At the turn of the century, Arrow, McGrath, and Berdahl portrayed teams as complex adaptive systems (CAS). And yet, despite broad agreement that this approach facilitates a better understanding of ...

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Teams as Complex Adaptive Systems: Reviewing 17 Years of Research

The effect of sustained vs. faded scaffolding on students' argumentation in ill-structured problem solving

In order to further validate and extend the application of recent knowledge structure (KS) measures to second language settings, this investigation explores how second language (L2, English) situation models are influenced by first language (L1, Korean) translation tasks. Fifty Korean low proficient English language learners were asked to read an L2 story and then complete L2 concept map and summary writing tasks, with or without an intervening L1 production tasks (Translated versus Directed conditions). Posttest comprehension was measured using the TOEFL multiple-choice items associated with the story (both in L2). KS elicited as concept maps and as text summaries were used to represent the situation models before, during, and after writing. For analysis, all of the participants’ maps and writing artifacts were converted into Pathfinder Networks (PFNets) that were analyzed using two distinctly different approaches, correlation of the raw proximity data and also degree centrality of the PFNets, in order to analyze the PFNets statistically and to visually describe KS cognitive state changes over time. The correlation results showed that the Translated Writing participants’ L2 KS relative to the Directed Writing condition are more similar to that of an expert and are significantly correlated with comprehension posttest scores. Including L1 tasks substantially improved the qualities of the L2 KS artifacts and underlying mental structures related to reading comprehension. In addition, the average centrality results showed that the KS ‘form’ of the participants’ PFNets who translated were relational network structures relative to the Directed Writing group’s (English only) PFNets that had a more linear structure that matched the text surface structure, suggesting a fundamental way that L1 and L2 cognitive processing differs.

Knowledge Structure Measures of Reader's Situation Models across Languages: Translation Engenders Richer Structure.

In contrast to well-structured problems which have pre-defined, correct answers, complex real-world problems are often ill-structured problems (ISPs). The open-ended nature of ISPs creates considerable barriers to assess and guide students in forming better solutions, which results in low adoption levels of inquiry-based learning. Students can structure and represent their knowledge for an ISP in the form of knowledge maps or causal maps, which articulate relevant concepts and their causal relations (i.e., antecedents and consequents). Assessing such maps can involve a referent-free evaluation (e.g., to encourage the creation of maps with high density of concepts) or a comparison to an expert map used as reference. This chapter starts with a review of theories and tools to compare a student’s map to the expert map. Previous approaches often compared individual connections (e.g., scoring the number of connections that a student has/misses in contrast with the expert) or general map metrics (e.g., one map is denser than the other). In contrast, the problem of comparing two maps has been studied in network theory and graph theory for several decades, yielding categories of algorithms that are currently underutilized in educational research. This chapter reviews three categories of algorithms (i.e., graph kernel, graph editing distance, graph embedding) in light of their application to assessment and student success. We discuss an implementation of these algorithms through a new set of digital tools, designed to support a community of practice in problem-based instruction.

Learning Analytics to Support Teachers’ Assessment of Problem Solving: A Novel Application for Machine Learning and Graph Algorithms

This study investigated, through both formative and practical evaluation, the relationships among the use of functions in computer-supported collaborative learning (CSCL), psychological factors, and learning behaviors related to applying the Community of Inquiry (CoI) framework. The goal was to increase active interaction among learners. In two experiments inside and outside the classroom, the authors examined an online discussion and collected data using questionnaires that assessed perceived psychological factors, as well as communication logs related to the efficacy of CoI. The results of a path analysis showed two points. First, cognitive learning tools support the enhancement of expressive cognitive presence that promotes the perception of CoI as formative evaluation. Second, the frequency of the use of the functions fostered expressive social and cognitive presence (which enhanced the perception of both), perceived contribution, and satisfaction with online discussion. This article is part of a special issue on social media for learning.

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A Computer-Supported Collaborative Learning Design for Quality Interaction

Problem solving likely involves at least two broad stages, problem space representation and then problem solution (Newell and Simon, Human problem solving, 1972). The metric centrality that Freeman (Social Networks 1:215–239, 1978) implemented in social network analysis is offered here as a potential measure of both. This development research study applied centrality measures to reanalyze existing concept maps from a recent investigation (Engelmann and Hesse, Computer-Supported Collaborative Learning 5:299–319, 2010). Participants (N = 120) were randomly assigned to interdependent (i.e. hidden profiles) or non-interdependent conditions to work online in triads using CmapTools software to create a concept map in order to solve a problem scenario. The centrality values of these group-created concept maps agreed with the common relations count analysis used in that investigation and allowed for additional comparisons as well as analysis by multidimensional scaling. Specifically, the interdependent triad maps resembled the fully explicated problem space, while the non-interdependent triad maps mainly resembled the problem solution. The results demonstrate that centrality is a useful measure of knowledge structure contained in these team concept map artifacts that allows researchers to infer problem representation start and goal state transitions during problem solving.

Using centrality of concept maps as a measure of problem space states in computer-supported collaborative problem solving

This investigation considers the effects of Interdependent and Independent group collaboration on the flow of information elicited as concept maps before (Premap), during (Group map), and after (Postmap) collaboration, and on the source, extent, and quality of Postmaps. Participants were undergraduate students (N = 37) randomly assigned by group (tetrads and a few triads) to the Independent condition who read and ‘mapped’ an entire textbook chapter as homework or to the Interdependent condition who read the whole chapter but mapped either the first half or second half. After completing their Premaps out of class, group members collaborated face-to-face in class to further learn the content by creating a group concept map on a large sheet of newsprint. Then immediately, students individually drew their Postmap from memory. The Independent condition participants’ Postmaps had more idiosyncratic terms and were more like their Premaps relative to the Interdependent participants’ Postmaps, and the Interdependent participants’ Postmaps were considerably more like their Group maps. Further, the Interdependent participants’ Postmaps were relatively more like the expert maps (62% vs. 52%) mainly due to a greater number of terms from the second half of the chapter, a possible primacy effect. Regarding knowledge convergence, the Interdependent Postmaps were relatively more like the Postmaps of others in their group (38% vs. 30% overlap). In this case, Interdependence resulted in less homework but nearly equivalent group maps and better and more similar

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Wu Yu

Papers

Using centrality of concept maps as a measure of problem space states in computer-supported collaborative problem solving

The effect of Independent and Interdependent group collaboration on knowledge extent, knowledge form and knowledge convergence