Assessing Student Problem-Solving Skills With Complex Computer-Based Tasks

Abstract

Valid formative assessment is an essential element in improving both student learning and the professional development of educators. Various shortcomings in common assessment modalities, however, hinder our ability to make and evaluate such formative decisions. The diffusion of computer technology into American classrooms offers new opportunities to evaluate student learning and a rich, new source of data upon which to make inferences about the formative interventions that will improve learning. The path from data to inference, however, requires appropriate methodologies that can fully exploit the data without discarding or oversimplifying the behavioral complexity of student activity. This study used IMMEX™, a computerized simulation and problem-solving tool, along with artificial neural networks as pattern recognizers to identify the common types of strategies high school chemistry students used to solve qualitative chemistry problems. Then, based on the calculated probabilities that students would transition between these strategy types over time, Markov hidden chain analysis allowed us to develop a model of the capacity of the current curriculum to produce students able to apply chemistry content to a real-world problem.