3,900 research outputs found
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis
Exploring data requires a fast feedback loop from the analyst to the system,
with a latency below about 10 seconds because of human cognitive limitations.
When data becomes large or analysis becomes complex, sequential computations
can no longer be completed in a few seconds and data exploration is severely
hampered. This article describes a novel computation paradigm called
Progressive Computation for Data Analysis or more concisely Progressive
Analytics, that brings at the programming language level a low-latency
guarantee by performing computations in a progressive fashion. Moving this
progressive computation at the language level relieves the programmer of
exploratory data analysis systems from implementing the whole analytics
pipeline in a progressive way from scratch, streamlining the implementation of
scalable exploratory data analysis systems. This article describes the new
paradigm through a prototype implementation called ProgressiVis, and explains
the requirements it implies through examples.Comment: 10 page
Medical students' cognitive load in volumetric image interpretation:Insights from human-computer interaction and eye movements
Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum
Digital ink and differentiated subjective ratings for cognitive load measurement in middle childhood
Background: New methods are constantly being developed
to adapt cognitive load measurement to different contexts.
However, research on middle childhood students' cognitive load measurement is rare. Research indicates that the
three cognitive load dimensions (intrinsic, extraneous, and
germane) can be measured well in adults and teenagers using
differentiated subjective rating instruments. Moreover, digital
ink recorded by smartpens could serve as an indicator for
cognitive load in adults.
Aims: With the present research, we aimed at investigating
the relation between subjective cognitive load ratings, velocity and pressure measures recorded with a smartpen, and
performance in standardized sketching tasks in middle childhood students.
Sample: Thirty-six children (age 7–12) participated at the
university's laboratory.
Methods: The children performed two standardized sketching tasks, each in two versions. The induced intrinsic cognitive load or the extraneous cognitive load was varied between
the versions. Digital ink was recorded while the children
drew with a smartpen on real paper and after each task, they
were asked to report their perceived intrinsic and extraneous
cognitive load using a newly developed 5-item scale.
Results: Results indicated that cognitive load ratings as well
as velocity and pressure measures were substantially related
to the induced cognitive load and to performance in both
sketching tasks. However, cognitive load ratings and smartpen measures were not substantially related. Conclusions: Both subjective rating and digital ink hold
potential for cognitive load and performance measurement.
However, it is questionable whether they measure the exact
same constructs
Modelling students' effort using behavioral data
International audienceStudents' effort is often considered a key factor for students' success. It has several related definitions, none of which is widely adopted. In this paper, we define students' effort as the experienced cognitive load, which is the total amount of cognitive resources used during the execution of a given task. We propose an effort model to quantify students' effort based on this construct. Our approach uses behavioral measures (i.e., interaction and eye gaze data). Our preliminary results show that the eye gaze measures have an intermediary relationship with effort, while the interaction measures have a weak relationship with effort and seem slightly complementary to eye gaze measures
NodeTrix: Hybrid Representation for Analyzing Social Networks
The need to visualize large social networks is growing as hardware
capabilities make analyzing large networks feasible and many new data sets
become available. Unfortunately, the visualizations in existing systems do not
satisfactorily answer the basic dilemma of being readable both for the global
structure of the network and also for detailed analysis of local communities.
To address this problem, we present NodeTrix, a hybrid representation for
networks that combines the advantages of two traditional representations:
node-link diagrams are used to show the global structure of a network, while
arbitrary portions of the network can be shown as adjacency matrices to better
support the analysis of communities. A key contribution is a set of interaction
techniques. These allow analysts to create a NodeTrix visualization by dragging
selections from either a node-link or a matrix, flexibly manipulate the
NodeTrix representation to explore the dataset, and create meaningful summary
visualizations of their findings. Finally, we present a case study applying
NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate
the capabilities of NodeTrix as both an exploration tool and an effective means
of communicating results
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