Taxonomy Of Tasks Research Articles

Understanding node-link and matrix visualizations of networks: A large-scale online experiment

AbstractWe investigated human understanding of different network visualizations in a large-scale online experiment. Three types of network visualizations were examined: node-link and two different sorting variants of matrix representations on a representative social network of either 20 or 50 nodes. Understanding of the network was quantified using task time and accuracy metrics on questions that were derived from an established task taxonomy. The sample size in our experiment was more than an order of magnitude larger (N = 600) than in previous research, leading to high statistical power and thus more precise estimation of detailed effects. Specifically, high statistical power allowed us to consider modern interaction capabilities as part of the evaluated visualizations, and to evaluate overall learning rates as well as ambient (implicit) learning. Findings indicate that participant understanding was best for the node-link visualization, with higher accuracy and faster task times than the two matrix visualizations. Analysis of participant learning indicated a large initial difference in task time between the node-link and matrix visualizations, with matrix performance steadily approaching that of the node-link visualization over the course of the experiment. This research is reproducible as the web-based module and results have been made available at: https://osf.io/qct84/.

Supporting the discoverability of open educational resources

Open Educational Resources (OERs), now available in large numbers, have a considerable potential to improve many aspects of society, yet one of the factors limiting this positive impact is the difficulty to discover them. This study investigates and proposes strategies to better support educators in discovering OERs, mainly focusing on secondary education. The literature suggests that the effectiveness of existing search systems could be improved by supporting high-level and domain-oriented tasks. Hence a preliminary taxonomy of discovery-related tasks was developed, based on the analysis of the literature, interpreted through Information Foraging Theory. This taxonomy was empirically evaluated with a few experienced educators, to preliminary identify an interesting class of Query By Examples (QBE) expansion by similarity tasks, which avoids the need to decompose natural high-level tasks in a complex sequence of sub-tasks. Following the Design Science Research methodology, three prototypes to support as well as to refine those tasks were iteratively designed, implemented, and evaluated involving an increasing number of educators in usability oriented studies. The resulting high-level and domain-oriented blended search/recommendation strategy, transparently replicates Google searches in specialized networks, and identifies similar resources with a QBE strategy. It makes use of a domain-oriented similarity metric based on shared schema.org/LRMI alignments to educational frameworks, and clusters results in expandable classes of comparable degree of similarity. The summative evaluation shows that educators appreciate this exploratory-oriented strategy because – balancing similarity and diversity – it supports their high-level tasks, such as lesson planning and personalization of education.

Compara

The OCAD University Visual Analytics Laboratory (VAL) has developed a taxonomy of end users, software systems, data types, tasks and interactivity within the domain of smart city transportation planning. This paper contributes to the taxonomy by creating Compara, an intuitive, interactive and searchable index that visualizes the attributes of software from a wide-range of applications and technologies. The taxonomy began as a spreadsheet that we transformed into a custom interactive data visualization that could help users find and understand existing tools and their attributes.
 The taxonomy and interactive index are a component of the iCity project which brings together academic, government, and industrial partners in order to improve the quality of life for urban residents and visitors through the development and integration of advanced IT infrastructure for the purpose of managing transit and transportation. The taxonomy and resulting tools discussed in this paper have expanded to include resources for urban planning which also impact transit and transportation.
 One of the primary uses of the taxonomy is to help various iCity project teams and stakeholders locate software that may be useful to their design and development process, as well as to understand the end-users of this technology. The taxonomy may be used to develop a city management dashboard for city planners and analysts, or equally, to design a city-services facing mobile service application.

New Impressions in Interaction Design: A Task Taxonomy for Elastic Displays

Abstract Novel shape-changing interfaces promise to provide a rich haptic experience for human-computer interaction. As a specific instance of shape-changing interfaces, Elastic Displays provide large interaction surfaces that can be temporally deformed using force-touch. The unique property of these displays is that they automatically return to their initial flat state. Recently, several review and position papers have stimulated a discussion towards consolidating the knowledge about shape-changing interfaces. The knowledge about Elastic Displays is similarly scattered across multiple publications from recent years. This paper contributes a task taxonomy based on productive uses of Elastic Displays found in literature, on the web, and in our interaction lab. This taxonomy emphasizes tasks, but also encompasses general aspects regarding content types, visualization technology, and interaction styles. All aspects of the taxonomy are illustrated using case studies from literature.

Cognitive-Motor Interference in Neurodegenerative Disease: A Narrative Review and Implications for Clinical Management.

This paper provides a narrative review of cognitive motor interference in neurodegeneration, including brain imaging findings specific to interference effects in neurodegenerative disease, and dual task assessment and intervention in Parkinson’s disease (PD), multiple sclerosis (MS), and Huntington’s disease (HD). In a healthy central nervous system the ability to process information is limited. Limitations in capacity to select and attend to inputs influence the ability to prepare and perform multiple tasks. As a result, the system balances demands, switching attention to the most task-relevant information as it becomes available. Limitations may become more apparent in persons with neurodegenerative diseases (ND) with system-specific impairments in PD, MS, and HD. These ND affect both cognitive and motor function and are thus particularly susceptible to dual task interference. Issues related to performer and task characteristics and implications of these findings for both the standard assessment of dual task abilities as well as development and evaluation of interventions aimed at improving dual task ability are discussed. In addition, we address the need for optimizing individualized assessment, intervention and evaluation of dual task function by choosing cognitive and motor tasks and measures that are sensitive to and appropriate for the individual’s level of function. Finally, we use current evidence to outline a 5-step process of clinical decision making that uses the dual task taxonomy as a framework for assessment and intervention.

Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults.

BackgroundIncreasingly, eHealth involves health data visualizations to enable users to better understand their health situation. Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the type of data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-data taxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists.ObjectiveThe aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion of older adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empirical evaluation and to facilitate a common understanding and language in eHealth data visualization.MethodsAnswers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations.ResultsConsultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts and older adults disagreed on the importance of mentoring (χ24=14.1, P=.002) and monitoring (χ24=22.1, P<.001). The answers to the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality management to the aforementioned tasks. Here, group differences in normalized code counts were identified for “monitoring” between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t96=2431, P=.02). Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned to eHealth tasks by both experimental groups.ConclusionsWe empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At the same time, the functionality dimension of the taxonomy for telemedicine—chosen as the basis for the construction of present taxonomy—was confirmed.

Designing Mathematical Thinking Tasks

Research has shown that the types of tasks assigned to students affect their learning. Various authors have described desirable features of mathematical tasks or of the activity they initiate. Others have suggested task taxonomies that might be used in classifying mathematical tasks. Drawing on this literature, we propose a set of task types that are deemed appropriate for undergraduate students and which foster mathematical habits of mind. These are: generating examples; analyzing reasoning; evaluating mathematical statements; conjecturing and/or generalizing; visualizing; and using definitions. We give rationales for our choices and examples of each type of task suitable for use in an introductory calculus course.

Meta-Analysis of the Effects of Computerized Cognitive Training on Executive Functions: a Cross-Disciplinary Taxonomy for Classifying Outcome Cognitive Factors.

The growing prevalence of neurodegenerative disorders associated with aging and cognitive decline has generated increasing cross-disciplinary interest in non-pharmacological interventions, such as computerized cognitive training (CCT), which may prevent or slow cognitive decline. However, inconsistent findings across meta-analytic reviews in the field suggest a lack of cross-disciplinary consensus and on-going debate regarding the benefits of CCT. We posit that a contributing factor is the lack of a theoretically-based taxonomy of constructs and representative tasks typically used. An integration of the Cattell-Horn-Carroll (CHC) taxonomy of broad and narrow cognitive factors and the Miyake unity-diversity theory of executive functions (EF) is proposed (CHC-M) as an attempt to clarify this issue through representing and integrating the disciplines contributing to CCT research. The present study assessed the utility of this taxonomy by reanalyzing the Lampit et al. (2014) meta-analysis of CCT in healthy older adults using the CHC-M framework. Results suggest that: 1) substantively different statistical effects are observed when CHC-M is applied to the Lampit et al. meta-analytic review, leading to importantly different interpretations of the data; 2) typically-used classification practices conflate Executive Function (EF) tasks with fluid reasoning (Gf) and retrieval fluency (Gr), and Attention with sensory perception; and 3) there is theoretical and practical advantage in differentiating attention and working-memory tasks into the narrow shifting, inhibition, and updating EF domains. Implications for clinical practice, particularly for our understanding of EF are discussed.

Pedagoški aspekti primene koncepta ishoda učenja u visokoškolskoj nastavi

U radu se razmatra problematika definisanja, planiranja i upotrebe ishoda učenja u visokoškolskoj nastavi, kao jednog o ključnih aspekata nastavnog rada. Koncept ishoda učenja posebno je aktuelizovan Bolonjskim procesom i predstavlja značajan alat za planiranje nastavnog rada i kreiranje kurikuluma kao i za prepoznavanje stečenih znanja i veština studenata. Analizirani su relevantni dokumenti u okviru reforme visokog obrazovanja kojima se ističe značaj ishoda učenja za usaglašavanje različitih sistema visokog obrazovanja u okviru Evrope. Akcenat je stavljen na pedagoške implikacije upotrebe ishoda učenja i na njihov značaj za nastavni rad, posebno iz perspektive pristupa usmerenog na studenta. Dat je prikaz konkretnih uputstava i smernica za kreiranje i definisanje ishoda učenja. Prikazana je SOLO taksonomija kao jedna od značajnijih taksonomija za pisanje ishoda učenja u visokom obrazovanju i za procenjivanje usvojenih znanja i veština studenata. Rezultati ukazuju na to da ishodi učenja predstavljaju konstrukt koji omogućava nastavnicima efikasno planiranje i organizovanje nastave, kao i procenjivanje stečenog znanja i kompetencija studenata. Sa druge strane, studentima omogućava mobilnost na druge univerzitete, priznavanje stečenih kvalifikacija, priznavanje prethodnog učenja i dr. Zaključuje se da adekvatna implementacija ishoda učenja u nastavnu praksu, studijske programe i predmete u značajnoj meri utiče na kvalitet obrazovanja.

On the track of human errors - Procedure and results of an innovative assembly planning method

Manual assembly operations are the endmost place of the production process and with it represent a melting pot for organizational, time-related and qualitative errors of manufacturing. Because competitive advantages are increasingly determined in the manual assembly, the creation of economical and reliable work steps is of fundamental importance for future business success. For that reason, at the institute of the authors, a procedure has been developed to use the knowledge of the Expert System for Task Taxonomy that was originally developed to evaluate the error rate of control and surveillance activities in safety critical areas for creating a computer-aided expert system that allows predicting human error probabilities for manual assembly operations. Applying this expert system, production processes can already be optimized during the phase of product design by avoiding critical process steps at an early stage of the planning process. On the basis of a case study, this paper introduces the conception of an Excel-based software tool that allows an automated application of the developed assembly planning method and thereby, generates reliable risk analyses of manual work tasks with little expenditure of personnel and time.

Towards a Future Reallocation of Work between Humans and Machines Taxonomy of Tasks and Interaction Types in the Context of Machine Learning

In today’s race for competitive advantages, more and more companies implement innovations in artificial intelligence and machine learning (ML). Although these machines take over tasks that have been executed by humans, they will not make human workforce obsolete. To leverage the potentials of ML, collaboration between humans and machines is necessary. Before collaboration processes can be developed, a classification of tasks in the field of ML is needed. Therefore, we present a taxonomy for the classification of tasks due to their complexity and the type of interaction. To derive insights about typical tasks and task-complexity, we conducted a literature review as well as a focus group workshop. We identified three levels of task-complexity and three types of interactions. Connecting them reveals three generic types of tasks. We provide prescriptive knowledge inherent in the task/interaction-taxonomy.

Developing the foundations of a cognition-based human reliability analysis model via mapping task types and performance-influencing factors: Application to radiotherapy

Most human reliability analysis methods have been developed for nuclear power plant applications; this challenges the application of the available techniques to other domains. Indeed, for application to a specific domain, a human reliability analysis method should address the relevant tasks and performance conditions. The aim of this article is to propose a methodology to develop a generic task type–performance-influencing factor structure, specific for application to a domain of interest and directly linked to an underlying cognitive framework of literature. The structure provides the foundation of a human reliability analysis method built on the generic task type concept; it identifies the sector-specific performance-influencing factor effects on the failure probability that the method needs to represent and quantify for each generic task type. The methodology is intended to support a systematic and traceable process to develop the generic task type–performance-influencing factor structure, to ease the review of the process and of its results and, in case, identify and implement changes to the structure. The proposed methodology is applied to the radiotherapy domain allowing the development of sector-specific taxonomies of representative critical tasks, their failure modes, underlying cognitive failure mechanism, and influencing performance-influencing factors. This is part of a broader activity carried out by the Risk and Human Reliability Group at the Paul Scherrer Institute of Switzerland to develop a human reliability analysis method, specific for the radiotherapy domain. The activity is conducted in close cooperation with Paul Scherrer Institute’s Center for Proton Therapy, where a first application of the method is foreseen.

Beyond Tasks: An Activity Typology for Visual Analytics.

As Visual Analytics (VA) research grows and diversifies to encompass new systems, techniques, and use contexts, gaining a holistic view of analytic practices is becoming ever more challenging. However, such a view is essential for researchers and practitioners seeking to develop systems for broad audiences that span multiple domains. In this paper, we interpret VA research through the lens of Activity Theory (AT)-a framework for modelling human activities that has been influential in the field of Human-Computer Interaction. We first provide an overview of Activity Theory, showing its potential for thinking beyond tasks, representations, and interactions to the broader systems of activity in which interactive tools are embedded and used. Next, we describe how Activity Theory can be used as an organizing framework in the construction of activity typologies, building and expanding upon the tradition of abstract task taxonomies in the field of Information Visualization. We then apply the resulting process to create an activity typology for Visual Analytics, synthesizing a wide range of systems and activity concepts from the literature. Finally, we use this typology as the foundation of an activity-centered design process, highlighting both tensions and opportunities in the design space of VA systems.

A taxonomy of visualization tasks for the analysis of biological pathway data

BackgroundUnderstanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity.ResultsHere, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy.ConclusionsOur taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts.

Users and uses of a global union catalog: A mixed‐methods study of WorldCat.org

This paper presents the first large‐scale investigation of the users and uses of WorldCat.org, the world's largest bibliographic database and global union catalog. Using a mixed‐methods approach involving focus group interviews with 120 participants, an online survey with 2,918 responses, and an analysis of transaction logs of approximately 15 million sessions from WorldCat.org, the study provides a new understanding of the context for global union catalog use. We find that WorldCat.org is accessed by a diverse population, with the three primary user groups being librarians, students, and academics. Use of the system is found to fall within three broad types of work‐task (professional, academic, and leisure), and we also present an emergent taxonomy of search tasks that encompass known‐item, unknown‐item, and institutional information searches. Our results support the notion that union catalogs are primarily used for known‐item searches, although the volume of traffic to WorldCat.org means that unknown‐item searches nonetheless represent an estimated 250,000 sessions per month. Search engine referrals account for almost half of all traffic, but although WorldCat.org effectively connects users referred from institutional library catalogs to other libraries holding a sought item, users arriving from a search engine are less likely to connect to a library.

Design and evaluation of line symbolizations for origin–destination flow maps

We present the results of a user study comparing variants of commonly used line symbolizations for directed origin–destination flow maps. Our design and evaluation consisted of five line symbolizations that employ a combination of following visual variables: arrowheads, origin–destination coloring (color hue, and value), line shortening, line width, tapered edges (varying width from wide to narrow, and narrow to wide), and curvature asymmetry and strength. To guide our evaluation, we used a task-by-type typology and chose four representative tasks that are commonly used in flow map reading: identifying dominant direction of flows, flows with the highest magnitude (volume), spatial focusing of long flows toward a destination, and clusters of high net-exports (net-outflow). We systematically analyzed user responses and task performance which we measured by task completion time and accuracy. We designed a web-based flow mapping and testing framework and recruited the participants from Amazon Mechanical Turk. To demonstrate the application and user experiment, we used 16 commodity flow data sets in the United States from 2007 and systematically rotated the layouts to evaluate the effect of layout orientation. From this study, we can conclude that there is potential usefulness for all of the five symbolizations we tested; however, the influence of the design on performance and perception depends on the type of the task. Also, we found that data and layout orientation have significant effects on performance and perception of patterns in flow maps which we attribute to the change in visual saliency of node and flow patterns in relation to the way users scan the map. We recommend that the choice of line symbolization should be guided by a task taxonomy which end users are expected to perform. We discuss various design trade-offs and recommendations and potential future work for designing and evaluating line symbolizations for flow mapping.

Evaluating Cartogram Effectiveness

Cartograms are maps in which areas of geographic regions, such as countries and states, appear in proportion to some variable of interest, such as population or income. Cartograms are popular visualizations for geo-referenced data that have been used for over a century to illustrate patterns and trends in the world around us. Despite the popularity of cartograms, and the large number of cartogram types, there are few studies evaluating the effectiveness of cartograms in conveying information. Based on a recent task taxonomy for cartograms, we evaluate four major types of cartograms: contiguous, non-contiguous, rectangular, and Dorling cartograms. We first evaluate the effectiveness of these cartogram types by quantitative performance analysis (time and error). Second, we collect qualitative data with an attitude study and by analyzing subjective preferences. Third, we compare the quantitative and qualitative results with the results of a metrics-based cartogram evaluation. Fourth, we analyze the results of our study in the context of cartography, geography, visual perception, and demography. Finally, we consider implications for design and possible improvements.

Robotics and STEM learning: students’ achievements in assignments according to the P3 Task Taxonomy—practice, problem solving, and projects

This study presents the case of development and evaluation of a STEM-oriented 30-h robotics course for junior high school students (n = 32). Class activities were designed according to the P3 Task Taxonomy, which included: (1) practice—basic closed-ended tasks and exercises; (2) problem solving—small-scale open-ended assignments in which the learner can choose the solution method or arrive at different answers; and (3) project-based learning—open-ended challenging tasks. The research aimed at exploring students’ working patterns, achievements in learning the course, and the impact of this experience on students’ motivation to learn STEM subjects. Evaluation tools included a final exam on factual, procedural and conceptual knowledge in the STEM subject learned in the course, class observations, interviews with the students, and administrating an attitude questionnaire before and after the course. Since the experimental class was quite heterogenic in regard to students’ prior learning achievements and motivation to learn, some of the students completed just the basic exercises, others coped well with the problem-solving tasks, and only a few took it upon themselves to carry out a complex project. However, all students showed high motivation to learn robotics and STEM subjects. In summary, robotics provides a very rich and attractive learning environment for STEM education. Yet, the realization of this potential depends largely on careful design of the course methodology and especially the students’ assignments in the class. One should recognize that often only some students are capable of learning a new subject on their own through project work, and these students also need to gain additional knowledge and skills before dealing with complex projects.

Development and evaluation of a specialized task taxonomy for spatial planning – A map literacy experiment with topographic maps

Topographic maps are among the most commonly used map types, however, their complex and information-rich designs depicting natural, human-made and cultural features make them difficult to read. Regardless of their complexity, spatial planners make extensive use of topographic maps in their work. On the other hand, various studies suggest that map literacy among the development planning professionals in South Africa is not very high. The widespread use of topographic maps combined with the low levels of map literacy presents challenges for effective development planning. In this paper we address some of these challenges by developing a specialized task taxonomy based on systematically assessed map literacy levels; and conducting an empirical experiment with topographic maps to evaluate our task taxonomy. In such empirical studies if non-realistic tasks are used, the results of map literacy tests may be skewed. Furthermore, experience and familiarity with the studied map type play a role in map literacy. There is thus a need to develop map literacy tests aimed at planners specifically. We developed a taxonomy of realistic map reading tasks typically executed during the planning process. The taxonomy defines six levels tasks of increasing difficulty and complexity, ranging from recognising symbols to extracting knowledge. We hypothesized that competence in the first four levels indicates functional map literacy. In this paper, we present results from an empirical experiment with 49 map literate participants solving a subset of tasks from the first four levels of the taxonomy with a topographic map. Our findings suggest that the proposed taxonomy is a good reference for evaluating topographic map literacy. Participants solved the tasks on all four levels as expected and we therefore conclude that the experiment based on the first four levels of the taxonomy successfully determined the functional map literacy of the participants. We plan to continue the study for the remaining levels, repeat the experiments with a group of map illiterate participants to confirm that the taxonomy can also be used to determine map illiteracy.

Visual Text Analysis in Digital Humanities

AbstractIn 2005, Franco Moretti introduced Distant Reading to analyse entire literary text collections. This was a rather revolutionary idea compared to the traditional Close Reading, which focuses on the thorough interpretation of an individual work. Both reading techniques are the prior means of Visual Text Analysis. We present an overview of the research conducted since 2005 on supporting text analysis tasks with close and distant reading visualizations in the digital humanities. Therefore, we classify the observed papers according to a taxonomy of text analysis tasks, categorize applied close and distant reading techniques to support the investigation of these tasks and illustrate approaches that combine both reading techniques in order to provide a multi‐faceted view of the textual data. In addition, we take a look at the used text sources and at the typical data transformation steps required for the proposed visualizations. Finally, we summarize collaboration experiences when developing visualizations for close and distant reading, and we give an outlook on future challenges in that research area.