Taxonomy Of Tasks Research Articles

TYPES AND DESCRIPTIVE ASPECTS OF EDUCATIONAL TASKS IN DEVELOPING LISTENING COMPREHENSION SKILLS

This article explores the diverse landscape of educational tasks aimed at fostering listening comprehension skills among learners. Through a comprehensive examination of task types and descriptive aspects, it unveils the intricacies that underpin effective pedagogical strategies in this domain. Drawing upon various methodologies and theoretical frameworks, the article offers insights into the design, implementation, and contextual considerations of these tasks. From structured exercises to authentic interactions, each task type serves a unique purpose in nurturing listening proficiency. By elucidating the taxonomy of educational tasks and examining their empirical evidence, this article equips educators with valuable resources to tailor instructional practices effectively, whether in traditional classroom settings or digital learning environments.

Benchmarking Large Language Models on Controllable Generation under Diversified Instructions

While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constraints that might be entailed in various instructions. As a significant aspect of LLM alignment, it is thus important to formulate such a specialized set of instructions as well as investigate the resulting behavior of LLMs. To address this vacancy, we propose a new benchmark CoDI-Eval to systematically and comprehensively evaluate LLMs' responses to instructions with various constraints. We construct a large collection of constraints-attributed instructions as a test suite focused on both generalization and coverage. Specifically, we advocate an instruction diversification process to synthesize diverse forms of constraint expression and also deliberate the candidate task taxonomy with even finer-grained sub-categories. Finally, we automate the entire evaluation process to facilitate further developments. Different from existing studies on controllable text generation, CoDI-Eval extends the scope to the prevalent instruction-following paradigm for the first time. We provide extensive evaluations of representative LLMs (e.g., ChatGPT, Vicuna) on CoDI-Eval, revealing their limitations in following instructions with specific constraints and there is still a significant gap between open-source and commercial closed-source LLMs. We believe this benchmark will facilitate research into improving the controllability of LLMs' responses to instructions. Our data and code are available at https://github.com/Xt-cyh/CoDI-Eval.

A Multi-Level Task Framework for Event Sequence Analysis.

Despite the development of numerous visual analytics tools for event sequence data across various domains, including but not limited to healthcare, digital marketing, and user behavior analysis, comparing these domain-specific investigations and transferring the results to new datasets and problem areas remain challenging. Task abstractions can help us go beyond domain-specific details, but existing visualization task abstractions are insufficient for event sequence visual analytics because they primarily focus on multivariate datasets and often overlook automated analytical techniques. To address this gap, we propose a domain-agnostic multi-level task framework for event sequence analytics, derived from an analysis of 58 papers that present event sequence visualization systems. Our framework consists of four levels: objective, intent, strategy, and technique. Overall objectives identify the main goals of analysis. Intents comprises five high-level approaches adopted at each analysis step: augment data, simplify data, configure data, configure visualization, and manage provenance. Each intent is accomplished through a number of strategies, for instance, data simplification can be achieved through aggregation, summarization, or segmentation. Finally, each strategy can be implemented by a set of techniques depending on the input and output components. We further show that each technique can be expressed through a quartet of action-input-output-criteria. We demonstrate the framework's descriptive power through case studies and discuss its similarities and differences with previous event sequence task taxonomies.

SpreadLine: Visualizing Egocentric Dynamic Influence.

Egocentric networks, often visualized as node-link diagrams, portray the complex relationship (link) dynamics between an entity (node) and others. However, common analytics tasks are multifaceted, encompassing interactions among four key aspects: strength, function, structure, and content. Current node-link visualization designs may fall short, focusing narrowly on certain aspects and neglecting the holistic, dynamic nature of egocentric networks. To bridge this gap, we introduce SpreadLine, a novel visualization framework designed to enable the visual exploration of egocentric networks from these four aspects at the microscopic level. Leveraging the intuitive appeal of storyline visualizations, SpreadLine adopts a storyline-based design to represent entities and their evolving relationships. We further encode essential topological information in the layout and condense the contextual information in a metro map metaphor, allowing for a more engaging and effective way to explore temporal and attribute-based information. To guide our work, with a thorough review of pertinent literature, we have distilled a task taxonomy that addresses the analytical needs specific to egocentric network exploration. Acknowledging the diverse analytical requirements of users, SpreadLine offers customizable encodings to enable users to tailor the framework for their tasks. We demonstrate the efficacy and general applicability of SpreadLine through three diverse real-world case studies (disease surveillance, social media trends, and academic career evolution) and a usability study.

2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualisations in Virtual Reality.

Relational information between different types of entities is often modelled by a multilayer network (MLN) - a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs.

An Analysis on the Kind of Tasks Found in Modul Pembelajar SMP Terbuka Bahasa Inggris: Modul 1 It’s English Time for 8th Grade

Tasks on language textbooks are essential for displaying materials, assisting students in using the language, and fostering critical and creative thinking. This study's objective is to examine the task in the English textbook, Modul Pembelajar SMP Terbuka Bahasa Inggris: Modul 1 It’s English Time, as well as the various types of assignments it offers in the eighth grade List making (24%) ordering and sorting (24%) comparing (20%) problem solving (32%) and sharing personal experiences (0%) were the least common task types offered in Modul Pembelajar SMP Terbuka Bahasa Inggris: Modul 1 It's English Time for 8th Grade, according to Willis's (2000) task taxonomy. In this study, we employed a descriptive qualitative and content analytic design. The study is also quantitative in that we calculated and tabulated the frequency and percentage of each sort of activity. The findings of this study are expected to increase English teachers' awareness of various task kinds that are crucial to the learning process. For developers to improve and add more communicative challenges to the existing English Learning teaching resources.

Machine Learning for Ancient Languages: A Survey

Abstract Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature. Technological aids have long supported the study of ancient texts, but in recent years advances in artificial intelligence and machine learning have enabled analyses on a scale and in a detail that are reshaping the field of humanities, similarly to how microscopes and telescopes have contributed to the realm of science. This article aims to provide a comprehensive survey of published research using machine learning for the study of ancient texts written in any language, script, and medium, spanning over three and a half millennia of civilizations around the ancient world. To analyze the relevant literature, we introduce a taxonomy of tasks inspired by the steps involved in the study of ancient documents: digitization, restoration, attribution, linguistic analysis, textual criticism, translation, and decipherment. This work offers three major contributions: first, mapping the interdisciplinary field carved out by the synergy between the humanities and machine learning; second, highlighting how active collaboration between specialists from both fields is key to producing impactful and compelling scholarship; third, highlighting promising directions for future work in this field. Thus, this work promotes and supports the continued collaborative impetus between the humanities and machine learning.

Do you want to promote recall, perceptions, or behavior? The best data visualization depends on the communication goal.

Data visualizations can be effective and inclusive means for helping people understand health-related data. Yet numerous high-quality studies comparing data visualizations have yielded relatively little practical design guidance because of a lack of clarity about what communicators want their audience to accomplish. When conducting rigorous evaluations of communication (eg, applying the ISO 9186 method), describing the process simply as evaluating "comprehension" or "interpretation" of visualizations fails to do justice to the true range of outcomes being studied. We present newly developed taxonomies of outcome measures and tasks that are guiding a large-scale systematic review of the health numbers communication literature. Using these taxonomies allows a designer to determine whether a specific data presentation format or feature supports or inhibits the desired audience cognitions, feelings, or behaviors. We argue that taking a granular, outcomes-based approach to designing and evaluating information visualization research is essential to deriving practical, actionable knowledge from it.

The State of the Art in Creating Visualization Corpora for Automated Chart Analysis

AbstractWe present a state‐of‐the‐art report on visualization corpora in automated chart analysis research. We survey 56 papers that created or used a visualization corpus as the input of their research techniques or systems. Based on a multi‐level task taxonomy that identifies the goal, method, and outputs of automated chart analysis, we examine the property space of existing chart corpora along five dimensions: format, scope, collection method, annotations, and diversity. Through the survey, we summarize common patterns and practices of creating chart corpora, identify research gaps and opportunities, and discuss the desired properties of future benchmark corpora and the required tools to create them.

Mobile Health Self-management Support for Spinal Cord Injury: Systematic Literature Review

Self-management plays a critical role in maintaining and improving the health of persons with spinal cord injury (SCI). Despite their potential, existing mobile health (mHealth) self-management support (SMS) tools for SCI have not been comprehensively described in terms of their characteristics and approaches. It is important to have an overview of these tools to know how best to select, further develop, and improve them. The objective of this systematic literature review was to identify mHealth SMS tools for SCI and summarize their characteristics and approaches to offering SMS. A systematic review of the literature published between January 2010 and March 2022 was conducted across 8 bibliographic databases. The data synthesis was guided by the self-management task taxonomy by Corbin and Strauss, the self-management skill taxonomy by Lorig and Holman, and the Practical Reviews in Self-Management Support taxonomy. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standards guided the reporting. A total of 24 publications reporting on 19 mHealth SMS tools for SCI were included. These tools were introduced from 2015 onward and used various mHealth technologies and multimedia formats to provide SMS using 9 methods identified by the Practical Reviews in Self-Management Support taxonomy (eg, social support and lifestyle advice and support). The identified tools focused on common SCI self-management areas (eg, bowel, bladder, and pain management) and overlooked areas such as sexual dysfunction problems and environmental problems, including barriers in the built environment. Most tools (12/19, 63%) unexpectedly supported a single self-management task instead of all 3 tasks (ie, medical, role, and emotional management), and emotional management tasks had very little support. All self-management skills (eg, problem-solving, decision-making, and action planning) had coverage, but a single tool addressed resource use. The identified mHealth SMS tools were similar in terms of number, introduction period, geographical distribution, and technical sophistication compared with SMS tools for other chronic conditions. This systematic literature review provides one of the first descriptions of mHealth SMS tools for SCI in terms of their characteristics and approaches to offering SMS. This study's findings highlight a need for increased coverage of key SMS for SCI components; adopting comparable usability, user experience, and accessibility evaluation methods; and related research to provide more detailed reporting. Future research should consider other data sources such as app stores and technology-centric bibliographic databases to complement this compilation by identifying other possibly overlooked mHealth SMS tools. A consideration of this study's findings is expected to support the selection, development, and improvement of mHealth SMS tools for SCI.

Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies.

Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever-growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a "roadmap" detailing the current research trends in the field of network visualization. We design our contribution as a meta-survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.

Measuring the cognitive effort associated with task switching in routine EHR-based tasks

ObjectiveClinical work involves performing overlapping, time-sensitive tasks that frequently require clinicians to switch their attention between multiple tasks. We developed a methodological approach using EHR-based audit logs to determine switch costs—the cognitive burden associated with task switching—and assessed its magnitude during routine EHR-based clinical tasks. MethodPhysician trainees (N = 75) participated in a longitudinal study where they provided access to their EHR-based audit logs. Physicians’ audit log actions were used to create a taxonomy of EHR tasks. These tasks were transformed into task sequences and the time spent on each task in a sequence was computed. Within these task sequences, instances of task switching (i.e., switching from one task to the next) and non-switching were identified. The primary outcome of interest was the time spent on a post-switch task. Using a mixed-effects regression model, we compared the durations of post-switch and non-switch tasks. Results2,781,679 audit log events over 117,822 sessions from 75 physicians were analyzed. Physicians spent most time on chart review (Median (IQR) = 5,439 (2,492–8,336) seconds), note review (1,936 (827–3,321) seconds), and navigating the EHR interface (1,048 (365.5–2,006) seconds) daily. Post task switch activity times were greater for documentation (Median increase = 5 s), order entry (Median increase = 3 s) and results review (Median increase = 3 s). Mixed-effects regression showed that time spent on tasks were longer following a task switch (β = 0.03; 95% CIlower = 0.027, CIupper = 0.034), with greater post-swtich task times for imaging, order entry, note review, handoff, note entry, chart review and best practice advisory tasks. DiscussionIncreased task switching time—an indicator of the cognitive burden associated with switching between tasks—is prevalent in routine EHR-based tasks. We discuss the cumulative impact of incremental switch costs have on overall EHR workload, wellness, and error rates. Relying on theoretical cognitive foundations, we suggest pragmatic design considerations for mitigating the effects of cognitive burden associated with task switching.

What workers do and how

This paper presents a new and enriched version of the JRC-Eurofound database of tasks indices across jobs in the EU15 economy. After a review of the existing measures and applications, we present the taxonomy of tasks which conceptually underlies the database. Next, the construction of the task indices is presented. We analyse the tasks profile of the European average worker, providing useful insights into work content and organisational methods, and how different indices are distributed across occupations.

Analyzing Task Types Used in Four High School English Textbooks in China

In the past decade, many scholars conducted research concerning cultural content, policy changes, and other issues in the field of EFL textbook evaluation in China. However, a limited number of studies shift their attention to task types used in EFL textbooks. This study employs Nunan’s (1999) task taxonomy to investigate how task types are presented in four main government-approved English textbooks at high school level in China and use Pearson’s Chi-square test in two stages to examine whether any significant difference exists in the four textbooks. The findings reveal that at a macro level, linguistics tasks are viewed are the most important type while domestic educators show a mixed attitude toward the rest four task types. Under closer scrutiny, some sub-task types are payed more attention to than others in different versions of textbooks. This should raise concern because each task type exerts its unique effects on language learning. The aim of the study is to evaluate the quality of the four textbooks and spot potential problems in these teaching materials. 
  

Visual notations for viewing RDF constraints with UnSHACLed

The quality of knowledge graphs can be assessed by a validation against specified constraints, typically use-case specific and modeled by human users in a manual fashion. Visualizations can improve the modeling process as they are specifically designed for human information processing, possibly leading to more accurate constraints, and in turn higher quality knowledge graphs. However, it is currently unknown how such visualizations support users when viewing RDF constraints as no scientific evidence for the visualizations’ effectiveness is provided. Furthermore, some of the existing tools are likely suboptimal, as they lack support for edit operations or common constraints types. To establish a baseline, we have defined visual notations to represent RDF constraints and implemented them in UnSHACLed, a tool that is independent of a concrete RDF constraint language. In this paper, we (i) present two visual notations that support all SHACL core constraints, built upon the commonly used visualizations VOWL and UML, (ii) analyze both notations based on cognitive effective design principles, (iii) perform a comparative user study between both visual notations, and (iv) present our open source tool UnSHACLed incorporating our efforts. Users were presented RDF constraints in both visual notations and had to answer questions based on visualization task taxonomies. Although no statistical significant difference in mean error rates was observed, all study participants preferred ShapeVOWL in a self assessment to answer RDF constraint-related questions. Furthermore, ShapeVOWL adheres to more cognitive effective design principles according to our performed comparison. Study participants argued that the increased visual features of ShapeVOWL made it easier to spot constraints, but a list of constraints – as in ShapeUML – is easier to read. However, also that more deviations from the strict UML specification and introduction of more visual features can improve ShapeUML. From these findings we conclude that ShapeVOWL has a higher potential to represent RDF constraints more effective compared to ShapeUML. But also that the clear and efficient text encoding of ShapeUML can be improved with visual features. A one-size-fits-all approach to RDF constraint visualization and editing will be insufficient. Therefore, to support different audiences and use cases, user interfaces of RDF constraint editors need to support different visual notations.

A Grammar‐Based Approach for Applying Visualization Taxonomies to Interaction Logs

AbstractResearchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their high‐level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to facilitate this mapping process, no formal methods exist for systematically applying these existing theories to user interaction logs. This paper seeks to bridge the gap between visualization task taxonomies and interaction log data by making the taxonomies more actionable for interaction log analysis. To achieve this, we leverage structural parallels between how people express themselves through interactions and language by reformulating existing theories as regular grammars. We represent interactions as terminals within a regular grammar, similar to the role of individual words in a language, and patterns of interactions or non‐terminals as regular expressions over these terminals to capture common language patterns. To demonstrate our approach, we generate regular grammars for seven existing visualization taxonomies and develop code to apply them to three public interaction log datasets. In analyzing these regular grammars, we find that the taxonomies at the low‐level (i.e., terminals) show mixed results in expressing multiple interaction log datasets, and taxonomies at the high‐level (i.e., regular expressions) have limited expressiveness, due to primarily two challenges: inconsistencies in interaction log dataset granularity and structure, and under‐expressiveness of certain terminals. Based on our findings, we suggest new research directions for the visualization community to augment existing taxonomies, develop new ones, and build better interaction log recording processes to facilitate the data‐driven development of user behavior taxonomies.

From Objective to Learning Outcome Through Application of Bloom's Taxonomy

The paper describes the concepts of goal and "learning outcomes" and their mutual relations, as well as relations with similar pedagogical concepts. A systematic analysis of taxonomies of teaching tasks, goals, and outcomes, formulated by Benjamin Bloom and his associates, is attached. It represents the most commonly used classification of cognitive domains, and the mental and practical abilities of students within each domain (cognitive, affective, and psychomotor). The paper also specifies the importance of Bloom's taxonomy in formulating learning outcomes, as well as in the grading process. The second part of the paper demonstrates the application of Bloom's taxonomy on the example of Thematic teaching (theme day) which was realized in an elementary school first-grade class. Through various activities, oriented toward problem-solving or acquiring new knowledge and skills in a selected topic, the outcomes planned by the teaching and learning program for a particular grade are achieved.

Психолого-педагогическое исследование особенностей сформированности критического мышления у девиантных подростков (на примере школьников России и Индонезии)

The article presents a comparative analysis of the study of the critical thinking skills among the deviant adolescents from Russia and Indonesia. The authors offer the analysis of ideas on the concept of «critical thinking», its features, compare the academic points of view on its origin and aspects in domestic and foreign psychology.
 The study involved 80 teenagers, 40 from each country. According to the expert assessment made by teachers, the samples were categorized as an asocial type of deviant behavior group according to E.V. Zmanovskaya. The following methods were used: Test of Everyday Reasoning (TER), the Dembo-Rubinstein self-assessment test, Kettell Culture Fair Intelligence test. The found differences between the groups have been analyzed in the context of cultural and historical factors, features of the construction of educational programs, psychological aspects of the personality of children.
 The study showed that there is a significant relationship between the degree of self-esteem of Russian and Indonesian students and indicators of critical thinking and IQ. In our opinion, the leading factor in the development of critical thinking is a properly structured task taxonomy, created in the learning process, as well as a specially organized learning process, mainly including interactive forms of work.

Understanding the Computational Demands Underlying Visual Reasoning.

Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual reasoning. We do this by systematically assessing the ability of modern deep convolutional neural networks (CNNs) to learn to solve the synthetic visual reasoning test (SVRT) challenge, a collection of 23 visual reasoning problems. Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different versus spatial-relation judgments) and the number of relations used to compose the underlying rules. Prior cognitive neuroscience work suggests that attention plays a key role in humans' visual reasoning ability. To test this hypothesis, we extended the CNNs with spatial and feature-based attention mechanisms. In a second series of experiments, we evaluated the ability of these attention networks to learn to solve the SVRT challenge and found the resulting architectures to be much more efficient at solving the hardest of these visual reasoning tasks. Most important, the corresponding improvements on individual tasks partially explained our novel taxonomy. Overall, this work provides a granular computational account of visual reasoning and yields testable neuroscience predictions regarding the differential need for feature-based versus spatial attention depending on the type of visual reasoning problem.

ПЕДАГОГІЧНІ ОСОБЛИВОСТІ ЗАСТОСУВАННЯ МЕТОДІВ ПРОДУКТИВНОГО НАВЧАННЯ ПІД ЧАС ПІДГОТОВКИ МАЙБУТНІХ ОФІЦЕРІВ НАЦІОНАЛЬНОЇ ГВАРДІЇ УКРАЇНИ

The article examines the essence and features of productive learning, defines the main approaches and characteristics of such learning. The characteristics of the methods, types, stages, features of the application of productive training methods in the training of cadets as future officers of the National Guard of Ukraine are given. It was established that productive learning based on creative and cognitive learning methods integrates activity and problem-based approaches, reflective, search-research, communicative methods, etc. Designing the educational activities of the cadets made it possible to determine the main stages of such training: initial, main, final. For the organization of productive training of cadets, the goals, principles, content, methods and forms of training, possible results are substantiated. Pedagogical conditions for ensuring the success of the application of productive work methods in the training of cadets in the discipline "Moral and psychological support of units" are proposed: he focus of training on the conscious motivation of cadets on the need to form professional readiness to conduct measures for moral and psychological support in NGU units; application in the educational process of technologies of productive and personally oriented training of cadets to conduct measures for moral and psychological support in units of NGU. The basis of pedagogical technology of training of cadets includes the characteristic of object of designing - educational process; taxonomy of tasks that gradually reflect a specific algorithm; pedagogical conditions that improve the training of cadets and create opportunities for their development in problematic situations; stages of individualized and group learning. Key words: future officers of the National Guard of Ukraine, professional training, methods of productive training, moral and psychological support.