Effects of the metacognitive computer-tool met.a.ware on the web search of laypersons

Today, laypersons often consult the Internet to inform themselves about health-related issues. However, the competent use of these often complex and heterogeneous information provisions cannot be taken for granted, because many Internet users are lacking the necessary metacognitive prerequisites. Therefore, we developed the metacognitive computer-tool met.a.ware, which supports laypersons’ Internet research for medical information by the means of metacognitive prompting and ontological classification. In an experimental investigation of met.a.ware a total of 118 participants with little medical knowledge were asked to conduct an Internet research on a medical topic. Participants were randomly assigned to four experimental groups that worked with met.a.ware and either received evaluation prompts, monitoring prompts, both types of prompts, or no prompts. All experimental conditions were additionally provided with ontological classification. One control group took paper and pencil notes. A further control group took notes using a blank text window. Results showed that laypersons receiving evaluation prompts outperformed controls in terms of knowledge about sources and produced more arguments commenting on the source of information in an essay task. In addition, laypersons receiving monitoring prompts acquired significantly more knowledge about facts, but did not perform better on a comprehension test than the controls. The availability of ontological categories helped to structure the notes laypersons in the conditions working with ontological classification took during Internet research. Analyses of the notes further demonstrated that the availability of ontological categories guided information search in direction of the selected categories. It is concluded, that met.a.ware is an effective tool that supports laypersons’ Internet research.     

An alternative,layout‐driven approach to the clustering of documents

Internet has become a huge repository of information and knowledge, based on the sharing of the electronic documents. Last trends in knowledge management focus on the knowledge representation based on the document content. In fact, most accustomed approaches achieve the document understanding by analyzing the “portions of information'' in the document which describe the content, through techniques of text parsing and extraction. This paper presents an alternative approach that departs from the consolidated techniques of document management and focuses on the logical structure of a PDF document as a discriminating source of document knowledge. The main idea is based on the fact, when the reader looks at a paper, his first perception is related to the layout of the document. The analysis of layout, typesetting, paginating, and graphical arrangement of a document provides interesting information about its content understanding; in general, the documents that are in the same category present similar page layout, fonts, and figures arrangement. In this sense, this work presents an alternative way to deal with documents recognition and understanding, through the analysis of the layout of electronic PDF documents and their classification. © 2008 Wiley Periodicals, Inc.     

Evaluation of a Meta-1-based automatic indexing method for medical documents.

This paper describes MetaIndex, an automatic indexing program that creates symbolic representations of documents for the purpose of document retrieval. MetaIndex uses a simple transition network parser to recognize a language that is derived from the set of main concepts in the Unified Medical Language System Metathesaurus (Meta-1). MetaIndex uses a hierarchy of medical concepts, also derived from Meta-1, to represent the content of documents. The goal of this approach is to improve document retrieval performance by better representation of documents. An evaluation method is described, and the performance of MetaIndex on the task of indexing the Slice of Life medical image collection is reported.     

Feature pattern based representation of multimedia documents for efficient knowledge discovery

The rapid growth of multimedia documents has raised huge demand for sophisticated multimedia knowledge discovery systems. The knowledge extraction of the documents mainly relies on the data representation model and the document representation model. As the multimedia document comprised of multimodal multimedia objects, the data representation depends on modality of the objects. The multimodal objects require distinct processing and feature extraction methods resulting in different features with different dimensionalities. Managing multiple types of features is challenging for knowledge extraction tasks. The unified representation of multimedia document benefits the knowledge extraction process, as they are represented by same type of features. The appropriate document representation will benefit the overall decision making process by reducing the search time and memory requirements. In this paper, we propose a domain converting method known as Multimedia to Signal converter (MSC) to represent the multimodal multimedia document in an unified representation by converting multimodal objects as signal objects. A tree based approach known as Multimedia Feature Pattern (MFP) tree is proposed for the compact representation of multimedia documents in terms of features of multimedia objects. The effectiveness of the proposed framework is evaluated by performing the experiments on four multimodal datasets. Experimental results show that the unified representation of multimedia documents helped in improving the classification accuracy for the documents. The MFP tree based representation of multimedia documents not only reduces the search time and memory requirements, also outperforms the competitive approaches for search and retrieval of multimedia documents.     

Embedding knowledge in Web documents

The paper argues for the use of general and intuitive knowledge representation languages (and simpler notational variants, e.g. subsets of natural languages) for indexing the content of Web documents and representing knowledge within them. We believe that these languages have advantages over metadata languages based on the Extensible Mark-up Language (XML). Indeed, the retrieval of precise information is better supported by languages designed to represent semantic content and support logical inference, and the readability of such a language eases its exploitation, presentation and direct insertion within a document (thus also avoiding information duplication). We advocate the use of Conceptual Graphs and simpler notational variants that enhance knowledge readability. To further ease the representation process, we propose techniques allowing users to leave some knowledge terms undeclared. We also show how lexical, structural and knowledge-based techniques may be combined to retrieve or generate knowledge or Web documents. To support and guide the knowledge modeling approach, we present a top-level ontology of 400 concept and relation types. We have implemented these features in a Web-accessible tool named WebKB², and show examples to illustrate them.     

Promoting college students’ knowledge acquisition and ill-structured problem solving: Web-based integration and procedure prompts

This study examined how web-based integration and procedure question prompts differentially affected students’ knowledge acquisition and ill-structured problem solving skills, particularly in representing problem(s), developing solutions, and monitoring and evaluating a plan of action within the social science context. Eighty-four undergraduate pre-service teachers were recruited and randomly assigned to one of the four conditions: (1) an IP condition that required students to complete integration prompts, (2) a PP condition that required students to complete procedure prompts, (3) an IPP condition that required students to complete both integration and procedure prompts, or (4) a control condition that did not provide access to any prompts. The findings show that students who received integration prompts outperformed those who did not receive any in knowledge acquisition and problem representation for solving an ill-structured problem. Integration prompts also helped the development and integration of cognitive schema, whereas procedure prompts helped direct students’ attention to specific features of the problem in order to arrive at the solution(s). In fact, the presence of an integration prompt alone is not sufficient to support successful ill-structured problem solving unless a procedure prompt is provided. Based on these findings, this study offers implications for designing Web-based learning environments, engineered to promote integrative knowledge and ill-structured problem solving skills.     

Short- and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance

This study seeks to promote learning in computer-based learning environments utilizing students’ self-directed metacognitive prompts. Such prompts are based on the idea of instructing students to design their own metacognitive scaffolds and learn with them afterward. In a pre-post experimental design, students in the experimental group (n = 35) were instructed to configure their own metacognitive prompts before learning whereas students in the control group (n = 35) learned without prompts. Log file analysis of navigation behavior indicates that students who learned with their individually designed, self-directed prompts visited relevant webpages significantly more often and spent a longer time on them compared with students in the control group. Moreover, participants in the experimental group attained better transfer performance immediately after learning. The long-term effect in transfer performance was even greater in a follow-up learning session conducted after three weeks without any instructional support in either group. These results are consistent with theories of metacognition and self-regulated learning and indicate that self-directed prompts can lead to sustainable effects.     

Iterative augmentation of a medical training simulator: Effects of affective metacognitive scaffolding

Experiential training simulators are gaining increasing popularity for job-related training due to their potential to engage and motivate adult learners. They are designed to provide learning experiences that are directly connected to users' work environments and support self-regulated learning. Nevertheless, learners often fail to transfer the knowledge gained in the simulated environment to real-world contexts. The EU-funded ImREAL project aimed to bridge that gap by developing a suite of intelligent services designed to enhance existing training simulators. This paper presents work that was a subset of this research project, reporting the iterative development and evaluation of a scaffolding service, which was integrated into a simulator for training medical students to perform diagnostic interviews. The study comprises three evaluation phases, comparing the pure simulator to a first version with metacognitive scaffolding and then to a final version with affective metacognitive scaffolding and enriched user modelling. The scaffolding service provides the learner with metacognitive prompts; affective elements are realized by an integrated affect reporting tool and affective prompts. Using a mixed-method approach by analysing questionnaires (N = 106) and log-data (N = 426), the effects of the services were investigated with respect to real-world relevance, self-regulated learning support, learning experience, and integration. Despite some limitations, the outcomes of this study demonstrate the usefulness of affective metacognitive scaffolding in the context of experiential training simulators; significant post-simulation increases in perceived relevance of the simulator, reflective note-taking, overall motivation, and feeling of success could be identified. Perceived usability and flow of the simulation increased, whereas overall workload and frustration decreased. However, low response rates to specific functions of the simulation point to a need to further investigate how to raise users' awareness and understanding of the provided tools, to encourage interaction with the services, and to better convey the benefits of using them. Thus, future challenges concern not so much technological developments for personalizing learning experiences, but rather new ways to change user attitudes towards an open approach to learning systems that enables them to benefit from all offered features.     

Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning

Using hidden Markov models (HMM), the current study looked at how learners' metacognitive monitoring is related to their physiological reactivity in the context of collaborative learning. The participants (N = 12, age 16–17 years, three females and nine males) in the study were high school students enrolled in an advanced physics course. The results show that during collaborative learning, the students engaged in monitoring in each self-regulated learning phase such as task understanding, planning and goal setting, task enactment, adaptation and reflection. The results of the HMM indicated that the learners' physiological reactivity was low when monitoring occurred. The associations between the states based on the HMM provide insights not only into how learners engage in metacognitive monitoring but also about their level of physiological reactivity in each state. In conclusion, exploring aspects of metacognitive monitoring in collaborative learning can be done with the help of physiological reactions.     

The internet-based knowledge acquisition and management method to construct large-scale distributed medical expert systems

The Internet offers an unprecedented opportunity to construct powerful large-scale medical expert systems (MES). In these systems, a cost-effective medical knowledge acquisition (KA) and management scheme is highly desirable to handle the large quantities of, often conflicting, medical information collected from medical experts in different medical fields and from different geographical regions. In this paper, we demonstrate that a medical KA/management system can be built upon a three-tier distributed client/server architecture. The knowledge in the system is stored/managed in three knowledge bases. The maturity of the medical know-how controls the knowledge flow through these knowledge bases. In addition, to facilitate the knowledge representation and application in these knowledge bases as well as information retrieval across the Internet, an 8-digit numeric coding scheme with a weight value system is proposed. At present, a medical KA and management system based on the proposed method is being tested in clinics. Current results have showed that the method is a viable solution to construct, modify, and expand a distributed MES through the Internet.     

A knowledge construction methodology to automate case-based learning using clinical documents

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.     

Differential impact of two types of metacognitive prompting provided during simulation-based training

The purpose of the current study was to test the differential impact of two forms of metacognitive prompts on knowledge acquisition and application during simulation-based training. Participants in the experimental conditions were prompted to construct sentences by connecting declarative words (Words Group) or conceptual phrases (Phrases Group) related to the training material from two columns. Performance was then compared across conditions during an assessment scenario that did not include prompting. Overall, results provide support for the effectiveness of metacognitive prompting generally, when compared to the Control Group that did not receive prompting. Further, some support was found for providing word-based prompts over more conceptual phrase-based prompts, suggesting that the phrases may have distracted or overloaded learners. Implications for further investigation into the effects of different types of metacognitive support are discussed.     

Enhancing computer-supported writing of learning protocols by adaptive prompts

Cognitive and metacognitive prompts are a central support procedure in eHELp a computer-based environment that supports the writing of learning protocols. In order to investigate the effectiveness of adapting prompts, 79 students revised a learning protocol in eHELp either supported by prompts that were adapted on the basis of the results of an integrated learning-strategy questionnaire or a meta-knowledge test, respectively, by randomly selected prompts, or without any support. Adaptive prompts improved the quality of the learning protocols and fostered the acquisition of declarative knowledge and deep understanding, irrespective of the applied diagnostic instrument. In conclusion, open-ended learning tasks like the writing of learning protocols can be made more effective by adaptive support based on prior strategy assessment.     

Knowledge acquisition, modelling and inference through the World Wide Web

The development of knowledge-based systems involves the management of a diversity of knowledge sources, computing resources and system users, often distributed geographically. The knowledge acquisition, modelling and representation communities have developed a wide range of tools relevant to the development and management of large-scale knowledge-based systems, but the majority of these tools run on individual workstations and use specialist data formats making system integration and knowledge interchange very problematic. However, widespread access to the Internet has led to a new era of distributed client–server computing. In particular, the introduction of support forformson World Wide Web in late 1993 has provided an easily programmable, cross-platform graphic-user interface that has become widely used in innovative interactive systems. This article reports on the development of open architecture knowledge management tools operating through the web to support knowledge acquisition, representation and inference through semantic networks and repertory grids.     

Transforming paper documents into XML format with WISDOM++

The transformation of scanned paper documents to a form suitable for an Internet browser is a complex process that requires solutions to several problems. The application of an OCR to some parts of the document image is only one of the problems. In fact, the generation of documents in HTML format is easier when the layout structure of a page has been extracted by means of a document analysis process. The adoption of an XML format is even better, since it can facilitate the retrieval of documents in the Web. Nevertheless, an effective transformation of paper documents into this format requires further processing steps, namely document image classification and understanding. WISDOM++ is a document processing system that operates in five steps: document analysis, document classification, document understanding, text recognition with an OCR, and transformation into HTML/XML format. The innovative aspects described in the paper are: the preprocessing algorithm, the adaptive page segmentation, the acquisition of block classification rules using techniques from machine learning, the layout analysis based on general layout principles, and a method that uses document layout information for conversion to HTML/XML formats. A benchmarking of the system components implementing these innovative aspects is reported. Received June 15, 2000 / Revised November 7, 2000     

Consensus-based table form recognition of low-quality historical documents

Given a set of low-quality line-delimited tabular documents of the same layout, we present a robust zoning algorithm which exploits both intra- and inter-document consensus to extract the structure of the table. The structure is captured in the form of a document template, that can then be snapped to a new document to perform automated “cookie cutter” data extraction. We also report a companion consensus-based algorithm for the classification of zone content as either machine print, handwriting or empty. Using scanned Census records from 1841 to 1881, the template is recovered with an efficiency of.076 [0, 1). Using consensus over about 10 documents from each data set, this error was reduced to.0076, or by 90%, which amounts to two missing line segments and one false positive. Similarly, the error for coverage was reduced from 0.098 to 0.016, or by 83%. Use of consensus also resulted in machine print classification accuracy of 100% for two of the three data sets. The classification error for handwriting averaged 0.1225 per document. By exploiting consensus within and between documents, automated zoning and labeling is greatly improved, providing field-level indexing of document content. Heath Nielson received his B.S. in 1998 and an M.S. degree in 2003 from Brigham Young University, Prove, Utah, in computer science. He is now working at the Church of Jesus Christ of Latter-day Saints on microfilm scanning technology at Salt Lake City, Utah. William Barrett received his Ph.D. (1978) in medical biophysics and computing and his undergraduate degree in mathematics from the University of Utah. He was a research fellow at the National Institutes of Health in the Division of Computer Research and Technology, where he worked with the National Heart, Lung, and Blood Institute. He is also a member of IEEE and ACM and has over 60 refereed publications. He is currently at BYU and heads an active research group that works in the areas of computer vision, pattern recognition, and image processing.     

A knowledge acquisition methodology to ontology construction for information retrieval from medical documents

Abstract: Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi-automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language-independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.     

ARAXA: Storing and managing Active XML documents

Active XML (AXML) documents combine extensional XML data with intentional data defined through Web service calls. The dynamic properties of these documents pose challenges to both storage and data materialization techniques. In this paper, we present ARAXA, a non-intrusive approach to store and manage AXML documents. We also define a methodology to materialize AXML documents at query time. The storage approach of ARAXA is based on plain relational tables and user-defined functions of Object-Relational DBMS to trigger the service calls. By using a DBMS we benefit from efficient storage tools and query optimization. Approaches without DBMS support have to process XML in main memory or provide for virtual memory solutions. One of the main advantages of ARAXA is that AXML documents do not need to be loaded into main memory at query processing time. This is crucial when dealing with large documents. The experimental results with ARAXA prototype show that our approach is scalable and capable of dealing with large AXML documents.     

Conceptual and content-based annotation of (multimedia) documents

This paper focuses on the techniques used in an NKRL environment (NKRL = Narrative Knowledge Representation Language) to deal with a general problem affecting the so-called “semantic/conceptual annotations” techniques. These last, mainly ontology-based, aim at “annotating” multimedia documents by representing, in some way, the “inner meaning/deep content” of these documents. For documents of sufficient size, the content modeling operations are separately executed on ‘significant fragments’ of the documents, e.g., “sentences” for natural language texts or “segments” (minimal units for story advancement) in a video context. The general problem above concerns then the possibility of collecting all the partial conceptual representations into a global one. This integration operation must, moreover, be carried out in such a way that the meaning of the full document could go beyond the simple addition of the ‘meanings’ conveyed by the single fragments. In this context, NKRL makes use of second order knowledge representation structures, “completive construction” and “binding occurrences”, for collecting within the conceptual annotation of a whole “narrative” the basic building blocks corresponding to the representation of its composing elementary events. These solutions, of a quite general nature, are discussed in some depth in this paper. This last includes also a short “state of the art” in the annotation domain and some comparisons with the different methodologies proposed in the past for solving the above ‘integration’ problem.