Beyond the 4 Skills: Looking at 21st century skills

The advent of internet and digital media has significantly added to the types of skills that students need to acquire in the ESL classroom to be successful in communication. The digital skills of viewing and representing should be added to the traditional four skills to prepare students for experiencing and creating multimodal texts

Symmetric function generalizations of graph polynomials

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1995.Includes bibliographical references (p. 67-70).by Timothy Yi-Chung Chow.Ph.D

Practical application of the Information Seeking and Communication Model (ISCM) in Higher Education - Westminster Business School Module Leader practice

Purpose: The purpose of this study is to test the Information Seeking and Communication Model’s (ISCM’s) validity in higher education (HE) and identify insights into the provision of a framework for a more integrated and aligned (structured) module leader practice (MLp). Design and Methodological Approach: This study adopts a qualitative approach in which the primary instrument of online indepth (Teams) semi-structured interviews were used to elicit academic practice in relation to the proposed Information Seeking and Communication Model in Higher Education. Participants to the interviews comprised of 13 full time academics who were also Leaders of small, medium, and large Modules within Westminster Business School, at the University of Westminster. The results were analysed using thematic (content) analysis supported by the NVivo analytical tool. Findings: are as follows: (1) The study supports the rationality of the model with minimal alterations. (2) The model provides practical insight into the information behaviour of MLs. (3) MLs are information actors with multi-purpose, act as information users and information providers. (4) MLs are information producers and service providers, thereby creating internal sources. (5) The ML training framework developed identifies ways in which information behaviour may be positively altered, more awareness built into the notion of how MLs source, use, provide, communicate information while building a sense of a community to support the practice. Originality and Value: This study provides practical value as an information behaviour model, ISCM being applied within higher education. It contributes with new knowledge through insights into its practical usefulness in the field of library and information science (LIS). It also answers the criticism that research in LIS often does not build on earlier research, this study does, and thereby suggest further practical use of ISCM

Effective Natural Language Interfaces for Data Visualization Tools

How many Covid cases and deaths are there in my hometown? How much money was invested into renewable energy projects across states in the last 5 years? How large was the biggest investment in solar energy projects in the previous year? These questions and others are of interest to users and can often be answered by data visualization tools (e.g., COVID-19 dashboards) provided by governmental organizations or other institutions. However, while users in organizations or private life with limited expertise with data visualization tools (hereafter referred to as end users) are also interested in these topics, they do not necessarily have knowledge of how to use these data visualization tools effectively to answer these questions. This challenge is highlighted by previous research that provided evidence suggesting that while business analysts and other experts can effectively use these data visualization tools, end users with limited expertise with data visualization tools are still impeded in their interactions. One approach to tackle this problem is natural language interfaces (NLIs) that provide end users with a more intuitive way of interacting with these data visualization tools. End users would be enabled to interact with the data visualization tool both by utilizing the graphical user interface (GUI) elements and by just typing or speaking a natural language (NL) input to the data visualization tool. While NLIs for data visualization tools have been regarded as a promising approach to improving the interaction, two design challenges still remain. First, existing NLIs for data visualization tools still target users who are familiar with the technology, such as business analysts. Consequently, the unique design required by end users that address their specific characteristics and that would enable the effective use of data visualization tools by them is not included in existing NLIs for data visualization tools. Second, developers of NLIs for data visualization tools are not able to foresee all NL inputs and tasks that end users want to perform with these NLIs for data visualization tools. Consequently, errors still occur in current NLIs for data visualization tools. End users need to be therefore enabled to continuously improve and personalize the NLI themselves by addressing these errors. However, only limited work exists that focus on enabling end users in teaching NLIs for data visualization tools how to correctly respond to new NL inputs. This thesis addresses these design challenges and provides insights into the related research questions. Furthermore, this thesis contributes prescriptive knowledge on how to design effective NLIs for data visualization tools. Specifically, this thesis provides insights into how data visualization tools can be extended through NLIs to improve their effective use by end users and how to enable end users to effectively teach NLIs how to respond to new NL inputs. Furthermore, this thesis provides high-level guidance that developers and providers of data visualization tools can utilize as a blueprint for developing data visualization tools with NLIs for end users and outlines future research opportunities that are of interest in supporting end users to effectively use data visualization tools

Equity by Design and Delivery Model in Online Learning: Educator and Student Perceptions and Behaviors as Leading Indicators of Systemic Change

The purpose of this study is to explore educator and student perceptions of and behaviors in the Equity by Design and Delivery (EDD) model and its online courses as leading indicators of systemic change. The EDD model is a pilot intervention to eliminate opportunity to learn gaps at the program level in a mid-sized northwestern college in the United States. It shifts instructional behavior from individual efforts to collective approaches to limit quality variances in online courses, theorized to be a major contributor of missed opportunities to learn at high levels, by developing and delivering reliable quality courses based on collective agreements to apply evidence-based practices. It improves course and credentialing outcomes (e.g., course grades, course and degree completion rates) as it eliminates significant outcome disparities between student groups in programs with a strong online learning presence. It uses systems theory, improvement and implementation sciences, as well as principles of adaptive leadership as an operational framework to increase the likely efficacy of the EDD model. A convergent mixed methods of a single-site case study research design is used. It collects primary and secondary quantitative and qualitative data to conduct a comprehensive analysis of and findings from the pilot. It ends with recommendations for implementation at scale and scholar-practitioner reflections of practice

A Systematic and Minimalist Approach to Lower Barriers in Visual Data Exploration

With the increasing availability and impact of data in our lives, we need to make quicker, more accurate, and intricate data-driven decisions. We can see and interact with data, and identify relevant features, trends, and outliers through visual data representations. In addition, the outcomes of data analysis reflect our cognitive processes, which are strongly influenced by the design of tools. To support visual and interactive data exploration, this thesis presents a systematic and minimalist approach. First, I present the Cognitive Exploration Framework, which identifies six distinct cognitive stages and provides a high-level structure to design guidelines, and evaluation of analysis tools. Next, in order to reduce decision-making complexities in creating effective interactive data visualizations, I present a minimal, yet expressive, model for tabular data using aggregated data summaries and linked selections. I demonstrate its application to common categorical, numerical, temporal, spatial, and set data types. Based on this model, I developed Keshif as an out-of-the-box, web-based tool to bootstrap the data exploration process. Then, I applied it to 160+ datasets across many domains, aiming to serve journalists, researchers, policy makers, businesses, and those tracking personal data. Using tools with novel designs and capabilities requires learning and help-seeking for both novices and experts. To provide self-service help for visual data interfaces, I present a data-driven contextual in-situ help system, HelpIn, which contrasts with separated and static videos and manuals. Lastly, I present an evaluation on design and graphical perception for dense visualization of sorted numeric data. I contrast the non-hierarchical treemaps against two multi-column chart designs, wrapped bars and piled bars. The results support that multi-column charts are perceptually more accurate than treemaps, and the unconventional piled bars may require more training to read effectively. This thesis contributes to our understanding on how to create effective data interfaces by systematically focusing on human-facing challenges through minimalist solutions. Future work to extend the power of data analysis to a broader public should continue to evaluate and improve design approaches to address many remaining cognitive, social, educational, and technical challenges

Looking East: Brice Marden, Michael Mazur, and Pat Steir

This is the catalogue of the exhibition "Looking East" at Boston University Art Gallery

Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations