215 research outputs found

    The Role of Geospatial Thinking and Geographic Skills in Effective Problem Solving with GIS: K-16 Education

    Get PDF
    Effective use of a Geographic Information System (GIS) is hampered by the limited geospatial reasoning abilities of students. The ability to reason with spatial relations, more specifically apply geospatial concepts, including the identification of spatial patterns and spatial associations, is important to geographic problem solving in a GIS context. This dissertation examines the broad influence of three factors on GIS problem solving: 1) affection towards computers, geography, and mathematics, 2) geospatial thinking, as well as 3) geographic skills. The research was conducted with 104 students in Waterloo, Ontario, Canada. Students were drawn from four educational levels: grade 9 students, 13 to 14 years of age; 1st year undergraduate university students, 3rd and 4th year undergraduate geography majors; and geography students at the graduate level ranging from 22 to 32 years of age. The level of affection is measured with modified scales borrowed from psychology. Results show that students in general exhibit positive sentiments toward computers and geography but less so towards mathematics. Spatial thinking and knowledge of geospatial concepts are measured by a 30-item scale differentiating among spatial thinkers along a novice-expert continuum. Scores on the scale showed an increase in spatial reasoning ability with age, grade, and level of education, such that grade 9 students averaged 7.5 out of 30 while the mean score of graduate students was 20.6. The final exercise assessed pertinent skills to geography namely inquiry, data collection, and analysis. In general, there was a positive correlation in the scores such that the skill proficiency increased with grade. Related analysis found three factors that affect problem-solving performance with a GIS. These include age, geographic skills (inquiry and analysis), and geospatial thinking (subscales analysis, representation, comprehension, and application). As well, the relationship(s) between performance on the geospatial scale and the observed problem-solving sequences and strategies applied on a GIS was examined. In general, students with lower scores were more apt to use basic visualization (zoom/measure tools) or buffer operations, while those with higher scores used a combination of buffers, intersection, and spatial queries. There were, however, exceptions as some advanced students used strategies that overly complicated the problem while others used visualization tools alone. The study concludes with a discussion on future research directions, followed by a series of pencil and paper games aimed to develop spatial thinking within a geographic setting

    Methods and Tools for Objective Assessment of Psychomotor Skills in Laparoscopic Surgery

    Get PDF
    Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentor–trainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons’ psychomotor skills. Ongoing development of tracking systems and software solutions has allowed for the expansion of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentals of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as well as optimize their already overloaded training schedules

    CoMoVA - A comprehension measurement framework for visualization systems

    Get PDF
    Despite the burgeoning interest shown in visualizations by many disciplines, there yet remains the unresolved question concerning comprehension. Is the concept that is being communicated through the visual easily grasped and clearly interpreted? Visual comprehension is that characteristic of any visualization system, which deals with how efficiently and effectively users are able to grasp the underlying concepts through suitable interactions provided for exploring the visually represented information. Comprehension has been considered a very complex subject, which is intangible and subjective in nature. Assessment of comprehension can help to determine the true usefulness of visualization systems to the intended users. A principal contribution of this research is the formulation of an empirical evaluation framework for systematically assessing comprehension support provided by a visualization system to its intended users. To assess comprehension i.e. to measure this seemingly immeasurable factor of visualization systems, we propose a set of criteria based on a detailed analysis of information flow from the raw data to the cognition of information in human mind. Our comprehension criteria are adapted from the pioneering work of two eminent researchers - Donald A. Norman and Aaron Marcus, who have investigated the issues of human perception and cognition, and visual effectiveness respectively. The proposed criteria have been refined with the help of opinions from experts. To gauge and verify the efficacy of these criteria in a practical sense, they were then applied to a bioinformatics visualization study tool and an immersive art visualization environment. Given the vast variety of users and their visualization goals, it may be noted that it is difficult for one to decide on the effectiveness of different visualization tools/techniques in a context independent fashion. We therefore propose an innovative way of evaluating a visualization technique by encapsulating it in a visualization pattern where it is seen as a solution to the visualization problem in a specific context. These visualization patterns guide the tool users/evaluators to compare, understand and select appropriate visualization tools/techniques. Lastly, we propose a novel framework named as CoMoVA (Comprehension Model for Visualization Assessment) that incorporates 'context of use', visualization patterns, visual design principles and important cognitive principles into a coherent whole that can be used to effectively tell us in a more quantifiable manner the benefits of visual representations and interactions provided by a system to the intended audience. Our approach of evaluation of visualization systems is similar to other questionnaire-based approaches such as SUMI (Software Usability Measurement Inventory), where all the questions deal with the measurement of a common trait. We apply this framework to two static software visualization tools in the software visualization domain to demonstrate the practical benefits of using such a framework

    Latent gaze information in highly dynamic decision-tasks

    Get PDF
    Die Digitalisierung durchdringt immer mehr Lebensbereiche. Aufgaben werden zunehmend digital erledigt und damit schneller, effizienter, aber auch zielorientierter und erfolgreicher erfüllt. Die rasante Entwicklung im Bereich der künstlichen Intelligenz in den letzten Jahren hat dabei eine große Rolle gespielt, denn sie hat viele hilfreiche Ansätze hervorgebracht, auf die immer weiter aufgebaut werden kann. Gleichzeitig werden die Augen, ihre Bewegungen und die Bedeutung dieser Bewegungen immer weiter erforscht. Die Verknüpfung dieser Entwicklungen hat zu spannenden Ansätzen in der Wissenschaft geführt. In dieser Dissertation stelle ich einige der Ansätze vor, an denen ich während meiner Promotion gearbeitet habe. Zunächst gebe ich einen Einblick in die Entwicklung von Modellen, die mit Hilfe künstlicher Intelligenz Verbindungen zwischen Augenbewegungsdaten und visueller Expertise herstellen. Dies wird anhand zwei verschiedener Bereiche, genauer gesagt zwei verschiedener Personengruppen, demonstriert: Sportler bei Entscheidungsfindungen und Chirurgen bei arthroskopischen Eingriffen. Die daraus resultierenden Modelle können als digitale Diagnosemodelle für die automatische Erkennung von visueller Expertise betrachtet werden. Darüber hinaus stelle ich Ansätze vor, die die Übertragbarkeit von Augenbewegungsmustern auf verschiedene Kompetenzbereiche untersuchen sowie wichtige Aspekte von Techniken zur Generalisierung. Schließlich befasse ich mich mit der zeitlichen Erkennung von Verwirrung auf der Grundlage von Augenbewegungsdaten. Die Ergebnisse legen eine Nutzung der Modelle als Zeitgeber für mögliche digitale Assistenzoptionen in der Ausbildung von Berufsanfängern nahe. Eine Besonderheit meiner Untersuchungen besteht darin, dass ich auf sehr wervolle Daten von DFB-Jugendkaderathleten sowie von langjährigen Experten in der Arthroskopie zurückgreifen konnte. Insbesondere die Arbeit mit den DFB-Daten stieß auf das Interesse von Radiound Printmedien, genauer, DeutschlandFunk Nova und SWR DasDing. Alle hier vorgestellten Beiträge wurden in international renommierten Fachzeitschriften oder auf Konferenzen veröffentlicht.Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences

    Learning from Teacher's Eye Movement: Expertise, Subject Matter and Video Modeling

    Full text link
    How teachers' eye movements can be used to understand and improve education is the central focus of the present paper. Three empirical studies were carried out to understand the nature of teachers' eye movements in natural settings and how they might be used to promote learning. The studies explored 1) the relationship between teacher expertise and eye movement in the course of teaching, 2) how individual differences and the demands of different subjects affect teachers' eye movement during literacy and mathematics instruction, 3) whether including an expert's eye movement and hand information in instructional videos can promote learning. Each study looked at the nature and use of teacher eye movements from a different angle but collectively converge on contributions to answering the question: what can we learn from teachers' eye movements? The paper also contains an independent methodology chapter dedicated to reviewing and comparing methods of representing eye movements in order to determine a suitable statistical procedure for representing the richness of current and similar eye tracking data. Results show that there are considerable differences between expert and novice teachers' eye movement in a real teaching situation, replicating similar patterns revealed by past studies on expertise and gaze behavior in athletics and other fields. This paper also identified the mix of person-specific and subject-specific eye movement patterns that occur when the same teacher teaches different topics to the same children. The final study reports evidence that eye movement can be useful in teaching; by showing increased learning when learners saw an expert model's eye movement in a video modeling example. The implications of these studies regarding teacher education and instruction are discussed.PHDEducation & PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145853/1/yizhenh_1.pd

    The background information on subjects in program comprehension studies

    Get PDF
    Program comprehension is a very important skill a software engineer need. Many researchers conduct experiments on program comprehension in order to improve tools, documentation, and maintenance guidelines supporting program comprehension. Individual programmers’ productivity might vary significantly even though they have similar background. Thus, the subjects’ background is very important when conducting and analyzing experiments on program comprehension. The survey presented in this short Master thesis identifies subjects background information reported in software experiments on program comprehension. The background information reported in 24 articles was systematically analyzed in order to answer what kind of background information is reported and how the background information was used in the analysis. The articles reports many different background variables, but the overall impression of the background information reported in program comprehension experiments is that it is rather arbitrary and small. The analysis shows that there is a need for standards and guidelines of how to collect and report subjects’ background information. The survey shows also that almost no background information of the subjects is used in the experiments’ analysis. The articles in this survey provide so little information about the subjects’ background that it is difficult to perform replications and meta-analysis. This thesis aims to make researchers more aware of the subjects’ background in their experiments and reports. On the basis of the results of the analysis I have suggested background variables that should be collected in comprehension studies and proposed a background questionnaire. The questionnaire was used in an experiment with 24 subjects from the industry. I report here experiences with the questionnaire

    #MPLP: a Comparison of Domain Novice and Expert User-generated Tags in a Minimally Processed Digital Archive

    Get PDF
    The high costs of creating and maintaining digital archives precluded many archives from providing users with digital content or increasing the amount of digitized materials. Studies have shown users increasingly demand immediate online access to archival materials with detailed descriptions (access points). The adoption of minimal processing to digital archives limits the access points at the folder or series level rather than the item-level description users\u27 desire. User-generated content such as tags, could supplement the minimally processed metadata, though users are reluctant to trust or use unmediated tags. This dissertation project explores the potential for controlling/mediating the supplemental metadata from user-generated tags through inclusion of only expert domain user-generated tags. The study was designed to answer three research questions with two parts each: 1(a) What are the similarities and differences between tags generated by expert and novice users in a minimally processed digital archive?, 1(b) Are there differences between expert and novice users\u27 opinions of the tagging experience and tag creation considerations?, 2(a) In what ways do tags generated by expert and/or novice users in a minimally processed collection correspond with metadata in a traditionally processed digital archive?, 2(b) Does user knowledge affect the proportion of tags matching unselected metadata in a minimally processed digital archive?, 3(a) In what ways do tags generated by expert and/or novice users in a minimally processed collection correspond with existing users\u27 search terms in a digital archive?, and 3(b) Does user knowledge affect the proportion of tags matching query terms in a minimally processed digital archive? The dissertation project was a mixed-methods, quasi-experimental design focused on tag generation within a sample minimally processed digital archive. The study used a sample collection of fifteen documents and fifteen photographs. Sixty participants divided into two groups (novices and experts) based on assessed prior knowledge of the sample collection\u27s domain generated tags for fifteen documents and fifteen photographs (a minimum of one tag per object). Participants completed a pre-questionnaire identifying prior knowledge, and use of social tagging and archives. Additionally, participants provided their opinions regarding factors associated with tagging including the tagging experience and considerations while creating tags through structured and open-ended questions in a post-questionnaire. An open-coding analysis of the created tags developed a coding scheme of six major categories and six subcategories. Application of the coding scheme categorized all generated tags. Additional descriptive statistics summarized the number of tags created by each domain group (expert, novice) for all objects and divided by format (photograph, document). T-tests and Chi-square tests explored the associations (and associative strengths) between domain knowledge and the number of tags created or types of tags created for all objects and divided by format. The subsequent analysis compared the tags with the metadata from the existing collection not displayed within the sample collection participants used. Descriptive statistics summarized the proportion of tags matching unselected metadata and Chi-square tests analyzed the findings for associations with domain knowledge. Finally, the author extracted existing users\u27 query terms from one month of server-log data and compared the generated-tags and unselected metadata. Descriptive statistics summarized the proportion of tags and unselected metadata matching query terms, and Chi-square tests analyzed the findings for associations with domain knowledge. Based on the findings, the author discussed the theoretical and practical implications of including social tags within a minimally processed digital archive

    The Example Guru: Suggesting Examples to Novice Programmers in an Artifact-Based Context

    Get PDF
    Programmers in artifact-based contexts could likely benefit from skills that they do not realize exist. We define artifact-based contexts as contexts where programmers have a goal project, like an application or game, which they must figure out how to accomplish and can change along the way. Artifact-based contexts do not have quantifiable goal states, like the solution to a puzzle or the resolution of a bug in task-based contexts. Currently, programmers in artifact-based contexts have to seek out information, but may be unaware of useful information or choose not to seek out new skills. This is especially problematic for young novice programmers in blocks programming environments. Blocks programming environments often lack even minimal in-context support, such as auto-complete or in-context documentation. Novices programming independently in these blocks-based programming environments often plateau in the programming skills and API methods they use. This work aims to encourage novices in artifact-based programming contexts to explore new API methods and skills. One way to support novices may be with examples, as examples are effective for learning and highly available. In order to better understand how to use examples for supporting novice programmers, I first ran two studies exploring novices\u27 use and focus on example code. I used those results to design a system called the Example Guru. The Example Guru suggests example snippets to novice programmers that contain previously unused API methods or code concepts. Finally, I present an approach for semi-automatically generating content for this type of suggestion system. This approach reduces the amount of expert effort required to create suggestions. This work contains three contributions: 1) a better understanding of difficulties novices have using example code, 2) a system that encourages exploration and use of new programming skills, and 3) an approach for generating content for a suggestion system with less expert effort

    AH 2003 : workshop on adaptive hypermedia and adaptive web-based systems

    Get PDF
    • …
    corecore