Dual mapping for support of problem solving and knowledge construction

Learning through problem solving has received increased attention in constructivist learning, especially in ill-structured domains such as medicine [1]. Previous studies indicate knowledge construction and problem solving can reciprocate each other. However, this is not easily achieved, especially for complex problem solving. Temporal problem solving activities can be easily forgotten, and knowledge situated in problem solving experience may not be possibly retrieved and reused to solve new problems. This paper addresses the problem by proposing a dual mapping visualization approach to support the connection between problem solving and knowledge construction. Diagnostic problem solving in medical education is selected as the application domain, and abdominal pain is used as the case of the study to demonstrate the proposed approach in an online learning environment. © 2011 IEEE.published_or_final_versionThe 11th IEEE International Conference on Advanced Learning Technologies (ICALT 2011), Athens, GA., 6-8 July 2011. In Proceedings of 11th IEEE ICALT, 2011, p. 207-20

Learning with worked-out problems in Manufacturing Technology: The effects of instructional explanations and self-explanation prompts on acquired knowledge acquisition, near and far transfer performance

In the present research, two different explanatory approaches – namely, instructional explanation and self-explanation prompts – were applied in worked-out-problem-based learning (learning with worked-out problems) in a computer-assisted instructional environment in the domain of manufacturing technology. This research aims at comparing the effects of both explanatory approaches on topic knowledge acquisition, near transfer performance, and far transfer performance. Additionally, this research also attempts to examine the impact of topic interest on the aforementioned variables, in addition to the relationships between topic interest, mental effort, and learning outcomes. A total of 76 second-year students were randomly assigned to experimental and control groups. The pre- and post-tests were used to measure topic knowledge acquisition, near-transfer performance, and far-transfer performance, whereas topic interest and mental effort were measured by means of Topic Interest Questionnaire and NASA Task Load Index (NASA-TLX) respectively. The analysis outcomes revealed that the self-explanation prompts approach was significantly superior to the instructional-explanation approach in terms of topic knowledge acquisition and near transfer performance. In addition, the results demonstrated that the impact of topic interest was significantly noticeable on far transfer tasks, but not on topic knowledge acquisition and near transfer tasks. On the other hand, the relationship between mental effort investment and test performance was not statistically significant. Finally, an equivocal relationship, which varied depending on the treatment conditions, was discovered between topic interest, mental effort, and test performance. (DIPF/orig.)In der vorliegenden Untersuchung wurden zwei unterschiedliche Lehrmethoden – instruktionale Erklärung und Aufforderung zur Selbsterklärung – angewandt auf das Lernen mit Lösungsbeispielen in einer computergestützten Lernumgebung, die thematisch im Bereich der Fertigungstechnik angesiedelt ist. Die computergestützte Lernumgebung bestand aus einer vom Autor erstellten Lernsoftware, die mit Macromedia Authorware entworfen und entwickelt wurde. Hauptziel der Studie war ein Vergleich der Effekte beider Lehrmethoden auf die Aneignung von Sachwissen sowie die Leistung beim nahen und weiten Transfer. Außerdem wurden die Auswirkungen von Gegenstandsinteresse auf die zuvor genannten Kriterien untersucht und die Beziehungen zwischen Gegenstandsinteresse, mentaler Anstrengung und Lernergebnissen. Insgesamt wurden 76 Studierende im zweiten Jahr ihres Studiums an der Fakultät für Technische Bildung, Universität Tun Hussein Onn Malaysia (UTHM), nach dem Zufallsprinzip in drei Gruppen aufgeteilt: Selbsterklärungsaufforderung (SE: n = 25), instruktionale Erklärung (IE: n = 25) und Kontrollgruppe (n = 26). Mit Pre- und Post-Tests wurden die Aneignung von Sachwissen sowie die nahe und weite Transferleistung erhoben. Gegenstandsinteresse und mentale Anstrengung wurden mit dem Topic Interest–Fragebogen und dem NASA-TLX gemessen. Das Statistik-Paket für die Sozialwissenschaften (SPSS) wurde verwendet, um die Hypothesen an den gesammelten Daten zu prüfen. Die Hypothesenprüfung erfolgte mittels quantitativ statistischer Auswertungsverfahren (Korrelation, Varianzanalyse). (DIPF/Orig.

Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval

In this paper we address the problem of learning robust cross-domain representations for sketch-based image retrieval (SBIR). While most SBIR approaches focus on extracting low- and mid-level descriptors for direct feature matching, recent works have shown the benefit of learning coupled feature representations to describe data from two related sources. However, cross-domain representation learning methods are typically cast into non-convex minimization problems that are difficult to optimize, leading to unsatisfactory performance. Inspired by self-paced learning, a learning methodology designed to overcome convergence issues related to local optima by exploiting the samples in a meaningful order (i.e. easy to hard), we introduce the cross-paced partial curriculum learning (CPPCL) framework. Compared with existing self-paced learning methods which only consider a single modality and cannot deal with prior knowledge, CPPCL is specifically designed to assess the learning pace by jointly handling data from dual sources and modality-specific prior information provided in the form of partial curricula. Additionally, thanks to the learned dictionaries, we demonstrate that the proposed CPPCL embeds robust coupled representations for SBIR. Our approach is extensively evaluated on four publicly available datasets (i.e. CUFS, Flickr15K, QueenMary SBIR and TU-Berlin Extension datasets), showing superior performance over competing SBIR methods

Improving the learning of clinical reasoning through computer-based cognitive representation

published_or_final_versio

Dynamic systems as tools for analysing human judgement

With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review on this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, it is shown how the task demands of system identification and system control can be realized in these environments and how psychometrically acceptable dependent variables can be derived

Born to be Wild: Using Communities of Practice as a Tool for Knowledge Management

This paper looks at what happens when Communities of Practice are used as a tool for Knowledge Management. The original concept of a Community of Practice appears to have very little in common with the knowledge sharing communities found in Knowledge Management, which are based on a revised view of 'cultivated' communities. We examine the risks and benefits of cultivating Communities of Practice rather than leaving them 'in the wild'. The paper presents the findings from two years of research in a small microelectronics firm to provide some insights into the wild vs domesticated dichotomy and discusses the implications of attempting to tame Communities of Practice in this way.Comment: Paper presented at the Ethicomp 2010: The 'Backwards, Forwards and Sideways' changes of ICT, Tarragona, Spain, April, 2010, pp. 71 - 80

Design Research and Domain Representation

While diverse theories about the nature of design research have been proposed, they are rarely considered in relation to one another across the broader disciplinary field. Discussions of design research paradigms have tended to use overarching binary models for understanding differing knowledge frameworks. This paper focuses on an analysis of theories of design research and the use of Web 3 and open content systems to explore the potential of building more relational modes of conceptual representation. The nature of this project is synthetic, building upon the work of other design theorists and researchers. A number of theoretical frameworks will be discussed and examples of the analysis and modelling of key concepts and information relationships, using concept mapping software, collaborative ontology building systems and semantic wiki technologies will be presented. The potential of building information structures from content relationships that are identified by domain specialists rather than the imposition of formal, top-down, information hierarchies developed by information scientists, will be considered. In particular the opportunity for users to engage with resources through their own knowledge frameworks, rather than through logically rigorous but largely incomprehensible ontological systems, will be explored in relation to building resources for emerging design researchers. The motivation behind this endeavour is not to create a totalising meta-theory or impose order on the ‘ill structured’ and ‘undisciplined’, domain of design. Nor is it to use machine intelligence to ‘solve design problems’. It seeks to create dynamic systems that might help researchers explore design research theories and their various relationships with one another. It is hoped such tools could help novice researchers to better locate their own projects, find reference material, identify knowledge gaps and make new linkages between bodies of knowledge by enabling forms of data-poesis - the freeing of data for different trajectories. Keywords: Design research; Design theory; Methodology; Knowledge systems; Semantic web technologies.</p

Measuring cognitive load and cognition: metrics for technology-enhanced learning

This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory's central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts

Improving the learning of clinical reasoning through computer-based cognitive representation

Objective: Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Methods: Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. Results: A significant improvement was found in students’ learning products from the beginning to the end of the study, consistent with students’ report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. Conclusions: The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction

Uncertainty and risk: politics and analysis

In environmental and sustainable development policy issues, and in infrastructural megaprojects and issues of innovative medical technologies as well, public authorities face emergent complexity, high value diversity, difficult-to-structure problems, high decision stakes, high uncertainty, and thus risk. In practice, it is believed, this often leads to crises, controversies, deadlocks, and policy fiascoes. Decision-makers are said to face a crisis in coping with uncertainty. Both the cognitive structure of uncertainty and the political structure of risk decisions have been studied. So far, these scientific literatures exist side by side, with few apparent efforts at theoretically conceptualizing and empirically testing the links between the two. Therefore, this exploratory and conceptual paper takes up the challenge: How should we conceptualize the cognitive structure of uncertainty? How should we conceptualize the political structure of risk? How can we conceptualize the link(s) between the two? Is there any empirical support for a conceptualization that bridges the analytical and political aspects of risk? What are the implications for guidelines for risk analysis and assessment