Goal oriented self-adaptive development of online assignments based on autonomous computational methods
Andrew Pownuk, ampownuk@utep.edu
Department of Mathematical Sciences, University of Texas at El Paso
Iwona Skalna, skalna@agh.edu.pl
Department of Business Informatics & Management, AGH University of Science and Technology, Krakow, Poland
Venkata Rama Rao Mallela, dr.mvrr@gmail.com
Department of Civil Engineering, Vasavi College of Engineering, Hyderabad, India
Significant part of currently available knowledge can be described by information in digital form (text, equations, graphics, digital signals, etc.).
There are many available methodologies that can process digital data (machine learning, numerical analysis, digital signal processing, etc.).
Autonomous computational methods can process selected algorithms and generate new algorithms in fully automated and automated way.
Practically all commonly used home desktop computers can generate several thousand pages of text per second,
because of that, by using existing technology it is possible to generate large number of useful results
by using cheap and widely available hardware which do not consume a lot of energy for calculations and storage.
Some algorithms can be updated adaptively to satisfy given constraints.
Self-adaptive autonomous computational methods can be used to generate some class of online assignments which are related to given topic.
Assignments can be created automatically according to current needs of teachers and students.
Online assignments are combinations of computer code, mathematical algorithms, and engineering examples in digital form.
Autonomous methods can generate large amounts of possibly useful assignments which can be used in online learning.
Some examples of presented methodology were implemented in online learning system which was developed by one of the authors of this presentation.
Examples related to numerical analysis, machine learning, and related applications in engineering will be presented.