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Two hundred million people in the US are overweight or obese mirroring a worldwide trend that is associated with high morbidity and mortality rates. Health and fitness mobile technology applications have great capacities for supporting dieters’ life-style changes and could profit from and provide input of health behavior theories. Those theories have been demonstrated with massive clinical evidence to be efficient for fostering healthy lifestyle changes and weight loss. This research reviewed the 100 most popular mobile technology applications from iTunes App Store’s Health and Fitness category in respect coverage of health behavior theories’ concepts und chose 14 of those for a complete analysis. Applications provide good support for athletes’ workouts and have great potential to be extended to serve overweight users as well. Missing features could be easily implemented given the current state of technology. These developments look promising for tackling sustained weight loss in many mobile technology users.

Misperceptions of stock and flow relationships are pervasive and an important problem to solve in system dynamics. Prior studies have shown that individuals perform poorly on accumulation problems, even when considering relatively simple systems, an effect termed the Stock–Flow (SF) failure. This study examines the effects of domain experience in overcoming the SF failure. We compared performance of medical students and undergraduates with no medical education on accumulation problems in medical and general domains. Medical students performed better than undergraduates only in some of the problems (including the general domain problems), and they performed equally poorly as undergraduates in problems that required medical domain experience. There was no correlation between performance in the stock and flow problems and either duration of medical education or age. Thus we conclude that domain experience is not a strong indicator for overcoming the SF failure. Copyright © 2010 John Wiley & Sons, Ltd.

Two experiments were conducted, contrasting a minimally guided discovery condition with a variety of instructional conditions. College students interacted with a computer-based tutor that presented algebralike problems in a novel graphical representation. Although the tutor provided no instruction in a discovery condition, it constrained the possible actions sufficiently that students could always discover the algebraic transformations they needed to learn. In Experiment I, with ample practice for each new transformation, students performed better in the discovery condition than any instructional condition. In Experiment 2, with only a little practice for each transformation, students performed worst in the discovery condition. The authors suggest that the high levels of practice in the 1st experiment made students more efficient at discovering the algebraic transformations. When the cognitive demands were manageable, the discovery students may have more often encoded the algebraic transformations in mathematically correct ways.

Background: Disaster triage embodies all key features of dynamic decision making. Multiple decisions have to be made under time pressure and workload. Situations are often unpredictable requiring trainees to apply learned routines to novel conditions. Up to this point, psychologic theories of learning can provide only little support on how to train disaster responders for these challenging situations.Methods: We summarize and illustrate several examples of dynamic decision-making research using simulations and microworlds as a starting point for a new theory of learning and skill acquisition in disaster triage. We describe MEDIC, a microworld in the context of medical diagnosis, and other simple tasks designed to gather people's understanding of accumulation, a basic component of dynamic tasks.Results: Using a microworld called MEDIC, we demonstrate the difficulties of learning to be effective at medical decision making and present a set of theoretical constructs that help to explain those difficulties. Implications for how to overcome them are also discussed. On the basis of this kind of research and our instance-based learning theory, we develop principles for the design of effective disaster training and for building a theoretical framework that can systematically predict how to best train for successful performance in disaster situations. Finally, we also demonstrate the difficulty of understanding dynamic systems; educated adults with medical expertise have trouble understanding even simple dynamic medical problems.Conclusions: Dynamic decision-making research can be used as a theoretical and empirical reference for advancing pediatric triage training to prepare trainees for disaster triage. Recommendations for effective learning derived from dynamic decision-making research are presented.

Computer-based studies usually produce log files as raw data. These data cannot be analyzed adequately with conventional statistical software. The Chemnitz LogAnalyzer provides tools for quick and comfortable visualization and analyses of hypertext navigation behavior by individual users and for aggregated data. In addition, it supports analogous analyses of questionnaire data and reanalysis with respect to several predefined orders of nodes of the same hypertext. As an illustration of how to use the Chemnitz LogAnalyzer, we give an account of one study on learning with hypertext. Participants either searched for specific details or read a hypertext document to familiarize themselves with its content. The tool helped identify navigation strategies affected by these two processing goals and provided comparisons, for example, of processing times and visited sites. Altogether, the Chemnitz LogAnalyzer fills the gap between log files as raw data of Web-based studies and conventional statistical software.

Zusammenfassung: In der Problemlöseforschung und Hypertextforschung zeigte sich wiederholt, dass Lerner mit unspezifischem bzw. spezifischem Bearbeitungsziel unterschiedlich vom bearbeiteten Material profitieren. Leser mit unspezifischem Ziel erwerben eher breites allgemeines und transferierbares Wissen, während sich Sucher, die nach spezifischen Details im Material suchten, eher gründliches aber nicht transferierbares Detailwissen aneignen. Die vorliegende Studie untersuchte, welche Bearbeitungsstrategien diesen Befunden zugrunde liegen. 40 Studenten bearbeiteten zwei Kapitel zur englischen Grammatik mit unspezifischem Leseziel bzw. spezifischem Suchziel, wobei sie Regeln, Beispiele und Übungen bearbeiteten. Passend zu den in der Literatur berichteten Unterschieden im erworbenen Wissen zeigten die Logfiles der Lernsitzungen, dass Leser zwar mehr Seiten besuchten als Sucher, sich allerdings auch kürzer dort aufhielten. Zusätzlich investierten beide Gruppen mehr Zeit in das Lesen der Regeln als in das Bearbeiten der Übungen. Navigationsstile und Verarbeitungszeiten werden in Zusammenhang mit den Lernergebnissen vor dem Hintergrund der Problemlöse- und Hypertextforschung diskutiert.

Hypertext research results suggest that building a correct representation of the hypertext structure enables users to navigate effectively within the text. Therefore, text comprehension processes involved in hypertext reading should be investigated. In an experimental study, we differentiated the text structure from the dimensions of a postulated coherent situation model in order to compare them. Three electronic text versions, varying in navigational facility, and text structure visualization were compared with respect to orientation, navigation, eye movements, mental representation of text structure and content (situation model). Results demonstrate that when text structure visualization was unavailable, a reorganization of readers’ representations of the text structure towards their situation model took place. Navigation within the text particularly affected mental representation of text structure and content.

Summary: This study investigated the ability to generalize acquired skills from training conditions to novel conditions, in a complex perceptual and cognitive task of luggage screening. We examined category and exemplar diversity during training for preparing learners to detect novel items during transfer. Category diversity was manipulated in terms of heterogeneity of training categories: Participants either trained with targets from one category or with targets from several categories. Exemplar diversity was manipulated between participants by presenting either a few or many exemplars for both category diversity conditions. Seventy-two participants were trained to identify threats in pieces of luggage. Thereafter they were transferred to novel stimuli. Results can be summarized in support for the diversity of training hypothesis for preparing for novelty: To the best training for novel luggage screening situations is achieved using fewer items in a variety of categories. Copyright # 2010 John Wiley & Sons, Ltd. Target detection and decision-making tasks are pervasive. They are as common as finding flaws as a quality inspector, and as important and relevant for our health and society, such as a physician identifying a tumour on an X-ray image, a soldier determining the presence of a combatant in unfamiliar terrain, and an airport security officer looking for threats in passenger luggage. The terrorist attacks of 11 September changed the way security is addressed in American airports. However, much of the threat detection in luggage screening is still done by visual inspection rather than by automated methods. This is partly due to the complexity of visual images, the uncertainty and variability of what constitutes a threat, and the intricacies of the human decision-making process. Research that improves human accuracy of detecting potential threats and optimizes detection time has become a priority. Our applied research goal in the airport security context is to find ways to transfer skills acquired during training to the accurate detection of unfamiliar, novel targets. This implies the need to prepare security officers to identify novel items of known categories. For instance, they should be able to detect not only images of guns or knives they have encountered during training but also novel images of guns and knives that they have not yet seen before. We will refer to this as exemplar diversity in the following. As we describe below, there exists some evidence that humans can indeed learn to detect novel items of familiar categories. More challenging, security officers have to prepare for another kind of novelty that we call category diversity. They have to detect not only novel items of familiar categories, but also novel items of categories that by definition cannot be practiced during training and that will potentially not look like any weapons encountered in previous training. However, we would not expect luggage screeners to be prepared to detect any novel object but a novel exemplar of a novel category within a meta-category such as weapons or threats. For example, the meta-category of ‘cutting instruments’ that are not allowed in an aircraft includes knives, box cutters, machetes, etc. For this category, luggage screeners are likely to be trained on a subset of objects. This subset of objects should help screeners to detect other members of the metacategory of ‘cutting instruments’. Similarly, we would expect that when screeners are trained in multiple categories of weapons or threats, they would be able to detect other members of a more generic meta-category of ‘threats’. In this research we investigated how to best train to detect this kind of novel items. To do so, we focused on category diversity and exemplar diversity of stimuli presented during training in a luggage screening task to facilitate detection of novel exemplars of a novel category. There exist several research areas relevant to the goal of preparing luggage screeners for detecting novel items, but none of those provide sufficient and theoretically sound advice to solve this problem. In the following, we will briefly review the literature on skill acquisition, especially for perceptual learning, transfer of skills and category learning, and the implications for preparing for exemplar and category diversity.

DEWEX is a server-based environment for developing Web-based experiments. It provides many features for creating and running complex experimental designs on a local server. It is freeware and allows for both using default features, for which only text input is necessary, and easy configurations that can be set up by the experimenter. The tool also provides log files on the local server that can be interpreted and analyzed very easily. As an illustration of how DEWEX can be used, a recent study is presented that demonstrates the system’s most important features. This study investigated learning from multiple hypertext sources and shows the influences of task, source of information, and hypertext presentation format on the construction of mental representations of a hypertext about a historical event.

This study investigated the role of specific and unspecific tasks for learning declarative knowledge and skills with a web based learning system. Results show that learners with specific tasks where better for both types of learning. Nevertheless, not all kinds of learning outcomes were equally influenced by instruction. Therefore, instructions should be selected carefully in correspondence with desired learning goals.

Using adaptive hypertext as a learning tool, the present study addresses the question of the effects of different processing goals, especially the goal of general reading a hypertext as opposed to the goal of searching for specific information, on learning content and skills at English grammar. Twenty students with German as mother tongue processed the present continuous chapter of the Chemnitz InternetGrammar. It has been shown that readers answer a higher amount of questions about details and, more importantly, they answer them in more detail than searchers. Nevertheless, searchers tend to produce more elaborated answers to complex questions than readers. Both groups performed better on skill tests after the session, showing no effect of the performed task. Based on this experimental evidence, the requirements on adaptive learning tools are discussed.

Understanding health statistics in graphical or numerical format is essential for evidence-based medical decision making. It has been reported repeatedly that physicians, medical students, and patients perform poorly when interpreting those kinds of information. With the current research we started to decompose skills and competencies needed for interpreting complex medical graphs. We invited students at Weill Cornell Medical College in Qatar to fill in a questionnaire on www.SurveyMonkey.com that contained three visual medical tasks and one well-investigated complex diagrammatic reasoning task from economics. We expected that students’ performance would improve with years at the medical college for medical tasks, but not necessarily for an unrelated diagrammatic reasoning task. 85 students (between 8 and 19 students per class) participated in this research. For the diagrammatic reasoning task, students performed as poorly as reported in the literature and there was no statistically significant difference between pre-medical and medical students. In contrast, medical students outperformed pre-medical students for a gastrointestinal anatomy test and for two general surgery tasks on steps and structures associated with cholecystectomy. These results replicate findings of low performance for interpreting complex graphs. At the same time, medical education seems to foster students’ understanding of simple graphs in their domain and might prepare them for understanding more complex graphs in that domain. Currently, we are working on extending our research to decompose students’, physicians’, and patients’ understanding of survival curves as complex graphs in the medical domain needed for evidence-based medical decision making.

Medical education is characterized by apprenticeship reflected in ‘see one, do one, teach one’. This research investigated effects of practice and individual guidance by a mentor for laparoscopic surgery using a simulation engine. Based on earlier research, we expected that it would take extensive training for self-directed, experience-based learning to compensate for individual, on-time instruction that is typical for medical education. Three groups of 3rd and 4th medical students trained to perform laparoscopic colecystectmy in 5 training sessions of 30 or 60 min. The mentored group received one-to-one individual guidance by a mentor during the complete training. A time-matched control group received exclusively feedback from the simulation engine. An extensive practice group was allotted double the time to compensate for missing guidance. Before and after training, their performance was analyzed for the first case in the system. Mentored students performed better during the pretest than students from both control groups. After training those students performed as well without guidance as during the pretest with guidance. Participants from both control groups improved performance from pre-test to post-test. In addition, students with extensive training performed almost as well as mentored students during the post-test. This implies that feedback provided by the simulation system is good enough for unsupervised students to reach a performance level comparable to mentored students, but it requires double the time for training. Next, we aim to improve the system's feedback to dramatically reducing training time while reaching the same level of performance. For example, this means to provide in-time warning before committing an error instead of presenting an error message after committing it. This will prevent students from automatizing suboptimal or dangerous routines.