Mary-Angela Papalaskari

Using puzzles in teaching algorithms

This paper advocates a wider use of puzzles and puzzle-like problems in teaching design and analysis of algorithms. It discusses a variety of puzzles and classifies them according to the general algorithm design techniques. Pedagogic issues are explored.

A repository that supports teaching and cooperation in the introductory AI course

This paper describes the development of FLAIR (Flexible Learning with an Artificial Intelligence Repository), a repository of educational material and of a highly visual computing environment for use in laboratories associated with the introductory undergraduate Artificial Intelligence (AI) course. This repository supports sharing of pedagogic material and of development tools, and cooperation in their use, while allowing diversity in content and in use at different institutions. Thus the development of the repository has stressed the production of system tools, extensible object-oriented libraries, and strong programming frameworks. Some modules currently available are on Search and Automated Reasoning. Examples of the presentation techniques used are conceptual maps, hypertext, and graphic animations of algorithms. Initial experience in the use of the repository in teaching the introductory AI course is taking place in the 1993/94 academic year.

Peer evaluation in an algorithms course

Peer evaluation is used in an algorithms course to highlight the importance of good documentation and professional presentation.

Broader and Earlier Access to Machine Learning

This poster presents the results of an ongoing project that has developed learning modules to provide access to the tools and techniques associated with machine learning to a broad category of undergraduate students. The changing nature of science and its reliance on massive data sets has led to the integral use of machine learning approaches in just about every discipline. Recognizing this shift, flexible teaching materials have been developed to provide educators and students in a wide variety of academic fields with relevant machine learning topics and discipline-ready activities. Goals, module design, example material, dissemination plans and evidence of student learning are presented.

Lessons learned from developing advanced topics for broad use

Two faculty teams worked to develop significant content that could be presented in a variety of contexts to students with interests in many areas. This poster reports on the opportunities and the challenges encountered.

Assessing attention in visual and textual programming using neuroeducation approaches

This poster will present results from an EEG study comparing visual and textual programming, carried out on informatics students. We observe brain rhythms as the subjects complete programming tasks using Python and Scratch. The present study belongs in the domain of Neuroeducation/Educational Neuroscience, which is an attempt to join cognitive science and education in order to create a sound grounding of education. Our goal is to gain a better understanding of different types of programming as one of the parameters that can impact professional involvement in software development, so as to help us formulate educational methodologies in software engineering education.