From design to narrative: the development of inquiry-based learning models

The University of Nottingham and the Open University are partners in a ca. £1.2m project to help school students learn the skills of modern science. The three-year project, Personal Inquiry (PI) (funded by the UK ESRC and EPSRC research councils), is developing a new approach of 'scripted inquiry learning', where children investigate a science topic with classmates by carrying out explorations between their classroom, homes and discovery centres, guided by a personal computer. This paper describes our progress to date on the development of four models for inquiry-based learning, as part of the PI project. These are being used as the basis for the development of educational scenarios and associated scripts to explore the use of mobile technologies in supporting an inquiry-based approach to teaching Scientific thinking across formal and informal learning

Data science in translational vision science and technology

What is Data Science? Data science involves the use of a variety of quantitative methods (e.g. mathematics, statistics, computer science) to extract useful information from structured and unstructured data.1 Typically, data scientists undertake exploratory data analysis by deploying machine learning principles and algorithms to identify patterns in rawdata with the purpose of understanding processes and predicting outcomes. These analytic approaches include predictive causal analytics, prescriptive analytics, and machine learning for pattern discovery and outcome prediction, and they require a large volume and variety of data (i.e. structured as well as unstructured data)

Active Learning in Sophomore Mathematics: A Cautionary Tale

Math 245: Multivariate Calculus, Linear Algebra, and Differential Equations with Computer I is the first half of a year-long sophomore sequence that emphasizes the subjects\u27 interconnections and grounding in real-world applications. The sequence is aimed primarily at students from physical and mathematical sciences and engineering. In Fall, 1998, as a result of my affiliation with the Science, Technology, Engineering, and Mathematics Teacher Education Collaborative (STEMTEC), I continued and extended previously-introduced reforms in Math 245, including: motivating mathematical ideas with real-world phenomena; student use of computer technology; and, learning by discovery and experimentation. I also introduced additional pedagogical strategies for more actively involving the students in their own learning—a collaborative exam component and in-class problem-solving exercises. The in-class exercises were well received and usually productive; two were especially effective at revealing normally unarticulated thinking. The collaborative exam component was of questionable benefit and was subsequently abandoned. Overall student performance, as measured by traditional means, was disappointing. Among the plausible reasons for this result is that too much material was covered in too short a time. Experience here suggests that active-learning strategies can be useful, but are unlikely to succeed unless one sets realistic limits to content coverage

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

Using Technology to Engage Preservice Elementary Teachers in Learning about Scientific Inquiry

Elementary teachers are often required to teach inquiry in their classrooms despite having had little exposure to inquiry learning themselves. In a capstone undergraduate science course preservice elementary teachers experience scientific inquiry through the completion of group projects, activities, readings and discussion, in order to develop a sense of how inquiry learning takes place. At the same time, they learn science content necessary for teacher licensure. The course exposes students to different pathways of scientific discovery and to the use of the computer both as a tool for conducting inquiry-based investigations and as a means of collecting and sharing student opinions. The students involved have many misconceptions about science and it is often difficult for them to distinguish science from pseudoscience. Computer simulations are used to help students understand that difference. In addition, a classroom response system using “clickers” is used to poll studentopinions on controversial issues and to stimulate discussion

The VITAL project: Visual Information Translation Analysis & Learning in Life Sciences

poster abstractIn many disciplines of science, especially in life sciences, research proceeds in a top-down approach, in which domain experts formulate hypotheses that are tested on relevant data. In contrast, research in Computer Science and Engineering often follows a data-driven bottom-up approach. In the bottom-up approach, various algorithms and computational tools are designed and utilized to perform unstructured knowledge discovery such as finding patterns and structure in data. In this presentation we give an overview of our research activities, namely how we combine novel bottom-up Computer Vision and Machine Learning methods with top-down domain knowledge in Physiology, Neuroscience and clinical Medicine to engender knowledge discovery. Specifically, we present our efforts towards answering the following questions: does brain control breathing? do genes control locomotion and touch sensation? can we reconstruct a model brain at single-cell resolution? how can we model protein-protein interactions in neurons, in situ? can we predict the biological effect of growth factor-delivering scaffolds for promoting angiogenesis? can we combine Magnetic Resonance imagery and biochemical spectroscopy for brain tumor radiation treatment planning? The VITAL project (PI: Tsechpenakis - web.mac.com/gavriil ) is a new research group, part of the Center for Visual Information Sensing and Computing (visc.cs.iupui.edu) at the Computer Science Department of IUPUI. The core theoretical background of our modeling and analysis methods is in Computer Vision, applied Machine Learning, Imaging and Signal Processing. Currently our research is funded by two NIH grants and the IUPUI School of Science; the PI’s research has been previously funded by NIH, NSF, NOAA, and the Wallace H. Coulter Foundation

Text2Onto - A Framework for Ontology Learning and Data-driven Change Discovery

Cimiano P, Völker J. Text2Onto - A Framework for Ontology Learning and Data-driven Change Discovery. In: Montoyo A, Munoz R, Metais E, eds. Natural language processing and information systems : 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15 - 17, 2005 ; proceedings. Lecture notes in computer science, 3513. Springer; 2005: 227-238

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions