Fostering reflection in the training of speech-receptive action

Dieser Aufsatz erörtert Möglichkeiten und Probleme der Förderung kommunikativer Fertigkeiten durch die Unterstützung der Reflexion eigenen sprachrezeptiven Handelns und des Einsatzes von computerunterstützten Lernumgebungen für dessen Förderung. Kommunikationstrainings widmen sich meistens der Förderung des beobachtbaren sprachproduktiven Handelns (Sprechen). Die individuellen kognitiven Prozesse, die dem sprachrezeptiven Handeln (Hören und Verstehen) zugrunde liegen, werden häufig vernachlässigt. Dies wird dadurch begründet, dass sprachrezeptives Handeln in einer kommunikativen Situation nur schwer zugänglich und die Förderung der individuellen Prozesse sprachrezeptiven Handelns sehr zeitaufwändig ist. Das zentrale Lernprinzip - die Reflexion des eigenen sprachlich-kommunikativen Handelns - wird aus verschiedenen Perspektiven diskutiert. Vor dem Hintergrund der Reflexionsmodelle wird die computerunterstützte Lernumgebung CaiMan© vorgestellt und beschrieben. Daran anschließend werden sieben Erfolgsfaktoren aus der empirischen Forschung zur Lernumgebung CaiMan© abgeleitet. Der Artikel endet mit der Vorstellung von zwei empirischen Studien, die Möglichkeiten der Reflexionsunterstützung untersucheThis article discusses the training of communicative skills by fostering the reflection of speech-receptive action and the opportunities for using software for this purpose. Most frameworks for the training of communicative behavior focus on fostering the observable speech-productive action (i.e. speaking); the individual cognitive processes underlying speech-receptive action (hearing and understanding utterances) are often neglected. Computer-supported learning environments employed as cognitive tools can help to foster speech-receptive action. Seven success factors for the integration of software into the training of soft skills have been derived from empirical research. The computer-supported learning environment CaiMan© based on these ideas is presented. One central learning principle in this learning environment reflection of one's own action will be discussed from different perspectives. The article concludes with two empirical studies examining opportunities to foster reflecti

Training soft skills with software

Most trainings of communicative behavior focus on fostering the observable speech productive behavior (i.e. speaking). The individual cognitive processes underlying speech receptive behavior (hearing and understanding utterances) thus are often neglected. This is due to the fact that speech receptive behavior cannot be accessed in the midst of a conversation and that its training is very time-consuming. Computer-supported learning environments employed as cognitive tools can help to foster speech receptive behavior. This article discusses the fostering of speech receptive behavior and the possibilities of using software for this purpose. The computer-supported learning environment CaiMan© which is based on these ideas is presented. Finally, seven factors of success for the integration of software into the training of soft skills are derived from empirical research.Kommunikationstrainings widmen sich meist der Förderung des beobachtbaren sprachproduktiven Handelns (d.h. des Sprechens). Die individuellen kognitiven Prozesse, die dem sprachrezeptiven Handeln (Hören und Verstehen) zugrunde liegen, werden häufig vernachlässigt. Dies wird dadurch begründet, dass sprachrezeptives Handeln in einer kommunikativen Situation nur schwer zugänglich und die Förderung der individuellen Prozesse sprachrezeptiven Handelns sehr zeitaufwändig ist. Computerunterstützte Lernumgebungen können als kognitive Tools die Förderung sprachrezeptiven Handelns unterstützen. Dieser Forschungsbericht erörtert Möglichkeiten und Probleme der Förderung sprachrezeptiven Handelns und des Einsatzes von computerunterstützten Lernumgebungen für dessen Förderung. Darauf aufbauend wird die computerunterstützte Lernumgebung CaiMan© vorgestellt und beschrieben. Abschließend werden sieben Erfolgsfaktoren aus der empirischen Forschung zur Lernumgebung CaiMan© abgeleitet

ALLY: An operator's associate for satellite ground control systems

The key characteristics of an intelligent advisory system is explored. A central feature is that human-machine cooperation should be based on a metaphor of human-to-human cooperation. ALLY, a computer-based operator's associate which is based on a preliminary theory of human-to-human cooperation, is discussed. ALLY assists the operator in carrying out the supervisory control functions for a simulated NASA ground control system. Experimental evaluation of ALLY indicates that operators using ALLY performed at least as well as they did when using a human associate and in some cases even better

Task analysis of discrete and continuous skills: a dual methodology approach to human skills capture for automation

There is a growing requirement within the field of intelligent automation for a formal methodology to capture and classify explicit and tacit skills deployed by operators during complex task performance. This paper describes the development of a dual methodology approach which recognises the inherent differences between continuous tasks and discrete tasks and which proposes separate methodologies for each. Both methodologies emphasise capturing operators’ physical, perceptual, and cognitive skills, however, they fundamentally differ in their approach. The continuous task analysis recognises the non-arbitrary nature of operation ordering and that identifying suitable cues for subtask is a vital component of the skill. Discrete task analysis is a more traditional, chronologically ordered methodology and is intended to increase the resolution of skill classification and be practical for assessing complex tasks involving multiple unique subtasks through the use of taxonomy of generic actions for physical, perceptual, and cognitive actions

Internet-delivered cognitive control training as a preventive intervention for remitted depressed patients : protocol for a randomized controlled trial

Background: Preventing recurrence of depression forms an important challenge for current treatments. Cognitive control impairments often remain present during remission of depression, putting remitted depressed patients at heightened risk for new depressive episodes by disrupting emotion regulation processes. Importantly, research indicates that cognitive control training targeting working memory functioning shows potential in reducing maladaptive emotion regulation and depressive symptomatology in clinically depressed patients and at-risk student samples. The current study aims to test the effectiveness of cognitive control training as a preventive intervention in a remitted depressed sample, exploring effects of cognitive control training on rumination and depressive symptomatology, along with indicators of adaptive emotion regulation and functioning. Methods/design: We present a double blind randomized controlled design. Remitted depressed adults will complete 10 online sessions of a cognitive control training targeting working memory functioning or a low cognitive load training (active control condition) over a period of 14 days. Effects of training on primary outcome measures of rumination and depressive symptomatology will be assessed pre-post training and at three months follow-up, along with secondary outcome measure adaptive emotion regulation. Long-term effects of cognitive control training on broader indicators of functioning will be assessed at three months follow-up (secondary outcome measures). Discussion: This study will provide information about the effectiveness of cognitive control training for remitted depressed adults in reducing vulnerability for depression. Furthermore, this study will address key questions concerning the mechanisms underlying the effects of cognitive control training, will take into account the subjective experience of the patients (including a self-report measure for cognitive functioning), and explore whether these effects extend to broad measures of functioning such as Quality of Life and disability

Evaluation of live human-computer music-making: Quantitative and qualitative approaches

NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Human-Computer Studies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Human-Computer Studies, [VOL 67,ISS 11(2009)] DOI: 10.1016/j.ijhcs.2009.05.00

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning