ITR/PE: A Learning Environment for Information Technology Concepts Using Intensive, Unobtrusive Assessment

University Of Washington, Seattle WA

Investigators

Abstract

EIA-0121345 Tanimoto, Steven University of Washington ITR/PE: A Learning Environment for Information Technology Concepts Using Intensive, Unobtrusive Assessment Although many learning environments have been developed that effectively engage students in assembling computational objects, simulations, or mathematical constructions, for the most part these systems fail to take pedagogical advantage of the wealth of assessment-related data that results from the fact that the students are working on computers. This is due less to the newness of these environments than to the challenges of effectively utilizing the event logs, student writing, and student constructions as evidence of cognitive state, learning preferences, and skills. We propose to integrate and extend two software systems for online education in order to perform a series of experiments that assess the impact of using intensive, unobtrusive assessment tightly integrated with a constructive learning environment upon learning outcomes and efficiencies. The integrated learning environment will be tested primarily with University of Washington freshmen but also with Mercer Island High School seniors, in problem solving and construction activities involving digital image representation and processing, web-based communication, and computer programming. The students' writing, online constructions, online sketches and computer-generated activity logs will be analyzed using a combination of computer-assisted and automated mark-up. The results of mark-up will trigger pedagogical recommendations to instructors, and in some cases, directly to the students. Such interventions may take a variety of forms; for example, a group of students stuck at the point of frustration in solving some problem may benefit by having a key step, performed well by a student in another group, called to their attention. Our objective will be to determine effective methods both for extracting pedagogically useful information from the products and byproducts of online learning and for designing the instruction to enable and benefit from the use of this information.