Transcription with Incremental Presentation of the Stimulus (TIPS) is an instrument to assess users’ graph comprehension through trace data. TIPS records cognitive micro-behaviours (e.g., keystrokes and pen-strokes) with millisecond accuracy across cycles of viewing and copying. These signals provide eight potential TIPS measures to study learners’ competence. In an experiment with 30 participants and visualizations of varying complexity, several TIPS measures, particularly when normalized using a test of rigid spatial transformations (Mental Rotation Test), showed moderate to strong correlations (r = 0.5–0.6) with an independent measure of graph comprehension. Results were most robust for complex stimuli. Potential practical benefits of TIPS include shorter testing time, no need to create new questions for different stimuli, and the ability to fully automate scoring, making the assessment more efficient and scalable.

In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE’s web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Trees with just one click, utilising a back-end engine based on RST. A table of cognitive costs, based on the cognitive Representational Interpretive Structure Theory (RIST), that a representation places on a particular profile of user is produced at the same time. MaRE is general and domain independent, applicable to other representations encoded in RST. It may enhance mathematical education and research, facilitating multi-modal knowledge representation and discovery.

Providing an approach to model the memory structures that humans build as they use visualizations could be useful for researchers, designers and educators in the field of information visualization. Cheng and colleagues formulated Representation Interpretive Structure Theory (RIST) for that purpose. RIST adopts a human information processing perspective in order to address the immediate, short timescale, cognitive load likely to be experienced by visualization users. RIST is operationalized in a graphical modeling notation and browser-based editor. This paper demonstrates the utility of RIST by showing that (a): RIST models are compatible with established empirical and computational cognitive findings about differences in human performance on alternative representations; (b) they can encompass existing explanations from the literature; and, (c) they provide new explanations about causes of those performance differences.

Existing data visualization design guidelines focus primarily on constructing grammatically-correct visualizations that faithfully convey the values and relationships in the underlying data. However, a designer may create a grammatically-correct visualization that still leaves audiences susceptible to reasoning misleaders, e.g. by failing to normalize data or using unrepresentative samples. Reasoning misleaders are especially pernicious when presenting public policy data, where data-driven decisions can affect public health, safety, and economic development. Through textual analysis, a formative evaluation, and iterative design with 19 policy communicators, we construct an actionable visualization design framework, V-FRAMER, that effectively synthesizes ways of mitigating reasoning misleaders. We discuss important design considerations for frameworks like V-FRAMER, including using concrete examples to help designers understand reasoning misleaders, and using a hierarchical structure to support example-based accessing. We further describe V-FRAMER’s congruence with current practice and how practitioners might integrate the framework into their existing workflows. Related materials available at: https://osf.io/q3uta/.

Competence Assessment by Chunk Hierarchy Evaluation with Transcription-tasks (CACHET) was proposed by Cheng [14]. It analyses micro-behaviors captured during cycles of stimulus viewing and copying in order to probe chunk structures in memory. This study extends CACHET by applying it to the domain of graphs and charts. Since drawing strategies are diverse, a new interactive stimulus presentation method is introduced: Transcription with Incremental Presentation of the Stimulus (TIPS). TIPS aims to reduce strategy variations that mask the chunking signal by giving users manual element-by-element control over the display of the stimulus. The potential of TIPS, is shown by the analysis of six participants transcriptions of stimuli of different levels of familiarity and complexity that reveal clear signals of chunking. To understand how the chunk size and individual differences drive TIPS measurements, a CPM-GOMS model was constructed to formalize the cognitive process involved in stimulus comprehension and chunk creation.

Access control (AC) policy creation and implementation is challenging because of its complex structure and specifications. This paper explores the potential of two novel interactive graphical displays whose design draws upon alternative principles of effective interface design. One tool (CDAC) exploits the power of Euler diagrams to visualize information and interrelate an organization's management structure and details of the file structure of digital resources. The other tool (DMAC) deploys a diagrammatic matrix to coordinate permissions allocations into a context of structural information. The third tool (SLAC) utilized a conventional visualization and served as the control group for comparison against the other two tools. An experiment with 91 relatively novice participants demonstrates that both tools, even with minimal training, support users' comprehension of AC policies and their implementation, as well as a more familiar traditional design of AC tools.

Pólya and others recognised that an appropriate representation of a problem is key for enabling us to solve it. But choosing the right representation is a problem that novice problem solvers find difficult, so must turn to experts for guidance. In this paper, we present a study that examines how human experts recommend representations. We asked high school mathematics teachers to order representational systems based on their suitability generally, and with respect to a student profile. We found the teachers updated their recommendations based on the problem and student profile, but were inconsistent with each other. This inconsistency highlights a need for more training and support in representational system selection.

Choosing an effective representation is fundamental to the ability of the representation's user to exploit it for the intended purpose. The major contribution of this paper is to provide a novel, flexible framework, rep2rep, that can be used by AI systems to recommend effective representations. What makes an effective representation is determined by whether it expresses the necessary information, supports the execution of tasks, and reflects the user's cognitive abilities. In general, there is no single `most effective' representation for every problem and every user, which makes it difficult to choose one from the plethora of possible representations. To address this, rep2rep includes: a domain-independent language for describing representations, algorithms that compute measures of informational suitability and overall cognitive cost, and uses these measures to recommend representations. We demonstrate the application of rep2rep in the probability domain. Importantly, our framework provides the foundations for personalised interaction with AI systems in the context of representation choice.

Mathematics and computing students learn new concepts and fortify their expertise by solving problems. The representation of a problem, be it through algebra, diagrams, or code, is key to understanding and solving it. Multiple-representation interactive environments are a promising approach, but the task of choosing an appropriate representation is largely placed on the user. We propose a new method to recommend representations based on correspondences: conceptual links between domains. Correspondences can be used to analyse, identify, and construct analogies even when the analogical target is unknown. This paper explains how correspondences build on probability theory and Gentner's structure-mapping framework; proposes rules for semi-automated correspondence discovery; and describes how correspondences can explain and construct analogies.

An assumption behind much work on the use of tangibles for learning is that there are individual cognitive benefits related to the physical manipulation of materials. However, previous work that has shown learning benefits in using physical materials often hasn't adequately controlled for the covariates of physicality.

Three semantic traits are considered central in the design of interfaces for scheduling: (1) expressive richness, (2) dependency constraints and (3) global homogeneity. These traits are exploited by diagrammatic representations that 'structurally integrate' systems of domain relations through analogous systems of diagrammatic relations. These traits are discussed in relation to ROLLOUT - a prototype representational system for bakery planning and scheduling. This article provides a semantic evaluation of ROLLOUT in terms of these representational traits and proposes explanations of how these traits lead to increased cognitive support in scheduling activities.

How should problem-solving representations for complex knowledge domains be designed? Traditional approaches typically address the problem of semantic complexity by designing systems that offer multiple and often heterogonous forms of representation. The REEP approach advocates structure preserving integration of the different classes and perspectives of a domain within a single representation. This work reports on a novel representational system for nurse rostering that was designed under the REEP approach. An empirical evaluation suggests the kinds of knowledge support provided by the representation and demonstrates that participants prefer fully integrated over selective views of information even though the former increases visual complexity. This knowledge support is explained in terms of more abstract domain independent cognitive benefits that we present as reasons for adopting the REEP approach.