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Measurement of Affective Empathy with Pictorial Empathy Test (PET)
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Empathy Center: Largest Collection of Empathy Measurements and Tests
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Comparing Credentials: An Update on the Credential Engine « WCET Frontiers
- The economy is changing rapidly, potential students are looking for ways to parse through options, and employer needs are shifting
- with institutions creating new degrees, badges, microcredentials, and certificates.
- Launched in 2016, Credential Engine is a nonprofit organization whose mission is to create credential transparency, reveal the credential marketplace, increase credential literacy, and empower everyone to make more informed decisions about credentials and their value
- transparent credentialing marketplace is the key to not only meeting the challenges students, employers, and educators face today, but to setting students up for long-term success.
- Our work to bring credential transparency starts with our technologies
- create a universal language to describe credentials
- Housing all of this data from the entire credentialing marketplace is no small feat.That’s why Credential Engine created the Credential Registry —a cloud-based storage system—that collects and connects data in new ways while ensuring that all credential data within the Registry is secure, accurate, and up-to-date.
- Credential Engine also supports an open platform for application development that opens up a world of possibilities for institutions
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Instructional design/SAMR Model/What is the SAMR Model? - Wikiversity
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Edu Trends Augmented and Virtual Reality — Observatory of Educational Innovation
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10 free online courses to shape the future of the education — Observatory of Educational Innovation
"Foundations of Computer Science for Teachers"
Saturday, May 26, 2018
Weekly Sporto bookmarks (weekly)
Saturday, May 19, 2018
Saturday, May 12, 2018
Weekly Sporto bookmarks (weekly)
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The Power Of AGILE Instructional Design Approach - eLearning Industry
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How Associations Bridge the Skills Gap With Digital Badges - Talented Learning
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Machine Learning Overview - Introduction to Machine Learning with Big Data | Coursera
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What Is Machine Learning? Definition and Examples
- Webster’s Dictionary defines artificial intelligence as “an area of computer science that deals with giving machines the ability to seem like they have human intelligence.”
- focus instead on how AI is being applied today
- Systems based on AI, sometimes referred to as cognitive systems, are helping us automate many tasks which, until recently, were seen as requiring human intelligence. However, AI allows us to not only automate and scale up tasks that so far have required humans, but it also lets us tackle more complex problems than most humans would be capable of solving
- Never before has so much information been available in digital form, ready for use.
- through crowdsourcing and online communities, we are also able to give feedback on the quality of the machines’ work at an unprecedented scale.
- Computing power and storage capacity continue to grow exponentially, and the cost for accessing these resources in the cloud are decreasing
- Research in algorithms has seen huge strides in giving us the ability to use these new computing resources on the massive data sets now available
- Machine learning is an AI technique getting significant attention today. The ultimate aim of machine learning is to enable software applications to become more accurate without being explicitly programmed
- The basic premise of machine learning is to build algorithms that can receive vast amounts of data, and then use statistical analysis to provide a reasonably accurate outcome
- Machine-learning algorithms are usually defined as supervised or unsupervised. Supervised algorithms need humans to provide both input and the desired output, in addition to providing the machine with feedback on the outcomes during the training phase. Once training is complete, the algorithm will apply what was learned to new data. Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions
- In reality, machine learning is about setting systems to the task of searching through data to look for patterns and adjusting actions accordingly
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What is Machine Learning? - An Informed Definition
A larger piece that discusses various definitions of machine learning. Discusses types, processes, etc. Provides bibliography.
- “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
- There are many different types of machine learning algorithms, with hundreds published each day, and they’re typically grouped by either learning style (i.e. supervised learning, unsupervised learning, semi-supervised learning) or by similarity in form or function (i.e. classification, regression, decision tree, clustering, deep learning, etc.). Regardless of learning style or function, all combinations of machine learning algorithms consist of the following
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- Representation (a set of classifiers or the language that a computer understands)
- Evaluation (aka objective/scoring function)
- Optimization (search method; often the highest-scoring classifier, for example; there are both off-the-shelf and custom optimization methods used)
- Below are some visual representations of machine learning models, with accompanying links for further information
- machine learning is not just, or even about, automation, an often misunderstood concept.
- miss the valuable insights that machines can provide and the resulting opportunities
- There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks (the latter of which has given way to deep learning), depending on what task you’re trying to accomplish and the type and amount of data that you have available
- selection of best-practices and concepts of applying machine learning
- features used to describe the data (which are domain-specific), and having adequate data to train your models in the first place
- Simplicity does not imply accuracy
- Whether or not we label data causal or correlative, the more important point is to predict the effects of our actions
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Excellent source for definitive definition, applications, classification, etc.
- field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed
- machine learning explores the study and construction of algorithms that can learn from and make predictions on data[4] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[5]:2
- Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,[6] optical character recognition (OCR),[7] learning to rank, and computer vision.
- Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field.
- Machine learning is sometimes conflated with data mining,[8] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning
- Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.
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What is Machine Learning - A definition
Short precise definition. Provides brief view of examples and methods. Uses appropriate language for people from other fields of study.
- Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
- The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
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