The "reflection protocol" - a way to develop learning and independent thinking?

It is now-a-days more and more common in the academic world to use new forms of “learning-tools”. One of those is the “reflection protocol”, which usually consist of a few pages of freely written text, related to something the students have read. There seems to be a lot of different opinions about the value to use this method. Some teachers and students are enthusiastic and others are rather critical. To write a “reflection protocol” is not in the first place to do a summery, a review, not even to analyze a text. Instead it is about to write down thoughts and questions that comes up as a result of the reading. It is also about doing associations, reflections and to interpret a text and relate this to a theme of some kind. The purpose to use “reflection protocols” is, as we see it, mainly for the student to practice independent thinking from a scientific point of view, but it also gives a possibility to a better understanding of another person’s thinking. This seems to open up for a fruitful dialogue and a way to learn. We will in this paper discuss if that could be the case.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.