Using Model-Tracing to Conduct Performance Assessment of Students' Inquiry Skills within a Microworld
Author: Gobert, Janice D.
Date: 2011-00-00
Type: Reports - Research
Subjects: Performance Based Assessment,Predictive Validity,High Stakes Tests,Rote Learning,Physical Sciences,Scoring,Science Process Skills,Grade 8,Inquiry,Science Instruction,Student Evaluation,Biological Sciences,Middle School Students,Computer Assisted Instruction,Educational Technology,Web Based Instruction,Computer Assisted Testing,Computer Simulation,Models,Pretests Posttests,Measurement Techniques,Rural Schools,Federal Aid,Correlation,Science Experiments

The National frameworks for science emphasize inquiry skills (NRC, 1996), however, in  typical classroom practice, science learning often focuses on rote learning in part because science process skills are difficult to assess (Fadel, Honey, &amp; Pasnick, 2007) and rote knowledge is prioritized on high-stakes tests. Short answer assessments of inquiry have been used (cf., Alonzo &amp; Aschbacher, 2004; Songer, 2006), however, these tend to not align well to current national frameworks (Quellmalz, Kreikemeier, DeBarger, &amp; Haertel, 2007) and it is unclear whether they properly identify inquiry skills (Black, 1999; Pellegrino, 2001). Hands-on performance assessments are more authentic (Baxter and Shavelson 1994; Ruiz-Primo &amp; Shavelson, 1996), however, these are seldom used in schools because of difficulty with reliable administration and the resulting high cost. The Science Assistments project (www.scienceassistments.org) has developed a rigorous, technology-based learning environment that assists and assesses hence, &quot;assistments&quot;) middle school students in Earth, Life, and Physical Science so that teachers can assess their students' skills rigorously, frequently, and during instruction--in the context in which they are developing (Mislevy et al, 2002). The authors' program of work represents a significant advance over other programs that utilize pencil and paper assessments because theirs makes use of a state-of-the art logging infrastructure to do web-based tutoring (Razzaq et al, 2005). As a proof of concept for automated assessment of scientific inquiry skills, the authors used model-tracing (Corbett &amp; Anderson, 1995; Koedinger &amp; Corbett, 2006) to develop a cognitive model of science inquiry skills, particularly, the control for variables strategy (Chen &amp; Klahr, 1999) and warranting claims with data. This model provides a rich qualitative, process-oriented scoring of students' inquiry &quot;moves&quot; within a guided scientific inquiry simulation for the domain of state change. They address the validity of this automated approach to performance assessment both quantitatively, in terms of reliability and predictive validity, and qualitatively, in terms of providing rich traces of student inquiry steps and &quot;mis-steps&quot; or haphazard inquiry (Buckley, Gobert et al, 2010). Participants were 78 eighth grade students, ranging in age from 12-14 years, from a public middle school in Central Massachusetts. In this paper the authors have shown that they can use model-tracing as a method of performance assessment for science inquiry skills, an ill-defined domain. This builds upon the extensive work that has been done to date for well-defined domains such as math (Corbett &amp; Anderson, 1995; Koedinger &amp; Corbett, 2006). Additionally: (1) the reliability of their machine-scored measures of inquiry are highly consistent across the 4 Assistment activities or &quot;trials&quot;, suggesting that they can reliably capture students' inquiry performance on these rich inquiry tasks, and (2) their measures are moderately correlated with post-test measures of inquiry performance for analogous concepts. Lastly, their data show that model-tracing can detect interesting patterns of student inquiry such as confirmation bias and overcoming confirmation basis. These are important data with respect to demonstrating auto-scoring of rich inquiry behaviors, but are also important, particularly the former, in terms of its implications for adaptive scaffolding of student inquiry, such as that being done by the Science Assistments group (www.scienceassistments.org; Gobert et al, 2007, 2009). This work makes contribution to theoretical understanding of scientific inquiry, to its assessment, and to technical methods to auto-score inquiry. This represents an advance in this area since to date there has been difficulty in separating inquiry from the domain-specific context in which it was learned (Mislevy et al., 2002; Gobert, Pallant, &amp; Daniels, 2010), and difficulty measuring inquiry skills due to their complexity and the amount of data required for reliable measurement (Shavelson et al, 1999). (Contains 1 table.)