The Meta Process of XLP

The Meta Process, or the generic process pattern of XLP is composed of the following consecutive stages:Early Success, Fail Early Fail Safe, Convergence, Demonstration. These four stages of processes is compatible with the Test Driven Development disicipline, where all processes are like software functions that must be tested and challenged for its operating boundaries before the software can be released for public use.

Early Success

In Extreme Learning Process (XLP), Early success is the presentation of ready-made solutions to incoming students. By offering them with tested solutions that are not priorly known by these students, the successful experience will challenge their cognitive habits, and start convincing themselves that there are things to be learned. This needs to be presented in the beginning of the learning cycle, so that students would be given the incentive to learn.

Fail Early Fail Safe

In the second stage of XLP, students need to realize that just adopting existing solutions that were prepared for them to rethink their assumptions is not sufficient. They need to directly experience some form of failures that would be compliant to the assumptions that there are new things to be learned, or new test cases to expand their mental model. Just like in Test Driven Development, the first set of test cases are test cases that demonstrate the failure modes of a system, so that they can be sure that the testing procedures would reveal some errors during tests. This failure first philosophy is necessary because it will reinforce the habit of system safety, where possible failure modes are considered to be the boundary cases, and at least some known boundary cases are explicitly documented and tested before a system is put into production. Similarly, learning a new concept or a new skill, requires the exposure to the boundary conditions of the system of interest. Designing a learning program that exposes boundary conditions to students, is a crucial part of learning result assessment. It is also a necessary stage of attaining mastery at a subject matter.

Convergence

Convergence of intention is often a group-based activity in XLP. Since XLP is often conducted in a group-based environment, where it involves many participants, the intention for learning something is almost always different given that everyone comes from different backgrounds and have their own private interest areas. However, learning a new thing together is an experience that requires accumulating and compilation of prior and ongoing experience. Therefore, all participants must identify certain commonality in their learning trajectory, otherwise, no tangible knowledge content can be retained.

The most common approach to enable convergence is to ensure that all learning take place using a single data repository. In case of practicing XLP, an instsance of Project Knowledge Container(PKC) should be serving the entire team from the challenge design stage, and the mission execution stage. This instance of PKC will then be a data space to capture and assess the formative and summative learning results.

Demonstration

The Demonstration part of XLP is about publishing the learning results. Using PKC to capture knowledge, allows participating members to use a common data processing infrastructure to present content to any target audience that will eventually want to see the results. Other forms to results can also be included in the PKC of a given learning project. For example, the personal blogs of learning participants, compiled and finalized group learning reports, and even video recordings of oral reports and demonstration show and tell parties. Any demonstrative data formats that could help other people to see the learning results, needs to be system compiled in a publicly recognizable data format, so that it can be delivered to the target audience with minimal logistic overhead. To protect intellectual properties and privacy rights, data content that are not suitable for public release must be pre-compiled and reviewed before presenting the final version of the data set. A set of consent forms for participants should also be prepared and signed by willing participants.