Integrating Real-Time Drawing and Writing Diagnostic Models: An Evidence-Centered Design Framework for Multimodal Science Assessment Andy Smith 1(&) , Osman Aksit 2 , Wookhee Min 1 , Eric Wiebe 2 , Bradford W. Mott 1 , and James C. Lester 1 1 Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA {pmsmith4,wmin,bwmott,lester}@ncsu.edu 2 Department of STEM Education, North Carolina State University, Raleigh, NC 27695, USA {oaksit,wiebe}@ncsu.edu Abstract. Interactively modeling science phenomena enables students to develop rich conceptual understanding of science. While this understanding is often assessed through summative, multiple-choice instruments, science note- books have been used extensively in elementary and secondary grades as a mechanism to promote and reveal reflection through both drawing and writing. Although each modality has been studied individually, obtaining a compre- hensive view of a student’s conceptual understanding requires analyses of knowledge represented across both modalities. Evidence-centered design (ECD) provides a framework for diagnostic measurement of data collected from student interactions with complex learning environments. This work utilizes ECD to analyze a corpus of elementary student writings and drawings collected with a digital science notebook. First, a competency model representing the core concepts of each exercise, as well as the curricular unit as a whole, was con- structed. Then, evidence models were created to map between student written and drawn artifacts and the shared competency model. Finally, the scores obtained using the evidence models were used to train a deep-learning based model for automated writing assessment, as well as to develop an automated drawing assessment model using topological abstraction. The findings reveal that ECD provides an expressive unified framework for multimodal assessment of science learning with accurate predictions of student learning. Keywords: Assessment Á Multimodalilty Á Evidence-centered design 1 Introduction Formative assessment can play a central role in enabling intelligent tutoring systems (ITSs) to provide students with personalized, adaptive learning experiences [1]. Effective formative assessment can be used to infer students’ underlying mental models as well as their movement through learning progressions [2, 3]. The models inferred © Springer International Publishing Switzerland 2016 A. Micarelli et al. (Eds.): ITS 2016, LNCS 9684, pp. 165–175, 2016. DOI: 10.1007/978-3-319-39583-8_16