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All pieces of hardware and software involve multiple systems and features working together to complete tasks. Therefore, innovations in computer science require breaking down large goals into small, manageable parts. This book shows readers how achieving programmatic goals is not so different from achieving personal goals, elaborating on the mechanics of computer programming in an easy-to-follow way.

In the manufacturing environments of today, human–machine systems are constituted with complex and advanced technology, which demands workers’ considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study’s contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user–system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants’ mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.

This book is about the role and potential of using digital technology in designing teaching and learning tasks in the mathematics classroom and explores mathematics task design when digital technology is part of the teaching and learning environment.

Consumer eHealth interventions are of a growing importance in the individual management of health and health behaviors. However, a range of access, resources, and skills barriers prevent health care consumers from fully engaging in and benefiting from the spectrum of eHealth interventions. Consumers may engage in a range of eHealth tasks, such as participating in health discussion forums and entering information into a personal health record. eHealth literacy names a set of skills and knowledge that are essential for productive interactions with technology-based health tools, such as proficiency in information retrieval strategies, and communicating health concepts effectively. We propose a theoretical and methodological framework for characterizing complexity of eHealth tasks, which can be used to diagnose and describe literacy barriers and inform the development of solution strategies. We adapted and integrated two existing theoretical models relevant to the analysis of eHealth literacy into a single framework to systematically categorize and describe task demands and user performance on tasks needed by health care consumers in the information age. The method derived from the framework is applied to (1) code task demands using a cognitive task analysis, and (2) code user performance on tasks. The framework and method are applied to the analysis of a Web-based consumer eHealth task with information-seeking and decision-making demands. We present the results from the in-depth analysis of the task performance of a single user as well as of 20 users on the same task to illustrate both the detailed analysis and the aggregate measures obtained and potential analyses that can be performed using this method. The analysis shows that the framework can be used to classify task demands as well as the barriers encountered in user performance of the tasks. Our approach can be used to (1) characterize the challenges confronted by participants in performing the tasks, (2) determine the extent to which application of the framework to the cognitive task analysis can predict and explain the problems encountered by participants, and (3) inform revisions to the framework to increase accuracy of predictions. The results of this illustrative application suggest that the framework is useful for characterizing task complexity and for diagnosing and explaining barriers encountered in task completion. The framework and analytic approach can be a potentially powerful generative research platform to inform development of rigorous eHealth examination and design instruments, such as to assess eHealth competence, to design and evaluate consumer eHealth tools, and to develop an eHealth curriculum.

\"Task-based language teaching is an approach which differs from traditional approaches by emphasizing the importance of engaging learners' natural abilities for acquiring language incidentally through the performance of tasks that draw learners' attention to form. Drawing on the multiple perspectives and expertise of five leading authorities in the field, this books provides a comprehensive and balanced account of task-based language teaching (TBLT). Split into five sections, the book provides an historical account of the development of TBLT and introduces the key issues facing the area. A number of different theoretical perspectives that have informed TBLT are presented, followed by a discussion on key pedagogic aspects - syllabus design, methodology of a task-based lesson, and task-based assessment. The final sections consider the research that has investigated the effectiveness of TBLT, addresses critiques and suggest directions for future research. Task-based language teaching is now mandated by many educational authorities throughout the world and this book serves as a core source of information for researchers, teachers and students\"-- Provided by publisher.

BackgroundVideo-based review is paramount for operative performance assessment but can be laborious when performed manually. Hierarchical Task Analysis (HTA) is a well-known method that divides any procedure into phases, steps, and tasks. HTA requires large datasets of videos with consistent definitions at each level. Our aim was to develop an AI model for automated segmentation of phases, steps, and tasks for laparoscopic cholecystectomy videos using a standardized HTA.MethodsA total of 160 laparoscopic cholecystectomy videos were collected from a publicly available dataset known as cholec80 and from our own institution. All videos were annotated for the beginning and ending of a predefined set of phases, steps, and tasks. Deep learning models were then separately developed and trained for the three levels using a 3D Convolutional Neural Network architecture.ResultsFour phases, eight steps, and nineteen tasks were defined through expert consensus. The training set for our deep learning models contained 100 videos with an additional 20 videos for hyperparameter optimization and tuning. The remaining 40 videos were used for testing the performance. The overall accuracy for phases, steps, and tasks were 0.90, 0.81, and 0.65 with the average F1 score of 0.86, 0.76 and 0.48 respectively. Control of bleeding and bile spillage tasks were most variable in definition, operative management, and clinical relevance.ConclusionThe use of hierarchical task analysis for surgical video analysis has numerous applications in AI-based automated systems. Our results show that our tiered method of task analysis can successfully be used to train a DL model.

\"This book describes research on training using cognitive psychology to build a complete empirical and theoretical picture of the training process. It includes a review of relevant cognitive psychological literature, a summary of recent laboratory experiments, a presentation of original theoretical ideas, and a discussion of possible applications to real-world training settings\"--Provided by publisher.

Many surgical procedures are prone to human error, particularly in the learning phase of skills acquisition. Task standardisation has been suggested as an approach to reducing errors, but it fails to account for the human factors associated with learning. Human reliability analysis (HRA) is a structured approach to assess human error during surgery. This study used HRA methodologies to examine skills acquisition associated with carpal tunnel decompression. The individual steps or subtasks required to complete a carpal tunnel decompression were identified using hierarchical task analysis (HTA). The systematic human error reduction and prediction approach (SHERPA) was carried out by consensus of subject matter experts. This identified the potential human errors at each subgoal, the level of risk associated with each task and how these potential errors could be prevented. Carpal tunnel decompression was broken down into 46 subtasks, of which 21 (45%) were medium risk and 25 (55%) were low risk. Of the 46 subtasks, 4 (9%) were assigned high probability and 18 (39%) were assigned medium probability. High probability errors (>1/50 cases) included selecting incorrect tourniquet size, failure to infiltrate local anaesthetic in a proximal-to-distal direction and completion of the World Health Organization (WHO) surgical sign-out. Three (6%) of the subtasks were assigned high criticality, which included failure to aspirate before anaesthetic injection, whereas 21 (45%) were assigned medium criticality. Remedial strategies for each potential error were devised. The use of HRA techniques provides surgeons with a platform to identify critical steps that are prone to error. This approach may improve surgical training and enhance patient safety.

How can you use the latest digital technology to create an environment in which people can learn European languages while performing a meaningful real-world task and experiencing the cultural aspect of learning to cook European dishes? This book explains how to do this from A to Z, covering how a real-world, immersive digital environment for language learning was designed and built, as well as showing the learning experiences of users in five European countries. The project makes language learning motivational and fun by tapping into people's interest in both cooking and technology - you can learn a language while cooking and interacting with a speaking digital kitchen. The kitchens provide spoken instructions in the foreign language on how to prepare European cuisine. Digital sensors are inserted in or attached to all the kitchen equipment and ingredients, so the digital kitchen detects what learners are doing and gives them feedback. Learners are also able to communicate with the kitchens and can ask for help via photos and videos if they don't understand any foreign language words. Written by experts in computing and linguistics, the book explains the principles and procedures involved, enabling others to design and implement a real-world digital learning environment in the same way. It includes numerous photographs of the system in use and evidence of how and what 250 users actually learnt.

IntroductionEducating residents on laparoscopic operations requires direct teaching and deliberate practice. Attending surgeons are often systematic when performing surgery, which creates a challenge when instructing surgery residents. The aim of this study was to use cognitive task analysis to expand laparoscopic cholecystectomy into microsteps reflecting expert surgeon cognition (perceptions, assessments, decisions, etc.) throughout the operation such that these could be better formalized and conveyed to residents in educational materials or assessments and to attending surgeons as teaching scripts.Materials and methodsOne surgeon, a surgical resident, and a human factors specialist conducted cognitive task analyses with three expert general surgeons and one hepatobiliary surgeon using semi-structured interviews. These interviews expanded an existing task model of laparoscopic cholecystectomy to specifically add patient safety aspects including injury prevention, risk management, and complication detection for each step. Interview analysis resulted in an expanded task diagram.ResultsCognitive task analysis expanded the current laparoscopic cholecystectomy task model from 19 to 97 microsteps. In addition to microsteps, an additional major step was identified, the planning step or step zero. Steps with the greatest number of microsteps included dissection with 15 microsteps and intraoperative cholangiogram with 10 microsteps.DiscussionLaparoscopic cholecystectomies are complex operations with multiple microsteps. Identification of these steps can lead to explicit strategies that can improve training of surgeons, with an end towards efficacy and safety. The identification of a planning step prior to beginning the operation is a significant finding that should arguably be included in all future cognitive task analyses regardless of operation or procedure, to emphasize to trainees what senior surgeons have learned through experience. These findings inform the development of interventions for surgical training and evaluation of competency.