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Adult learners value the flexibility and convenience offered to them as online learners, and many learners are required to absent themselves from their online classes during courses in order to accommodate demanding schedules. What factors and tensions contribute to learners' decision-making at these times? This qualitative study considered the planned absences of learners engaged in an online graduate course at a large university. Working within the framework provided by cognitive, instructional, and social presences, findings showed the following: (1) learners understood and accommodated the relationship and importance of the affective domain to their cognitive successes in learning, (2) successful learners demonstrated insightful self-knowledge in using metacognitive strategies, and (3) learners' external support systems were fundamental to their ability to continue to learn when absences occurred. The study's findings corroborate other recent research that similarly stresses the complexity and interrelated nature of the adult learning process.

Computer-supported collaborative learning (CSCL) is a pedagogical approach wherein learning takes place via social interaction using a computer or through the Internet. This kind of learning is characterized by the sharing and construction of knowledge among participants using technology as their primary means of communication or as a common resource.[1] CSCL can be implemented in online and classroom learning environments and can take place synchronously or asynchronously.

As teachers we hope our lessons are engaging and that our students are engaged. We understand that positive learning experiences are more likely to occur when we are engaged cognitively and affectively by what we are doing and that when we are, new ideas and skills are more likely to stick. Engagement is an important consideration in learning and as such it is worth taking time to consider what it means to be engaged and perhaps how we bring the benefits of engagement to our teaching and our learning. 

Introduction to Concept Mapping Used as a learning and teaching technique, concept mapping visually illustrates the relationships between concepts and ideas. Often represented in circles or boxes, concepts are linked by words and phrases that explain the connection between the ideas, helping students organize and structure their thoughts to further understand information and discover new relationships. Most concept maps represent a hierarchical structure, with the overall, broad concept first with connected sub-topics, more specific concepts, following. Concept Map Example Definition of a Concept Map A concept map is a type of graphic organizer used to help students organize and represent knowledge of a subject. Concept maps begin with a main idea (or concept) and then branch out to show how that main idea can be broken down into specific topics. Benefits of Concept Mapping Concept mapping serves several purposes for learners: Helping students brainstorm and generate new ideas Encouraging students to discover new concepts and the propositions that connect them Allowing students to more clearly communicate ideas, thoughts and information Helping students integrate new concepts with older concepts Enabling students to gain enhanced knowledge of any topic and evaluate the information How to Build a Concept Map Concept maps are typically hierarchical, with the subordinate concepts stemming from the main concept or idea. This type of graphic organizer however, always allows change and new concepts to be added. The Rubber Sheet Analogy states that concept positions on a map can continuously change, while always maintaining the same relationship with the other ideas on the map. Start with a main idea, topic, or issue to focus on. A helpful way to determine the context of your concept map is to choose a focus question-something that needs to be solved or a conclusion that needs to be reached. Once a topic or question is

What does it mean to have learned something? What occurs within the individual as they are learning and what changes occur as a result of that learning? At some point in the teaching/learning cycle we need to ask this question and ponder our definition of learning and the consequences that follow from our conclusions.

The dangers of making learning so safe and so easy it lacks any real sense of challenge. When we do this we deny our learners the opportunities they need to learn from mistakes and grow their minds. 

As children, we ask "Why?" a lot. It is a part of childhood, that special time when the many forces acting upon our cognitive development converge around a singular desire to ask "Why". It becomes the central focus of our conversational style, an incessant exclamation into the void which tests the patience of any nearby adult. But asking "Why" offers so much more.

At the heart of mathematics are a set of connected thinking dispositions. The mathematician uses these dispositions as the cognitive tools of their trade. While the traditional imagining of mathematics might be all about the accurate application of well-rehearsed algorithms and processes, in the real world of mathematics, it is all about the thinking. As we consider what our students need from their mathematical education, we should not overlook the importance of these dispositions. 

Walk into any McDonald's, anywhere in the world, and you know where you are and what to expect. For the homesick traveller, the consistency of McDonald's' design aesthetic is comforting. You know how this is going to work, you understand what to do, and you know what you are likely to get. McDonald's requires minimal cognitive load on the customer's behalf.

The Cognitive Toolkit is an open source toolkit, focused on deep learning and artificial intelligence similar to Google's TensorFlow, which Microsoft released last year.

This much-maligned question seems so appropriate for education's recent history. All that was normal, everything that was routine, all of our structures, have been turned upside down and hurled into the wind of COVID19. From having spoken of a future dominated by volatility, uncertainty, complexity and ambiguity (VUCA), we have found ourselves living in it. Innovation and creativity became the new normal as we "Apollo 13" schooling into a model that met the demands of emergency remote learning. The pressure, the workload, the demands on our time and the cognitive load have all been immense, and so it seems fitting to ask "Are we there yet?".

CaptionBot utilizes Computer Vision API to identify the components of the photo, and with data from the Bing Image API, it runs through Emotion API to spot the image description.