IMPROVING PEER LEARNING AND KNOWLEDGE SHARING IN STEM COURSES VIA PATTERN BASED GRAPH VISUALIZATION

High quality education in Science, Technology, Engineering and Math (STEM) majors expects not only the acquisition of comprehensive domain knowledge, but also the mastery of skills to solve open-ended and even ill-defined problems in real world. Problem-based Learning (PBL) is usually adopted to achieve such goals by encouraging students to learn by solving real-life problems. However, successful PBL requires sustained and in-depth involvement of faculty members, hence making PBL not scalable. Even though discussion forums and Q&A systems can help address the scalability problem of faculty involvement on large class sizes, it introduces new problems. First, as knowledge bases grow in size, the sheer size of the accumulated knowledge makes it harder to locate the desired information. Second, existing knowledge discovery techniques do not provide effective facilities for the capture and reuse of solutions to recurring problems. To address these challenges, we developed MicroBrowser, an innovative and interactive Question & Answer (Q&A) system augmented with pattern-based expertise-sharing interfaces and 2D knowledge graph discussion visualization. MicroBrowser provides a set of pattern-based expertise-sharing interfaces to allow both learners and instructors to refine, reuse, and share knowledge. MicroBrowser also allows learners to browse and navigate important discussions based on topic similarity encoded by node proximity in a knowledge graph. Results of empirical evaluations of our proposed solution show that ask difficulty improves with MicroBrowser when compared with a state-of-the-art Q&A system for knowledge discovery and reuse tasks. In addition, success rate for knowledge discovery tasks using keywords was higher with MicroBrowser. Moreover, we show that, students found the pattern-based expertise-sharing interface easy to use and were able to contribute new knowledge in the form of new knowledge connections and even recommend new design patterns

Modeling physical interaction and understanding peer group learning dynamics: Graph analytics approach perspective

Physical interaction in peer learning has been proven to improve students’ learning processes, which is pertinent in facilitating a fulfilling learning experience in learning theory. However,observation and interviews are often used to investigate peer group learning dynamics from a qualitative perspective. Hence, more data-driven analysis needs to be performed to investigate the physicalinteraction in peer learning. This paper complements existing works by proposing a frameworkfor exploring students’ physical interaction in peer learning based on the graph analytics modeling approach focusing on both centrality and community detection, as well as visualization of the grap model for more than 50 students taking part in group discussions. The experiment was conducted during a mathematics tutorial class. The physical interactions among students were captured through an online Google form and represented in a graph model. Once the model and graph visualization were developed, findings from centrality analysis and community detection were conducted to identify peer leaders who can facilitate and teach their peers. Based on the results, it was found that five groups were formed during the physical interaction throughout the peer learning process, with at least one student showing the potential to become a peer leader in each group. This paper also highlights the potential of the graph analytics approach to explore peer learning group dynamics and interaction patterns among students to maximize their teaching and learning experience

Mapping the Current Landscape of Research Library Engagement with Emerging Technologies in Research and Learning: Final Report

The generation, dissemination, and analysis of digital information is a significant driver, and consequence, of technological change. As data and information stewards in physical and virtual space, research libraries are thoroughly entangled in the challenges presented by the Fourth Industrial Revolution:1 a societal shift powered not by steam or electricity, but by data, and characterized by a fusion of the physical and digital worlds.2 Organizing, structuring, preserving, and providing access to growing volumes of the digital data generated and required by research and industry will become a critically important function. As partners with the community of researchers and scholars, research libraries are also recognizing and adapting to the consequences of technological change in the practices of scholarship and scholarly communication. Technologies that have emerged or become ubiquitous within the last decade have accelerated information production and have catalyzed profound changes in the ways scholars, students, and the general public create and engage with information. The production of an unprecedented volume and diversity of digital artifacts, the proliferation of machine learning (ML) technologies,3 and the emergence of data as the “world’s most valuable resource,”4 among other trends, present compelling opportunities for research libraries to contribute in new and significant ways to the research and learning enterprise. Librarians are all too familiar with predictions of the research library’s demise in an era when researchers have so much information at their fingertips. A growing body of evidence provides a resounding counterpoint: that the skills, experience, and values of librarians, and the persistence of libraries as an institution, will become more important than ever as researchers contend with the data deluge and the ephemerality and fragility of much digital content. This report identifies strategic opportunities for research libraries to adopt and engage with emerging technologies,5 with a roughly fiveyear time horizon. It considers the ways in which research library values and professional expertise inform and shape this engagement, the ways library and library worker roles will be reconceptualized, and the implication of a range of technologies on how the library fulfills its mission. The report builds on a literature review covering the last five years of published scholarship, primarily North American information science literature, and interviews with a dozen library field experts, completed in fall 2019. It begins with a discussion of four cross-cutting opportunities that permeate many or all aspects of research library services. Next, specific opportunities are identified in each of five core research library service areas: facilitating information discovery, stewarding the scholarly and cultural record, advancing digital scholarship, furthering student learning and success, and creating learning and collaboration spaces. Each section identifies key technologies shaping user behaviors and library services, and highlights exemplary initiatives. Underlying much of the discussion in this report is the idea that “digital transformation is increasingly about change management”6 —that adoption of or engagement with emerging technologies must be part of a broader strategy for organizational change, for “moving emerging work from the periphery to the core,”7 and a broader shift in conceptualizing the research library and its services. Above all, libraries are benefitting from the ways in which emerging technologies offer opportunities to center users and move from a centralized and often siloed service model to embedded, collaborative engagement with the research and learning enterprise

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

MOOC (Massive Open Online Courses)

Massive Open Online Courses (MOOCs) are free online courses available to anyone who can sign up. MOOCs provide an affordable and flexible way to learn new skills, advance in careers, and provide quality educational experiences to a certain extent. Millions of people around the world use MOOCs for learning and their reasons are various, including career development, career change, college preparation, supplementary learning, lifelong learning, corporate e-Learning and training, and so on

Building visualization skills through investigating the Journal of the Medical Library Association coauthorship network from 2006–2017

Objective: The primary objective of this study was to explore different dimensions of Journal of the Medical Library Association (JMLA) authorship from 2006-2017. Dimensions that were evaluated using coauthorship networks and affiliation data included collaboration, geographical reach, and relationship between Medical Library Association (MLA) member and nonmember authors. A secondary objective was to analyze the practice and practical application of data science skills. Methods: A team of librarians who attended the 2017 Data Science and Visualization Institute used JMLA bibliographic metadata extracted from Scopus, together with select MLA membership data from 2006-2017. Data cleaning, anonymization, analysis, and visualization were done collaboratively by the team members to meet their learning objectives and to produce insights about the nature of collaborative authorship at JMLA. Results: Sixty-nine percent of the 1,351 JMLA authors from 2006-2017 were not MLA members. MLA members were more productive and collaborative, and tended to author articles together. The majority of the authoring institutions in JMLA are based in the United States. Global reach outside of the United States and Canada shows higher authorship in English-speaking countries (e.g., Australia, United Kingdom), as well as in Western Europe and Japan. Conclusions: MLA support of JMLA may benefit a wider network of health information specialists and medical professionals than is reflected in MLA membership. Conducting coauthorship network analyses can create opportunities for health sciences librarians to practice applying emerging data science and data visualization skills