THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK

Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Managing the KM Trade-Off: Knowledge Centralization versus Distribution

KM is more an archipelago of theories and practices rather than a monolithic approach. We propose a conceptual map that organizes some major approaches to KM according to their assumptions on the nature of knowledge. The paper introduces the two major views on knowledge ­objectivist, subjectivist - and explodes each of them into two major approaches to KM: knowledge as a market, and knowledge as intellectual capital (the objectivistic perspective); knowledge as mental models, and knowledge as practice (the subjectivist perspective). We argue that the dichotomy between objective and subjective approaches is intrinsic to KM within complex organizations, as each side of the dichotomy responds to different, and often conflicting, needs: on the one hand, the need to maximize the value of knowledge through its replication; on the other hand, the need to keep knowledge appropriate to an increasingly complex and changing environment. Moreover, as a proposal for a deeper discussion, such trade-off will be suggested as the origin of other relevant KM related trade-offs that will be listed. Managing these trade-offs will be proposed as a main challenge of KM

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.

Directional adposition use in English, Swedish and Finnish

Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellä (in front of) and jäljessä (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003). When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellä (in front of) and jäljessä (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected. We asked native English, Swedish and Finnish speakers’ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers. All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion. We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion. Levinson, S. C. (1996). Frames of reference and Molyneux’s question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press. Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press. Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Assessing schematic knowledge of introductory probability theory

[Abstract]: The ability to identify schematic knowledge is an important goal for both assessment and instruction. In the current paper, schematic knowledge of statistical probability theory is explored from the declarative-procedural framework using multiple methods of assessment. A sample of 90 undergraduate introductory statistics students was required to classify 10 pairs of probability problems as similar or different; to identify whether 15 problems contained sufficient, irrelevant, or missing information (text-edit); and to solve 10 additional problems. The complexity of the schema on which the problems were based was also manipulated. Detailed analyses compared text-editing and solution accuracy as a function of text-editing category and schema complexity. Results showed that text-editing tends to be easier than solution and differentially sensitive to schema complexity. While text-editing and classification were correlated with solution, only text-editing problems with missing information uniquely predicted success. In light of previous research these results suggest that text-editing is suitable for supplementing the assessment of schematic knowledge in development