Modern Educational Philosophy Applied to Adaptive Microprocessor Laboratory Learning
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This Master’s Thesis deals with the creation of educational laboratories for learning about microprocessors and programming. The lab exercises are based on the educational theories of David Kolb. Kolb created the Experiential Learning Model (ELM) and the Learning Style Inventory (LSI). The ELM describes learning as a process based on a learner’s experience. With the LSI it is possible to determine the learning style of every learner. By using this knowledge from Kolb’s theories the lab exercises are designed adapted to the four learning types Kolb pointed out. The microcomputer used for the laboratories is the BeagleBone Black, a small and powerful microcomputer from Texas Instruments based on the 1GHz AM335x ARM® Cortex™-A8 microprocessor. The chosen programming language is Python™, which is a high-level programming language. Through its abstractness and its expressivity Python is easy for students to learn. The realization of Kolb’s theories is explained later in this thesis. The main focus by explaining this realization is to describe the connections between the outlined characterizations of every learning type and the methods used for the lab exercises. Only little research has been done using Kolb’s educational theories for engineering classes so far. Therefore, with this thesis new scientific territory will be entered. Before the lab exercises are described in detail, an explanation how adaptive lab exercises can be set up is presented. The idea is to provide a manual for creating new lab exercises by using the methods based on the ELM and the LSI. For describing the four lab exercises created for this thesis, a general description of every lab exercise and descriptions of every project are given.
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
NPS Report NumberNPS-SP-14-002CR
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