A contextualized project-based approach for improving student engagement and learning in AI courses

  • Authors:

  • Ingrid Russell;Susan Coleman;Zdravko Markov

  • Affiliations:

  • University of Hartford;University of Hartford;Central Connecticut State University

  • Venue:
  • Proceedings of Second Computer Science Education Research Conference
  • Year:
  • 2012

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Abstract

Project MLeXAI, Machine Learning Experiences in Artificial Intelligence, is a multi-institutional project that has been funded by a grant from the National Science Foundation. The goal is to develop a framework for teaching core Artificial Intelligence (AI) topics with a unifying theme of machine learning. The objectives are to enhance student learning experiences in the AI course by implementing a unifying theme of machine learning to tie together the diverse and seemingly disconnected topics in the AI course, to increase student interest and motivation to learn AI, and to introduce students to an increasingly important research area. To that end, a suite of hands-on term-long projects and associated curricular modules have been developed. Each project involves the design and implementation of a learning system which enhances a particular commonly-deployed AI application. In addition, the projects provide students with an opportunity to address not only core AI topics but also many of the issues central to computer science, including algorithmic complexity and scalability problems. Phase I of the project involved the development and testing of a prototype at three institutions: a small liberal arts college, a medium comprehensive university, and a state university. Phase II builds on phase I work and involves further development and testing of an adaptable framework for the presentation of core AI through a unifying theme of machine learning. The goal of phase II was to increase the number of modules and expand the implementation and testing to include a larger and more diverse set of colleges and universities. In this paper we present results of phase II assessment of the collaborative development, dissemination, and separate testing of the projects at the institutions of twenty participating faculty members. The institutions are geographically dispersed and represent a broad range of institutions and student body.