Martina A. Rau

dblp:95/7336 · also Martina Angela Rau · DBLP profile ↗
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30ranked-venue papers
18as first author
6since 2021 · last 2025
0000-0001-7204-3403ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 28 · 17 first-author · 6 since 2021Human-computer interaction and ubiquitous computing · 17 · 11 first-author · 3 since 2021Artificial intelligence and machine learning · 3 · 2 since 2021
YearPublicationVenuePosition
2025 How Instructional Sequence and Personalized Support Impact Diagnostic Strategy Learning
Fatma Betül Güres, Tanya Nazaretsky, Bahar Radmehr, Martina A. Rau, Tanja Käser
AIED (6)4
2025 Perceptually Training Viewers against Misleading Data Visualizations with Informative feedback
Jihyun Rho, Shubham Kumar Bharti, Shiyun Cheng, Martina A. Rau, Jerry Zhu
CogSci4
2024 Various Misleading Visual Features in Misleading Graphs: Do they truly deceive us?
Jihyun Rho, Martina A. Rau, Shubham Kumar Bharti, Rosanne Luu, Jeremy McMahan, Jerry Zhu
CogSci2
2022 Embodied Learning with Physical and Virtual Manipulatives in an Intelligent Tutor for Chemistry
Joel P. Beier, Martina A. Rau
AIED (1)2
2022 Preparing Future Learning with Novel Visuals by Supporting Representational Competencies
Jihyun Rho, Martina A. Rau, Barry D. Van Veen
AIED (1)2
2022 Investigating Growth of Representational Competencies by Knowledge-Component Model
Jihyun Rho, Martina A. Rau, Barry Vanveen
EDM2
2019 Using Machine Learning to Overcome the Expert Blind Spot for Perceptual Fluency Trainings
Martina A. Rau, Ayon Sen, Xiaojin Zhu 0001
AIED (1)1
2019 Disentangling Conceptual and Embodied Mechanisms for Learning with Virtual and Physical Representations
Martina A. Rau, Tara A. Schmidt
AIED (1)1
2019 Adaptive Support for Representation Skills in a Chemistry ITS Is More Effective Than Static Support
Martina A. Rau, Miranda Zahn, Edward Misback, Judith Burstyn
AIED (1)1
2018 For Teaching Perceptual Fluency, Machines Beat Human Experts
Ayon Sen, Purav Patel, Martina A. Rau, Blake Mason, Robert D. Nowak, Timothy T. Rogers, Jerry Zhu
CogSci3
2018 Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice
Ayon Sen, Purav Patel, Martina A. Rau, Blake Mason, Robert D. Nowak, Timothy T. Rogers, Xiaojin Zhu 0001
EDM3
2016 Pattern mining uncovers social prompts of conceptual learning with physical and virtual representations
Martina A. Rau
EDM1
2016 How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations
Martina A. Rau, Blake Mason, Robert D. Nowak
EDM1
2016 Adding eye-tracking AOI data to models of representation skills does not improve prediction accuracy
Martina A. Rau, Zachary A. Pardos
EDM1
2015 Understanding Student Success in Chemistry Using Gaze Tracking and Pupillometry
Joshua C. Peterson, Zachary A. Pardos, Martina A. Rau, Anna Swigart, Colin Gerber, Jon McKinsey
AIED3
2015 ITS Support for Conceptual and Perceptual Connection Making Between Multiple Graphical Representations
Martina A. Rau, Sally P. W. Wu
AIED1
2015 Why Do the Rich Get Richer? A Structural Equation Model to Test How Spatial Skills Affect Learning with Representations
Martina A. Rau
EDM1
2014 Multi-methods Approach for Domain-Specific Grounding: An ITS for Connection Making in Chemistry
Martina A. Rau, Amanda Siebert-Evenstone
Intelligent Tutoring Systems1
2013 Complementary Effects of Sense-Making and Fluency-Building Support for Connection Making: A Matter of Sequence?
Martina A. Rau, Vincent Aleven, Nikol Rummel
AIED1
2013 How to Use Multiple Graphical Representations to Support Conceptual Learning? Research-Based Principles in the Fractions Tutor
Martina A. Rau, Vincent Aleven, Nikol Rummel
AIED1
2013 Why interactive learning environments can have it all: resolving design conflicts between competing goals
abstract
Designing interactive learning environments (ILEs; e.g., intelligent tutoring systems, educational games, etc.) is a challenging interdisciplinary process that needs to satisfy multiple stakeholders. ILEs need to function in real educational settings (e.g., schools) in which a number of goals interact. Several instructional design methodologies exist to help developers address these goals. However, they often lead to conflicting recommendations. Due to the lack of an established methodology to resolve such conflicts, developers of ILEs have to rely on ad-hoc solutions. We present a principled methodology to resolve such conflicts. We build on a well-established design process for creating Cognitive Tutors, a highly effective type of ILE. We extend this process by integrating methods from multiple disciplines to resolve design conflicts. We illustrate our methodology's effectiveness by describing the iterative development of the Fractions Tutor, which has proven to be effective in classroom studies with 3,000 4th-6th graders.
Martina A. Rau, Vincent Aleven, Nikol Rummel, Stacie Rohrbach
CHI1
2013 Student Profiling from Tutoring System Log Data: When do Multiple Graphical Representations Matter?
Ryan Carlson, Konstantin Genin, Martina A. Rau, Richard Scheines
EDM3
2013 Does Representational Understanding Enhance Fluency - Or Vice Versa? Searching for Mediation Models
Martina A. Rau, Richard Scheines, Vincent Aleven, Nikol Rummel
EDM1
2012 TimeBlocks: mom, can I have another block of time
abstract
Time is a difficult concept for parents to communicate with young children. We developed TimeBlocks, a novel tangible, playful object to facilitate communication about concepts of time with young children. TimeBlocks consists of a set of cubic blocks that function as a physical progress bar. Parents and children can physically manipulate the blocks to represent the concept of time. We evaluated TimeBlocks through a field study in which six families tried TimeBlocks for four days at their homes. The results indicate that TimeBlocks played a useful role in facilitating the often challenging task of time-related communication between parents and children. We also report on a range of observed insightful novel uses of TimeBlocks in our study.
Eiji Hayashi, Martina A. Rau, Zhe Han Neo, Nastasha Tan, Sriram Ramasubramanian, Eric Paulos
CHI2
2012 Investigating Practice Schedules of Multiple Fraction Representations Using Knowledge Tracing Based Learning Analysis Techniques
Martina A. Rau, Zachary A. Pardos
EDM1
2012 Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations
Martina A. Rau, Richard Scheines
EDM1
2012 Sense Making Alone Doesn't Do It: Fluency Matters Too! ITS Support for Robust Learning with Multiple Representations
Martina A. Rau, Vincent Aleven, Nikol Rummel, Stacie Rohrbach
ITS1
2011 Thinking with Your Hands: Interactive Graphical Representations in a Tutor for Fractions Learning
Laurens Feenstra, Vincent Aleven, Nikol Rummel, Martina A. Rau, Niels Taatgen
AIED4
2010 Blocked versus Interleaved Practice with Multiple Representations in an Intelligent Tutoring System for Fractions
Martina A. Rau, Vincent Aleven, Nikol Rummel
Intelligent Tutoring Systems (1)1
2009 Intelligent Tutoring Systems with Multiple Representations and Self-Explanation Prompts Support Learning of Fractions
abstract
Although a solid understanding of fractions is foundational in mathematics, the concept of fractions remains a challenging one. Previous research suggests that multiple graphical representations (MGRs) may promote learning of fractions. Specifically, we hypothesized that providing students with MGRs of fractions, in addition to the conventional symbolic notation, leads to better learning outcomes as compared to instruction incorporating only one graphical representation. We anticipated, however, that MGRs would make the students' task more challenging, since they must link the representations and distill from them a common concept or principle. Therefore, we hypothesized further that self-explanation prompts would help students benefit from working with MGRs. To investigate these hypotheses, we conducted a classroom study in which 112 6th-grade students used intelligent tutors for fraction conversion and fraction addition. The results of the study show that students learned more with MGRs of fractions than with a single representation, but only when prompted to self-explain how the graphics relate to the symbolic fraction representations.
Martina A. Rau, Vincent Aleven, Nikol Rummel
AIED1