Abstract
The purpose of this chapter is to introduce library-led data scholarship and student engagement, demonstrate the extra-curricular achievements of students, and evaluate the pedagogical framework for student investigations. The objective of this chapter is to examine key aspects of the data scholarship ecosystem, including building data scholarship as a partnership between librarians, students, and academics; developing the knowledge, skills, and experiences of both librarians and students in data scholarship; and delivering a case study from specific Chinese contexts in the shaping of the extra-curriculum for student success, which includes student works and the student voice.
TopBackground
Data science has shifted pedagogical practices, services, and roles in higher education libraries. Librarians, students, and researchers are learning by making and remaking through data analysis and visualization (Scranton, 2015, p. 99), educating students to become data literate consumers and producers of visualizations (Magnuson, 2016, p. xiii), and making and creating with new and emerging technologies (New Media Consortium et al., 2017). These trends connect learning, services and roles in datafied scholarship (Pinfield et al., 2017), requiring a steep learning curve for librarians to be embedded in research processes (Jantz, 2017), and a distinctive set of new skills and competencies (King, 2018). These include knowledge, skills, and experiences in meaningful data visualization (Yau, 2013), models (Page, 2018), and storytelling (Knaflic, 2015, Dykes, 2020). It also includes social interoperability skills in collaboration, communication, and mutual understanding for cross-campus relationship building (Bryant et al., 2020), connecting people, stitching people to one another through the shared experience of discovering a connection that wasn’t visible before (Palmer, 2014). Academic libraries are positioned to provide data science and scholarship services (Oliver et al., 2019), including strategies for the integration of digital technology and literacy in classrooms (Glass & Hickman, 2020) and spaces to share information and discoveries (Aghassibake et al., 2020). Student attitudes, however, need to be at the heart of innovation to ensure resources are not wasted or become an obstacle to learning (Walker, 2020, p. 116). Nevertheless, data visualization has emerged as a research support service in academic libraries at world-class universities (Zakaria, 2021), and there are many examples of librarians using network visualization as a tool to understand interactions in complex systems (Zoss et al., 2018) or specific projects, such as research evaluation metrics (Wu et al., 2019), open access books (Zhou et al., 2018), or visible learning (Yu et al., 2020). Higher education institutions, however, tend to be more adept at researching and discussing innovation than implementing it, with few having significant experience with intensive collaborative networking in pedagogy (Rubin, 2020, p. 189).
Key Terms in this Chapter
Student Engagement: The investment students make in their learning at university. It includes the learning related to the formal degree programme and achievement of the degree grade. It is also about the curiosity, creativity, and passion that students bring beyond the formal degree programme, including extra-curriculum engagement, such as the Data Scholarship Award. It is about a commitment to higher learning and scholarship, including practice, pedagogy, and publications.
Data Scholarship Ecosystem: A community of people collaborating in learning with data-based research investigations in physical and digital environments to create new scholarship.
Extra-Curriculum: The programmes of study outside the formal degree modules at universities.
Librarian: An inspirational thought leader, committed to global change in knowledge creation and discovery through learning, teaching, and scholarship.
Student Research Investigations: The research process of students identifying a research question, collecting data, analyzing data, and drawing conclusions, which they learn to communicate through academic writing and showcase presentations.
Formative Assessment: A range of formal and informal assessment procedures conducted by teachers during the learning process in order to modify teaching and learning activities to improve student learning.
Research Data Postcards: The student-created digital artefacts designed for touch-table technology. They resemble traditional postcards, with a data visualization on one side and explanatory text of method, results, or revelatory discovery on the other.
Curriculum: The subjects students study in formal degree programmes at university.
Data Scholarship: The creative process of transitions from no data or little data to scholarship.
Open Scholarship: The interrelated strategies, policies, and practices that go beyond access to outputs, which enable public scholarship that is digital, online, free of charge, free of most copyright and licencing restrictions, and citable in scholarship. It can encompass open access, open research, open science, and open educational resources. It includes scholarship made open to a global public through universities, building on core socialist values.
Student: A person who is studying at university.
Assessment: The evaluation of student work or librarian leadership.