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

Understanding the errors of SHAPE-directed RNA structure modeling

Single-nucleotide-resolution chemical mapping for structured RNA is being rapidly advanced by new chemistries, faster readouts, and coupling to computational algorithms. Recent tests have shown that selective 2'-hydroxyl acylation by primer extension (SHAPE) can give near-zero error rates (0-2%) in modeling the helices of RNA secondary structure. Here, we benchmark the method using six molecules for which crystallographic data are available: tRNA(phe) and 5S rRNA from Escherichia coli, the P4-P6 domain of the Tetrahymena group I ribozyme, and ligand-bound domains from riboswitches for adenine, cyclic di-GMP, and glycine. SHAPE-directed modeling of these highly structured RNAs gave an overall false negative rate (FNR) of 17% and a false discovery rate (FDR) of 21%, with at least one helix prediction error in five of the six cases. Extensive variations of data processing, normalization, and modeling parameters did not significantly mitigate modeling errors. Only one varation, filtering out data collected with deoxyinosine triphosphate during primer extension, gave a modest improvement (FNR = 12%, and FDR = 14%). The residual structure modeling errors are explained by the insufficient information content of these RNAs' SHAPE data, as evaluated by a nonparametric bootstrapping analysis. Beyond these benchmark cases, bootstrapping suggests a low level of confidence (<50%) in the majority of helices in a previously proposed SHAPE-directed model for the HIV-1 RNA genome. Thus, SHAPE-directed RNA modeling is not always unambiguous, and helix-by-helix confidence estimates, as described herein, may be critical for interpreting results from this powerful methodology.Comment: Biochemistry, Article ASAP (Aug. 15, 2011

Web-based learning in the field of empirical research methods

This study focuses on the development of a complex web-based learning environment aimed at promoting the acquisition of applicable knowledge in the context of studying empirical research methods at university. This learning environment was then modified further on an empirical basis. The main focus of the present article is to describe the conceptualisation of the learning environment and research activities which were guided by an integrative research paradigm. The learning environment consisted of highly structured, complex texts in which the process of empirical research was illustrated in a detailed manner. By combining these texts with other instructional measures, the learning environment is given a flexible hypertext-structure. The effectiveness of the learning environment as a whole was investigated in three studies (two evaluation studies in the field and one experimental study in the laboratory). It was demonstrated that the additional instructional measures (e.g. a specific feedback-guidance and time-management measures) were not effective. The importance of cognitive, motivational and emotional learning prerequisites for the successful utilisation of the learning environment was highlighted. The implementation of special training and additional preparatory modules is recommended in order to optimise the fit between students' prerequisites and learning environmIm Zentrum der vorliegenden Arbeit steht zum einen die Konzeptualisierung einer Lernumgebung zur Förderung des Erwerbs anwendbaren Wissens im Kontext der universitären Ausbildung in empirischen Forschungsmethoden. Zum anderen werden ausgehend von einem integrativen Forschungsparadigma Forschungsaktivitäten beschrieben, die die empirische Basis zur Weiterentwicklung der Lernumgebung bereitstellen. Die Lernumgebung besteht aus hoch strukturierten, komplexen Texten, in welchen der Prozess empirischer Forschung auf detaillierte Weise veranschaulicht wird. Diese Texte wurden mit anderen instruktionalen Maßnahmen kombiniert, wodurch die Lernumgebung eine flexible, hypertextartige Struktur bekam. Die Effektivität der gesamten Lernumgebung wurde im Rahmen dreier empirischer Studien untersucht, von denen zwei als Evaluationsstudien im Feld durchgeführt wurden; die dritte war eine experimentelle Laborstudie. Es wurde gezeigt, dass die zusätzlichen instruktionalen Maßnahmen (z. B. eine spezifische Feedback-Anleitung und eine Zeitmanagement-Maßnahme) nicht wirksam waren. Die Bedeutung kognitiver, motivationaler und emotionaler Lernvoraussetzungen für die erfolgreiche Nutzung der Lernumgebung konnte nachgewiesen werden. Um die Passung zwischen den Eingangsvoraussetzungen der Studierenden und der Lernumgebung zu verbessern, wurde die Implementation eines speziellen Trainings und eines zusätzlichen vorbereitenden Moduls vorgeschlag