Mobile computing, cloud computing, and data-rich repositories have altered ideas about where and how learning takes place.
designers can find themselves filling a variety of roles. They might design large, complex systems or work with faculty and departments to develop courses and curricula. They might migrate traditional resources to mobile or adaptive platforms. They might help administrators understand the value and potential of new learning strategies and tools. Today’s instructional designer might work with subject-matter experts, coders, graphic designers, and others. Moreover, the work of an instructional designer increasingly continues throughout the duration of a course rather than taking place upfront
Given the expanding role and landscape of technology—as well as the growing body of knowledge about learning and about educational activities and assessments—dedicated instructional designers are increasingly common and often take a stronger role.
Competency based learning allows students to progress at their own pace and finish assignments, courses, and degree plans as time and skills permit. Data provided by analytics systems can help instructional designers predict which pedagogical approaches might be most effective and tailor learning experiences accordingly. The use of mobile learning continues to grow, enabling new kinds of learning experiences.
In some contexts, instructional designers might work more directly with students, teaching them lifelong learning skills. Students might begin coursework by choosing from a menu of options, creating their own path through content, making choices about learning options, being more hands-on, and selecting best approaches for demonstrating mastery. Educational models that feature adaptive and personalized learning will increasingly be a focus of instructional design.
Instructional designers bring a cross-disciplinary approach to their work, showing faculty how learning activities used in particular subject areas might be effective in others. In this way, instructional designers can cultivate a measure of consistency across courses and disciplines in how educational strategies and techniques are incorporated.
Are you seeing enrollments change? Which technologies hold the most promise? Will your campus become politically active? What collaborations might power up teaching and learning?
the big technological issues for the next year?
robotics? automation in education? big data / analytics?
organizational transformation. David Stone (Penn State) – centralization vs decentralization. technology is shifting everywhere, even the registrar. BA – where should be the IT department? CFO or Academic Department.
difference between undergrads and grad students and how to address. CETL join center for academic technologies.
faculty role, developing courses and materials. share these materials and make more usable. who should be maintaining these materials. life cycle, compensation for development materials. This is in essence the issues of the OER Open Education Resources initiative in MN
BA: OER and Open Access to Research has very similar models and issues. Open access scholarship both have a lot of impact on campus finances. Library and faculty budges.
Amanda Major is with Division of Digital Learning as part of Academic Affairs at UCF: Are there trends in competency-based learning, assessing quality course and programs, personalized adaptive learning, utilizing data analytics for retention and student success? BA: CBL continue to grow at state U’s and community colleges.
BA for group discussions: what are the technological changes happening this coming year, not only internally on campus, but global changes and how thy might be affecting us. Amazon Dash button, electric cars for U fleet, newer devices on campus
David Stone: students are price-sensitive. college and U can charge whatever they want and text books can raise prices.
Tobin, T. J., Mandernach, B. J., & Taylor, A. H. (2015). Evaluating Online Teaching: Implementing Best Practices (1 edition). San Francisco, CA: Jossey-Bass.
5 measurable faculty competencies for on line teaching:
attend to unique challenges of distance learning
Be familiar with unique learning needs
Achieve mastery of course content, structure , and organization
Respond to student inquiries
Provide detailed feedback
Communicate effectively
Promote a safe learning environment
Monitor student progress
Communicate course goals
Provide evidence of teaching presence.
Best practices include:
Making interactions challenging yet supportive for students
Asking learners to be active participants in the learning process
Acknowledging variety on the ways that students learn best
Providing timely and constructive feedback
Evaluation principles
Instructor knowledge
Method of instruction
Instructor-student rapport
Teaching behaviors
Enthusiastic teaching
Concern for teaching
Overall
8. The American Association for higher Education 9 principle4s of Good practice for assessing student learning from 1996 hold equally in the F2F and online environments:
the assessment of student learning beings with educational values
assessment is most effective when it reflects an understanding of learning as multidimensional, integrated and revealed in performance over time
assessment works best when the programs it seeks to improve have clear, explicitly stated purposes.
Assessment requires attention to outcomes but also and equally to the experiences that lead to those outcomes.
Assessment works best when it is ongoing, not episodic
Assessment fosters wider improvement when representatives from across the educational community are involved
Assessment makes a difference when it begins with issues of use and illumines questions that people really care bout
Assessment is most likely to lead to improvements when it is part of the large set of conditions that promote change.
Through assessment, educators meet responsibilities to students and to the public.
9 most of the online teaching evaluation instruments in use today are created to evaluate content design rather than teaching practices.
29 stakeholders for the evaluation of online teaching
faculty members with online teaching experience
campus faculty members as a means of establishing equitable evaluation across modes of teaching
contingent faculty members teaching online
department or college administrators
members of faculty unions or representative governing organizations
administrative support specialists
distance learning administrators
technology specialists
LMS administrators
Faculty development and training specialists
Institutional assessment and effectiveness specialists
Students
Sample student rating q/s
University resources
Rate the effectiveness of the online library for locationg course materials
Based on your experience,
148. Checklist for Online Interactive Learning COIL
150. Quality Online Course Initiative QOCI
151 QM Rubric
154 The Online Insturctor Evaluation System OIES
163 Data Analytics: moving beyond student learning
# of announcments posted per module
# of contributions to the asynchronous discussion boards
Quality of the contributions
Timeliness of posting student grades
Timelines of student feedback
Quality of instructional supplements
Quality of feedback on student work
Frequency of logins
180 understanding big data
reliability
validity
factor structure
187 a holistics valuation plan should include both formative evaluation, in which observations and rating are undertaken with the purposes of improving teaching and learning, and summative evaluation, in which observation and ratings are used in order to make personnel decisions, such as granting promotion and tenure, remediation, and asking contingent faculty to teach again.
195 separating teaching behaviors from content design
Studying Connections between Student Well-Being,
Performance, and Active Learning
Amy Godert, Cornell University; Teresa Pettit, Cornell University
Treasure in the Sierra Madre? Digital Badges and Educational
Development
Chris Clark, University of Notre Dame; G. Alex Ambrose, University
of Notre Dame; Gwynn Mettetal, Indiana University South Bend;
David Pedersen, Embry-Riddle Aeronautical University; Roberta
(Robin) Sullivan, University of Buffalo, State University of New York
Learning and Teaching Centers: The Missing Link in Data
Analytics
Denise Drane, Northwestern University; Susanna Calkins,
Northwestern University
Identifying and Supporting the Needs of International Faculty
Deborah DeZure, Michigan State University; Cindi Leverich, Michigan
State University
Online Discussions for Engaged and Meaningful Student
Learning
Danilo M. Baylen, University of West Georgia; Cheryl Fulghum,
Haywood Community College
Why Consider Online Asynchronous Educational Development?
Christopher Price, SUNY Center for Professional Development
Online, On-Demand Faculty Professional Development for Your
Campus
Roberta (Robin) Sullivan, University at Buffalo, State University of
New York; Cherie van Putten, Binghamton University, State
University of New York; Chris Price, State University of New York
The Tools of Engagement Project (http://suny.edu/toep) is an online faculty development model that encourages instructors to explore and reflect on innovative and creative uses of freely-available online educational technologies to increase student engagement and learning. TOEP is not traditional professional development but instead provides access to resources for instructors to explore at their own pace through a set of hands-on discovery activities. TOEP facilitates a learning community where participants learn from each
other and share ideas. This poster will demonstrate how you can implement TOEP at your campus by either adopting your own version or joining the existing project.
Video Captioning 101: Establishing High Standards With
Limited Resources
Stacy Grooters, Boston College; Christina Mirshekari, Boston
College; Kimberly Humphrey, Boston College
Recent legal challenges have alerted institutions to the importance of ensuring that video content for instruction is properly captioned. However, merely meeting minimum legal standards can still fall significantly short of the best practices defined by disability rights
organizations and the principles of Universal Design for Learning. Drawing from data gathered through a year-long pilot to investigate the costs and labor required to establish “in-house” captioning support at Boston College, this hands-on session seeks to give
participants the tools and information they need to set a high bar for captioning initiatives at their own institutions.
Sessions on mindfulness
52 Cognitive Neuroscience Applications for Teaching and Learning (BoF)
53 Contemplative Practices (BoF) Facilitators: Penelope Wong, Berea College; Carl S. Moore, University of the District of Columbia
79 The Art of Mindfulness: Transforming Faculty Development by Being Present Ursula Sorensen, Utah Valley University
93 Impacting Learning through Understanding of Work Life Balance Deanna Arbuckle, Walden University
113 Classroom Mindfulness Practices to Increase Attention, Creativity, and Deep Engagement Michael Sweet, Northeastern University
132 Measuring the Impacts of Mindfulness Practices in the Classroom Kelsey Bitting, Northeastern University; Michael Sweet, Northeastern University
Significant Challenges Impeding Technology Adoption in K–12 Education
Improving Digital Literacy.
Schools are charged with developing students’ digital citizenship, ensuring mastery of responsible and appropriate technology use, including online etiquette and digital rights and responsibilities in blended and online learning settings. Due to the multitude of elements comprising digital literacy, it is a challenge for schools to implement a comprehensive and cohesive approach to embedding it in curricula.
Rethinking the Roles of Teachers.
Pre-service teacher training programs are also challenged to equip educators with digital and social–emotional competencies, such as the ability to analyze and use student data, amid other professional requirements to ensure classroom readiness.
p. 28 Improving Digital Literacy
Digital literacy spans across subjects and grades, taking a school-wide effort to embed it in curricula. This can ensure that students are empowered to adapt in a quickly changing world
Education Overview: Digital Literacy Has to Encompass More Than Social Use
The American Library Association (ALA) defines digital literacy as “the ability to use information and communication technologies to find, evaluate, create, and communicate or share information, requiring both cognitive and technical skills.” While the ALA’s definition does align to some of the skills in “Participate”, it does not specifically mention the skills related to the “Open Practice.”
The library community’s digital and information literacy standards do not specifically include the coding, revision and remixing of digital content as skills required for creating digital information. Most digital content created for the web is “dynamic,” rather than fixed, and coding and remixing skills are needed to create new content and refresh or repurpose existing content. Leaving out these critical skills ignores the fact that library professionals need to be able to build and contribute online content to the ever-changing Internet.
p. 30 Rethinking the Roles of Teachers
Teachers implementing new games and software learn alongside students, which requires
a degree of risk on the teacher’s part as they try new methods and learn what works
p. 32 Teaching Computational Thinking
p. 36 Sustaining Innovation through Leadership Changes
shift the role of teachers from depositors of knowledge to mentors working alongside students;
p. 38 Important Developments in Educational Technology for K–12 Education
Consumer technologies are tools created for recreational and professional purposes and were not designed, at least initially, for educational use — though they may serve well as learning aids and be quite adaptable for use in schools.
Drones > Real-Time Communication Tools > Robotics > Wearable Technology
Digital strategies are not so much technologies as they are ways of using devices and software to enrich teaching and learning, whether inside or outside the classroom.
> Games and Gamification > Location Intelligence > Makerspaces > Preservation and Conservation Technologies
Enabling technologies are those technologies that have the potential to transform what we expect of our devices and tools. The link to learning in this category is less easy to make, but this group of technologies is where substantive technological innovation begins to be visible. Enabling technologies expand the reach of our tools, making them more capable and useful
Affective Computing > Analytics Technologies > Artificial Intelligence > Dynamic Spectrum and TV White Spaces > Electrovibration > Flexible Displays > Mesh Networks > Mobile Broadband > Natural User Interfaces > Near Field Communication > Next Generation Batteries > Open Hardware > Software-Defined Networking > Speech-to-Speech Translation > Virtual Assistants > Wireless Powe
Internet technologies include techniques and essential infrastructure that help to make the technologies underlying how we interact with the network more transparent, less obtrusive, and easier to use.
Bibliometrics and Citation Technologies > Blockchain > Digital Scholarship Technologies > Internet of Things > Syndication Tools
Learning technologies include both tools and resources developed expressly for the education sector, as well as pathways of development that may include tools adapted from other purposes that are matched with strategies to make them useful for learning.
Adaptive Learning Technologies > Microlearning Technologies > Mobile Learning > Online Learning > Virtual and Remote Laboratories
Social media technologies could have been subsumed under the consumer technology category, but they have become so ever-present and so widely used in every part of society that they have been elevated to their own category.
Crowdsourcing > Online Identity > Social Networks > Virtual Worlds
Visualization technologies run the gamut from simple infographics to complex forms of visual data analysis
3D Printing > GIS/Mapping > Information Visualization > Mixed Reality > Virtual Reality
p. 46 Virtual Reality
p. 48 AI
p. 50 IoT
+++++++++++++++
more on NMC Horizon Reports in this IMS blog
More data doesn’t automatically lead to better decisions. A shortage of skilled data scientists has hindered progress towards translation of information into actionable business insights. In addition, traditionally dense spreadsheets and linear slideshows are ineffective to present discoveries when dealing with Big Data’s dynamic nature. We need to evolve how we capture, analyze and communicate data.
Large-scale visualization platforms have several advantages over traditional presentation methods. They blur the line between the presenter and audience to increase the level of interactivity and collaboration. They also offer simultaneous views of both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets.
Visualization walls enable presenters to target people’s preferred learning methods, thus creating a more effective communication tool. The human brain has an amazing ability to quickly glean insights from patterns – and great visualizations make for more efficient storytellers.
Grant: Visualizing Digital Scholarship in Libraries and Learning Spaces
Award amount: $40,000
Funder: Andrew W. Mellon Foundation
Lead institution: North Carolina State University Libraries
Due date: 13 August 2017
Notification date: 15 September 2017
Website: https://immersivescholar.org
Contact: immersivescholar@ncsu.edu
Project Description
NC State University, funded by the Andrew W. Mellon Foundation, invites proposals from institutions interested in participating in a new project for Visualizing Digital Scholarship in Libraries and Learning Spaces. The grant aims to 1) build a community of practice of scholars and librarians who work in large-scale multimedia to help visually immersive scholarly work enter the research lifecycle; and 2) overcome technical and resource barriers that limit the number of scholars and libraries who may produce digital scholarship for visualization environments and the impact of generated knowledge. Libraries and museums have made significant strides in pioneering the use of large-scale visualization technologies for research and learning. However, the utilization, scale, and impact of visualization environments and the scholarship created within them have not reached their fullest potential. A logical next step in the provision of technology-rich, visual academic spaces is to develop best practices and collaborative frameworks that can benefit individual institutions by building economies of scale among collaborators.
The project contains four major elements:
An initial meeting and priority setting workshop that brings together librarians, scholars, and technologists working in large-scale, library and museum-based visualization environments.
Scholars-in-residence at NC State over a multi-year period who pursue open source creative projects, working in collaboration with our librarians and faculty, with the potential to address the articulated limitations.
Funding for modest, competitive block grants to other institutions working on similar challenges for creating, disseminating, validating, and preserving digital scholarship created in and for large-scale visual environments.
A culminating symposium that brings together representatives from the scholars-in-residence and block grant recipient institutions to share and assess results, organize ways of preserving and disseminating digital products produced, and build on the methods, templates, and tools developed for future projects.
Work Summary
This call solicits proposals for block grants from library or museum systems that have visualization installations. Block grant recipients can utilize funds for ideas ranging from creating open source scholarly content for visualization environments to developing tools and templates to enhance sharing of visualization work. An advisory panel will select four institutions to receive awards of up to $40,000. Block grant recipients will also participate in the initial priority setting workshop and the culminating symposium. Participating in a block grant proposal does not disqualify an individual from later applying for one of the grant-supported scholar-in-residence appointments.
Applicants will provide a statement of work that describes the contributions that their organization will make toward the goals of the grant. Applicants will also provide a budget and budget justification.
Activities that can be funded through block grants include, but are not limited to:
Commissioning work by a visualization expert
Hosting a visiting scholar, artist, or technologist residency
Software development or adaptation
Development of templates and methodologies for sharing and scaling content utilizing open source software
Student or staff labor for content or software development or adaptation
Curricula and reusable learning objects for digital scholarship and visualization courses
Travel (if necessary) to the initial project meeting and culminating workshop
User research on universal design for visualization spaces
Funding for operational expenditures, such as equipment, is not allowed for any grant participant.
Application
Send an application to immersivescholar@ncsu.edu by the end of the day on 13 August 2017 that includes the following:
Statement of work (no more than 1000 words) of the project idea your organization plans to develop, its relationship to the overall goals of the grant, and the challenges to be addressed.
List the names and contact information for each of the participants in the funded project, including a brief description of their current role, background, expertise, interests, and what they can contribute.
Project timeline.
Budget table with projected expenditures.
Budget narrative detailing the proposed expenditures
Selection and Notification Process
An advisory panel made up of scholars, librarians, and technologists with experience and expertise in large-scale visualization and/or visual scholarship will review and rank proposals. The project leaders are especially keen to receive proposals that develop best practices and collaborative frameworks that can benefit individual institutions by building a community of practice and economies of scale among collaborators.
Awardees will be selected based on:
the ability of their proposal to successfully address one or both of the identified problems;
the creativity of the proposed activities;
relevant demonstrated experience partnering with scholars or students on visualization projects;
whether the proposal is extensible;
feasibility of the work within the proposed time-frame and budget;
whether the project work improves or expands access to large-scale visual environments for users; and
the participant’s ability to expand content development and sharing among the network of institutions with large-scale visual environments.
Awardees will be required to send a representative to an initial meeting of the project cohort in Fall 2017.
The Internet of Things (IoT), augmented reality, and advancements in online learning have changed the way universities reach prospective students, engage with their current student body, and provide them the resources they need.
The Internet of Things has opened up a whole new world of possibilities in higher education. The increased connectivity between devices and “everyday things” means better data tracking and analytics, and improved communication between student, professor, and institution, often without ever saying a word. IoT is making it easier for students to learn when, how, and where they want, while providing professors support to create a more flexible and connected learning environment.
Virtual/Augmented Reality
Virtual and augmented reality technologies have begun to take Higher Ed into the realm of what used to be considered science fiction.
A Strategist’s Guide to Industry 4.0. Global businesses are about to integrate their operations into a seamless digital whole, and thereby change the world.
Industrial revolutions are momentous events. By most reckonings, there have been only three. The first was triggered in the 1700s by the commercial steam engine and the mechanical loom. The harnessing of electricity and mass production sparked the second, around the start of the 20th century. The computer set the third in motion after World War II.
Henning Kagermann, the head of the German National Academy of Science and Engineering (Acatech), did exactly that in 2011, when he used the term Industrie 4.0 to describe a proposed government-sponsored industrial initiative.
The term Industry 4.0 refers to the combination of several major innovations in digital technology
These technologies include advanced robotics and artificial intelligence; sophisticated sensors; cloud computing; the Internet of Things; data capture and analytics; digital fabrication (including 3D printing); software-as-a-service and other new marketing models; smartphones and other mobile devices; platforms that use algorithms to direct motor vehicles (including navigation tools, ride-sharing apps, delivery and ride services, and autonomous vehicles); and the embedding of all these elements in an interoperable global value chain, shared by many companies from many countries.
Companies that embrace Industry 4.0 are beginning to track everything they produce from cradle to grave, sending out upgrades for complex products after they are sold (in the same way that software has come to be updated). These companies are learning mass customization: the ability to make products in batches of one as inexpensively as they could make a mass-produced product in the 20th century, while fully tailoring the product to the specifications of the purchaser
.
Three aspects of digitization form the heart of an Industry 4.0 approach.
• The full digitization of a company’s operations
• The redesign of products and services
• Closer interaction with customers
Making Industry 4.0 work requires major shifts in organizational practices and structures. These shifts include new forms of IT architecture and data management, new approaches to regulatory and tax compliance, new organizational structures, and — most importantly — a new digitally oriented culture, which must embrace data analytics as a core enterprise capability.
Klaus Schwab put it in his recent book The Fourth Industrial Revolution (World Economic Forum, 2016), “Contrary to the previous industrial revolutions, this one is evolving at an exponential rather than linear pace.… It is not only changing the ‘what’ and the ‘how’ of doing things, but also ‘who’ we are.”
This great integrating force is gaining strength at a time of political fragmentation — when many governments are considering making international trade more difficult. It may indeed become harder to move people and products across some national borders. But Industry 4.0 could overcome those barriers by enabling companies to transfer just their intellectual property, including their software, while letting each nation maintain its own manufacturing networks.
+++++++++++++++++++++++++++
more on the Internet of Things in this IMS blog https://blog.stcloudstate.edu/ims?s=internet+of+things
Because the questionnaire data comprised both Likert scales and open questions, they were analyzed quantitatively and qualitatively. Textual data (open responses) were qualitatively analyzed by coding: each segment (e.g. a group of words) was assigned to a semantic reference category, as systematically and rigorously as possible. For example, “Using an iPad in class really motivates me to learn” was assigned to the category “positive impact on motivation.” The qualitative analysis was performed using an adapted version of the approaches developed by L’Écuyer (1990) and Huberman and Miles (1991, 1994). Thus, we adopted a content analysis approach using QDAMiner software, which is widely used in qualitative research (see Fielding, 2012; Karsenti, Komis, Depover, & Collin, 2011). For the quantitative analysis, we used SPSS 22.0 software to conduct descriptive and inferential statistics. We also conducted inferential statistics to further explore the iPad’s role in teaching and learning, along with its motivational effect. The results will be presented in a subsequent report (Fievez, & Karsenti, 2013)
The 20th century notion of conducting a qualitative research by an oral interview and then processing manually your results had triggered in the second half of the 20th century [sometimes] condescending attitudes by researchers from the exact sciences.
The reason was the advent of computing power in the second half of the 20th century, which allowed exact sciences to claim “scientific” and “data-based” results.
One of the statistical package, SPSS, is today widely known and considered a magnificent tools to bring solid statistically-based argumentation, which further perpetuates the superiority of quantitative over qualitative method.
At the same time, qualitative researchers continue to lag behind, mostly due to the inertia of their approach to qualitative analysis. Qualitative analysis continues to be processed in the olden ways. While there is nothing wrong with the “olden” ways, harnessing computational power can streamline the “olden ways” process and even present options, which the “human eye” sometimes misses.
Below are some suggestions, you may consider, when you embark on the path of qualitative research.
excellent guide to the structure of a qualitative research
Palys, T., & Atchison, C. (2012). Qualitative Research in the Digital Era: Obstacles and Opportunities. International Journal Of Qualitative Methods, 11(4), 352-367.
Palys and Atchison (2012) present a compelling case to bring your qualitative research to the level of the quantitative research by using modern tools for qualitative analysis.
1. The authors correctly promote NVivo as the “jaguar’ of the qualitative research method tools. Be aware, however, about the existence of other “Geo Metro” tools, which, for your research, might achieve the same result (see bottom of this blog entry).
2. The authors promote a new type of approach to Chapter 2 doctoral dissertation and namely OCR-ing PDF articles (most of your literature as of 2017 is mostly either in PDF or electronic textual format) through applications such as
Abbyy Fine Reader, https://www.abbyy.com/en-us/finereader/
OmniPage, http://www.nuance.com/for-individuals/by-product/omnipage/index.htm
Readirus http://www.irislink.com/EN-US/c1462/Readiris-16-for-Windows—OCR-Software.aspx
The text from the articles is processed either through NVIVO or related programs (see bottom of this blog entry). As the authors propose: ” This is immediately useful for literature review and proposal writing, and continues through the research design, data gathering, and analysis stages— where NVivo’s flexibility for many different sources of data (including audio, video, graphic, and text) are well known—of writing for publication” (p. 353).
In other words, you can try to wrap your head around huge amount of textual information, but you can also approach the task by a parallel process of processing the same text with a tool.
+++++++++++++++++++++++++++++
Here are some suggestions for Computer Assisted / Aided Qualitative Data Analysis Software (CAQDAS)for a small and a large community applications):
text mining: https://en.wikipedia.org/wiki/Text_mining Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. https://ischool.syr.edu/infospace/2013/04/23/what-is-text-mining/
Qualitative data is descriptive data that cannot be measured in numbers and often includes qualities of appearance like color, texture, and textual description. Quantitative data is numerical, structured data that can be measured. However, there is often slippage between qualitative and quantitative categories. For example, a photograph might traditionally be considered “qualitative data” but when you break it down to the level of pixels, which can be measured.
word of caution, text mining doesn’t generate new facts and is not an end, in and of itself. The process is most useful when the data it generates can be further analyzed by a domain expert, who can bring additional knowledge for a more complete picture. Still, text mining creates new relationships and hypotheses for experts to explore further.
Pros and Cons of Computer Assisted Qualitative Data Analysis Software
+++++++++++++++++++++++++
more on quantitative research:
Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
++++++++++++++++++++++++
literature on quantitative research:
St. Cloud State University MC Main Collection – 2nd floor
AZ195 .B66 2015
p. 161 Data scholarship in the Humanities
p. 166 When Are Data?
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015
