COMPLEX-IT: A Case-Based Modelling and Scenario Simulation Platform for Social Inquiry

COMPLEX-IT is a case-based, mixed-methods platform for applied social inquiry into complex data/systems, designed to increase non-expert access to the tools of computational social science (i.e., cluster analysis, artificial intelligence, data visualization, data forecasting, and scenario simulation). In particular, COMPLEX-IT aids applied social inquiry though a heavy emphasis on learning about the complex data/system under study, which it does by (a) identifying and forecasting major and minor clusters/trends; (b) visualizing their complex causality; and (c) simulating scenarios for potential interventions. COMPLEX-IT is accessible through the web or can be run locally and is powered by R and the Shiny web framework

What Can Be Learned from Computer Modeling? Comparing Expository and Modeling Approaches to Teaching Dynamic Systems Behavior

Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment designed to reveal the benefits of either mode of instruction. The assessment addresses proficiency in declarative knowledge, application, construction, and evaluation. The subscales differentiate between simple and complex structure. The learning task concerns the dynamics of global warming. We found that, for complex tasks, the modeling group outperformed the expository group on declarative knowledge and on evaluating complex models and data. No differences were found with regard to the application of knowledge or the creation of models. These results confirmed that modeling and direct instruction lead to qualitatively different learning outcomes, and that these two modes of instruction cannot be compared on a single “effectiveness measure”

Science Leadership: Impact of the New Science Coordinators Academy

This article discusses the impact of the New Science Coordinators Academy (NSCA) on two cohorts of participants. The NSCA is one of four components of the Virginia Initiative for Science Teaching and Achievement (VISTA), a United States Department of Education (USED) science education reform grant. The NSCA is designed to support new school district science coordinators (with less than five years of experience) and to continue building the state science education infrastructure. Research in education leadership traditionally focuses on teacher leaders, principals, and district office personnel. Interestingly, research on district office personnel rarely distinguishes between the different roles of district personnel. This article seeks to inform the field by sharing the impact of an academy designed for new science coordinators on their learning, and to begin to understand their role and impact in their district. The five-day Academy engaged participants in a variety of experiences designed to facilitate the following: 1) build leadership skills; 2) build a common understanding and vision for hands-on science, inquiry, problem-based learning, and nature of science in the science classroom; 3) investigate data to improve student learning goals; 4) and, develop a science strategic plan. The data indicate that the NSCA was successful at meeting its goals to support the participants and to build a common language among these new coordinators. Initial data also support the variety of responsibilities of these participants and the positive impact of the Academy on their district work

Data Visualization Tools for Science and Math

As the computers available in schools become more powerful, more and more exciting tools are available to science and math students and teachers. Visualization tools, such as image processing, geographic information systems, modeling, and simulation software, are a class of tools with particular promise. These tools are being used in schools across the country to integrate computer use with the curriculum and to bring more hands-on inquiry to the students. A primary goal of using these computer-based tools is to aid students in developing a deeper understanding of the science and math (not the computers) and to help make difficult concepts a little easier to grasp (and visualize). In particular, these tools allow students to collect, analyze, and manipulate data, a fundamental requirement of the Virginia Standards of Learning [1]. More importantly, these tools allow students with a variety of different learning styles, especially visual learners, to help make abstract concepts into concrete expressions. Teachers can use the computers as a laboratory to study phenomena they could never fit into their classroom (like remote sensing of Earth to study land use and geology from space). One of the challenges in bringing these tools to students is how to do the faculty development to bring the tools to teachers. In this session, we\u27ll explore the possibilities that these tools offer, examine the challenges, and try to understand how to prepare future teachers to use these and other tools in their classrooms

Support of the collaborative inquiry learning process: influence of support on task and team regulation

Regulation of the learning process is an important condition for efficient and effective learning. In collaborative learning, students have to regulate their collaborative activities (team regulation) next to the regulation of their own learning process focused on the task at hand (task regulation). In this study, we investigate how support of collaborative inquiry learning can influence the use of regulative activities of students. Furthermore, we explore the possible relations between task regulation, team regulation and learning results. This study involves tenth-grade students who worked in pairs in a collaborative inquiry learning environment that was based on a computer simulation, Collisions, developed in the program SimQuest. Students of the same team worked on two different computers and communicated through chat. Chat logs of students from three different conditions are compared. Students in the first condition did not receive any support at all (Control condition). In the second condition, students received an instruction in effective communication, the RIDE rules (RIDE condition). In the third condition, students were, in addition to receiving the RIDE rules instruction, supported by the Collaborative Hypothesis Tool (CHT), which helped the students with formulating hypotheses together (CHT condition). The results show that students overall used more team regulation than task regulation. In the RIDE condition and the CHT condition, students regulated their team activities most often. Moreover, in the CHT condition the regulation of team activities was positively related to the learning results. We can conclude that different measures of support can enhance the use of team regulative activities, which in turn can lead to better learning results

Reinventing discovery learning: a field-wide research program

© 2017, Springer Science+Business Media B.V., part of Springer Nature. Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective “video games,” and intercorporeal master–student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts

Information system support in construction industry with semantic web technologies and/or autonomous reasoning agents

Information technology support is hard to find for the early design phases of the architectural design process. Many of the existing issues in such design decision support tools appear to be caused by a mismatch between the ways in which designers think and the ways in which information systems aim to give support. We therefore started an investigation of existing theories of design thinking, compared to the way in which design decision support systems provide information to the designer. We identify two main strategies towards information system support in the early design phase: (1) applications for making design try-outs, and (2) applications as autonomous reasoning agents. We outline preview implementations for both approaches and indicate to what extent these strategies can be used to improve information system support for the architectural designer

Modeling Memes: A Memetic View of Affordance Learning

This research employed systems social science inquiry to build a synthesis model that would be useful for modeling meme evolution. First, a formal definition of memes was proposed that balanced both ontological adequacy and empirical observability. Based on this definition, a systems model for meme evolution was synthesized from Shannon Information Theory and elements of Bandura\u27s Social Cognitive Learning Theory. Research in perception, social psychology, learning, and communication were incorporated to explain the cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances. The PMFServ agent based model was used to examine two scenarios: a simulation to test for potential memes inside the Stanford Prison Experiment and a simulation of pro-US and anti-US meme competition within the fictional Hamariyah Iraqi village. The Stanford Prison Experiment simulation was designed, calibrated, and tested using the original Stanford Prison Experiment archival data. This scenario was used to study potential memes within a real-life context. The Stanford Prison Experiment simulation was complemented by internal and external validity testing. The Hamariyah Iraqi village was used to analyze meme competition in a fictional village based upon US Marine Corps human terrain data. This simulation demonstrated how the implemented system can infer the personality traits and contextual factors that cause certain agents to adopt pro-US or anti-US memes, using Gaussian mixture clustering analysis and cross-cluster analysis. Finally, this research identified significant gaps in empirical science with respect to studying memes. These roadblocks and their potential solutions are explored in the conclusions of this work