational organization the ability to modified and republish single courses to various audiences while maintaining different versions and history. The objects stored in the centralized repository can be made available to course developer and content experts throughout and organization for potential reuse and re purpose. The eliminates duplicate development effort and allow for the rapid assemble of customized content which is very cost-effective.


I do not know all the capabilities of Blackboard due to limited usages, but I find it to be okay. I really do not have anything to compare it to. It has met all of my educational needs.

According to Renee Greene of ehow.com, here are some to the cons with LMS.Learning does not happen in a vacuum,~~ according to Godfrey Parkin, a specialist in online strategy and marketing innovation. "...an LMS, as available today, is not a universal solution for a corporation's e-learning problems. In fact, an LMS is often the albatross around the neck of progress in technology-enhanced learning." He believes that the vendors of these software systems remove control from end-users--the instructors and learners. In an exaggerated effort to "do it all," LMS appears to cause more confusion than it resolves. Centralized learning, Parkin says, limit options in that they are more focused on tools and the warm bodies who use them than the needs of the instructor to maintain flexibility in his teaching methods.
I agree.
All though there are some cons, the positives of LMS are tremendous. According to an article that I found on a website called NetDimension, these just to few of the pros to using a LMS and their product which is Enterprise Knowledge Platform (EKP).

A learning management system is now used not only to implement an organization's learning development but also to measure and report on training delivery, ensure employee compliance, automate reporting and tracking, as well as deliver assessment and testing.

Whether your organization is starting a departmental pilot or rolling-out a global installation, NetDimensions' Enterprise Knowledge Platform (EKP) learning management system, is secure, reliable, easy to use and quick to implement.
EKP enables you to manage:

* Performance appraisals
* Training programs
* License and certification requirements
* Competencies
* Compliance initiatives
* Succession planning

NetDimensions' learning management system will help you:

* Improve the efficiency of compliance programs.
* Increase knowledge retention and make learning more efficient.
* Measure the effectiveness of training.
* Identify skills and competencies.
* Decrease operational and travel costs.



EKP is a powerful, multilingual learning management system that manages the entire training and development process from delivering and tracking to testing and reporting.
EKP is optimized for:

* Reporting & Tracking
* Assessments, compliance procedures and licensing
* Managing employee competencies and performance
* Centralizing global knowledge
* Transforming the learning process
* Managing information – Integrate & Disseminate
* Enhancing the front-end learning experience (EKP Portal Toolkit)
* e-Commerce Management

EKP Features List:

* Automated reporting and tracking
* Standardized procedures
* Centralized global knowledge into one system
* Increased organizational interaction and exchange of knowledge
* Tracked regulatory compliance
* Access, monitor, review and update materials anytime, anywhere 24/7
* Integrated management of your entire training process
* Supports 30+ languages

Change Management

I was unfamiliar with the term change management in respect to instructional technology. I want to find a simple explanation for this terminology.

According to website wikipedia, change management is an IT Service Management discipline. The objective of Change Management in this context is to ensure that standardized methods and procedures are used for efficient and prompt handling of all changes to controlled IT infrastructure, in order to minimize the number and impact of any related incidents upon service. Changes in the IT infrastructure may arise reactively in response to problems or externally imposed requirements, e.g. legislative changes, or proactively from seeking imposed efficiency and effectiveness or to enable or reflect business initiatives, or from programs, projects or service improvement initiatives. Change Management can ensure standardized methods, processes and procedures are used for all changes, facilitate efficient and prompt handling of all changes, and maintain the proper balance between the need for change and the potential detrimental impact of changes.

I have find the article explaining ten principles of change management.

10 Principles of Change Management
By John Jones, DeAnne Aguirre, and Matthew Calderone

4/15/04 Tools and techniques to help companies transform quickly.

In this article, the authors discuss how in 2004 senior executives in large companies had a simple goal for themselves and their organizations: stability. This stability would give their shareholders the safe, more than predictable earnings growth. Five years later, during the recession, that is still have very prevalent concern.

This presents most senior executives with an unfamiliar challenge. In major transformations of large enterprises, they and their advisors conventionally focus their attention on devising the best strategic and tactical plans. But to succeed, they also must have an intimate understanding of the human side of change management — the alignment of the company’s culture, values, people, and behaviors — to encourage the desired results. Plans themselves do not capture value; value is realized only through the sustained, collective actions of the thousands — perhaps the tens of thousands — of employees who are responsible for designing, executing, and living with the changed environment.

Long-term structural transformation has four characteristics:

• scale (the change affects all or most of the organization),

• magnitude (it involves significant alterations of the status quo),

• duration (it lasts for months, if not years), and

• strategic importance.

Yet companies will reap the rewards only when change occurs at the level of the individual employee.

No single methodology fits every company, but there is a set of practices, tools, and techniques that can be adapted to a variety of situations. What follows is a “Top 10” list of guiding principles for change management. Using these as a systematic, comprehensive framework, executives can understand what to expect, how to manage their own personal change, and how to engage the entire organization in the process.

1. Address the “human side” systematically.

• Any significant transformation creates “people issues.” New leaders will be asked to step up, jobs will be changed, new skills and capabilities must be developed, and employees will be uncertain and resistant. Dealing with these issues on a reactive, case-by-case basis puts speed, morale, and results at risk.
  

• A formal approach for managing change — beginning with the leadership team and then engaging key stakeholders and leaders — should be developed early, and adapted often as change moves through the organization.
  

• This demands as much data collection and analysis, planning, and implementation discipline as does a redesign of strategy, systems, or processes.
  

• The change-management approach should be fully integrated into program design and decision making, both informing and enabling strategic direction. It should be based on a realistic assessment of the organization’s history, readiness, and capacity to change.
  

2. Start at the top. Because change is inherently unsettling for people at all levels of an organization, when it is on the horizon, all eyes will turn to the CEO and the leadership team for strength, support, and direction. The leaders themselves must embrace the new approaches first, both to challenge and to motivate the rest of the institution. They must speak with one voice and model the desired behaviors. The executive team also needs to understand that, although its public face may be one of unity, it, too, is composed of individuals who are going through stressful times and need to be supported.

Executive teams that work well together are best positioned for success. They are aligned and committed to the direction of change, understand the culture and behaviors the changes intend to introduce, and can model those changes themselves. At one large transportation company, the senior team rolled out an initiative to improve the efficiency and performance of its corporate and field staff before addressing change issues at the officer level. The initiative realized initial cost savings but stalled as employees began to question the leadership team’s vision and commitment. Only after the leadership team went through the process of aligning and committing to the change initiative was the work force able to deliver downstream results.

3. Involve every layer. As transformation programs progress from defining strategy and setting targets to design and implementation, they affect different levels of the organization. Change efforts must include plans for identifying leaders throughout the company and pushing responsibility for design and implementation down, so that change “cascades” through the organization. At each layer of the organization, the leaders who are identified and trained must be aligned to the company’s vision, equipped to execute their specific mission, and motivated to make change happen.

A major multiline insurer with consistently flat earnings decided to change performance and behavior in preparation for going public. The company followed this “cascading leadership” methodology, training and supporting teams at each stage. First, 10 officers set the strategy, vision, and targets. Next, more than 60 senior executives and managers designed the core of the change initiative. Then 500 leaders from the field drove implementation. The structure remained in place throughout the change program, which doubled the company’s earnings far ahead of schedule. This approach is also a superb way for a company to identify its next generation of leadership.

4. Make the formal case. Individuals are inherently rational and will question to what extent change is needed, whether the company is headed in the right direction, and whether they want to commit personally to making change happen. They will look to the leadership for answers. The articulation of a formal case for change and the creation of a written vision statement are invaluable opportunities to create or compel leadership-team alignment.

Three steps should be followed in developing the case: First, confront reality and articulate a convincing need for change. Second, demonstrate faith that the company has a viable future and the leadership to get there. Finally, provide a road map to guide behavior and decision making. Leaders must then customize this message for various internal audiences, describing the pending change in terms that matter to the individuals.

A consumer packaged-goods company experiencing years of steadily declining earnings determined that it needed to significantly restructure its operations — instituting, among other things, a 30 percent work force reduction — to remain competitive. In a series of offsite meetings, the executive team built a brutally honest business case that downsizing was the only way to keep the business viable, and drew on the company’s proud heritage to craft a compelling vision to lead the company forward. By confronting reality and helping employees understand the necessity for change, leaders were able to motivate the organization to follow the new direction in the midst of the largest downsizing in the company’s history. Instead of being shell-shocked and demoralized, those who stayed felt a renewed resolve to help the enterprise advance.

5. Create ownership. Leaders of large change programs must overperform during the transformation and be the zealots who create a critical mass among the work force in favor of change. This requires more than mere buy-in or passive agreement that the direction of change is acceptable. It demands ownership by leaders willing to accept responsibility for making change happen in all of the areas they influence or control. Ownership is often best created by involving people in identifying problems and crafting solutions. It is reinforced by incentives and rewards. These can be tangible (for example, financial compensation) or psychological (for example, camaraderie and a sense of shared destiny).

At a large health-care organization that was moving to a shared-services model for administrative support, the first department to create detailed designs for the new organization was human resources. Its personnel worked with advisors in cross-functional teams for more than six months. But as the designs were being finalized, top departmental executives began to resist the move to implementation. While agreeing that the work was top-notch, the executives realized they hadn’t invested enough individual time in the design process to feel the ownership required to begin implementation. On the basis of their feedback, the process was modified to include a “deep dive.” The departmental executives worked with the design teams to learn more, and get further exposure to changes that would occur. This was the turning point; the transition then happened quickly. It also created a forum for top executives to work as a team, creating a sense of alignment and unity that the group hadn’t felt before.

6. Communicate the message. Too often, change leaders make the mistake of believing that others understand the issues, feel the need to change, and see the new direction as clearly as they do. The best change programs reinforce core messages through regular, timely advice that is both inspirational and practicable. Communications flow in from the bottom and out from the top, and are targeted to provide employees the right information at the right time and to solicit their input and feedback. Often this will require overcommunication through multiple, redundant channels.

In the late 1990s, the commissioner of the Internal Revenue Service, Charles O. Rossotti, had a vision: The IRS could treat taxpayers as customers and turn a feared bureaucracy into a world-class service organization. Getting more than 100,000 employees to think and act differently required more than just systems redesign and process change. IRS leadership designed and executed an ambitious communications program including daily voice mails from the commissioner and his top staff, training sessions, videotapes, newsletters, and town hall meetings that continued through the transformation. Timely, constant, practical communication was at the heart of the program, which brought the IRS’s customer ratings from the lowest in various surveys to its current ranking above the likes of McDonald’s and most airlines.

7. Assess the cultural landscape. Successful change programs pick up speed and intensity as they cascade down, making it critically important that leaders understand and account for culture and behaviors at each level of the organization. Companies often make the mistake of assessing culture either too late or not at all. Thorough cultural diagnostics can assess organizational readiness to change, bring major problems to the surface, identify conflicts, and define factors that can recognize and influence sources of leadership and resistance. These diagnostics identify the core values, beliefs, behaviors, and perceptions that must be taken into account for successful change to occur. They serve as the common baseline for designing essential change elements, such as the new corporate vision, and building the infrastructure and programs needed to drive change.

8. Address culture explicitly. Once the culture is understood, it should be addressed as thoroughly as any other area in a change program. Leaders should be explicit about the culture and underlying behaviors that will best support the new way of doing business, and find opportunities to model and reward those behaviors. This requires developing a baseline, defining an explicit end-state or desired culture, and devising detailed plans to make the transition.

Company culture is an amalgam of shared history, explicit values and beliefs, and common attitudes and behaviors. Change programs can involve creating a culture (in new companies or those built through multiple acquisitions), combining cultures (in mergers or acquisitions of large companies), or reinforcing cultures (in, say, long-established consumer goods or manufacturing companies). Understanding that all companies have a cultural center — the locus of thought, activity, influence, or personal identification — is often an effective way to jump-start culture change.

A consumer goods company with a suite of premium brands determined that business realities demanded a greater focus on profitability and bottom-line accountability. In addition to redesigning metrics and incentives, it developed a plan to systematically change the company’s culture, beginning with marketing, the company’s historical center. It brought the marketing staff into the process early to create enthusiasts for the new philosophy who adapted marketing campaigns, spending plans, and incentive programs to be more accountable. Seeing these culture leaders grab onto the new program, the rest of the company quickly fell in line.

9. Prepare for the unexpected. No change program goes completely according to plan. People react in unexpected ways; areas of anticipated resistance fall away; and the external environment shifts. Effectively managing change requires continual reassessment of its impact and the organization’s willingness and ability to adopt the next wave of transformation. Fed by real data from the field and supported by information and solid decision-making processes, change leaders can then make the adjustments necessary to maintain momentum and drive results.

A leading U.S. health-care company was facing competitive and financial pressures from its inability to react to changes in the marketplace. A diagnosis revealed shortcomings in its organizational structure and governance, and the company decided to implement a new operating model. In the midst of detailed design, a new CEO and leadership team took over. The new team was initially skeptical, but was ultimately convinced that a solid case for change, grounded in facts and supported by the organization at large, existed. Some adjustments were made to the speed and sequence of implementation, but the fundamentals of the new operating model remained unchanged.

10. Speak to the individual. Change is both an institutional journey and a very personal one. People spend many hours each week at work; many think of their colleagues as a second family. Individuals (or teams of individuals) need to know how their work will change, what is expected of them during and after the change program, how they will be measured, and what success or failure will mean for them and those around them. Team leaders should be as honest and explicit as possible. People will react to what they see and hear around them, and need to be involved in the change process. Highly visible rewards, such as promotion, recognition, and bonuses, should be provided as dramatic reinforcement for embracing change. Sanction or removal of people standing in the way of change will reinforce the institution’s commitment.

Most leaders contemplating change know that people matter. It is all too tempting, however, to dwell on the plans and processes, which don’t talk back and don’t respond emotionally, rather than face up to the more difficult and more critical human issues. But mastering the “soft” side of change management needn’t be a mystery.

Competency Modeling

Organizations typically envision competency models as providing a unifying framework among the different human resource functions. As a building block toward an integrated human resource system, competency models provide a broad overview of the capabilities required to perform successfully within an organization. Given that many personnel functions are subject to legal scrutiny and to ensure the utility of these programs, HumRRO follows a rigorous process when developing competency models for clients.

Entrenched with traditional job analytic information (i.e., knowledge, skills, abilities and other characteristics [KSAOs]), the resulting competency models provide a solid foundation upon which various human resource programs can be based. To achieve this end, HumRRO works with job incumbents, supervisors, managers and executives to identify the critical KSAOs. This includes conducting interview and focus groups, as well as administering surveys to capture the requisite KSAOs. Then, in collaboration with the client, HumRRO translates these data into meaningful competencies. The resulting competency model and associated definitions are reviewed by both the client and HumRRO to ensure clarity, relevance and consistency with the job analytic data.

HumRRO has extensive experience in developing customized competency models tailored to the client's individual needs and goals. Recently, for the Bureau of Alcohol, Tobacco, and Firearms (ATF), HumRRO developed a competency model specific to the agency's criminal investigator positions. HumRRO also developed a nontechnical competency model for the Federal Aviation Administration (FAA). This competency model was developed for all of the FAA's employees, nonsupervisory through executive ranks. For the Education and Training Administration of the Department of Labor, HumRRO developed a competency model that focuses on the leadership requirements for all levels of employees. And, for the Girl Scouts of the USA, HumRRO completed a competency modeling study of the council membership job.

The competency models that resulted from these efforts served as the foundation for a variety of human resource programs including selection, training, performance management, and the development. The diversity of clients as well as purposes for the competency models accents the benefits of our tailored and thorough methodologies.

For more information, contact:
Dr. Suzanne Tsacoumis or Research Notes

Competency modeling

For most of my blogs thus far, I have been seek out the most technical information that I could find. I have read about ten articles on each topic before I blogged it. I have gotten the full understanding of one them yet. So I am trying a new approach. This is a research paper on the competency modeling approach.

Career and Competency Pathing: The Competency Modeling Approach
By Maggie LaRocca

Maggie LaRocca
Learning Program Manager
Hewlett-Packard Company
maggie.larocca@hp.com

LaRocca opens by explaining that competencies are behaviors that encompass the knowledge, skills, and attributes required for successful performance. In addition to intelligence and aptitude, the underlying characteristics of a person, such as traits, habits, motives, social roles, and self-image, as well as the environment around them, enable a person to deliver superior performance in a given job, role, or situation. I am able to clearly understand all of the factors that she has attributed to the approach. The focus of her paper is to describe how organizations identify their core competencies and how they are applying this competency data to improve performance, which she clearly stated.

Chris Wright is the founder, President and CEO of Reliant (www.reliantlive.com). Dr. Wright leads the development of Reliant's Strategic Talent Management products and content.

Leadership is an outcome

For example, Leadership is not a competency. Effective leadership is an outcome. To be an effective leader, one must possess many core competencies including, but not limited to motivating intrinsically, strategic planning, initiation, decisiveness, people reading, public presentation, information integration, and industry knowledge.

Good competency models will contain definitions and behavioral elements that are highly specific and well defined. If a competency model is based on scientific research, the vendor can provide you with the corresponding articles.

You may also find it useful to create a competency model based on your specific organization. Below is a step-by-step process for creating a competency model and utilizing that model within your organization.

1. Identify the high-impact jobs or positions within your organization.
2. Determine the outcomes expected as a result of successful performance within this position. These outcomes should be directly linked to the organizational objectives.
3. Identify the technical and behavior competencies necessary in achieving the desired outcomes.
4. Assess the candidate using behavioral assessments that have been validated against your competency model.
5. Create and utilize behaviorally-based interview questions that compliment the behavioral assessments and are mapped directly to the competencies.
6. Develop learning opportunities to foster growth in the core competencies necessary for success.
7. Institute performance management and developmental processes around the competency model.
8. Integrate the competency model within your succession planning strategies to identify and develop candidates for succession into your critical jobs.

Competency modeling is an important step in the development of an effective talent management strategy. It can greatly increase the hiring manager’s success rates in finding and developing the talent needed to keep their organizations competitive.

Knowledge Management Systems

How to Create a Knowledge Management System
By Adaptive Leadership SystemsLLC, eHow Member


According to the website, www.ehow.com, knowledge can be defined as actionable information which has been refined for a specific purpose. It was expressed that leaders, managers, and subordinate employees of many organizations may possess information that can be refined for specific business use. The author or the article feel that by sharing this knowledge it may be possible to elevate the collective ability of employees within the organization. Knowledge Management systems are designed to efficiently share this information with other employees to help them with their work responsibilities. This article listed six step needed in creating a knowledge management system.

Step One-Determine a design for knowledge management system

*A system may come in various forms (intranet, internet, virtual private network)
*Design should be comparable to organizational needs
It was noted that an Information Technology (IT) consultant who is well-informed on information security may be able to assist with determining what type of users should be granted access and the best type of information security systems for the organization’s needs.
The tools should not be too complicated for employees to learn. This would hinder the Knowledge Management efforts.

Step Two- Determine the knowledge to be shared based on organizational goals

Determine the design of the system, leaders and managers should consider the type of knowledge that should be placed on the network. The knowledge should be industry or organization-specific information and should be designed to help employees do their jobs better. It may also be designed to allow multiple employees perform the same or similar work functions. This may result in increased efficiency.

Step Three-Hire Information Technology consultants or use in-house IT staff to build the network.

Organizations must determine whether it is more cost-effective for them to build the system themselves or to hire an outside consultant to perform the endeavor. The organization may lack the appropriate staff to build the database, set up the user permissions, and maintain the system. If this is the case it may be more effective to hire a consultant. Requests For Proposals (RFP’s) may be distributed to several vendors to determine the best price and the most qualified company. I like!

Step Four- Train employees how access and to load information into the Knowledge Management system.

Employee should be trained on how effective use the system or it will not be useful.

Step Five- Obtain feedback in the form of surveys and/or informal meetings to determine the effectiveness of the Knowledge Management system.

The system should be helping to improve employee effectiveness, efficiency, and overall productivity. If it is not accomplishing this, it may need to be adapted.

Step Six- Implement changes and make improvement and feedback an iterative process.

Organizations may decide to consider making improvements on the Knowledge Management system based on feedback provided by users. If this type of system is not improved over time and adapted to fit the changes in the organization it may become obsolete. Incremental improvements may help the system to become better over time and may help keep employees motivated to continue sharing their knowledge to improve the organization as a whole.


This article was quite useful in assisting my understanding the uses of a knowledge management system. I work for an organization that could use this type of system. We are constantly having to re-do tasks do to lack of understanding of information by everyone involved. We are always 3 steps behind the pack. Administration would benefit for a knowledge management system. There is little motivation on the behalf of employees to do better than the minimal.

Knowledge Management Systems

I have to try to find and simple definition due to lack of background on these topic. Wikipedia shares the definition of a knowledge management system (KM System) refers to a (generally IT based) system for managing knowledge in organizations, supporting creation, capture, storage and dissemination of information. It can comprise a part (neither necessary or sufficient) of a Knowledge Management initiative.The idea of a KM system is to enable employees to have ready access to the organization's documented base of facts, sources of information, and solutions.

I came across several universities' who were teaching a course in the subject with course description that resembled this on for the University of Texas-Austin.

This course surveys Knowledge Management systems that enable the access and coordination of knowledge assets. Technologies reviewed will include intranets, groupware, weblogs, instant messaging, content management systems and email in both individual and organizational contexts. Students will use these KM technologies, review case studies, research methods of knowledge organization and analyze and design KM processes and systems.

Objectives: [Top]
1. Participate generously in discussions, attend class regularly, and complete assignments according to the course schedule.
2. Understand the history, state-of-the-art and future of Knowledge Management System applications.
3. Use and evaluate Knowledge Management Systems to facilitate individual and group work.
4. Develop a thorough review of Knowledge Management application type, both historical and speculative.
5. Review a book related to the topics discussed in this course.
6. Originate and distribute research on a Knowledge Management System topic.

Knowledge Acquisition 2

Knowledge acquisition from databases is a research frontier for both database technology and machine learning techniques, and has seen sustained research in recent years. It also acts as a link between the two fields, thus offering a dual benefit. First, because database technology has already found wide application in many fields, machine learning research obviously stands to gain from this greater exposure and established technological foundation. Second, machine learning techniques can augment the ability of existing database systems to represent, acquire, and process a collection of expertise such as those that form part of the semantics of many advanced applications, for example, computer-aided design (CAD) and computer-aided manufacturing (CAM).
This book contains three parts. Part I surveys the area of knowledge acquisition from databases and figures out some of the major problems. Part II provides an overview of symbolic methods in machine learning and describes two types of rule induction algorithms to facilitate the acquisition of knowledge from databases: the decision tree-based ID3-like algorithms and the extension matrix-based induction algorithms. The author's own HCV induction algorithm based on the newly developed extension matrix approach is described as a counterpart to ID3-like algorithms. Two practical issues, noise handling and processing real-valued attributes in the context of knowledge acquisition from databases, are addressed in detail, and a performance comparison of different learning algorithms (ID3, C4.5, NewID, and HCV) is also provided in terms of rule compactness and accuracy on a battery of experimental data sets including three famous classification problems, the MONK's problems. Finally, in Part III, an intelligent learning database system, KEshell2, which makes use of the HCV algorithm and couples machine learning techniques with database and knowledge base technology, is described with examples.

The parts of the book have different but interrelated objectives and suit different levels of readership. Part II can be adopted as an inductive learning module in an artificial intelligence (AI) related undergraduate and/or postgraduate course. Part III can be integrated into a machine learning or advanced database course. Together with the brief overview in Part I, this book as a whole should be of interest to the whole intelligent databases and machine learning community and to students in machine learning, expert systems, and advanced database courses. Knowledge acquisition from databases could well form an independent honors or postgraduate course in a computer science or information systems program, and therefore this book could be adopted as a textbook.

by Xindong Wu (Monash University, Australia),
1995

Knowledge Acquisition

Information presented in this module is largely summarized from:
Jones, P.H. 1989. Knowledge Acquisition. In: Barrett, J.R. and D.D. Jones. Knowledge Engineering in Agriculture. ASAE Monograph No. 8, ASAE, St. Joseph, MI.

Introduction
Knowledge acquisition is the process of extracting, structuring and organizing knowledge from one source, usually human experts, so it can be used in software such as an ES. This is often the major obstacle in building an ES.
There are three main topic areas central to knowledge acquisition that require consideration in all ES projects. First, the domain must be evaluated to determine if the type of knowledge in the domain is suitable for an ES. Second, the source of expertise must be identified and evaluated to ensure that the specific level of knowledge required by the project is provided. Third, if the major source of expertise is a person, the specific knowledge acquisition techniques and participants need to be identified.


Theoretical Considerations
An ES attempts to replicate in software the reasoning/pattern-recognition abilities of human experts who are distinctive because of their particular knowledge and specialized intelligence. ES should be heuristic and readily distinguishable from algorithmic programs and databases. Further, ES should be based on expert knowledge, not just competent or skillful behavior.
Domains
Several domain features are frequently listed for consideration in determining whether an ES is appropriate for a particular problem domain. Several of these caveats relate directly to knowledge acquisition. First, bona fide experts, people with generally acknowledge expertise in the domain, must exist. Second, there must be general consensus among experts about the accuracy of solutions in a domain. Third, experts in the domain must be able to communicate the details of their problem solving methods. Fourth, the domain should be narrow and well defined and solutions within the domain must not require common sense.
Experts
Although an ES knowledge base can be developed from a range of sources such as textbooks, manuals and simulation models, the knowledge at the core of a well developed ES comes from human experts. Although multiple experts can be used, the ideal ES should be based on the knowledge of a single expert. In light of the pivotal role of the expert, caveats for choosing a domain expert are not surprising. First, the expert should agree with the goals of the project. Second, the expert should be cooperative and easy to work with. Third, good verbal communication skills are needed. Fourth, the expert must be willing and able to make the required time commitment (there must also be adequate administrative/managerial support for this too).
Knowledge Acquisition Technique
At the heart of the process is the interview. The heuristic model of the domain is usually extracted through a series of intense, systematic interviews, usually extending over a period of many months. Note that this assumes the expert and the knowledge engineer are not the same person. It is generally best that the expert and the knowledge engineer not be the same person since the deeper the experts' knowledge, the less able they are in describing their logic. Furthermore, in their efforts to describe their procedures, experts tend to rationalize their knowledge and this can be misleading.
General suggestions about the knowledge acquisition process are summarized in rough chronological order below:

Observe the person solving real problems.
Through discussions, identify the kinds of data, knowledge and procedures required to solve different types of problems.
Build scenarios with the expert that can be associated with different problem types.
Have the expert solve a series of problems verbally and ask the rationale behind each step.
Develop rules based on the interviews and solve the problems with them.
Have the expert review the rules and the general problem solving procedure.
Compare the responses of outside experts to a set of scenarios obtained from the project's expert and the ES.
Note that most of these procedures require a close working relationship between the knowledge engineer and the expert.

Practical Considerations
The preceding section provided an idealized version of how ES projects might be conducted. In most instances, the above suggestions are considered and modified to suit the particular project. The remainder of this section will describe a range of knowledge acquisition techniques that have been successfully used in the development of ES.
Operational Goals
After an evaluation of the problem domain shows that an ES solution is appropriate and feasible, then realistic goals for the project can be formulated. An ES's operational goals should define exactly what level of expertise its final product should be able to deliver, who the expected user is and how the product is to be delivered. If participants do not have a shared concept of the project's operational goals, knowledge acquisition is hampered.
Pre-training
Pre-training the knowledge engineer about the domain can be important. In the past, knowledge engineers have often been unfamiliar with the domain. As a result, the development process was greatly hindered. If a knowledge engineer has limited knowledge of the problem domain, then pre-training in the domain is very important and can significantly boost the early development of the ES.
Knowledge Document
Once development begins on the knowledge base, the process should be well documented. In addition to tutorial a document, a knowledge document that succinctly state the project's current knowledge base should be kept. Conventions should be established for the document such as keeping the rules in quasi-English format, using standard domain jargon, giving descriptive names to the rules and including supplementary, explanatory clauses with each rule. The rules should be grouped into natural subdivisions and the entire document should be kept current.
Scenarios
An early goal of knowledge acquisition should be the development of a series of well developed scenarios that fully describe the kinds of procedures that the expert goes through in arriving at different solutions. If reasonably complete case studies do not exist, then one goal of pre-training should be to become so familiar with the domain that the interviewer can compose realistic scenarios. Anecdotal stories that can be developed into scenarios are especially useful because they are often examples of unusual interactions at the edges of the domain. Familiarity with several realistic scenarios can be essential to understanding the expert in early interviews and the key to structuring later interviews. Finally, they are ultimately necessary for validation of the system.
Interviews
Experts are usually busy people and interviews held in the expert's work environment are likely to be interrupted. To maximize access to the expert and minimize interruptions it can be helpful to hold meetings away from the expert's workplace. Another possibility is to hold meetings after work hours and on weekends. At least initially, audiotape recordings ought to be made of the interviews because often times notes taken during an interview can be incomplete or suggest inconsistencies that can be clarified by listening to the tape. The knowledge engineer should also be alert to fatigue and limit interviews accordingly.
In early interviews, the format should be unstructured in the sense that discussion can take its own course. The knowledge engineer should resist the temptation to impose personal biases on what the expert is saying. During early discussions, experts are often asked to describe the tasks encountered in the domain and to go through example tasks explaining each step. An alternative or supplemental approach is simply to observe the expert on the job solving problems without interruption or to have the expert talk aloud during performance of a task with or without interruption. These procedures are variations of protocol analysis and are useful only with experts that primarily use verbal thought processes to solve domain problems.

For shorter term projects, initial interviews can be formalized to simplify rapid prototyping. One such technique is a structured interview in which the expert is asked to list the variables considered when making a decision. Next the expert is asked to list possible outcomes (solutions) from decision making. Finally, the expert is asked to connect variables to one another, solutions to one another and variables to solutions through rules.

A second technique is called twenty questions. With this technique, the knowledge engineer develops several scenarios typical of the domain before the interview. At the beginning of the interview, the expert asks whatever questions are necessary to understand the scenario well enough to determine the solution. Once the expert begins the questions, the expert is asked to explain why each question is asked. When the interviewer perceives a rule, he interrupts and restates the rule to ensure that it is correct.

A third technique is card sorting. In this procedure, the knowledge engineer prepares a stack of cards with typical solutions to problems in the domain. The expert is asked to sort the cards according to some characteristic important to finding solutions to the problem. After each sort, the expert is asked to identify the sorting variable. After each sort, the expert is asked to repeat the process based on another variable. Note that this technique is usually not as effective as the 2 previous.

In large projects, later interviews cannot be expected to be as productive as early interviews. Typically, later interviews should become increasingly structured and follow a cyclical pattern where bits of knowledge are elicited, documented and tested. During this phase of knowledge acquisition, the interviewer must begin methodically to uncover the more subtle aspects of the knowledge. Typically, this process is based on scenarios. By modifying the scenarios in different ways, the interviewer can probe the expert's sensitivity.

During interviews, it may be helpful to work at a whiteboard to flexibly record and order the exact phraseology of rules or other representations. It may also be helpful to establish recording conventions for use such as color coding different aspects of a rule and using flags to note and defer consideration of significant but peripheral details.

Structured interviews should direct the course of a meeting to accomplish specific goals defined in advance. For instance, once a prototypic knowledge base is developed, the expert can be asked to evaluate it line by line. Other less obvious structures can be imposed on interviews, such as asking the expert to perform a task with limited information or during a limited period of time. Even these structured interviews can deviate from the session's intended goals. Sometimes such deviations show subtleties in the expert's procedures and at other times the interview simply becomes sidetracked, requiring the knowledge engineer to redirect the session.

Questionnaires
When specific information is needed, a questionnaire can sometimes be used effectively. Questionnaires are generally used in combination with other techniques such as interviews.
Decision Trees
Decision trees are widely recognized to be useful tools for the knowledge engineer in prototyping knowledge representations. In addition, they can be useful in knowledge acquisition on several different levels. Some knowledge engineers have found that experts can more readily relate to decision trees than rules.
Rule Development
Although complex representation techniques might eventually be used, rules are generally easier to use for characterizing knowledge during knowledge acquisition. Prototypic rules should be developed as soon as possible to serve as a focal point for directing the course of the knowledge acquisition process. The initial knowledge base can be developed from written materials or from example cases described by the expert during early unstructured interviews. Initial rules should be treated as approximations and their wording should be general to avoid pressuring the expert. As additional cases are described during interviews, the rule base can be expanded. Once a stable rule base begins to develop, it can provide feedback for structuring interviews. Initially the rules and procedures can be traced through by hand with the expert considering each step. The same pattern of tracing through rules should continue once a version of the knowledge base is developed on a computer and it frequent use should become part of the process.
Conclusions
Recognition of the central role of knowledge acquisition in the development of ES is an unavoidable prerequisite for any knowledge engineering project. If domains are defined and experts chosen with this in mind, a project's chances for success will be greatly increased. Once an appropriate domain is identified and a cooperative, available expert with the necessary stamina is found, then the practical approaches to knowledge acquisition outlined should be of help.
There are several points that deserve to be emphasized:

Projects need to be well planned. The knowledge engineer has the responsibility initially to define explicit operational goals that are consistent with the resources available to a project. Early in the interview process, the purpose of the project and the roles of the participants should be carefully discussed. The discussion should lead to a consensus opinion on what is expected in a final product, who its users should be and how it should be delivered. As the project develops, the operational goals should be consciously reconsidered on a regular basis.
The knowledge acquisition process should be carefully planned to match the requirements of the project's domain expert. For example, time lines that allow for the necessary pre-training, unstructured interviewing and the structured interview phases can be developed. Documentation procedures to be used during the project should be defined.
Specific interviews or knowledge gathering events should be planned to accomplish specific goals. In pre-training, the goal might be to identify several realistic scenarios and during the first unstructured interviews one goal might be to develop a glossary of the expert's terminology. Later, the goals might be to obtain specific bits of information to explain apparent discrepancies. In planning individual interviews, the knowledge engineer should try to get explicit feedback.
Regardless of its size or the intended application, the knowledge acquisition process cannot be avoided. Recognition of this is the first step toward the successful development of a functional ES.
This article caught my attention because of the various models that can be used.It also gives options that can be used for problem with just about anything.You can take a "layered" approach to just about any problem. I feel that if you apply this to just about any situation you can fix any problem.

Summary of Four Architectures of Instruction

Four Architectures of Instruction
Ruth Colvin Clark

According to the author of this article, their are four basic strategies to learning thus also to instruction. She identifies that of all of the strategies, not one is best for all learners. Approaches or architectures should be based in the the audience's background, cognitive abilities, motivation for training and end product.

Receptive Architecture
Receptive instruction assumes that learners can absorb knowledge and skills when they are exposed to them such as when listening to a lecture,watching a video, or reading text. This is a very instructor-controlled environment. Clark notes that receptive training varies a great deal in its use of specific instructional methods such as examples, analogies, visuals, and sequencing of information. Challenges of the model is that it may lead to cognitive or information overload as well as long-term memory encoding failure. Which simply means that it may just be too much information to retain and save to be useful in practice. This method is found to best used in situations of briefing. A researched team in the article found that four main factors that predicted successful learning from reading text were:

1. metacognitive ability to recognize learning deficiencies,

2. working memory capacity,

3. inferencing ability (e.g. the ability to extend and connect information in a reading beyond the context of the reading itself), and

4. prior knowledge in the specific subject domain of the reading.

Behavioral Architectures

Behavioral instruction assumes that learning occurs by a gradual bottom up building and association of skills, which are strengthened by correct learner responses to carefully constructed and tested interactions. Thus the role of the learner is to respond correctly to frequent interactions embedded into the instruction. Behavioral architectures tend to emphasize:

1. bottom up hierarchies in which prerequisite knowledge and skills are sequenced before more
complex knowledge and skills

2. chunking of instruction into relatively short lessons that build on each other

3. frequent interactions to build the skill hierarchies in the learner

4. effective feedback to provide knowledge of results and promote subsequent adjustments by the learner

This was a easier concept for me, being a pre-K teacher, this is method that I use daily. I also belief that is the way that I learn. A cognitive impact of the architectures is most positive encoding into the long-term memory through repetition. Clark states that while this will be helpful for novice (new) learners, individuals with more experience find the approach to
be “overkill” with their motivation and subsequent learning may be depressed.

Situated Guided Discovery Architectures
In laymen terms can be described as small group work. In this architecture, the instructor is used as a point of resources, reflection and enlightenment on the topic area. This is a much more student guided approach than the earlier mention architectures.

Cognitive Impact
Guided Discovery architectures may challenge cognitive load and will demand good metacognitive skills by learners. Because they are case based, by design they should promote encoding into and transfer from long-term memory of job-relevant skills. In combination with high levels of learner control this technique may cause an overload working memory to new learners. If the audience background is different in the degree of knowledge then opportunities to access a more behavioral instructional approach should be included into the instruction.

Exploratory Architectures
This design is setup with the highest level learner control. Internet classes or instruction is an example of an exploratory architectures which is an inherently learner controlled environment. Clark states that depending on the design and structure of the topics in an exploratory architecture, overload can result. Keeping
topics brief and adding frequent optional practice exercise provides an opportunity for load control.
Thus exploratory architectures may be risky for learners who lack background in the material being taught and who lack effective self-regulatory skills.

In other words, all architectures should be provided based on the performance outcomes and learning audience.

Human Performance Improvement 2

www.eogogics.com





Human Performance Improvement Study
How often does one see an individual sent off to training for help with a perceived performance problem when the real problem lies elsewhere?
Management and human resource practices, quality processes, and corporate culture are among the factors that can interplay to create serious performance issues. For instance, when team-members appear to lack motivation or cohesiveness, the management response often is to send them to a teambuilding course.
When training does not resolve a performance issue or when a large number of individuals in an organization exhibit performance problems, it’s time to take a holistic approach to identifying and fixing organizational problems. Human Performance Improvement (HPI) study is just such an approach. It includes systematic needs analysis to determine the environment, needs, current and expected culture, policies and processes, and other issues that impact work performance.
From this analysis emerge recommendations for performance enhancement that may or may not include training. HPI is a scientific, quantitative, and holistic approach to uncover and address performance issues. HPI believes that learning should make a difference!



This particular article is important in that it lays out what you might need to do as a individual ,when faced with a problem that might be from another area. It suggests that you take the overall approach of looking at all the angles of a particular problem and address it from that perspective. Once you look at it this way you or your organization might get a better understanding where the problems lie and adjust accordingly

Human Performance Improvement

http://www.purdue.edu/physicalfacilitiestraining/human_performance_improvement.htm


Human Performance Improvement
What is Human Performance Improvement?
Human performance improvement is the systematic process of discovering and analyzing important human performance gaps, planning for future improvements in human performance, designing and developing cost-effective and ethically justifiable interventions to close performance gaps, implementing the interventions, and evaluating the financial and non-financial results.from ASTD Models for Human Performance Improvement, Second Edition William J. Rothwell, ed.
What does HPI mean for organizations?
HPI specialists work with your staff to identify the root performance cause and help to identify solutions/interventions that will best close the gap in performance. It is a partnership of departments working together to find the best solution.
What kinds of interventions are we talking about?
An intervention can be as simple as changing the layout of a form, to an entire training program on a new process. The intervention is selected based upon the root cause of the performance deficiency and the elimination of gaps the intervention will remove. To learn more about HPI program and the courses.

Human performance improvement or technology is seem as a process in which individuals are being trained to become more focus on the interworking of an organization. According to the courses descriptions for Purdue University, at the completion of this degree, one who be a consultant to an organization or the head of a human resource department. In this process, problems can be found and dealt with in a effective manner to eliminate future distractions. This can be done by looking at the overall problems sometimes with the help of interventions that can just change a few simple things.

Still unsure...

I am still new to the whole blogging experience. I am doing this for the class and do not fully understand it purpose yet. I am not sure what I should be keep a journal on in reference to the class. What should I be discussion with the virtual world?I have read the syllabus several times and I am still missing something.
I have begun to researching the material for assignment one in this class. It seems to be a lot of information right off, but from read just a little information, it seems to be necessary for proving one's credentials . I am not sure but I will keep reading...........Oh what is mean but an Executive Summary exactly?

Introduction

Greetings all! My name is Miranda Mosby. I am first year, first semester, first class graduate in the IT program at SIUE. I have a Bachelor Degree in Community Health from EIU. I have also completed everything but the oral portion of my Masters in Family Services. I am a full-time employee of SIUE in the Head Start program. I am what is call a home visitor. It is a combination of a teacher and social worker. I have a17 month older daughter, Amirra. I am interested in seeing how this semester pans out.