1. Scratch
1.1. Data gathering and training
1.1.1. 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.
1.1.1.1. Analyse those data in order to ..
1.1.1.1.1. 1. Develop the manual of script to make it easier for users and provide variety functions that fit users need and preference.
1.1.1.1.2. 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.
1.1.1.1.3. 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 .
2. 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.
2.1. games tie into the math and science curriculums
3. Making programming visual provides very quick rewards for the efforts, making this perfect for young children who often have trouble with patience
4. Machine Learning
4.1. AutoDraw
4.1.1. what's it?
4.1.1.1. Pairs AI machine Learning + Talented Artist
4.1.1.2. App for creating ..
4.1.1.2.1. postcard
4.1.1.2.2. poster
4.1.1.2.3. picture
4.1.2. How does it work?
4.1.2.1. Collecting data from people' doodles and predict the picture people are going to draw and create that proper picture
4.1.3. What's their algorithm
4.1.3.1. Using 'Artificial Neural Networks'
4.1.3.1.1. 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
4.2. High level programming and Games
4.2.1. Thingifying
4.2.1.1. using Object oriented programming techniques
4.2.1.1.1. Object
4.2.2. Games in term of Teaching and Learning
4.2.2.1. Gamification or Game-based learning
4.2.2.1.1. It may impact...
4.2.2.1.2. CLIL (content and language integrated learning).
4.2.2.1.3. Games
4.3. machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data.
4.4. Algorithms
4.4.1. Supervised Learning
4.4.1.1. Decision tree
4.4.1.1.1. classification
4.4.1.1.2. Training data
4.4.1.1.3. Find optimal separations
4.4.1.1.4. To make prediction
5. Machine Code, Modelling and Mathematical Perspectives
5.1. mathetics
5.1.1. mathematics is a language, as much as computational programming languages and our spoken languages.
5.1.2. algorithms that support abstract, mathematical representations
5.1.3. mathematics of Machine Learning is important as
5.1.3.1. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.
5.2. StarLogo
5.3. NetLogo
5.4. TURTLE
5.4.1. easy to start creating shapes using the LOGO language
5.5. The important in terms of teaching and learning
5.5.1. Teaching coding via games would grounding students....
5.5.1.1. for learning about Computational thinking (CT) skills
5.5.1.1.1. CT is essential to be used to support problem-solving across all disciplines, including math, science, and the humanities.
5.5.1.2. To enhance important critical thinking abilities
5.5.1.2.1. as it's logically learning structure. reasonable and rational.
5.5.1.3. Prepare them to get used to the complex programming landscape (as there are well over 100 languages in use)
6. Coding For All
6.1. Raspberry Pi
6.1.1. Yearners
6.1.1.1. Benefits
6.1.1.1.1. Learn more and gain in understanding about computing as a practice even no interest in computing before
6.1.1.1.2. Exploring aspect of digital tool, they use without knowing the underlying techno work
6.1.2. Schoolers
6.1.2.1. Benefits
6.1.2.1.1. open spaces of informal learning approaches to all staffs as it naturally facilitate creativity and collaboration.
6.1.3. Community
6.1.3.1. the community contributions, inspired from Raspberry Pi like Raspberry Jams, Astro Pi etc.
6.1.3.1.1. 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.
6.2. 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.
6.3. 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.
6.4. 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.
6.5. The important in terms of teaching and learning
6.5.1. Informal learning in formal learning setting
6.5.1.1. 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.
6.5.1.2. Curriculum , criteria and assessment in order to evaluate proficiency is the thing have to consider
7. Computational Literacy
7.1. Educational context
7.1.1. 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.
7.2. Computational Thinking
7.2.1. 'logically reasoning + systematically + problem solving + creativity + critically' ( Awful lots if things!, Janette Wing's)
7.2.2. The important in terms of teaching and learning
7.2.2.1. Educational Context
7.2.2.1.1. 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.
8. Ethical AI concerns
8.1. 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.
8.1.1. 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.
8.2. Solution on Ethics in Machine Learning
8.2.1. 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
8.2.2. Create the ethical robot that learn to predict consequences, as they were programmed to learn basic ethical dilemma.
8.3. Final thought! The increasing of machine intelligence will shed the light on human morals. Morality, empathy and creativity. More importantly, make people questioning on their practice. Look back on our BLACK BOX and try to under it from the root.
9. Bubble using AI on the tutorial session
10. intuition + physicality
11. Right job + Right task --> accomplishment
12. 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.
13. AI and machine learning in educational context
13.1. 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.
13.1.1. " 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
13.2. Algorithms improve education, approach to learning
13.2.1. 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)
13.2.2. Using AI machine learning to Personalised learning experiences called ' Intelligent tutoring systems'
13.2.2.1. Challenging about 'Privacy'
13.2.2.1.1. 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.
13.2.2.2. Self-direct learning, Self-organising system
13.2.2.2.1. learning through self- instruction and peer-shared knowledge
13.2.2.2.2. Self-organized learning happens through peer-sharing, emergent phenomenon happening from the inside
13.2.3. artificial intelligence and natural language procession
13.2.3.1. Chatbots
13.2.3.1.1. It personalizes institutional learning
13.2.3.1.2. 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.
13.2.4. Using algorithms to do the grading automated systems
14. Data is central to computing
14.1. Machine Learning starts from ...
14.1.1. Data storing
14.1.1.1. Having Data base, filled with user information then..
14.1.1.1.1. Data organising
14.1.2. Data store, Data organising. Data training
14.1.2.1. split up dataset into test and training sets then..
14.1.2.1.1. Data Training
15. http://gunkelweb.com/coms493/texts/AI_Dummies.pdf
16. UX and Bubble
16.1. User Experience, the machine learning brought the idea of learn-ability and cognetics
16.2. UX and machine learning
16.2.1. 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)
17. Physical computing and IoT ( Internet of things)
17.1. controlling something physical
17.2. IoT ( The Internet of Things)
17.2.1. Intelligent systems of systems can shares data and analyze it
17.2.2. 'Connectionism'
17.2.2.1. Encapsulate how we construct or make our knowledge, or how we make connections.
17.2.2.1.1. Computational theories of mind
17.2.2.2. the knowledge of a system resides in the weights of connections within a network, rather than being stored in nodes.
17.2.2.2.1. This link to connectionism and connectivism and the users engagement
17.2.2.3. Connectionism in educational context
17.2.2.3.1. 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.
17.2.2.3.2. Laurillard's Conversational Model
17.2.3. People <---> Machine <--> Data
17.2.4. The IoT interms of Learning and Teaching
17.2.4.1. The impact for the school and institution IoT improves the education itself and brings advanced value to the physical environment and structures.
17.2.4.1.1. 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 )
17.2.4.1.2. 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.
17.3. Computational models are being used to DEMONSTRATE that ABSTRACT NOTIONS of reasoning are LEARNABLE
17.4. Marvin Minsky
17.4.1. Example of patctern recognition with letters
17.4.1.1. connectionist model of activation relative to recognised form
17.4.1.1.1. i.e. Delay recognising letters when they are not part of recognisable words
17.5. Codebug
17.5.1. debugging , learning to do algorithm , solving problem