DataSense, a data management platform developed by Brightbytes.
DataSense is a set of professional services that work with K-12 districts to collect data from different data systems, translate them into unified formats and aggregate that information into a unified dashboard for reporting purposes.
DataSense traces its origins to Authentica Solutions, an education data management company founded in 2013.
A month later, BrightBytes acquired Authentica. The deal was hailed as a “major milestone in the industry” and appeared to be a complement to BrightBytes’ flagship offering, Clarity, a suite of data analytics tools that help educators understand the impact of technology spending and usage on student outcomes.
Of the “Big Five” technology giants, Microsoft has become the most acqui-hungry as of late in the learning and training space. In recent years it purchased several consumer brand names whose services reach into education, including LinkedIn (which owns Lynda.com, now a part of the LinkedIn Learning suite), Minecraft (which has been adapted for use in the classroom) and Github (which released an education bundle).
Last year, Microsoft also acquired a couple of smaller education tools, including Flipgrid, a video-discussion platform popular among teachers, and Chalkup, whose services have been rolled into Microsoft Teams, its competitor to Slack.
Submissions are invited for the IOLUG Spring 2019 Conference, to be held May 10th in Indianapolis, IN. Submissions are welcomed from all types of libraries and on topics related to the theme of data in libraries.
Libraries and librarians work with data every day, with a variety of applications – circulation, gate counts, reference questions, and so on. The mass collection of user data has made headlines many times in the past few years. Analytics and privacy have, understandably, become important issues both globally and locally. In addition to being aware of the data ecosystem in which we work, libraries can play a pivotal role in educating user communities about data and all of its implications, both favorable and unfavorable.
The Conference Planning Committee is seeking proposals on topics related to data in libraries, including but not limited to:
Using tools/resources to find and leverage data to solve problems and expand knowledge,
Data policies and procedures,
Harvesting, organizing, and presenting data,
Data-driven decision making,
Learning analytics,
Metadata/linked data,
Data in collection development,
Using data to measure outcomes, not just uses,
Using data to better reach and serve your communities,
Session 2: The Digital Age: The Impact and Future Possibilities Offered by Data and Technology
Thank you for registering to participate in the second Reimagining Minnesota State forum. The Forums have been designed to spark not only individual reflection but what we hope can serve as catalysts for discussions in a variety of venues. The Forum will be recorded and available for viewing on the Reimagining website.
Below are the directions whether you are attending in person or by live stream.
Catherine Haslag: Is there any research to show students retention in an online class vs a face-to-face course?
the challenge is not collecting, but integrating, using data.
silos = cylinder of excellence.
technology innovation around advising. iPASS resources.
adaptive learning systems – how students advance through the learning process.
games and simulations Bryan Mark Gill. voice recognition,
next 3 to 5 years AR. by 2023 40% with AR and VR
AI around the controversial. Chatbot and Voice assistants.
Unizin: 13 founding members to develop platform, Canvas, instructional services, data for predictive analytic, consistent data standard among institutions,
University innovation Alliance. Analytics as the linchpin for students’ success. graduation rates increase. racial gap graduation. Georgia State.
digital ethics. Mark Gill and Susana Nuccetelli. digital ethics: Susana Nuccetelli brought her students from the Philosophy Dept to Mark Gill’s SCSu Vizlab so we can discuss ethics and AI, last semester. jobrien@educause.edu
assistant vice president for student success and prevention Morgan State U
the importance of training in technology adoption
Dr. Peter Smith, Orkand Endowed Chair and Professor of Innovative Practices in Higher Education at University of Maryland University College
social disruption, national security issue,
Allan Taft Candadian researcher, 700 hours / year learning something. 14 h/w.
learners deserve recognition
free range learning.
how do we get a value on people from a different background? knowledge discrimination. we value it on where they learned it. then how you learned it and what you can do with it. talent and capacity not recognized.
we, the campus, don’t control the forces for a very first time. MIT undergrad curricula is free, what will happen. dynamics at work here. declining student numbers, legislation unhappy. technology had made college more expensive, not less. doing the right thing, leads to more disruption. local will be better, if done well. workplace can become a place for learning.
learning is a social activity. distance learning: being on the farthest raw of 300 Princeton lecture. there is a tool and there is people; has to have people at the heart.
what will work not only for MN, but for each of the campuses, the personalization.
staying still is death.
Panel discussion
what is the role of faculty in the vendor and discussions about technology. a heat map shows that IT people were testing the vendor web site most, faculty and student much less.
Elsevier, the information analytics business specializing in science and health, has acquired Science-Metrix Inc., a research evaluation firm that provides science research evaluation and analytics to assess science and technology activities. Headquartered in Montréal, Canada, Science-Metrix is known for high-quality and independent bibliometric analysis and research evaluation.
Science-Metrix works for governmental, educational, nonprofit and private organizations that perform scientific research or deal with funding and management of science and technology. Its services enable evidence-based decision-making, strategic planning and outcome assessment processes for governments, international organizations, universities, scientific societies, publishers and technology companies.
As part of the acquisition of Science-Metrix Inc., Elsevier has also acquired 1science, a business started in 2015 to develop research intelligence products. Customers of 1science products will benefit from synergies with the Elsevier technology stack.
Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come.
Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.
Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.
Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learningand/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.
Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement. For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.
Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.
Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans.
Storytelling with Data: An Introduction to Data Visualization
Mar 04 – Mar 31, 2019
Delivery Mode : Asynchronous Workshop Levels : Beginner,Intermediate Eligible for Online Teaching Certificate elective : No
Data visualization is about presenting data visually so we can explore and identify patterns in the data, analyze and make sense of those patterns, and communicate our findings. In this course, you will explore those key aspects of data visualization, and then focus on the theories, concepts, and skills related to communicating data in effective, engaging, and accessible ways.
This will be a hands-on, project-based course in which you will apply key data visualization strategies to various data sets to tell specific data stories using Microsoft Excel or Google Sheets. Practice data sets will be provided, or you can utilize your own data sets.
Week 1: Introduction and Tool Setup
Week 2: Cognitive Load and Pre-Attentive Attributes
Week 3: Selecting the Appropriate Visualization Type
Week 4: Data Stories and Context
Learning Objectives:
Upon completion of this course, you will be able to create basic data visualizations that are effective, accessible, and engaging. In support of that primary objective, you will:
Describe the benefits of data visualization for your professional situation
Identify opportunities for using data visualization
Use appropriate accessibility strategies for data tables
Prerequisites
Basic knowledge of Microsoft Excel or Google Sheets is required to successfully complete this course. Resources will be included to help you with the basics should you need them, but time spent learning the tools is not included in the estimated time for completing this course.
What are the key takeaways from this course?
The ability to explain how data visualization is connected to data analytics
The ability to identify key data visualization theories
Creating effective and engaging data visualizations
Applying appropriate accessibility strategies to data visualizations
Who should take this course?
Instructional designers, faculty, and higher education administrators who need to present data in effective, engaging, and accessible ways will benefit from taking this course