Community College Information Technology Education: Curriculum Mapping, a Learning Science Framework, and AI Learning Technologies

• In Event: AI Across Educational Contexts

Abstract

Most jobs in the digital economy require 4-year university degrees, excluding many community college students. To help these students join the digital economy, our project team is developing AI-based learning technology using a novel approach. First, we employ curriculum mapping to analyze courses and identify knowledge components (KCs) that are positioned to impact student outcomes. We triangulate our results using student learning data and expert-provided qualitative assessment. We then employ the Knowledge, Learning and Instruction framework to align KCs with individual tutoring and collaborative learning. This analysis is guiding us in developing intelligent tutors and collaborative learning technology, empirically-tested forms of AI-based learning technology, to support IT students. In this paper, we describe our innovative approach and results thus far.

Authors

• Bruce McLaren, Carnegie Mellon University

• Lauren Herckis, Carnegie Mellon University

• Leah Teffera, Carnegie Mellon University

• Lee Branstetter, Carnegie Mellon University

• Carolyn Penstein Rose, Carnegie Mellon University

• Majd Sakr, Carnegie Mellon University

• Matthew Kisow, Community College of Allegheny County

• Reis Reis, Community College of Allegheny County

• Michael Rinsem, Community College of Allegheny County

• Lex Miller, Mount Holyoke College

• Marcus Alenius, Carnegie Mellon University