Detecting students who are conducting inquiry Without Thinking Fastidiously (WTF) in the Context of Microworld Learning Environments

In recent years, there has been increased interest and research on identifying the various ways that students can deviate from expected or desired patterns while using educational software. This includes research on gaming the system, player transformation, haphazard inquiry, and failure to use key features of the learning system. Detection of these sorts of behaviors has helped researchers to better understand these behaviors, thus allowing software designers to develop interventions that can remediate them and/or reduce their negative impacts on student learning. This work addresses two types of student disengagement: carelessness and a behavior we term WTF (“Without Thinking Fastidiously”) behavior. Carelessness is defined as not demonstrating a skill despite knowing it; we measured carelessness using a machine learned model. In WTF behavior, the student is interacting with the software, but their actions appear to have no relationship to the intended learning task. We discuss the detector development process, validate the detectors with human labels of the behavior, and discuss implications for understanding how and why students conduct inquiry without thinking fastidiously while learning in science inquiry microworlds. Following this work we explore the relationship between student learner characteristics and the aforementioned disengaged behaviors carelessness and WTF. Our goal was to develop a deeper understanding of which learner characteristics correlate to carelessness or WTF behavior. Our work examines three alternative methods for predicting carelessness and WTF behaviors from learner characteristics: simple correlations, k-means clustering, and decision tree rule learners

Discovery with Models: A Case Study on Carelessness in Computer-based Science Inquiry

In recent years, an increasing number of analyses in Learning Analytics and Educational Data Mining (EDM) have adopted a "Discovery with Models" approach, where an existing model is used as a key component in a new EDM/analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its potential to enhance research on learning and learners, and key lessons learned in how discovery with models can be conducted validly and effectively. We illustrate these issues through discussion of a case study where discovery with models was used to investigate a form of disengaged behavior, i.e., carelessness, in the context of middle school computer-based science inquiry. This behavior has been acknowledged as a problem in education as early as the 1920s. With the increasing use of high-stakes testing, the cost of student carelessness can be higher. For instance, within computer-based learning environments careless errors can result in reduced educational effectiveness, with students continuing to receive material they have already mastered. Despite the importance of this problem, it has received minimal research attention, in part due to difficulties in operationalizing carelessness as a construct. Building from theory on carelessness and a Bayesian framework for knowledge modeling, we use machine-learned detectors to predict carelessness within authentic use of a computer-based learning environment. We then use a discovery with models approach to link these validated carelessness measures to survey data, to study the correlations between the prevalence of carelessness and student goal orientation. The second construct, carelessness, refers to incorrect answers given by a student on material that the student should be able to answer correctly Rodriguez-Fornells & Maydeu-Olivares, 2000). The application of discovery with models involves two main phases. First, a model of a construct is developed using machine learning or knowledge engineering techniques, and is then validated, as discussed below. Second, this validated model is applied to data and used as a component in another analysis: For example, for identifying outliers through model predictions; examining which variables best predict the modeled construct; finding relationships between the construct and other variables using correlations, predictions, associations rules, causal relationships or other methods; or studying the contexts where the construct occurs, including its prevalence across domains, systems, or populations. For example, in One essential question to pose prior to a discovery with model analysis is whether the model adopted is valid, both overall, and for the specific situation in which it is being used. Ideally, a model should be validated using an approach such as cross-validation, where the model is repeatedly trained on one portion of the data and tested on a different portion, with model predictions compared to appropriate external measures, for example assessments made by humans with acceptably high inter-rater reliability, such as field observations of student behavior for gaming the system (cf. Even after validating in this fashion, validity should be re-considered if the model is used for a substantially different population or context than was used when developing the model.. An alternative approach is to use a simpler knowledge-engineered definition, rationally deriving a function/rule that is then applied to the data. In this case, the model can be inferred to have face validity. However, knowledge-engineered models often DISCOVERY WITH MODELS: A CASE STUDY ON CARELESSNESS 6 produce different results than machine learning-based models, for example in the case of gaming the system. Research studying whether student or content is a better predictor of gaming the system identified different results, depending on which model was applied (cf. Baker, 2007a

PathEx: a novel multi factors based datasets selector web tool

<p>Abstract</p> <p>Background</p> <p>Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.</p> <p>Description</p> <p>Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach.</p> <p>Conclusion</p> <p>This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.</p

The role of the user within the medical device design and development process: medical device manufacturers' perspectives

Copyright @ 2011 Money et al.Background: Academic literature and international standards bodies suggest that user involvement, via the incorporation of human factors engineering methods within the medical device design and development (MDDD) process, offer many benefits that enable the development of safer and more usable medical devices that are better suited to users' needs. However, little research has been carried out to explore medical device manufacturers' beliefs and attitudes towards user involvement within this process, or indeed what value they believe can be added by doing so.Methods: In-depth interviews with representatives from 11 medical device manufacturers are carried out. We ask them to specify who they believe the intended users of the device to be, who they consult to inform the MDDD process, what role they believe the user plays within this process, and what value (if any) they believe users add. Thematic analysis is used to analyse the fully transcribed interview data, to gain insight into medical device manufacturers' beliefs and attitudes towards user involvement within the MDDD process.Results: A number of high-level themes emerged, relating who the user is perceived to be, the methods used, the perceived value and barriers to user involvement, and the nature of user contributions. The findings reveal that despite standards agencies and academic literature offering strong support for the employment formal methods, manufacturers are still hesitant due to a range of factors including: perceived barriers to obtaining ethical approval; the speed at which such activity may be carried out; the belief that there is no need given the 'all-knowing' nature of senior health care staff and clinical champions; a belief that effective results are achievable by consulting a minimal number of champions. Furthermore, less senior health care practitioners and patients were rarely seen as being able to provide valuable input into the process.Conclusions: Medical device manufacturers often do not see the benefit of employing formal human factors engineering methods within the MDDD process. Research is required to better understand the day-to-day requirements of manufacturers within this sector. The development of new or adapted methods may be required if user involvement is to be fully realised.This study was in part funded by grant number Ref: GR/S29874/01 from the Engineering and Physical Sciences Research Council. This article is made available through the Brunel University Open Access Publishing Fund

History and physics -- the covered bridge at Old Sturbridge Village.

The purpose of this project was both to research the history of covered bridges in New England and to explore ways that the engineering principles in these covered bridges could be explained to visitors of Old Sturbridge Village. The project focused particularly on the Vermont Bridge at Old Sturbridge Village

Gene therapy clinical trials worldwide 1989-2004-an overview

Summary In 1989, Rosenberg et al. performed the first human gene therapy trial when they used a retrovirus to introduce the gene coding for resistance to neomycin into human tumor-infiltrating lymphocytes before infusing them into five patients with advanced melanoma. This study demonstrated the feasibility of using retroviral gene transduction in humans and set the stage for further studies. Since then, over 900 clinical trials have been completed, are ongoing or have been approved worldwide. These trials have been designed to establish feasibility and safety, to demonstrate the reality of expression of therapeutic protein(s) in vivo by the genes transferred and, in some cases, to show therapeutic benefit. There is no single source of information that presents an overview of all the clinical trials undertaken worldwide. In 1997 we set up a database to bring all the information on clinical trials together as comprehensively and as globally as possible. The data were compiled and are regularly updated from official agency sources, the published literature, presentations at conferences and from information kindly provided by investigators or trial sponsors themselves. As of January 31, 2004, we have identified 918 trials in 24 countries. The USA accounts for two-thirds of these trials. Cancer is by far the most common disease indication, followed by inherited monogenic diseases, and cardiovascular diseases. Viral vectors have been the most frequently used vehicles for transferring genes into human cells, with retroviruses and adenoviruses representing the vast majority. Plasmid (naked) DNA and other non-viral vectors have been used in one-quarter of the trials. Over 100 distinct genes have been transferred. This article aims to provide a descriptive overview of the clinical trials that, to the best of our knowledge, have been or are being performed worldwide. Details of the data presented, including an interactive, searchable database that currently holds information on 918 trials, can be found on The Journal of Gene Medicine clinical trials websit

Exploring the impact of a learning dashboard on student affect

Research highlights that many students experience negative emotions during learning activities, and these can have a detrimental impact on behaviors and outcomes. Here, we investigate the impact of a particular kind of affective intervention, namely a learning dashboard, on two deactivating emotions: bore

Involving end-users in the design of an audit and feedback intervention in the emergency department setting – a mixed methods study

Abstract Background Long length of stays (LOS) in emergency departments (ED) negatively affect quality of care. Ordering of inappropriate diagnostic tests contributes to long LOS and reduces quality of care. One strategy to change practice patterns is to use performance feedback dashboards for physicians. While this strategy has proven to be successful in multiple settings, the most effective ways to deliver such interventions remain unknown. Involving end-users in the process is likely important for a successful design and implementation of a performance dashboard within a specific workplace culture. This mixed methods study aimed to develop design requirements for an ED performance dashboard and to understand the role of culture and social networks in the adoption process. Methods We performed 13 semi-structured interviews with attending physicians in different roles within a single public ED in the U.S. to get an in-depth understanding of physicians’ needs and concerns. Principles of human-centered design were used to translate these interviews into design requirements and to iteratively develop a front-end performance feedback dashboard. Pre- and post- surveys were used to evaluate the effect of the dashboard on physicians’ motivation and to measure their perception of the usefulness of the dashboard. Data on the ED culture and underlying social network were collected. Outcomes were compared between physicians involved in the human-centered design process, those with exposure to the design process through the ED social network, and those with limited exposure. Results Key design requirements obtained from the interviews were ease of access, drilldown functionality, customization, and a visual data display including monthly time-trends and blinded peer-comparisons. Identified barriers included concerns about unintended consequences and the veracity of underlying data. The surveys revealed that the ED culture and social network are associated with reported usefulness of the dashboard. Additionally, physicians’ motivation was differentially affected by the dashboard based on their position in the social network. Conclusions This study demonstrates the feasibility of designing a performance feedback dashboard using a human-centered design approach in the ED setting. Additionally, we show preliminary evidence that the culture and underlying social network are of key importance for successful adoption of a dashboard