The first phase of the traditional ADDIE instructional design model is Analysis. In the case of course design, this means: Analysis of your student learners—who are they, what level of knowledge and skills can be assumed
May 09, 2022 · Course-Level Analysis. This is typically conducted before development begins on a particular course or small series of courses. The discussion focuses on job tasks specific to the topic, the business processes, and ultimately what learners need to know or do to be successful. It results in task-level learning objectives and is typically one ...
THE COURSE OF ACTION WITH THE HIGHEST PROBABILITY FOR SUCCESS. To speed the comparison, prepare blank matrices and identify the criteria for the operation.
May 02, 2022 · Written by Coursera • Updated on May 2, 2022. Share. Share. Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. "It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in …
Course of Action Simulation Analysis (CASA) task wa s created to research metrics identification, data representation and scoring approaches. This paper introduces concepts behind CASA, chronicles task results to date, and finishes with a discussion of the scoring methodologies and capabilities developed during the CASA prototyping effort.
The first step in the design process involves completing an analysis of your learners/audience, course/subject area, and the learning environment. After this analysis you should:
The second step in the design process involves identifying and describing assessments, activities, and resources that will provide the specific group of learners with the best opportunities to meet the course goals.
The information gathered through technical analysis is used to predict the likely outcomes of a trade so you can make better trading decisions in an unemotional and unbiased way. It is used in different ways depending on your investment objectives. For example, it could be used by day traders trying to capture short-term profits between the opening and closing bells of the market. Swing traders use it to monitor price changes and identify trends over a more extended period of time. Portfolio managers use technical analysis alongside fundamental analysis to identify investment opportunities for their clients. Generally, any investor who used technical analysis is trying to maximize their return on investment.
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The Charting School is an extensive library of dozens of articles organized as course chapters, such as Chart Analysis, Technical Indicators & Overlays, and Market Analysis. In another section, you can learn how to use the various charting tools and resources, such as StockCharts' award-winning financial charting tool and its full-screen, interactive Advanced Charting Platform. The section goes on to teach you how to use the many chart analysis tools available through StockCharts.
Successful traders look to technical analysis to unlock the key to stock price movements in order to identify potentially profitable trading opportunities. Technical analysis is a complex discipline involving price trend lines, chart patterns, and calculated indicators that need to be interpreted to know the optimum time to enter and exit a trade. While it's not an exact science, successful traders who master technical analysis get it right much more often than they get it wrong.
The first phase of the traditional ADDIE instructional design model is Analysis. In the case of course design, this means:
An assessment allows students to demonstrate their learning–if not, one needs to revise the assessment.
Clarify learning objectives and intended outcomes as they are manifested in course activities, assignments, and assessments and explain the purpose of instructional content and activities. Share your expertise and knowledge but put the focus on facilitating your students’ own learning rather than just imparting information.
If they are not, faculty will design learning activities that are only remotely related to the stated learning outcomes, students will have good reason to wonder why they are being asked to do a task, and in the end, student work will not match up with the intended outcomes for the class.
The vast majority of SPS students are adult learners, busy with both work and family responsibilities. So it’s a good idea to incorporate adult learning principles into the design of your course—not only in regard to the assignments and learning activities, but also to factor these into your approach to communicating and providing feedback to your students, and setting up the class so that students are easily able to share and communicate with their classmates. A few of these ideas are that adult learners are
In either case, it is an advantage to understand and know how learning outcomes are relevant to the learning activities, assignments, and assessments you create for your course. It comes down to a simple fact: Learning outcomes, instructional activities, and assessment all need to be aligned. If they are not, faculty will design learning activities ...
DISCUSSION: Wargaming is the most valuable step within the course-of-action analysis. Observations from the CTCs indicate that few staffs understand how to war-game effectively, and that many staff officers are not involved in the procedure. By wargaming , the staff takes a course of action and begins to develop a detailed plan. Additionally, it can better synchronize the course of action when the entire staff is involved in wargaming . Information recorded during the warga me provides the information for the development of paragraph three (execution) of the operations order, the execution or synchronization matrices, and the decision support template. Because of the importance of its results, and the time it requires, more time is allocated than for any other step. Wargaming results in the identification of tasks, combat power requirements, critical events and priority efforts, task organization and command and support relationships, decision points and possible fratricide locations.
To develop a complete course of action, the staff must identify what, when, where, how, and why the unit will execute. A technique to quickly develop complete courses of action is for the XO to assemble the staff and follow the five-step method. The staff develops the courses of action together. While the S-3 develops the scheme of maneuver, the remainder of the staff integrates its assets within its functional area of responsibility.
Course-of-action development is the foundation of the plan. Eliminating or inadequately conducting this step produces inferior estimates which impact on the remainder of the MDMP in the following ways. The commander, recognizing courses of action that do not adhere to his planning guidance or are not feasible, responds by having the staff do the work again, which wastes time. Or, in the absence of adequate planning time, the commander develops a course of action himself.
After courses of action are war-gamed, the staff determines which one to recommend to the commander. This requires the staff to continue to analyze and compare each course of action. A quick and effective method to do this is to use a decision matrix. The staff develops criteria for comparison using commanders' guidance, critical events, and other significant factors pertaining to the mission. The staff uses criteria to determine advantages and disadvantages of each course of action. It is the comparison of the advantages and disadvantages that helps the staff determine the course of action with the highest probability of success.
Staff: Develop courses of action that identify what, when, where, how, and why the unit will execute.
Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. Sherlock Holmes once said (in a story by Arthur Conan Doyle), “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”.
Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions?
Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.
Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.
Data analysis can help a bank to personalize customer interactions, a healthcare system to predict future health needs, or an entertainment company to create the next big streaming hit.
Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans.
Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistics can help set you up for success.
Attrition-based scoring represents one approach to answering the need to identify a common set of scoring metrics that allow disparate COAs to be directly compared. The attrition-based scoring approach attempts to consider the kinetic effects of missions, both positive and negative. In researching this approach, several templates were constructed to account for how the results of kinetic actions affected numerous facets of the battle space, including but not limited to, adversary forces; civilian populations; economics; and political, religious, and cultural infrastructures. What quickly became obvious was that each examined application of kinetic force had numerous exceptions. When the templates were combined and revised to attempt to account for all variations, they became very large and were generally sparsely populated and unwieldy. Their sparse nature forced abstraction to allow for direct comparison, with each abstraction specific to the COAs under examination. Additionally, attempting to allow for EBO considerations expanded both template size and complexity. Following numerous failed attempts to find a means to use this scoring approach, a more fundamentally abstract approach was researched.
The main emphasis in the task and effect-based scoring model is to account for the relative importance of the individual tasks on achieving the overall desired effects. The viewpoint here is that although a given COA may inflict dramatically more casualties than are sustained, it could still prove to be a poor COA. For example, suppose the purpose of a COA is to destroy the enemy’s ability to utilize weapons of mass destruction (WMD). It achieves 25% success on all missions without losing a single asset, but the COA would not be considered a success. The enemy will still be able to utilize WMD even though they sustained more damage than they inflicted.
In an effort to represent data beyond two dimensions, ontologies were evaluated. An ontology is a relational model of data. Instances are created and grouped into classes based on their attributes. Inheritance-based specification of these classes ensure uniformity and data independence in the constructs. These logical groupings of data into higher-level concepts provide a clear correlation of data into information.
The analysis lasts about two months. During the analysis, equal emphasis is placed on defining and understanding the problem, brainstorming its possible causes, analyzing causes and effects, and devising a solution to the problem. During the analysis period, the team meets at least weekly, sometimes two or three times a week.
Events and causal factor analysis: Widely used for major, single-event problems, such as a refinery explosion, this process uses evidence gathered quickly and methodically to establish a timeline for the activities leading up to the accident. Once the timeline has been established, the causal and contributing factors can be identified.
It's important to note that root cause analysis in itself will not produce any results; it must be made part of a larger problem-solving effort for quality improvement.
Root cause analysis (RCA) is defined as a collective term that describes a wide range of approaches, tools, and techniques used to uncover causes of problems. Some RCA approaches are geared more toward identifying true root causes than others, some are more general problem-solving techniques, and others simply offer support for the core activity ...
What is Root Cause Analysis (RCA)? A root cause is defined as a factor that caused a nonconformance and should be permanently eliminated through process improvement. The root cause is the core issue—the highest-level cause—that sets in motion the entire cause-and-effect reaction that ultimately leads to the problem (s).
Management oversight and risk tree analysis: One aspect of this approach is the use of a tree diagram to look at what occurred and why it might have occurred.
Change analysis: This approach is applicable to situations where a system’s performance has shifted significantly. It explores changes made in people, equipment, information, and more that may have contributed to the change in performance.