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IES Grant

Title: Now I See It: Supporting Flexible Problem Solving in Mathematics through Perceptual Scaffolding in ASSISTments
Center: NCER Year: 2023
Principal Investigator: Closser, Avery Awardee: Purdue University
Program: Digital Learning Platforms to Enable Efficient Education Research Network      [Program Details]
Award Period: 2 years (09/01/2023 – 08/31/2025) Award Amount: $400,000
Type: Exploration Award Number: R305N230034
Description:

Co-Principal Investigator: Purpura, David

Related Network Teams: This project is part of the Digital Learning Platforms to Enable Efficient Education Research Network (Digital Learning Platforms Network), which aims to leverage existing, widely used digital learning platforms for rigorous education research, and includes the following other projects — The SEER Research Network for Digital Learning Platforms (R305N210034), The Canvas+Terracotta LMS-Based Experimental Education Research Platform (R305N210035), The ASU Learning at Scale (L@S) Digital Learning Network (R305N210041), MATHia: A Digital Learning Platform Supporting Core and Supplemental Instruction in Middle and High School Mathematics (R305N210045), Revisions to the ASSISTments Digital Learning Platform to Expand Its Support for Rigorous Education Research (R305N210049), Efficient Education Research via the OpenStax Learning Platform  (R305N210064), Investigating the Impact of Metacognitive Supports on Students' Mathematics Knowledge and Motivation in MATHia (R305N240024), The Educational Divide: Transition, Retention, and Course Selection in Digital and On-Campus Immersion Students (R305N240042), Examining the Relationship Between Individual Characteristics and Self-Regulated Learning Across Multiple OpenStax Courses (R305N240049), Effects of Enhanced Representations in Digital Mathematics Practice Items (R305N240050), A Multipronged Approach to Small-Teaching Interventions for Reducing Academic Procrastination: A Randomized Control Study via Terracotta (R305N240063)

Purpose: The purpose of this research is to explore whether exposure to perceptual cues, referred to as perceptual scaffolding, in mathematics notation (e.g., using color to highlight key terms such as the inverse operators in an expression) may disrupt in-the-moment fluency, lead learners to pause and notice structural patterns, then ultimately practice more flexible and efficient problem solving. This project will yield evidence on how, when, and for whom perceptual scaffolding works to inform classroom practice and the development of materials for digital learning platforms. The research team will work with ASSISTments, a digital learning platform, to design, implement, and test perceptual scaffolding that directs learners' attention towards important information in expressions to help learners break away from a left-to-right solving strategy, attend to structural patterns in expressions, and reinforce their procedural and conceptual understanding of concepts.

Project Activities: The research team will pilot and conduct two studies within ASSISTments to test the impact of using perceptual scaffolding to support learning two mathematics concepts: equivalence and inversion. These studies address whether the presence of perceptual cues, as an intervention feature, supports learning above and beyond a control condition.

Products and Dissemination: Products include evidence on the effects of perceptual scaffolding on learners' mathematics outcomes in digital learning platforms and preliminary evidence on whether and how to implement perceptual scaffolding in online activities for elementary mathematics. Products will also include publications in interdisciplinary journals and presentations at international conferences. The materials, analyses, output, and de-identified data will be shared publicly on the Open Science Framework. Third, based on the results, evidence-based recommendations will be shared through online dissemination and professional development sessions for researchers, teachers, and content developers who wish to implement or adapt the materials to use in ASSISTments, other digital learning platforms, or their classroom.

Structured Abstract

Setting: The project will take place in public elementary schools in an urban district in Indiana.

Sample: Each intervention will include approximately 700 learners in grades 2 through 4 in general education classes across 129 teachers.

Innovation/Factors: The research team will develop two online instructional activities for foundational elementary mathematics skills—one on equivalence and one on inverse operators— that leverage perceptual scaffolding as an intervention feature. Grouping cues and color will be used as perceptual scaffolds.

Research Design and Methods: The researchers will conduct two studies concurrently designed and deployed in ASSISTments to respectively test the effects of perceptual scaffolding in the context of (a) equivalence and (b) inverse operations content. The studies will both use a between-subjects design with learner-level randomization within ASSISTments that holds learners within condition across three problem sets (a pretest, intervention, and posttest). The research team will test for the effects of perceptual scaffolding on learners' mathematics performance and learning as well as examine potential moderators.

Control Condition: Learners in the control conditions will receive the same content (i.e., worked examples and practice problems) without any perceptual scaffolding.

Key Measures: The key measures are from the learner log data in ASSISTments, including learners' performance (i.e., accuracy and average response time) on the intervention problem set and learning (i.e., posttest score). The research team will collect learners' calculation skills and pretest scores. The team will also note learners' teacher and grade level. The project team will also check for differences in fidelity (i.e., completion) by condition in the preliminary analyses.

Data Analytic Strategy: The researchers will examine the effects of perceptual scaffolding on performance and learning, as well as potential moderators using analyses of variance, controlling for learners' teacher, grade level, and prior knowledge.


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