AI Thinking for Cloud Education Platform with Personalized Learning

Artificial Intelligence (AI) thinking is a framework beyond procedural thinking and based on cognitive and adaptation to automatically learn deep and wide rules and semantics from experiments. This paper presents Cloud-eLab, an open and interactive cloud-based learning platform for AI Thinking, aiming to inspire i) Deep and Wide learning, ii) Cognitive and Adaptation learning concepts for education. It has been successfully used in various machine learning courses in practice, and has the expandability to support more AI modules. In this paper, we describe the block diagram of the proposed AI Thinking education platform, and provide two education application scenarios for unfolding Deep and Wide learning as well as Cognitive and Adaptation learning concepts. Cloud-eLab education platform will deliver personalized content for each student with flexibility to repeat the experiments at their own pace which allow the learner to be in control of the whole learning process

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

An Intelligent Facial Recognition System using Stacked Auto Encoder with Convolutional Neural Network (CNN) Approach

The act of identifying an emotional feeling  is described as facial expression.  one of the effective techniques for interperson communication. They serve as indications that regulate interactions with those around. As a result, they are crucial in creating effective relationships.Facial expression recognition system to identify the expressions by evaluating the changes in facial characteristics and extracting features from facial images. This system  essential for enhancing computer-human interaction. The majority of facial emotion recognition research mainly relies on  reference face model and well known facial landmarks. Due to  intricacy of the face musculature, finding the most noticeable facial landmarks can be difficult and requires physical intervention for improved accuracy. So, this research work provides  new dimension to deal with the above issues by proposing a Stacked Auto-Encoder with Convolutional Neural Network based approach that does not rely on the landmarks or a reference model. The proposed approach outperforms the existing techniques