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In this paper, we propose an interactive drawing based activity in BookRoll, an e-book-based teaching-learning platform. Participants who were researchers drew an overview of their research as a handwritten memo. We demonstrate the affordances of the learning analytics (LA) enhanced platform to facilitate practice and research of such a drawing activity focused on a higher-order thinking task to organize knowledge (research overview in this case). We share the multi-modal data of 4 participants to illustrate the outcomes of the activity and their perception of the activity. An initial analysis of the handwritten memo is visualised. Further reflections show how the use of learning analytics-based platforms can enrich the learning design of a knowledge organisation task.

In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation method that is based on a combination of course knowledge structures, reading behavior, and assessment analysis to create optimal heterogenous and homogeneous working groups using a genetic algorithm. The characteristics of each group are presented for explanation as a visualized knowledge map showing the strengths and weaknesses of each group, and are in the structure form of curriculum. We also present a case study of applying the method to junior high school mathematics log data, and provide explanation in a visualized form of standardized curriculum of group characteristics that are often referenced for learning design by teachers.

Self-direction skills in the context of learning can be supported with data in this digital era. This study analyzes the behaviors of learners during a self-directed reading and summarization task. Our work investigates an undergraduate course (n=72) where students worked on a reading and summarizing assignment while planning and monitoring the task in GOAL, a platform synthesizing learner’s activity data from learning and physical activity contexts. This study focuses on the initial cohort analysis of the students’ behavior based on the fine grain interaction data collected in the different systems using visual analytics techniques. Such a data-rich narrative of self-directed in-semester activities is not discussed yet in the literature to our knowledge. We discuss the implications of the trends that is found in our collected dataset for designing AI-support for self-direction skills with the GOAL platform and the scope of deeper analysis to further understand the process.

Digitized learning materials are a core part of modern education and also can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open ebook test from the perspective of performance and identifying strategies that are used by both high and low performing learners. We create models to predict the performance of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that 1) strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment; and 2) low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment.

Learning context has evident to be an essential part in vocabulary development, however describing learning context for each vocabulary is considered to be difficult. In the human brain, it is relatively easy to describe learning contexts using pictures because pictures describe an immense amount of details at a quick glance that text annotations cannot do. Therefore, in an informal language learning system, pictures can be used to overcome the problems that language learners face in describing learning contexts. The present study aimed to develop a support system that generates and represents learning contexts automatically by analyzing the visual contents of the pictures captured by language learners. Automatic image captioning, a technology of artificial intelligence that connects computer vision and natural language processing is used for analyzing the visual contents of the learners’ captured images. A neural image caption generator model called Show and Tell is trained for image-to-word generation and to describe the context of an image. The three-fold objectives of this research are: First, an intelligent technology that can understand the contents of the picture and capable to generate learning contexts automatically; Second, a leaner can learn multiple vocabularies by using one picture without relying on a representative picture for each vocabulary, and Third, a learner’s prior vocabulary knowledge can be mapped with new learning vocabulary so that previously acquired vocabulary be reviewed and recalled while learning new vocabulary., 7th International Conference, DAPI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings

This article contends that the profile of a foreign language learner can contain valuable information about possible problems they will face during the learning process, and could be used to help personalize feedback. A particularly important attribute of a foreign language learner is their native language background as it defines their known language knowledge. Native language identification serves two purposes: to classify a learners' unknown native language; and to identify characteristic features of native language groups that can be analyzed to generate tailored feedback. Fundamentally, this problem can be thought of as the process of identifying characteristic features that represent the application of a learner's native language knowledge in the use of the language that they are learning. In this article, the authors approach the problem of identifying characteristic differences and the classification of native languages from the perspective of 15 automatically predicted writing errors by online language learners.

Analysis of publicly available language learning corpora can be useful for extracting characteristic features of learners from different proficiency levels. This can then be used to support language learning research and the creation of educational resources. In this paper, we classify the words and parts of speech of transcripts from different speaking proficiency levels found in the NICT-JLE corpus. The characteristic features of learners who have the equivalent spoken proficiency of CEFR levels A1 through to B2 were extracted by analyzing the data with the support vector machine method. In particular, we apply feature selection to find a set of characteristic features that achieve optimal classification performance, which can be used to predict spoken learner proficiency.

The difficulty in protecting and enforcing Intellectual Property (IP) rights has been a major obstacle to sharing intellectual works in digital forms. In this work, we present a prototype design for a decentralized e-learning market-place where teachers, authorized publishers and authors can publish their learning materials, establish conditions for allowing access, and grant or revoke access based on adherence or infringement of their rights. Using the Blockchain of Learning Logs (BOLL) and a digital book reader (BookRoll), we demonstrate how authors can be empowered to deploy smart contracts that protect and enforce these rights on their intellectual works in a distributed manner. We particularly use the blockchain in order to enable trust, trace-ability, rights protection, transparency and collaboration between authors and users of their work with no third party interference. Finally, we examine the implications of our proposed design, its limitations and directions for future work.

For the 21st century learner, developing self-direction skill is crucial for both academic activities and maintaining one's healthy lifestyle. While there are technology supports for specific self-regulated learning tasks and health monitoring, research is limited on how to support development of meta-skill of self-direction process itself. In our work, we focus on designing seamless technology infrastructure to foster self-directedness of learners. We consider learning and physical activities data as a context and DAPER (data collection-analyze-plan-execution monitoring-reflect), as a data-driven self-direction skill execution and acquisition model. We bridge Learning Analytics and Quantified-Self approaches to develop the GOAL (Goal Oriented Active Learner) system to support synchronize-visualize-analyze multisource data regarding learners' learning and physical activities. This paper proposes a measurement rubric as a basis of adaptive scaffolding for skill development during the process.

Association rule mining is the process of discovering new knowledge and non-trivial patterns hidden in textual datasets. This approach is often used by learning analytics researchers and practitioners for exploring unique types of data collected from various educational environments. By leveraging association rule mining, this study aims at providing a solution to a critical challenge that most of the location-based informal learning tools face namely, the limited scopes to learn new words. Hence, the purpose of this work is to design a word recommendation system that can analyze a learner's previously learned words in order to suggest new vocabulary that can be acquired in a particular learning location. We designed a study and explored a dataset for uncovering the insight of language learners' learning behaviors. 20 EFL (English as Foreign Language) learners' data, whose learning location was Tokushima region of Japan, is analyzed by using association rule mining technique. The result indicates that-First, in informal learning context, EFL learners' word learning interests vary much from each other although the learning location is same; and Second, a word recommendation system can be introduced by using association rule mining analysis; however, the possibility of getting highly associated words are low compared with random words.

It is important for education systems to analyze and provide an appropriate level of feedback to meet the needs of learners. Predicting a learner’s proficiency level can be used to inform learner’s about their progress, and can also aid other parts of the characteristic analysis and feedback process, such as: focused analysis on learner proficiency subgroups. In this paper, we propose a measure based on the frequency of words in the sentences produced by learners during speaking exams to predict the learner’s language proficiency. The proposed measure is compared to the learner’s vocabulary size by correlation analysis. The results suggest that there is a stronger correlation between the proposed measure and the proficiency of the learner than the learner’s vocabulary size.

In this paper, we present a method to extract the possible relationships between knowledge points by analyzing e-book log and mining quiz data and mining Wikipedia articles. This method will be implemented in an ontology-based visualization support system to support the instructor to construct course-centered ontologies semi-automatically.

It is important for language learners to determine and reflect on their writing errors in order to overcome weaknesses. Each language learner has their own unique writing error characteristics and therefore has different learning needs. In this paper, we analyze the writing errors of foreign language learners on the language learning SNS website Lang-8 to investigate the characteristics of errors by native language. 142,465 sentences were collected from Lang-8 for analysis. For each native language, the predicted scores of 15 error categories from SVM machine learning models are used as a vector representation of each sentence. These score vectors are then clustered to determine error co-occurrence within the same sentence. The results were then analyzed to determine the error characteristics of different native languages.

Kyushu University installed a private cloud system, named “campus cloud system”, using VCL and CloudStack. For a graduate school exercise course on web search engine, the authors prepared a virtual machine on VCL, which had apache web server and GETA indexer preinstalled. This paper introduces an outline of the cloud system, the exercise, and also reports advantages and disadvantages of cloud based education.

The authors are interested in online novel services as a user-generated media on the Web. A large number of novels are being uploaded, and a few novels become major. Novel writers like to create novel of current popular genre, then current popular genre words may frequently appear. In this paper, the authors apply the time series analysis to the keywords words given to an online novel by the creator. The authors construct a trend analysis tool. The tool not only shows the trend of posted query word(s), but also shows the trends of similar terms. This paper describes the trend analysis system, the used data, and some interesting analyses.

Due to the explosive growth and popularity of the deep web, information extraction from deep web page has gained more and more attention. However, the HTML structure of web page has become more complicated, making it difficult to recognize target content by only analyzing the HTML source code. In this paper, we propose a method to extract the informative blocks from a deep web using the layout feature. We consider the visual rectangular region of an HTML element as a visual block in web page. We transform the elements’ layout of a visual block into a layout tree. By calculating the similarity of layout trees, we cluster the visual blocks that have similar layout feature. Finally, the cluster which has the largest area is extracted as the informative block cluster. The experiment results show that this method is optimal when the threshold of layout tree similarity is 0.4.

Recent research into writing tools to support foreign language learners of English has focused on prevalent errors in learner writing, while other errors, such as word order errors, have received little attention. As the word order of some languages are similar, there are also large differences between languages which can affect foreign language learning. In this paper, we automatically extract corrected sentences that contain word order errors samples from a language learning SNS to create a word order error corpus for machine learning. This corpus is then analyzed to train and evaluate the effectiveness of SVM classifiers to automatically classify word order errors in learner writing.

Background In Australia and New Zealand, surgical trainees are expected to develop competencies across 9 domains. Although structured training is provided in several domains, there is little or no formal program for professionalism, communication, collaboration, and management and leadership. The Australian federal Department of Health and Aging funded a pilot course in simulation-based education to address these competencies for surgical trainees. This article describes the course and evaluation. Methods Course development: Content and methods drew on best-evidence for teaching and learning these competencies from other disciplines. Course evaluation: Participants completed surveys using rating scales and free text comments to identify aspects of the course that worked well and those that needed improvement. Results Eleven of 12 participants completed evaluation forms immediately after the course. Participants reported largely meeting learning objectives and valuing the educational methods. High levels of realism in simulations contributed to the ease with which participants immersed themselves in scenarios. Conclusions This study demonstrates that a course designed to teach competencies in communication, teamwork, leadership, and the encompassing professionalism to surgical trainees is feasible. Although participants valued the content and methods, they identified areas for development. Limitations of the evaluation are highlighted, and further areas for research are identified.

Airway complications are a leading cause of morbidity and mortality in anaesthesia1. Effective management of a difficult airway requires the timely availability of suitable airway equipment. The Australian and New Zealand College of Anaesthetists has recently developed guidelines for the minimum set of equipment needed for the effective management of an unexpected difficult airway (TG4 [2010] www.anzca.edu.au/resources/professionaldocuments ). TG4 [2010] is based on expert consensus, underpinned by wide consultation and an extensive review of the available evidence, which is summarised in a Background Paper (TG4 BP [2010] www.anzca.edu.au/resources/professional-documents ). TG4 [2010] will be reviewed at the end of one year and thereafter every five years or more frequently if necessary. The current paper is reproduced directly from the Background Paper (TG4 BP [2010]).

A 51-year-old female patient with Guillain-Batré syndrome was given three times the intended dose of intravenous human immunoglobulin while admitted to a tertiary intensive care unit. The error went unnoticed for seven hours and appears to have been the result of several successive breakdowns in communication between key staff. The patient, fortunately, made a full recovery. This report analyses the communication failure and explores possible ways of avoiding similar occurrences in the future.

Suboptimal communication between health professionals has been identified as a significant causative factor in incidents compromising patient safety. The use of a structured method of communication has been suggested to improve the quality of information exchange, particularly with inexperienced practitioners. One structure that has been suggested to improve communication is the situational briefing tool SBAR. This tool was developed by the US Navy for standardizing important and urgent communication in nuclear submarines. Despite its widespread uptake in some areas of healthcare, its effectiveness has until recently been unproven. This paper describes the benefits and pitfalls of methods we have used to prove the effectiveness of these communication tools.

It is important for education systems to analyze and provide an appropriate level of support to meet the needs of learners. An example of this is how the effectiveness of automatic language learner error detection and correction can vary depending on the learner's proficiency level. Covering a wide range of language complexity makes the task of error detection difficult. By predicting the learner's proficiency level, different error models can be applied for different proficiency levels. In this paper, we propose a measure based on the frequency of words in the sentences produced by learners during speaking exams to predict the learner's language proficiency. The proposed measure is compared to the learner's vocabulary size by correlation analysis. The results suggest that there is a stronger correlation between the proposed measure and the proficiency of the learner than the learner's vocabulary size.

It has been easy for us to send information thanks to the growth of the Internet. Information includes reputation. Analysis of the large amount of reputation and making summary of the analysis is very helpful for consumers to decide which goods they should buy. They are also helpful for companies in order to make marketing decisions. Many researches try to classify documents on whether they have positive or negative sentiment. In this paper, we focus on more complex sentiment. We will show a result of machine classification of wine reviews from the point of view of "aroma" using Support Vector Machine (SVM).

Summary The Effective Management of Anaesthetic Crises (EMAC) course was developed in response to several factors. The increased incorporation of workplace-based assessment into the whole assessment process revealed difficulties when trying to deal with important areas such as the management of anaesthetic crises. At the same time the development of simulation centres combined with an increased interest and understanding of the role of human factors in anaesthesia offered a potential solution. A partnership between the Australia and New Zealand College of Anaesthetists (ANZCA) and the Australasian simulation centres through a working party and a workshop created a blueprint for a course. Five main topics were identified—human factors, cardiovascular emergencies, airway emergencies, anaesthetic emergencies and trauma. A two and a half day course was developed using simulation, skills stations, presentations, demonstrations and feedback-led discussion as the major teaching methods. The main aim of the course was to teach not only what to do but importantly how to do it. The course was piloted and extensive evaluation yielded positive findings with the result that it was accepted by ANZCA for both trainee and career grade anaesthetists. Further evaluations are in place and will result in a revision of the course.

Background This paper examines the role of high fidelity simulation and crisis resource management in bridging the gap between theory and practice. Patient safety is fundamental to healthcare professional practice and is a common goal for healthcare providers. It provides a focus to motivate practitioners. Patient safety issues are not a priority in undergraduate curricula. Raising the profile at this level is crucial to improving the safety and quality of healthcare delivery. This paper explores the role of simulation in providing a realistic, safe environment for participants with different levels of experience to manage evolving crises in the context of their work environment. Methods The Southern Health Simulation and Skills Centre uses a patient safety focus in delivering a specialised educational programme adapted from aviation to healthcare. The programme, crisis resource management, enables participants to consolidate knowledge, attitudes and skills to achieve a deeper understanding of how their performance impacts on patient safety and the quality of healthcare provided. Self-reported written evaluation data was collected from participants of three different courses at Southern Health. Results Participants consistently report that these courses offer unique learning experiences that address aspects of workplace learning in ways that have not previously been possible. A video-assisted reflective process powerfully reinforces learning. Conclusion Crisis resource management courses demonstrate the value of simulation in bridging the gap between ‘knowing’ and ‘doing’ and keeping the focus on patient safety. Recommendations are made for ways in which the core elements of crisis resource management philosophy can influence the conceptualisation of a new medical curriculum.

Objective To introduce the concept of simulator training to gynaecological surgeons and to inform them of the current state of worldwide research into the development of a laparoscopic simulator. Attributes Virtual-reality technology enables the surgeon not only to perform diagnostic procedures, but also to practice entire surgical operations on a mannikin, while observing and interacting with a realistic computer-generated gynaecological environment on-screen. The benefits of such simulator training are twofold: surgeons will be at a higher point on the learning curve before their first operation on a patient, and patients should enjoy better quality of care. Research Great confidence in the potential of virtual-reality technology in gynaecology, and medicine in general, has generated considerable international interest. As a result, much research and development effort is going into the creation of a gynaecological endoscopy simulator. Although several laparoscopic simulators are near their completion stage, there are no commercially available systems as yet. Medical education A plan for the successful implementation of simulator training in gynaecology, through preceptorship and credentialling programs, is outlined. Use of simulator training in gynaecology, and other medical fields, is expected to become more popular with further advances in technology.

Page segmentation has received great attention in recent years. However, most research has been based on some pre-defined heuristics or visual cues which may be not suitable for large-scale page segmentation. In this chapter, we proposed two parameters: seam degree and content similarity, to indicate the coherent degree of a page block. Instead of analyzing pre-defined heuristics or visual cues, our method utilizes the visual and content features to determine whether a page block should be divided into smaller blocks. We also proposed a principled page segmentation method using these two parameters. An experiment was conducted to determine the relationship between the two parameters and the number of segment results. The empirical results also show that our segmentation method can effectively segment a page into different semantic parts.

It is very important for any academic researches to do research surveys. In this paper, we proposed an SNS based search engine to support doing research surveys, called SNSearch. Our SNSearch system not only supports analysis of research trends, but also offers learners opportunities to reflect on their search behaviors. By browsing the experts' survey history, students can learn search skills.

Page segmentation has received great attention in recent years. However, most research has been based on some pre-defined heuristics or visual cues which may be not suitable for large-scale page segmentation. In this paper, we proposed two parameters: seam degree and content similarity, to indicate the coherent degree of a page block. Instead of analyzing pre-defined heuristics or visual cues, our method utilizes the visual and content features to determine whether a page block should be divided into smaller blocks. We also proposed a principled page segmentation method using these two parameters. An experiment was conducted to determine the relationship between the two parameters and the number of segment results. The empirical results also show that our segmentation method can effectively segment a page into different semantic parts.

When extracting information from a web page, IE systems usually need to perform pattern recognition to identify the elements that have similar patterns. However, most of them are mainly based on analyzing HMTL source code, DOM tree, tag tree or Xpath of web pages. These methods are language-dependent, or more precisely, HTML-dependent. They have some insuperable limitations. In order to overcome these limitations, we propose a notion of layout-tree and a pattern recognition method to identify visual blocks with similar visual pattern using layout tree. In this paper, we call a visible rectangular region in a web page a visual block or block for short. We consider if the elements of two blocks are displayed in a similar layout, we define that the two blocks are visually similar. We first transform the layout into a layout tree. By calculating the similarity of the layout trees of two blocks, we can determine whether the two blocks are visually similar or not. The result of experiment shows that the layout tree is an effective method to identify visually similar blocks.

The value of an open event is in active communication between participants. However, it is difficult to look for those who have a common interest or a common friend. You would miss an opportunity to speak directly to your SNS friends or to those who have common SNS friends, unless you notice that there are such participants and you recognize them. This paper proposes a participant managerial system for using the human relations in SNS in an actual event site. Each participant makes his registration to an event using his SNS ID. In the event site on the day, the participant only has to touch his mobile terminal to a NFC tag. The participant and his SNS are bound by this action. The organizer as well as the participants can understand the background and the interests of each participant using SNS information. NFC tag provides real time attendance-and-absence status of the participants. It is expectable that the proposed system promotes exchange between participants.

It is not so simple to get an appropriated level of search result among a large number of targets which might be too general or too specific. Hints for the query are valuable for a user to expand or shrink his next search step, if the hints would be shown with their levels compared with the user's original query. This paper proposes a method to extract feature words of the same level as user's query using restricted bootstrap. Examples are shown to demonstrate the effectiveness of the method on tourism blogs. The paper proposes an evaluation measure for the similarity of levels for words based on WordNet

When starting new research or summarizing the results of research, it is necessary to review related work in the same research field. The research review requires several point of views such as ``problem'', ``method'', ``result''. Simple search by keywords is not effective to specify and narrow-down the scope of search for these meta purpose. In this paper, we focus on sentences to improve the efficiency of research survey with multiple viewpoints. We developed a search engine for scientific articles which classifies sentences by multiple viewpoints.

A smart phone and a tablet terminal with a NFC function are spreading by standardization of NFC technology. The authors are interested in the exchange support between the participants in the off-line meeting. The authors focus on participants' mutual communication in off-line meetings. It is rare to perform exchange in an actual meeting place, although there is a relation called a friend or a follower on on-line. This will be because of gap between identity on SNS and identity in the real world. The authors are interested in bridging the gap between the online identity and the identity in the real world. The authors are developing a system, which matches human on online SNS ID and a real person, using a smart phone or a mobile terminal with NFC technology. Our system will display the human relations on the on-line SNS, and connect a meeting and a meeting participant.

As the amount of web page increases, searching for semi-structured documents is gaining greater attention. The traditional approach for extracting data from web page documents is to write specialized programs, called wrappers that identify data of interest and map them to some suitable format. However, developing wrappers manually has many well known shortcomings, mainly due to the difficulty in writing and maintaining them for continually changing web data. Moreover, there is no one wrapper program that can treat all kinds of web pages. In this paper, we aim to extract relevant and meaningful snippets from as many web pages as possible, using the shallow feature of HTML documents to discover and analyze the relevant components. Also, we introduced a new feature called GAP and verified the effectiveness of GAP by conducting a SVM learning experiment.

Not only specialists but also ordinary people have interests in the stock price changes. The present paper analyzes the influence of the frequency of characteristic words that appear in the investor relation (IR) documents of companies. Is it true that the positive words cause the rise of a stock price, and the negative words cause the fall? We focused on 57 medical-supplies-related companies which have listed on Tokyo Stock Exchange. The stock price data and IR documents of the period in 2009 to 2011 were collected. We prepared 10 positive words and 10 negative words to evaluate the score of IR documents. The rate of the stock price compared with the previous day and the document score was analyzed. It is confirmed that the correlation of the rate and the document score is higher for the companies with high stock price ratio of change.

Identifying research trends is an integral part of the planning phase of academic research and can be a daunting task for those not familiar with the process. This paper proposes the use of the Milky Way search engine to assist novice students who are beginning to undertake research. Using data-processing and data-mining techniques with data from SciVerse Scopus, the Milky Way search engine can provide students with a way to investigate research trends and test trend hypothesis' in their field. We evaluated the use of the Milky Way search engine in the field of mobile learning to analyze research trends.

Background: The use of high-fidelity patient simulators for training health-care professionals has increased rapidly in recent years. Approximately 150 simulation training centres operate internationally. Australasia has acquired four centres since 1997. A large component of simulator-based training is experiential. Methods: Participants manage clinical scenarios on lifelike computer-controlled mannikins within realistic clinical environments. Afterwards they actively reflect upon the experience, an exercise that is facilitated by observation of a video replay of the event. Results: This approach to training promotes a consideration of broader issues which can influence clinical practice and patient outcomes. This has particular relevance to emergencies. Here, events that are by nature infrequent and unscheduled can be addressed in a controlled fashion, in an environment that is supportive and separated from actual patients. Conclusions: A broad range of skills can be addressed with this resource. Of key importance are situational management and team effectiveness skills. Deficiencies with respect to these ‘non-clinical’ skills are being increasingly identified for their contribution to preventable adverse events within the health-care environment. Multidisciplinary operation-room team training has the potential to address these issues as they relate to the perioperative environment.

All health services rely on efficient and accurate communication between health professionals to ensure safe and effective patient care. Our health service introduced a standardised technique, ISBAR (Identify, Situation, Background, Assessment, Request), for telephone communication. We describe and evaluate the implementation of this project; evaluation was undertaken using program logic mapping. Recommendations for other health services planning to introduce communication tools into routine clinical use are also provided.

INTRODUCTION: Suboptimal communication between health professionals has been recognised as a significant causative factor in incidents compromising patient safety. The use of a structured method of communication has been suggested to improve the quality of information exchange. The aim of this study was to determine if the teaching of a communication tool, ISBAR (Identify, Situation, Background, Assessment, Recommendation), a modification of SBAR (Situation, Background, Assessment, Recommendation), improved the content and clarity of a telephone referral in an immersive simulated clinical scenario conducted in real time. METHOD: Seventeen teams of final-year medical students were randomised into two groups. The intervention group participated in a 40 min education session about the ISBAR communication tool. A control group received no training. Each team of five students participated in a simulated clinical scenario using a patient simulator in a mocked-up clinical environment. During each scenario, one student made a telephone referral seeking assistance from a senior colleague. Audio data for the telephone referrals (n = 17 students) were captured during the scenario for both groups. During a blinded review of the data, communication was scored on both content and clarity. RESULTS: Communication content was higher from a mean score of 10.2 to 17.4 items (p

The Effective Management of Anesthetic Crises (EMAC) course is a joint initiative between the Australian and New Zealand College of Anesthetists (ANZCA) and simulation centers. This standardized 2.5-day course has become an integral component of training for Fellowship of ANZCA and as such is an innovative development on the global anesthesia scene.Since its inception in 2002, over 600 anesthetists, with equal numbers of specialists and trainees, have attended EMAC throughout Australia, New Zealand, and Hong Kong. Course evaluations from 499 anesthetists and a follow-up survey showed strong support for the course and its relevance to clinical practice. The course is perceived by participants as changing their practice and improving their management of anesthetic crises.Exposure to the concepts of effective crisis management is now widespread in the anesthetic community in the region and should contribute to improved patient safety.

The spectrum of uses of high-fidelity simulation in emergency medicine has increased in the past 10 years and ranges from teaching isolated skills to improving complex team behaviours in critical situations. This review will describe these published uses and explore the evidence supporting the use of high-fidelity simulation, as well as the future directions and difficulties of using this technology in our specialty.

This study attempts to describe the effects of a computerized patient simulator education program on interns' perceptions of their own performance and confidence in managing and/or participating in a cardiac arrest incident during simulated and real events. The study design was qualitative using thematic analysis of debriefing sessions and individual interviews. The setting for the research was an education program for interns working at Southern Health, Victoria, Australia utilising a high-fidelity patient simulator. The participants were junior doctors (n = 30) in the first three-month rotation of their first postgraduate year (interns). The results describe the interns' self-reported experiences and perceptions relating to: (1) cardiac arrest experiences prior to the simulation scenario; (2) a simulated cardiac arrest scenario; (3) real-life cardiac arrest experiences after the simulation scenario. The interns expressed perceptions of improved self-confidence in: managing this particular critical situation; their own decision making during a critical incident; their ability to prioritize tasks. They also reported improved awareness and understanding with regards to: the need for leadership and effective teamwork; and the need for effective communication with senior staff during a critical incident. The interns considered that they had improved their ability to effectively handle a cardiac arrest situation as a result of the simulator program. The interns interviewed expressed decreased levels of anxiety and improved confidence in their decision making and their ability to prioritize tasks during a similar real-world situation as a result of the simulator program.