Post-Midterm Flashcards
Computational thinking
A way that humans (not computers) think
-solving problems, designing systems, understanding human behaviour by drawing on concepts fundamental to computer science
-abstraction, decomposition, evaluation, pattern recognition, logic, algorithm design
Computational thinking Learning (with/about/from)
in K-12 curriculum CT is Learning ABOUT and WITH technology
Jeannette Wing
Computational thinking will be a fundamental skill used by everyone in the world by the middle of the 21st C
-CT is thinking like a computer scientist
-Abstraction is the most important and high level thought process inn CT
-Computational thinking (concept)–>Operationalization–> computer science
CT Concept: Logic and Logical Thinking
-Analyzing situations to make a decision or reach a conclusion about a situation
-Boolean logic: AND, OR, NOT
CT Concept: Decomposition
-Breaking down complex ideas down into subcomponents (or more manageable parts)
-ex. concept mapping, building a garden box with steps
CT Concept: Pattern Recognition
-Looking for similarities
-ways to organize information
-forming categories
-helps us organize the world and make predictions
-can lead to definition of generalizable solution that can leverage automation
-repeating patterns (incorporate iteration/recursion)
CT Concept: Algorithm Design
- set of rules to be followed
-algorithms: a series of logical, precise, repeatable steps that delivers an expected result
-recipe with steps to take
-3 basic building blocks: Sequence, selection and repetition
-if-then-else (conditional checks), do-while, for, repeat, repeat-until (looping actions - perform repetitive actions)
CT Concept: Abstraction and generalization
-Carefully selecting the qualities we care about and ignoring the rest of the details
-ex. 3 min thesis, synopsis
-Abstraction: information hiding (black-boxing details allows one to focus on input and output)
CT Concept: Evaluation
-solutions must be evaluated for correctness and appropriateness based on goals and constraints
-more than analysis and analytical thinking- efficiency contraints (time to completion, resource usage and human factors, user experience considerations
CT Concept: Automation
-Computing is the automation of our abstractions
-working toward a solution that will be executed by a machine
-recognizing when automation is needed and what abstractions and data representations will best help develop an automated solution
CT skills include
- Gathering and organizing data to investigate questions and communicate findings
- expressing procedures as algorithms (a series of logical, precise, repeatable steps that delivers an expected result) to reliably create and analyze processes
- Create computational models that use data and algorithms to simulate complex systems
- Using and comparing computational models to develop new insights about a subject
Operationalize a concept
the process of defining a fuzzy concept to make it clearly distinguishable or measurable, and to understand it in terms of empirical observations
-articulate the operationalization process: operational definition
Operational Definition
a description of something in terms of the operations (procedures, actions, processes) by which it could be observed and measured
Computational thinking (concept–>Operationalization through–>Computer Science (knowledge and skills to build Computational models)–>Measure learning (processes and product of building computational models–>
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CT Practice: Problem Decomposition
-breaking a problem down into smaller subproblems makes the problem more tractable and problem-solving process more manageable
-writing pieces of code separately and bring parts together when solution is composed
CT Practice: Creating computational artifacts
-creating solutions to be executed by a computer is often the natural end goal of CT and problem solving
-often simulation or model or interactive prototype to eventually be a physical artifact
CT Practice: Testing and Debugging
-Evaluating one’s solution for accuracy, detecting flaws in a faulty solution and fixing them
CT Practice: Iterative Refinement (incremental development)
-similar to problem decomposition
-it focuses less about making sub-problems and more on growing the solution or program iteratively with frequency testing and debugging in between to develop improvements
CT Practice: Collaboration and creativity
-Norms of collaboration in pair programming require programmers to alternate between taking the lead on typing or reviewing code are beneficial to problem solving processes
Assistive technology (AT)
-AT used in the classroom to support needs of students with disabilities
-tool for communication, social interaction, physical access to resources
AT: any item, piece of equipment, product system (commercial/off the shelf, modified, customized) used to maintain or improve the functional capabilities of children with disabilities
Low-Tech AT
-devices and tools that support students but do not require extensive training or high cost, easily accessed and replaced
-consider low-tech first to meet students’ needs
-ex. pencil grips, graphic organizers, highlighting pens, adapted paper
Mid-tech AT
-Generally doesn’t require extensive training to use and is reasonably prices
-has power sources but not overly complex
-ex. single-phrase communication systems, digital recording devices, talking calculators, audiobooks
-considered high tech if user must program the device
High-tech AT
-best suited for people with significant disabilities or have extensive functional needs
-requires training and is more costly
-ex. powered wheelchair, speech-to-text, eye gaze systems, head pointers
-costly due to small market and large research and development costs incurred in design and manufacture of devices
-low tech alternatives should be available in case of tech difficulties
Selection of AT devices
-Person making decision needs knowledge of device and the needs of individual
Considerations:
-budget available and cost of tech
-scope of training required by used and facilitator
-environment where AT will be used
-capacity to modify or personalize AT
-preferences and attitudes of all stakeholders of the ATZ
AT selection/Evaluation
SETT framework (student, environment, task, tool), MPT (matching person technology)
-Environmental factors: cultural expectations, legislation and policy, attitudes toward AR of other stakeholders
-maintenance of devices, teacher support to best use AT
Inclusive Education
-Education environments that accommodate for the needs of all students within mainstream classrooms (prevent marginalized/excluded groups being discriminated against and denied)
-children should learn together whenever possible, regardless of any difficulties or differences they may have
Features of Inclusive Ed
-attendance at the local school
-welcoming all children in the school
-all students situated in the mainstream classroom
instruction and curricular modified or adapted for all students
-support for social inclusion in the classroom
-Appropriate resources and training to support inclusion
Inclusive Education and Assistive Technology
-Belonging: ensure students in inclusive classrooms have a sense of belonging (value difference, social relationships, engagement, contribute)
-Planning: UDL framework, consider tech to cater needs of all students)
-teaching and learning: where students are at, high-quility teaching processes, how to monitor progress and evaluation
UDL Principles and Practices
Universal Design for Learning
-curricula developed for “average” student but there is no average
-the power to be very flexible, allow choices/different paths (universal and everybody learns)
Elements of Belonging and Assistive Technology
-Difference is valued
-school climate
-social relationships
-contribution and engagement
UDL 3 Broad Principles
- Multiple means of representation: symbols, language, diagrams, physical manipulatives
- Multiple means of action and expression
- Provide multiple means of engagement (methods, materials, media, assessment)
AT: Text-to-Speech (TTS)
-converts digital text into speech (read text out loud)
-allows students to work more independently (confident, motivated)
-decoding is a sub-skill of reading
AT: Word Prediction
-Predicts a word as students start to type and provides spoken feedback
-spelling can stop students from writing or slow the process (eliminate frustration)
-software becomes more accurate over time
-not fixing grammar but reads sentences out to you
AT: Visual thinking tools
-Images and text for understanding, creating, explaining, communicating, problem solving
-concept mapping
AT: Speech recognition
-Speech to text, spoken work to digitized text
-learning punctuation and editing their work
-shift to expressing thoughts and knowledge
-increased legibility, greater writing output
Artificial Intelligence (AI)
-they make data-driven decisions as opposed to rule-based decisions
-users need to be conscious of how data flows through AI systems and question where the data comes from
AI Training data
-AI systems learn from diverse data sets during their initial training phase to acquire knowledge
-where is data coming from
AI: Input Data
AI systems analyze incoming data
-typically comes from human users (what you type into Chatgpt)
AI: Output Data
AI produced predictions, recommendations, or decisions (a list, an essay etc.)
AI Steps
1: Training Data set
2: Learning algorithm (what patterns are observed), rigorous training and testing
3: Classification (what it is, what it is not)
AI or not AI
- Does it sense or observe its environment? What data was used to make this possible
- Is it trained to make its own decisions?
- Can it learn/adapt over time
Generative AI
-Creates new and transformative things
-supervised (we are tagging images and building data set on what it is and what is not)
-unsupervised (put in a whole bunch of data and it will classify its own patterns)
ChatGPT
-a fine-tuned language model trained to produce text
-reinforcement learning with human feedback
-not a search engine, not a continually evolving data set
AI Foundational Models: Core AI models:
fundamental machine learning models serving as building blocks for various AI applications
AI Foundational Models: Versatility
designed for a wide range of tasks, from natural language processing to computer vision
AI Foundational Models: pre-trained knowledge
trained on extensive data, enabling faster development of specialized AI system
AI Foundational Models: customization
can be fine-tuned for specific applications and industries
Data-Driven AI
Large data set and criterion for improvement a computer can gradually find a model that optimizes its predictions
gradient: for each system parameter, the direction of maximal change
Knowledge-Based AI
Human knowledge and expertise can be represented in a form that can be processed by computer programs
Student focused AIED
Intelligent Tutoring systems (ITS)
AI assisteed apps
AI assisted simulations
AI to support disabilities
Automatic essay writing
chatbots
automatic formative assessment
learning new orchestrators
Dialogue-based tutoring systems
Exploratory learning environments
AI assisted lifelong learning assistants
Teacher focused AIED
Very few available
Plagiarism detection
Smart curation of learning materials
classroom monitoring
automatic summative assessment
AI teaching and assessment assistant
classroom orchestration
Institution focused AIED
allocation of financial aid, course planning, scheduling, timetabling, identifying dropouts/students at risk
Admissions
E-proctoring
Digital Citizenship and Copyright: Citizenship
state of being. citizen of a particular social group
political or national community
citizenship carries both rights and responsibilities
Copyright
-teachers/school may communicate, reproduce on paper/electronic short excepts from a copyrighted work for purpose of research, private study, criticism, review, news reporting, education, satire, parody
Copyright
-cannot copy multiple short excerpts if copying entire work
-short except is = or < 10% of copyrighted work
one chapter from book, entire news article, poem, musical score
-no one-time use materials copied (workbook)
Face-to-face learning
-traditional teaching, direct instruction, lecturing
-counterpoint to online learning
-online instruction is supplemental (struggling or high achieving students)
Online Learning
-structured learning environment
-students engage with teachers in 1+ courses online
-planning and implementation of instruction and assessment of student learning in relation to outcomes of AB curriculum
Learning Management System (LMS)
Online and blended learning require management system
-LMS: a software application that us used to administer, track, report and deliver training
ex. Google Classroom, eClass
LMS common functions
-track attendance
-record marks/calculate averages
-act as database for activities/docs/media
-depot for uploads
-student and teacher communication
Blended learning
30-80% face-to-face/online
-face-to-face and online activities are integrated
-hybrid learning, mixed-mode learning
Synchronous Learning
-learning is defined and learned event where learner and instructor are in the same place at the same time
Asynchronous Learning
instructor facilitated
-not conducted in real time, students and teacher can engage in course related activities at their convenience, not during specific coordinated class sessions
Models of Blending: Rotation Model
students rotate on a fixed schedule from one delivery method to the next (online, self-paced and face-2-face
-face-2-face teacher oversees everything
Models of Blending: the MOOC Classroom
Massive open online course
-lectures viewed asynchronously
-labs semi synchronous
-assessments are completed online
other activities are completed online
Issues with Blended/Online Learning
-Lack of automation: current blending requires a lot of human intervention
-Lack of large scale blended learning experiences and surveys: lack of publications/growing outside of universities
-Lack of resources and managerial decision power
-Lack of privacy/security
-Lack of internet and technology access
Reasons to use blended learning
-increased student motivation and engagement
-provides students with immediate feedback
-reduced operational costs (after initial setup)
-personalized learning experience
-enhance competency-based learning
Models of Blending: Self Blend
students opt to take online courses, allows students to take subjects not offered in school district
-students take initiative (student led interest)
-often in rural communities
Models of Blending: Flex
allows for asynchronous learning
-assignments completed independently on computers
-students work at their own pace
-teacher supervises everything and provides one-on-one or group interaction when needed
Models of Blending: Online Driver
-Most reliant on technology
-teacher takes facilitator role
-students decide where to work
-all instruction completed virtually
-may only meet instructor during exam period
Concerns and limitations of online learning
-budget and infrastructure
-tech failure
-burn out (lots of preplanning)
-plagiarism and copyright
-privacy and security
Print-based learning
-without reliable internet access
-all materials printed (needs to be engaging)
-needs a strong component of teacher interaction
Digital games Learning and technology
Learning FROM and WITH technology
Learning with entertainment games
-entertainment games repurposed in an educational setting
-COTS (commercial off the shelf)
Learning from educational Games
-EDUtainment
-games developed especially to teach something
-COTS or developed by individual/uni etc.
-math-blaster, Oregon trail
Learning from playing games
-analysis of informal learning that takes place during rhe playing of games for fun
-memory skills, perceptual skills, attention skills, reasoning (problem-solving), motor skills
*Serious games: primary purpose to experience something- pulse for Dr’s
Learning inspired by games
using games in context of understanding learning and problem solving
-sliding puzzle, towers of Hanoi, chess
Learning about games
studying them in that context (like books, literature and media
Learning about game design principles
-how game design principles might be applied in learning situations
-levelling up, badges etc.
-good digital games incorporate good learning principles
-gamification
Learning within game communities
groups and communities that form both online and in the world
-they can result in communities of practice, focuses around a game
-collaboration, communication, supportive learning
Learning with game creation
-learning processes that take place in the construction of games (design, development, building)
-building a digital game involves development and use of computational thinking skills (high-level thinking)
Gamification
uses the elements of games in non-game contexts
-goals and rules, conflict, points, badges
-reward cards -gamification in marketing (Starbucks rewards)
-Fitbit (gamification in fitness)
Game elements: Goals and rules
purpose, focus, a way to measure success
Game elements: Conflict, competition, cooperation
not too difficult or easy
Game elements: Points, badges, leaderboards
-encourages competition
give feedback
Game elements: Feedback
progress bars, regualr and frequent
can continue learning in more focused way
teachers can better support students
Game elements: Levels
master specific set of skills before going on to next harder task (scaffolding)
Game elements: story/narrative
context
understand how everything is connected
characters/plot/tension
Game elements: curve of interest
opportunities to catch learners interest early on and keep it
consider motivation
Game elements: Aesthetics
art, beauty, symmetry, visual appeal and visual cues
site that is user friendly
Game elements: time
motivated action
see the result of their choices
Game elements: replay or do-over
allow students to fail (discovery based learning)
Gamification- Motivation
self-determination theory- to be motivated to do something
extrinsic motivation
when you do something to attain a separable outcome
to gain something you want or avoid something negative
Intrinsic Motivation
doing an activity for its inherent satisfaction rather that for separable consequences
(something you enjoy doing)
Most important types of learning and games
Learning from Educational games
learning from playing games
learning from game design principles
learning with game creation
Principle of online games: Achievement
learners are continuously rewarded for skill mastery and advancing knowledge
sense of competence and feeling appreciation for their participation
Principle of online games: Interactions
learners grow though interactions with others including technology
-collaboration with peers- students learn from each other and extend their knowledge
Principle of online games: Multiple routes
learners given more than one way to progress and learn
-increases learner autonomy
increased student motivation and engagement
Principle of online games: practice
learners spend time practicing in an interesting context
creates a safe context for learning
Principle of online games: Probing
learners engage in cycles in inquiry, hypothesis building and doing
-build, test and explore
environment to test hypothesis, learn the results and build new hypothesis to test later
Principle of online games: Challenge
the game should push learners outside of their current comfort zone in an attainable manner
pleasantly frustrating tasks, challenge to match learners’ abilities to accomplish tasks while providing motivational tension
Course Design: Levelling Up
Achievement
Multiple Routes
Practice
levels gradually increase in difficulty
Course Design: Badges and Awards
Achievement
Challenge
for skill mastery for advancement of knowledge
Course Design: Mastery-focused
Probing
Challenge
ability to resubmit work
experience earned only when project meets states requirements
Course Design: Quests
Interactions
Practice
small group work
sharing recent technologies and their uses for learning
Course Design: Boss Level
Practice
Probing
final project
challenge to develop, implement and evaluate a learning activity
