1. Bubble using AI on the tutorial session
2. Scratch
2.1. Data gathering and training
2.1.1. let users create the work openly and free so would be able to gather alot of data on what most users have created, got the data of what is most and less popular tools, scripts or the style of animation most users seem like to make and avoid to use.
2.1.1.1. Analyse those data in order to ..
2.1.1.1.1. 1. Develop the manual of script to make it easier for users and provide variety functions that fit users need and preference.
2.1.1.1.2. 2. Make a predictions and suggestions for the users to reach the script and make the project easier , helping them to shorten their time to create the work.
2.1.1.1.3. 3. Develop the project to match the target group properly ie. Kids age 5-10 years old can reach to these set of script, providing them the basic guide for beginner users .
3. intuition + physicality
4. It facilitates the creation and understanding of simulations of complex systems. Its 3-D graphics, sound, blocks-based interface and keyboard input make StarLogo a great tool for programming educational video games.
4.1. games tie into the math and science curriculums
5. Making programming visual provides very quick rewards for the efforts, making this perfect for young children who often have trouble with patience
6. Right job + Right task --> accomplishment
7. Help Teachers to do their jobs ( grading) faster, easier and accurate and they have more time to do others important job like teaching plane and develop intercommunication skills with the students.
8. AI and machine learning in educational context
8.1. The tutor can not only express what they know well but both tutor and learner will learn together as they get to know well what they try to express.
8.1.1. " the complexity of the task of knowledge communication and its universality as a manifestation of human intelligence make computational models of the process revealing testbeds for theories and techniques for all the disciplines involved' - - Etienne Wenger
8.2. Algorithms improve education, approach to learning
8.2.1. ie. Instead of give students the generic curriculum of textbooks, give them Digital text books with knowledge engines ( machine learning algorithms) as the machine will learn from students' performance and create content based on their needs ( specific need)
8.2.2. Using AI machine learning to Personalised learning experiences called ' Intelligent tutoring systems'
8.2.2.1. Challenging about 'Privacy'
8.2.2.1.1. As trying to create AI that deeply understand students in order to personalize and adapt and optimize them. it has to learn about them from their engagement and experiences so it's risky like two side of dilemma situation between what should it be learning and adapting to them to benefit and what or how does it protect their privacy in their security.
8.2.2.2. Self-direct learning, Self-organising system
8.2.2.2.1. learning through self- instruction and peer-shared knowledge
8.2.2.2.2. Self-organized learning happens through peer-sharing, emergent phenomenon happening from the inside
8.2.3. artificial intelligence and natural language procession
8.2.3.1. Chatbots
8.2.3.1.1. It personalizes institutional learning
8.2.3.1.2. to Find the main core content of learning subject called 'Main domain" like in Algebra, learning from students algorithm of solving problem in various ways.
8.2.4. Using algorithms to do the grading automated systems
9. Data is central to computing
9.1. Machine Learning starts from ...
9.1.1. Data storing
9.1.1.1. Having Data base, filled with user information then..
9.1.1.1.1. Data organising
9.1.2. Data store, Data organising. Data training
9.1.2.1. split up dataset into test and training sets then..
9.1.2.1.1. Data Training
10. Machine Learning
10.1. AutoDraw
10.1.1. what's it?
10.1.1.1. Pairs AI machine Learning + Talented Artist
10.1.1.2. App for creating ..
10.1.1.2.1. postcard
10.1.1.2.2. poster
10.1.1.2.3. picture
10.1.2. How does it work?
10.1.2.1. Collecting data from people' doodles and predict the picture people are going to draw and create that proper picture
10.1.3. What's their algorithm
10.1.3.1. Using 'Artificial Neural Networks'
10.1.3.1.1. The app collected data with Pictionary clone called Quick, Draw! They practically got training data for free by gamifying the process,gathering a bunch of different samples of people's drawings
10.2. High level programming and Games
10.2.1. Thingifying
10.2.1.1. using Object oriented programming techniques
10.2.1.1.1. Object
10.2.2. Games in term of Teaching and Learning
10.2.2.1. Gamification or Game-based learning
10.2.2.1.1. It may impact...
10.2.2.1.2. CLIL (content and language integrated learning).
10.2.2.1.3. Games
10.3. machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data.
10.4. Algorithms
10.4.1. Supervised Learning
10.4.1.1. Decision tree
10.4.1.1.1. classification
10.4.1.1.2. Training data
10.4.1.1.3. Find optimal separations
10.4.1.1.4. To make prediction
11. http://gunkelweb.com/coms493/texts/AI_Dummies.pdf
12. UX and Bubble
12.1. User Experience, the machine learning brought the idea of learn-ability and cognetics
12.2. UX and machine learning
12.2.1. it meets the specific needs of the users( learners), by generating data and providing various kind of information from wider range of sources combining with the users experience to fulfil their needs and goals ( personalized learning)
13. Physical computing and IoT ( Internet of things)
13.1. controlling something physical
13.2. IoT ( The Internet of Things)
13.2.1. Intelligent systems of systems can shares data and analyze it
13.2.2. 'Connectionism'
13.2.2.1. Encapsulate how we construct or make our knowledge, or how we make connections.
13.2.2.1.1. Computational theories of mind
13.2.2.2. the knowledge of a system resides in the weights of connections within a network, rather than being stored in nodes.
13.2.2.2.1. This link to connectionism and connectivism and the users engagement
13.2.2.3. Connectionism in educational context
13.2.2.3.1. This could be embed in creating learning ecology as provide space or setting for students to get access to knowledge freely and accessibly and also can share and give feedback in real-time.
13.2.2.3.2. Laurillard's Conversational Model
13.2.3. People <---> Machine <--> Data
13.2.4. The IoT interms of Learning and Teaching
13.2.4.1. The impact for the school and institution IoT improves the education itself and brings advanced value to the physical environment and structures.
13.2.4.1.1. using IoT in terms of linking data base (course module information) with the users( students, teachers, school staffs) and connect them via Machine ( devices ie. ipad, smartboard )
13.2.4.1.2. Universities can use connected devices to monitor their students, staff, and resources and equipment at a reduced operating cost, which saves everyone money. And these tracking capabilities should also lead to safer campuses.
13.3. Computational models are being used to DEMONSTRATE that ABSTRACT NOTIONS of reasoning are LEARNABLE
13.4. Marvin Minsky
13.4.1. Example of patctern recognition with letters
13.4.1.1. connectionist model of activation relative to recognised form
13.4.1.1.1. i.e. Delay recognising letters when they are not part of recognisable words
13.5. Codebug
13.5.1. debugging , learning to do algorithm , solving problem
14. Machine Code, Modelling and Mathematical Perspectives
14.1. mathetics
14.1.1. mathematics is a language, as much as computational programming languages and our spoken languages.
14.1.2. algorithms that support abstract, mathematical representations
14.1.3. mathematics of Machine Learning is important as
14.1.3.1. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.
14.2. StarLogo
14.3. NetLogo
14.4. TURTLE
14.4.1. easy to start creating shapes using the LOGO language
14.5. The important in terms of teaching and learning
14.5.1. Teaching coding via games would grounding students....
14.5.1.1. for learning about Computational thinking (CT) skills
14.5.1.1.1. CT is essential to be used to support problem-solving across all disciplines, including math, science, and the humanities.
14.5.1.2. To enhance important critical thinking abilities
14.5.1.2.1. as it's logically learning structure. reasonable and rational.
14.5.1.3. Prepare them to get used to the complex programming landscape (as there are well over 100 languages in use)
15. Coding For All
15.1. Raspberry Pi
15.1.1. Yearners
15.1.1.1. Benefits
15.1.1.1.1. Learn more and gain in understanding about computing as a practice even no interest in computing before
15.1.1.1.2. Exploring aspect of digital tool, they use without knowing the underlying techno work
15.1.2. Schoolers
15.1.2.1. Benefits
15.1.2.1.1. open spaces of informal learning approaches to all staffs as it naturally facilitate creativity and collaboration.
15.1.3. Community
15.1.3.1. the community contributions, inspired from Raspberry Pi like Raspberry Jams, Astro Pi etc.
15.1.3.1.1. Showing that this digital tool inspired many users to create and develop the tools in broader aspects to fulfil their needs and facilitate their learning and teaching.
15.2. Coding is like Thingnified ' thought' and 'practice' to be able to explore it, manipulate it and to understand it. To convert those abstract into a thing; to make concrete and tangible.
15.3. using computational expressing the rhythm and music so it would be shown that computing could be the way of express the abstract idea to be more tangible in every kind of form of contents.
15.4. Social political condition play a role in the community and society we live in. not just about how good the device is but how people in the society engage and contribute.
15.5. The important in terms of teaching and learning
15.5.1. Informal learning in formal learning setting
15.5.1.1. In the school, having 'Code club' teaching kids, coding and computational skills. it's like having 'music club and dancing club' Offering the interest in computing to kids like in music and arts.
15.5.1.2. Curriculum , criteria and assessment in order to evaluate proficiency is the thing have to consider
16. Computational Literacy
16.1. Educational context
16.1.1. It applies to every other type of reasoning, to understand what computer can do for us and how we can use this criteria for our own creations and problem solving in daily life.
16.2. Computational Thinking
16.2.1. 'logically reasoning + systematically + problem solving + creativity + critically' ( Awful lots if things!, Janette Wing's)
16.2.2. The important in terms of teaching and learning
16.2.2.1. Educational Context
16.2.2.1.1. more likely is about the practice, a way of doing, a way of being able to think at multiple levels of abstraction that can manifest at different levels. (Wing, 2006; Denning, 2009.
17. Ethical AI concerns
17.1. Intelligence comes from learning, whether we’re human or machine. Systems usually have a training phase in which they "learn" to detect the right patterns and act according to their input.
17.1.1. the training phase can be fooled in ways that humans wouldn't be. For example, random dot patterns can lead a machine to “see” things that aren’t there. If we rely on AI to bring us into a new world of labour, security and efficiency, we need to ensure that the machine performs as planned, and that people can’t overpower it to use it for their own ends. Again, Who's programming? Validity and trustworthiness is the issue here. We shouldn’t forget that AI systems are created by humans, who can be biased and judgemental.
17.2. Solution on Ethics in Machine Learning
17.2.1. It might be useful to make the law about AI to measure and evaluate the practices, perhaps we can create better standards in broader perspective on political issue and privacy protection. The one who write the law should listen to the programmer that will inform the risk that AI can be in order to launch the right, accurate rule
17.2.2. Create the ethical robot that learn to predict consequences, as they were programmed to learn basic ethical dilemma.