Task Definitions Research Articles

Investigating how college students’ task definitions and plans relate to self-regulated learning processing and understanding of a complex science topic

Winne and Hadwin (2008) identified four phases of self-regulated learning (SRL) including defining the task, setting goals and making plans, studying (i.e., learning), and adaptation. The vast majority of SRL research has focused on processing during the third phase, studying. In this study, we developed coding rubrics that allowed us to examine how the results of college students’ processing during phases one and two of Winne and Hadwin’s model, task definitions and plans, affected their SRL processing while learning with a hypermedia learning environment (HLE), and their subsequent academic performance. Our findings showed that, on average, participants gained both factual knowledge and integrated conceptual understanding over the course of learning with the HLE. The quality of participants’ task definitions and plans at pretest, as well as their SRL processing during learning, were related to learning outcomes. On average, participants’ task definitions improved from pretest to posttest. The findings from this study and the associated coding methodologies have implications for educational research and practice.

Recent progress in road and lane detection: a survey

The problem of road or lane perception is a crucial enabler for advanced driver assistance systems. As such, it has been an active field of research for the past two decades with considerable progress made in the past few years. The problem was confronted under various scenarios, with different task definitions, leading to usage of diverse sensing modalities and approaches. In this paper we survey the approaches and the algorithmic techniques devised for the various modalities over the last 5 years. We present a generic break down of the problem into its functional building blocks and elaborate the wide range of proposed methods within this scheme. For each functional block, we describe the possible implementations suggested and analyze their underlying assumptions. While impressive advancements were demonstrated at limited scenarios, inspection into the needs of next generation systems reveals significant gaps. We identify these gaps and suggest research directions that may bridge them.

Intelligent Job Shop Scheduling in Auto Blanking Plant

Job shop scheduling belongs to the large class of NP-complete (nondeterministic polynomial time complete) problems and the complexity of job shop scheduling characterizes many real-life situations. In auto blanking plant, intelligent scheduling system is needed to deal with the automation of production. The core structure of job shop scheduling system is designed. The scheduling rules such as mission-critical priority rule, first come first serve rule, the highest task priority rule and the urgent tasks priority rule are built with the definitions of express tasks, urgent tasks, middle urgent tasks and normal tasks according to the time features of tasks. The heuristic optimization algorithm is designed to follow the above rules. The intelligent logistics management system is built to optimize job shop scheduling in auto blanking plant.

Normalized recognition of speech and audio events

An invariant feature is a nonlinear projection whose output shows less intra-class variability than its input. In machine learning, invariant features may be given a priori, on the basis of scientific knowledge, or they may be learned using feature selection algorithms. In the task of acoustic feature extraction for automatic speech recognition, for example, a candidate for apriori invariance is provided by the theory of phonological distinctive features, which specifies that any given distinctive feature should correspond to a fixed acoustic correlate (a fixed classification boundary between positive and negative examples), regardless of context. A learned invariance might, instead, project each phoneme into a high-dimensional Gaussian mixture supervector space, and in the high-dimensional space, learn an inter-phoneme distance metric that minimizes the distances among examples of any given phoneme. Results are available for both tasks, but it is not easy to compare them: learned invariance outperforms a priori invariance for some task definitions, and underperforms for other task definitions. As future work, we propose that the a priori invariance might be used to regularize a learned invariance projection.

Beyond Capacity Limitations II: Effects of Lexical Processes on Word Recall in Verbal Working Memory Tasks in Children With and Without Specific Language Impairment

This study investigated the impact of lexical processes on target word recall in sentence span tasks in children with and without specific language impairment (SLI). Participants were 42 children (ages 8;2-12;3 [years;months]): 21 with SLI and 21 typically developing peers matched on age and nonverbal IQ. Children completed a sentence span task in which target words to be recalled varied in word frequency and neighborhood density. Two measures of lexical processes were examined: the number of nontarget competitor words activated during a gating task (lexical cohort competition) and word definitions. Neighborhood density had no effect on word recall for either group. However, both groups recalled significantly more high- than low-frequency words. Lexical cohort competition and specificity of semantic representations accounted for unique variance in the number of target word recalled in the SLI and chronological age-matched (CA) groups combined. Performance on verbal working memory span tasks for both SLI and CA children is influenced by word frequency, lexical cohorts, and semantic representations. Future studies need to examine the extent to which verbal working memory capacity is a cognitive construct independent of extant language knowledge representations.

Application of a driving simulator to the development of in-vehicle human–machine-interfaces

The use of a driving simulator in the development of human–machine-interfaces (HMI) such as a navigation, information or entertainment system is discussed. Such use addresses the need to study and evaluate the characteristics of a candidate HMI early in the R&D and design stage to ensure that it is likely to meet various objectives and requirements, and to revise the HMI as may be necessary. Those HMI requirements include such things as usability, driver comfort, and an acceptable level of attentional demand in dual task conditions (driving while using an HMI). Typically, such an HMI involves an information display to the driver, and a means for driver input to the HMI. Corresponding simulator requirements are discussed, along with typical simulator features and components. The latter include a cab, control feel systems, visual image generator, real time scenario control (task definitions), a motion system (if provided), and data acquisition. Both fixed and moving base systems are described, together with associated benefits and tradeoffs. Considerations in the design of the evaluation experiment are discussed, including definition of primary and secondary tasks, and number of driver subjects (experimental participants). Possible response and performance measures for the primary and secondary tasks are noted, together with subjective measures such as task difficulty and ease of using the HMI. The advantages of using a driving simulator to support R&D are summarized. Some typical and example simulator uses are noted.

Extracting Protein Interactions from Text with the Unified AkaneRE Event Extraction System

Currently, relation extraction (RE) and event extraction (EE) are the two main streams of biological information extraction. In 2009, the majority of these RE and EE research efforts were centered around the BioCreative II.5 Protein-Protein Interaction (PPI) challenge and the "BioNLP event extraction shared task." Although these challenges took somewhat different approaches, they share the same ultimate goal of extracting bio-knowledge from the literature. This paper compares the two challenge task definitions, and presents a unified system that was successfully applied in both these and several other PPI extraction task settings. The AkaneRE system has three parts: A core engine for RE, a pool of modules for specific solutions, and a configuration language to adapt the system to different tasks. The core engine is based on machine learning, using either Support Vector Machines or Statistical Classifiers and features extracted from given training data. The specific modules solve tasks like sentence boundary detection, tokenization, stemming, part-of-speech tagging, parsing, named entity recognition, generation of potential relations, generation of machine learning features for each relation, and finally, assignment of confidence scores and ranking of candidate relations. With these components, the AkaneRE system produces state-of-the-art results, and the system is freely available for academic purposes at http://www-tsujii.is.s.u-tokyo.ac.jp/satre/akane/.

The relationship between the leadership styles of the school administrators and the organizational citizenship behaviours of teachers

The aim of this study is to determine the relationship between the leadership styles of the school administrators and the organizational citizenship behaviors of teachers. The concept of organizational citizenship is not one of the task definitions of teachers but they are the actions which they perform with their free will. Besides, these actions increase job satisfaction and productivity of teachers in their working environment. The style of leadership of school administrators has a significant effect on the gaining process of organizational citizenship for teachers. The study group of this research model was formed with 204 volunteer primary school teachers who work in Samsun city center. The data of this research have been collected by looking into the “Organizational Citizenship Behaviors Scale” and “The Leadership Style Questionnaire”. Descriptive statistics, t test, Kruskal Wallis test have been used in the analysis of the data which have been collected. According to the findings which have been gathered, the participants have positive outlooks and opinions about the organizational citizenship behaviors in the schools they work in. However, participants’ opinions have had a significant diversity in terms of gender, branch, education level and variable seniority. There is a meaningful and positive relationship between the organizational behaviors of teachers and transactional and transformational leadership styles of school administrators. It cannot be denied that school administrators have an important role in making teachers feel that they are the part of the organization and in their working more efficiently. Consequently, it is important that school administrators are the people who should have clear objectives, be open to transformation, be able to give ethical decisions and respect and appraise teachers’ opinions.

Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subsets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches. It also shows that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision. The commonalities (and differences) between the approaches are showcased on a selection of best known variants of CSM, EPM and SD algorithms. The paper also provides a critical survey of existing supervised descriptive rule discovery visualization methods.

Recreational diving: Re-evaluation of task, environment, and equipment definitions

Recreational diving has developed extensively over the years, becoming a popular sport and leisure activity, attracting millions of people around the world. A questionnaire aiming to identify problematic stages during recreational diving and to map the relative importance of the diving equipment features was distributed among divers of different genders, ages, certification, and professional experience (N=130). It was found that the most problematic stages for recreational divers (out of 32 tasks) were on-land activities and transitional phases between water and terrestrial interfaces: carrying equipment on shore, underwater orientation, exiting from water to boat and reef, surface swimming, and donning the wetsuit. Safety/reliability, individual adjustment, performance, and simplicity were rated the most important features for diving equipment, while design/style and brand name were rated the least. We suggest that redefinition of the task, equipment, and environment according to the current profile of recreational divers will increase pleasure, reduce fatigue and related diving accidents, improve safety, and increase the number of dedicated active divers.

Managing Real Estate Investments: A Review of International Concepts and Definitions

Despite its evident importance for a profitable investment in real estate, there are no clear-cut definitions of management tasks in real estate to date. This article reviews the existing definitions and attempts to develop a unified framework that integrates the disparate definitions in an international perspective. It sets out to describe the scope of real estate management tasks by drawing together evidence from both academic studies and current industry standards and practices in an international perspective. The reviewed concepts include real estate portfolio management, real estate investment management, real estate asset management corporate, private and public REM, facility management as well as property management.

Metric issues in the study of eye movements in psychiatry

This review provides a description of the measurement methods, task definitions and measurement parameters in the study of smooth eye pursuit and saccade–antisaccade tasks in psychiatry. The large heterogeneity in task definitions and definitions of parameters and its potential impact on the large variability of the parameter measures is presented. Finally issues relating to data aggregation, practice effects and reliability of these smooth eye pursuit and saccade–antisaccade task performance measures are also discussed. The main conclusion of this review is that oculomotor function testing is still lacking standardization of methods, tasks and parameters affecting its usefulness in certain areas of psychiatric research. A future research program could derive a set of tentative recommendations for test standardization.

Metacognitive calibration—an extended conceptualization and potential applications

In this theoretical paper, I present a short critical review of research on calibration. Based on this conceptual analysis I argue for two extensions of this construct: In addition to traditional applications, the methodology should be transferred to also measure calibration between students’ metacognitive control processes (traditionally, only students’ metacognitive judgments were considered) and important external criteria (traditionally, judgments were only compared to students’ own performance). As an illustrative example, one application context will be highlighted where these proposed extensions would alleviate potential problems with the traditional conceptualization: While students’ idiosyncratic task definitions constitute unwanted error variance in the traditional account it would be possible to investigate them as a primary research question within this extended notion. More specifically, it would be possible to investigate how well students’ learning processes match objective task demands. I will put forward theoretical and empirical arguments in favor of these suggestions.

RESEARCHING PEDAGOGIC TASKS: SECOND LANGUAGE LEARNING, TEACHING, AND TESTING. Martin Bygate, Peter Skehan, and Merrill Swain (Eds.). Harlow, UK: Pearson Education, 2001. Pp. x + 258. $32.00 paper.

This volume presents a series of empirical studies that reflect the editors' commitment to the exploration of pedagogic tasks from three interrelated perspectives—learning, teaching, and testing. The introductory chapter by the editors sets the tone of the volume by advocating this view of tasks and provides the basis for a definition that differs from previous, generic definitions in its emphasis on “the different purposes to which tasks are used” (p. 5). Therefore, according to the editors, tasks are inherently distinct and highly dependent on the situational context in which they develop. The departure from former task definitions aims: (a) to avoid continuous misinterpretations between the pedagogy and the research fields, (b) to create a space for understanding the purpose that tasks serve in different areas, and (c) to elude “inappropriate standards for task evaluation” (p. 12). The rest of the chapters are grouped into three sections according to area of emphasis (learning, teaching, or testing). Each section begins with a brief introduction to the chapters it includes.

Dynamic process management for engineering environments

The research presented in this paper proposes a concept for dynamic process management as part of an integrated approach to engineering process support. The theory of information management is the starting point for the development of a process management system based on evolution of information content. The use of an ontology enables the formalization of information content. Based on this ontology, transition relations and task definitions can be instantiated. A methodology for the generation of task networks is presented. From these networks, process models can be derived in the form of task chains.

Assessment of the intrarater and interrater reliability of an established clinical task analysis methodology.

Task analysis may be useful for assessing how anesthesiologists alter their behavior in response to different clinical situations. In this study, the authors examined the intraobserver and interobserver reliability of an established task analysis methodology. During 20 routine anesthetic procedures, a trained observer sat in the operating room and categorized in real-time the anesthetist's activities into 38 task categories. Two weeks later, the same observer performed task analysis from videotapes obtained intraoperatively. A different observer performed task analysis from the videotapes on two separate occasions. Data were analyzed for percent of time spent on each task category, average task duration, and number of task occurrences. Rater reliability and agreement were assessed using intraclass correlation coefficients. Intrarater reliability was generally good for categorization of percent time on task and task occurrence (mean intraclass correlation coefficients of 0.84-0.97). There was a comparably high concordance between real-time and video analyses. Interrater reliability was generally good for percent time and task occurrence measurements. However, the interrater reliability of the task duration metric was unsatisfactory, primarily because of the technique used to capture multitasking. A task analysis technique used in anesthesia research for several decades showed good intrarater reliability. Off-line analysis of videotapes is a viable alternative to real-time data collection. Acceptable interrater reliability requires the use of strict task definitions, sophisticated software, and rigorous observer training. New techniques must be developed to more accurately capture multitasking. Substantial effort is required to conduct task analyses that will have sufficient reliability for purposes of research or clinical evaluation.

Introduction to the Special Issue: Overview of the TREC Routing and Filtering Tasks

This paper introduces the special issue, and reviews the routing and filtering tasks as defined and evaluated at TREC. The tasks attempt to simulate a specific service situation: the system is assumed to process an incoming stream of documents against profiles of user interest, strictly in the time order in which they arrive, and immediately refer any matching document to the user. In the adaptive filtering version of the task, the user is assumed to provide a relevance judgement instantly. The rationale for the task definitions and the evaluation measures used is discussed.

Using data to enhance the expert panel process. Rating indications of alcohol-related problems in older adults.

To enhance the validity of a well-known expert panel process, we used data from patient surveys to identify and correct rating errors. We used the two-round RAND/UCLA panel method to rate indications of harmful (presence of problems), hazardous (at risk for problems), and nonhazardous (no known risks) drinking in older adults. Results from the panel provided guidelines for classifying older individuals as harmful, hazardous, or nonhazardous drinkers, using a survey. The classifications yielded unexpectedly high numbers of harmful and hazardous drinkers. We hypothesized possible misclassifications of drinking risks and used the survey data to identify indications that may have led to invalid ratings. We modified problematic indications and asked three clinician panelists to evaluate the clinical usefulness of the modifications in a third panel round. We revised the indications based on panelist response and reexamined drinking classifications. Using the original indications, 48% of drinkers in the sample were classified as harmful, 31% as hazardous, and 21% as nonhazardous. A review of the indications revealed framing bias in the original rating task and vague definitions of certain symptoms and conditions. The modified indications resulted in classifications of 22% harmful, 47% hazardous, and 31% nonhazardous drinkers. Analysis of survey data led to identification and correction of specific errors occurring during the panel-rating process. The validity of the RAND/UCLA method can be enhanced using data-driven modifications.

Goal-Oriented Workflows

In recent years workflow management systems have become a well known option of work automatisation. However, there are several disadvantages that lessen improvements in work organisation achieved by application of the workflow management systems. The main problem is the strict definitions of tasks imposed by the systems. The strictness of the definition hinders employees’ productivity and creativity. Different attempts are made by workflow specialists to overcome those difficulties. The hypothesis under investigation is that the goal-oriented workflow definition can solve the problem at least partly. In the goal-oriented definition goals, not tasks are structured and assigned by the workflow adminstrator.

The NIST 1999 Speaker Recognition Evaluation—An Overview

Martin, Alvin, and Przybocki, Mark, The NIST 1999 Speaker Recognition Evaluation—An Overview, Digital Signal Processing10(2000), 1–18.This article summarizes the 1999 NIST Speaker Recognition Evaluation. It discusses the overall research objectives, the three task definitions, the development and evaluation data sets, the specified performance measures and their manner of presentation, the overall quality of the results. More than a dozen sites from the United States, Europe, and Asia participated in this evaluation. There were three primary tasks for which automatic systems could be designed: one-speaker detection, two-speaker detection, and speaker tracking. All three tasks were performed in the context of mu-law encoded conversational telephone speech. The one-speaker detection task used single channel data, while the other two tasks used summed two-channel data. About 500 target speakers were specified, with 2 min of training speech data provided for each. Both multiple and single speaker test segments were selected from about 2000 conversations that were not used for training material. The duration of the multiple speaker test data was nominally 1 min, while the duration of the single speaker test segments varied from near zero up to 60 s. For each task, systems had to make independent decisions for selected combinations of a test segment and a hypothesized target speaker. The data sets for each task were designed to be large enough to provide statistically meaningful results on test subsets of interest. Results were analyzed with respect to various conditions including duration, pitch differences, and handset types.