Successfully carrying out complex learning tasks through guiding teams’ qualitative and quantitative reasoning

Slof, B., Erkens, G., Kirschner, P. A., Janssen, J., & Jaspers, J. G. M. (2012). Successfully carrying out complex learning tasks through guiding teams' qualitative and quantitative reasoning. Instructional Science, 40, 623-643. DOI: 10.1007/s11251-011-9185-2This study investigated whether and how scripting learners’ use of representational tools in a Computer Supported Collaborative Learning (CSCL)-environment fostered their collaborative performance on a complex business-economics task. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely defining the problem and proposing multiple solutions, followed by determining suitability of the solutions and coming to a definitive problem solution. Two tools facilitated construction of causal or mathematical domain representations. Each was suited for carrying out the part-task demands of one specific problem-solving phase; the causal was matched to problem-solution phase and the mathematical (in the form of a simulation) to the solution-evaluation phase. Teams of learners (N = 34, Mean age = 15.7) in four experimental conditions carried out the part-tasks in a predefined order, but differed in the representational tool/tools they received during the collaborative problem-solving process. The tools were matched, partly matched or mismatched to the part-task demands. Teams in the causal-only (n = 9) and simulation-only (n = 9) conditions received either a causal or a simulation tool and were, thus, supported in only one of the two part-tasks. Teams in the simulation-causal condition (n = 9) received both tools, but in an order that was mismatched to the part-task demands. Teams in the causal-simulation condition (n = 7) received both tools in an order that matched the part-task demands of the problem phases. Results revealed that teams receiving part-task congruent tools constructed more task-appropriate representations and had more elaborated discussions about the domain. As a consequence, those teams performed better on the complex learning-task

Design and effects of representational scripting on group performance

This study investigated the effects of representational scripting on non-expert student learning while collaboratively carrying out complex learning-tasks. The premise underlying this research is that effective cognitive activities would be evoked when complex learning-tasks are structured into phase-related part-tasks and are supported by providing students with part-task-congruent external representations for each phase; representational scripting. It was hypothesized that this approach would lead to increased individual learning and better complex learning-task performance. In groups, 96 secondary education students worked on a complex business-economics problem in four experimental conditions, namely one condition in which the groups received representations that were part-task-congruent for all three phases and three conditions in which the groups received one of these representations for all three phases (i.e., part-task-incongruent for two of the three phases). The results indicate that groups receiving part-task-congruent representations in a phased order performed better on the complex learning-task, though this did not result in increased individual learning

Guiding students’ online complex learning-task behavior through representational scripting

Slof, B., Erkens, G., Kirschner, P. A., Jaspers, J. G. M., & Janssen, J. (2010). Guiding students’ online complex learning-task behavior through representational scripting. Computers in Human Behavior, 26(5), 927-939. doi:10.1016/j.chb.2010.02.2007This study investigated the effects of representational scripting on students’ collaborative performance of a complex business-economics problem. The scripting structured the learning-task into three part-tasks, namely (1) determining core concepts and relating them to the problem, (2) proposing multiple solutions to the problem, and (3) coming to a final solution to the problem. Each provided representation (i.e., conceptual, causal, or simulation) was suited for carrying out a specific part-task. It was hypothesized that providing part-task congruent support would guide student interaction towards better learning-task performance. Groups in four experimental conditions had to carry out the part-tasks in a predefined order, but differed in the representation they received. In three mismatch conditions, groups only received one of the representations and were, thus, only supported in carrying out one of the part-tasks. In the match condition, groups received all three representations in the specified order (i.e., representational scripting). The results indicate that groups in the match condition had more elaborated discussions about the content of the knowledge domain (i.e., concepts, solutions and relations) and were better able to share and to negotiate about their knowledge. As a consequence, these groups performed better on the learning-task. However, these differences were not obtained for groups receiving only a causal representation of the domain

Fostering complex learning-task performance through scripting student use of computer supported representational tools

Slof, B., Erkens, G., Kirschner, P. A., Janssen, J., & Phielix, C. (2010). Fostering complex learning-task performance through scripting student use of computer supported representational tools. Computers & Education, 55(4), 1707-1720.This study investigated whether scripting student use of computer supported representational tools fostered students’ collaborative performance of a complex business-economics problem. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a final solution. The representational tools facilitated students in constructing specific representations of the domain (i.e., conceptual, causal, or mathematical) and were each suited for carrying out the part-task demands of a specific phase. Student groups in four experimental conditions had to carry out all part-tasks in a predefined order, but differed in the representational tool(s) they received during their collaborative problem-solving process. In three mismatch conditions, student groups received either a conceptual, causal, or simulation representational tool which supported them in only carrying out one of the three part-tasks. In the match condition, student groups received the three representational tools in the specified order, each matching the part-task demands of a specific problem phase. The results revealed that student groups in the match condition constructed more task-appropriate representations and had more elaborated and meaningful discussions about the domain. As a consequence, those student groups performed better on the complex learning-task. However, similar results were obtained by student groups who only received a representational tool for constructing causal representations for all part-tasks

Integrating verbal and nonverbal communication in a dynamic neural field architecture for human–robot interaction

How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer’s motor system are crucially involved in our ability to understand actions of others’, to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human–robot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and nonverbal communication skills of the robot in a joint assembly task in which the human–robot team has to construct toy objects from their components. The experiments focus on the robot’s capacity to anticipate the user’s needs and to detect and communicate unexpected events that may occur during joint task execution.Fundação para a Ciência e a Tecnologia (FCT) - Bolsa POCI/V.5/A0119/2005 and CONC-REEQ/17/2001European Commission through the project JAST (IP-003747

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling