final exam Flashcards
(165 cards)
1
Q
How is Computational thinking integrated into education
A
- Learning about the technology( how computer works, coding)
-Learning with the technology (support and enhance problem solving)
2
Q
What curriculum is Computational thinking part of
A
Science for k-6
3
Q
Jeanette Wing uses Computational thinking as shorthand for what
A
“thinking like a computer scientist.”
4
Q
What is Jeanette Wings grand vision for Computational thinking
A
Computational thinking will be a fundamental skill for everyone by the mid 21st century: taught in school like writing
5
Q
Computational thinking is useful for ?
A
Computational thinking is a useful tool in learning any subject that involves problem-solving
6
Q
Who is Computational thinking meant for?
A
Computational thinking is a fundamental skill that can be employed for everybody, not only for computer scientists
7
Q
Jeanette Wing definition for Computational thinking
A
involves
-solving problems
-designing systems
-understanding human behavior, by drawing on the concepts fundamental to computer science
-It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use
8
Q
Royal Society definition for Computational thinking
A
- the process of recognising aspects of computation in the world that surrounds us,
-applying tools and techniques from Computer Science to understand and reason about both natural and artificial systems and processes
9
Q
Computational thinking involves (3)
A
Solving problems
Designing systems
Understanding human behavior
10
Q
Concepts of Computational thinking(6)
A
- Logic (predicting and analyzing)
-Algorithms (making steps and rules)
-Decomposition (breaking down into parts)
-Patterns (spotting and using similarities)
-Abstraction (removing unnecessary detail)
-Evaluation (making judgement)
11
Q
Computational thinking approaches
A
- Tinkering (experimenting)
- Creating (designing)
- Debugging (finding and fixing errors)
- Persevering (keep going)
-Collaborating (working together)
12
Q
4 core concepts
A
DECOMPOSITION,
ABSTRACTION,
PATTERN RECOGNITION,
ALGORITHMS
13
Q
Decomposition
A
The strategy of breaking a complex idea or challenging problem down into its more manageable parts.
(e.g. writing a paper, teaching reading, planning a wedding, building raised garden boxes, building a dog ramp :).
14
Q
Pattern Recognition
A
Humans are biological pattern recognition machines. We see patterns everywhere… they help organize the world and make predictions…
Examples: forming categories, self-driving vehicles, Amazon recommendations, facial recognition…
Pattern recognition is one of the core elements of AI (machine learning)
15
Q
Algorithm Design
A
Set of rules to be followed: For example, when a chef writes a recipe for a dish, she is creating an algorithm that others can follow to replicate the dish … a sequence of steps.
16
Q
Abstraction
A
Carefully selecting the qualities we care about and ignoring the rest of the details (e.g., Google maps, a synopsis of story, explain you idea in 30 seconds, recreate the Golden Gate Bridge in Minecraft)
17
Q
how is Computational thinking the most effectively learned and most apparent
A
through the rigorous creative process of writing code.
18
Q
Computational thinking skills: broad context
A
Gathering and organizing data to investigate questions and communicate findings.
- student wants to learn about the solar system: start by investigating and asking what planets are in it, etc
- communicate findings by making a model
Expressing procedures as algorithms (that is, a series of logical, precise, repeatable steps that deliver an expected result) to reliably create and analyze processes.
- in additon to making physical model, they can make computational model that contain algorithms for planets that go around the sun
- they go in an elipse, 1st draft of a computational model
Creating computational models that use data and algorithms to simulate complex systems.
- if you are going to build a computational model, you need to know learn the computer science skills to do it and how the solar system
-you need both
- coding is one way of learning this
Using and comparing computational models to develop new insights about a subject.
- when they build the model, they can explain it, revise the model, have varieties in the model, and change the model
19
Q
Operationalize
A
is the process of defining a fuzzy concept (computational thinking) so as to make it clearly distinguishable or measurable, and to understand it in terms of empirical observations.
one way to do articulate this process and to define the fuzzy concept is to create a operational definition
20
Q
operational definition
A
description of something(computational thinking) in terms of the operations (procedures, actions, or processes) by which it could be observed and measured
21
Q
how do you operationally define anxiety
A
through tests, surveys, questionnaires, physical measurements
22
Q
how do you operationally define Intelligence
A
head measurements, tests, IQ,
23
Q
how do you operationally define computational thinking for k-12 students
A
Formulating problems in a way that enables us to use a computer and other tools to help solve them
Logically organizing and analyzing data
Representing data through abstractions such as models and simulations
Automating solutions through algorithmic thinking (a series of ordered steps)
Identifying, analyzing, and implementing possible solutions to achieve the most efficient and effective combination of steps and resources
Generalizing and transferring this problem-solving process to a wide variety of problems
24
Q
Dispositions or attitudes that are essential dimensions of CT
A
Confidence in dealing with complexity
Persistence in working with difficult problems
Tolerance for ambiguity
The ability to deal with open-ended problems
The ability to communicate and work with others to achieve a common goal or solution
25
The primary way CT is integrated into the K-12 curriculum is by
Learning About and Learning With Technology
26
According to Jeannette Wing, computational thinking is a useful tool in learning:
Any subject that involves problem solving.
27
Operationalization is the process of defining a fuzzy _______ so as to make it clearly distinguishable or measurable, and to understand it in terms of ________ observations.
concept, empirical
28
computational thinking 4 point cycle
computational thinking (concept)
to
operationalzation through
to
computer science (knowledge/skills)
to
measure learning (process and product of building computational models)
29
Conceptual learning progression follows a ____ curriculum.
spiral
30
what is a spiral curriculum
Content, and Skills increase in complexity as age increases
31
Simple set of computational models
1. Scratch as a Mindtool
- in control of what they do
2. Constructionist Learning Theory
- building something
3. Abstraction
- draw diagrams
4. Decomposition
- break up problem
5. Pattern Recognition
- constant pattern
6. Algorithm
32
Misconceptions and challenges in coding
Only programmers can teach coding
Students already have too much screen time
Only future programmers benefit from coding
Coding doesn't fit the curriculum or testing regimen
Teaching computer science is too expensive
Tech changes too fast to teach
33
Cancode Initiative program
200 million dollar investment
Support for educational opportunities for coding and digital skills
PD for K-12 teachers
34
Assistive Technology definition
any item, piece of equipment or product system, whether acquired commercially off the shelf, modified, or customized that is used to increase, maintain, or improve the functional capabilities of children with disabilities
35
Inclusive education definition
term used to describe educational environments which accommodate for the needs of all students within mainstream classrooms
36
Low-tech AT
devices and tools that support students but don’t require extensive training or high cost which are easily accessed and replaced. Ex: pencil grips, highlighting pens.
37
Mid-tech AT
don’t require extensive training to use and are reasonably priced. Have a power source (battery) but not too complex. Ex: communication systems, recording devices.
38
High-tech AT:
best for those with extensive disabilities and functional needs (minimal movement). Requires training, more costly.
39
how to select devices for assistive technology
requires the person
determining the selection to have appropriate knowledge of the device
sufficiently detailed knowledge of the needs of the individual, while working collaboratively with all stakeholders to ensure appropriate match
40
Three Principles of UDL
How does the learner pick up information? - Provide multiple means of representation
How do they act upon that information? - Provide multiple means of actions and expressions
How are they engaging in learning?- Provide multiple means of engagement?
41
UDL: Multiple Means of Representation
Make sure to present information in many ways to everyone can learn
math: manipulatives and symbolic
42
UDL: Multiple Means of action and expression
Vary greatly on how they can express what they know
43
UDL: Multiple Means of engagement
if they do not get engaged, none of the others matter
44
Text-to-Speech Assistive Technology
Text can be read aloud to student
Instead of DECODING, students focus on COMPREHENSION
Can participate in discussions
Helps student pay attention and reduce frustration
Work Independently
Teacher can focus on conversation and concept development
45
Word Prediction - Assistive Technology
Predicts a word while the student is typing
Reads the word aloud as well
Improves legibility
Students have the freedom of choice
Can add class-specific words
Potential is achieved
Helps students who are not strong spellers
46
Visual Thinking Tools - Assistive Technology
Concept maps (example)
Use of images and texts for problem-solving, communication, explaining etc
Build on background knowledge
Fosters collaboration
Focus attention on key elements
Memory prompts and structured thinking
47
Speech Recognition - Assistive Technology
Transfers voice to written text
Efficiently communicate ideas
Alternate between typing and speaking
Heightened expression and organization
Independence and inclusion and confidence
Reduces labor of writing
48
CT Concepts: Logic and Logical Thinking
Analyzing situations to make a decision or reach a conclusion about a situation
Students can build these skills by working on logical puzzles and problem-solving
Using scratch codes like AND, OR, and NOT
49
CT Concepts: Algorithms and Algorithmic Thinking
Algorithms - step-by-step plans or procedures to solve a problem or reach a goal
Skill involved in developing an algorithm (like a cooking recipe)
Most algorithms consist of the building blocks, sequence, selection, and repetition
like conditional checks or looping
50
what is a conditional check in coding
Conditional checks: if-then-else
51
what is looping in coding
Looping: do-while, for, repeat, or repeat-until.
52
CT concepts: Patterns and Pattern Recognition
Recognizing a repeating pattern can lead to finding a generalizable solution
AI recognizes patterns in data
53
Most of the algorithms that students encounter in the context of K-12 CT learning involve three basic building blocks
sequence, selection, and repetition.
54
CT concepts: Abstraction and Generalization
Most important & high-level thought process in CT
Abstraction is “information hiding”
Focus only on the input and output, and thus, simplify and manage complexity
Helps to generalize similarities and differences
Every algorithm is an abstraction
55
CT concepts: Evaluation
Solutions to problems in the form of algorithms or abstractions as programs must be evaluated for correctness and appropriateness
Solutions to problems are evaluated for correctness and accuracy
Ex. Algorithm that provides directions - evaluated for speed, accuracy, most scenic,
56
CT concepts: Automation
“Computing is the automation of our abstractions”
The application of tech, programs, etc to achieve outcomes
57
Other ways than programming to foster computational thinking
Logical Puzzles
Word Problems
Puzzles
58
What CT is not!
thinking is an inherently human trait that involves reasoning. Computers do not think, so CT is NOT ‘thinking like a computer’, rather it is about thinking like a computer scientist.
59
CT Practices defition
- Approaches that computer scientists often use when they engage in computational problem-solving
60
5 CT practices
Problem Decomposition
Creating Computational Artifacts
Testing and Debugging
Incremental Development (Iterative Refinement)
Collaboration and Creativity
61
Collaboration and Creativity
Fostered through “pair programming” - alternate taking the lead on typing or reviewing code
Encourage out of the box thinking, creation of computational artifact, and alternative approaches to problem solving
62
Incremental Development (Iterative Refinement)
Slowly writing up and growing the solution or program with frequent testing and debugging in between to develop improvements
Preferable to writing large chunks of code that make it difficult to isolate the bugs
Incremental growth
63
Testing and Debugging
Evaluating, running tests, and changing/making tweaks to solve a problem
64
Creating Computational Artifacts
Creating a solution that can be executed by a computer is often the end goal of CT and problem-solving
Sometimes the computational artifact is the end goal
Could also be a model or simulation of something that will eventually be a physical artifact
Demonstration of computational competencies
65
Problem Decomposition
Breaking a problem down into smaller subproblems makes the problem more digestible
Sub-tasks contain their own set of actions independent of the other and which happen in the same sequence every day
Programming context - pieces of code being written separately and independently of each other
66
CAST
Centre for Applied Special Technology
67
AI
Artificial Intelligence
68
AIED
AI in Education (and Learning)
69
ITS
Intelligent Tutoring Systems
70
AI is the biggest invention since
the Paleolithic age.
71
AI Literacy
The Teaching of AI in Education
72
ITS focuses on
providing automated, adaptive and individualized instruction
73
Heuristic Operations
technique designed for problem solving more quickly when classic methods are too slow
74
The technological origins of student-focused AIED can be traced back to
Sidney Pressey (Multiple-choice machine)
B. F. Skinner (Teaching Machine)
Gordon Pask (self-adaptive keyboard instructor or SAKI)
Jaime Carbonell (SCHOLAR)
75
Who came up with heuristics processes
Georg Polya
76
who were the key pioneers of AI and cognoitve science
Alain Newell and Herbert Simon
77
The ultimate rationale of using AI
it can lead to learning gains in specific knowledge domains independently of human teachers.
78
Function of Education with technology(3)
Qualification: providing students with “the knowledge, skills, and understandings... that allow them to ‘do something’” (Acquisition of pre-defined knowledge)
Socialization: the many ways in which, through education, we become part of particular social, cultural and political ‘orders’
Subjectification or Individuation: the process that allow[s] those educated to become more autonomous and independent in their thinking and acting
79
Benjamin Bloom
A key conceptual starting point for student-focused AIED has been ‘mastery learning’, a pedagogic model
80
mastery learning requires:
individual-level differentiation or ‘personalisation’ of instruction, for which AIED systems have been proposed and designed as an answer
81
the 2 sigma-effect
individualized tutoring combined with mastery learning leads to two standard deviations higher learning gains than traditional whole-class teaching
82
Student Focused AIED
not all AI-assisted technologies used by students have been designed for students. Instead, it might be said that these technologies have been ‘repurposed’ for learning (YouTube/WhatsApp/Google Docs).
83
Teacher focused AIED
There are few examples of genuinely teacher-focused AIED. Here, we discuss six possibilities, many of which are controversial: plagiarism detection, smart curation of learning materials, classroom monitoring, automatic summative assessment, AI teaching assistants, and classroom orchestration.
84
Institution focused AIED
Institution-focused AIED includes technologies that support the allocation of financial aid, course-planning, scheduling, and timetabling and identifying dropouts and students at risk.
85
AIED Colonialism
AIED colonialism: Global North companies exporting their AIED tools into contexts in the Global South, creating asymmetries in power across and between nations.
86
Personalization:
Personalization, more broadly understood, is about subjectification and helping each individual student to achieve their potential, to self-actualize, and to enhance their agency.
87
Characteristics AI Systems Share
Data utilization → A. I. systems rely on large datasets to learn, make predictions, and automate tasks.
Learning adaptations → A. I. systems can learn from data and adapt their behaviour over time (similar to humans)
Automation → A. I. systems can automate tasks and make decisions without direct human intervention, increasing efficiency.
88
data is key: types
Training data→ A. I. systems learn from diverse datasets during their initial training phase to “acquire knowledge”
Input data→ A.I. systems analyze incoming data. This data usually comes from human users
Output data→ AI produces predictions, recommendations or decisions as output
89
AI Training Process
training dataset then (make data about what a fork is)
put it into a learning algorithm (teach it what a fork is)
then classification of item( get it to classify fork or not fork)
90
Foundation Models for AI
Core A.I. models→ Fundamental machine learning models are building blocks for various A.I. applications
Versatility→ designed for a wide range of tasks, from natural language processing to computer vision
Pre-trained knowledge→ trained on extensive data, enabling faster development of specialized A.I. systems
Customization→ can be fine-tuned for specific applications and industries
91
Core A.I. models
Core A.I. models→ Fundamental machine learning models are building blocks for various A.I. applications
92
Foundation Models: Versatility→ designed for a wide range of tasks, from natural language processing to computer vision
Pre-trained knowledge→ trained on extensive data, enabling faster development of specialized A.I. systems
Customization→ can be fine-tuned for specific applications and industries
Foundation Models: Versatility→ designed for a wide range of tasks, from natural language processing to computer vision
93
Foundation Models: Pre-trained knowledge→
Pre-trained knowledge→ trained on extensive data, enabling faster development of specialized A.I. systems
94
Foundation Models:
Customization→
can be fine-tuned for specific applications and industries
95
How can ai be more accurate
Pose is better than image recognition models (there is no problem of background overlap).
96
A.I. or NOT A.I: what to ask
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?
97
Digital Citizenship definition
Citizenship occurs within a given community
For members of the community, there are rights (Free Speech)
With these rights come responsibilities and boundaries
98
Jason Ohler important quote
The nature of citizenship as a basis for developing digital citizenship
99
Jason Ohhler’s Nature & Requirements of Citizenship: (8)
high moral principles
balancing personal empowerment and responsibility with community well-being
Participation
Education through guidance
Citizenship is ever-evolving and thus requires ongoing conversation and debate
Must be inclusive
Has a close linkage with media advancement
Intimately tied to community
100
Citizenship vs. Digital Citizenship: working to high moral principles
Citizenship:
– individuals who must work to make the community effective
- community must be principled members to create an effective community
Digital Citizenship:
Requires similar moral principles to effectively work within online, time separated and geographically independent, multicultural, global communities
101
Citizenship vs. Digital Citizenship:balancing personal empowerment and responsibility with community well-being
Citizenship
good of the individual and the good of the community must reside in a state of equilibrium
Digital Citizenship:
Individual members can affect unforeseen outcomes upon the community and other individuals. These effects are often not obvious, given geographic and time independence.
102
Citizenship vs. Digital Citizenship: participation
Citizenship
Communities, whether local, regional, national, social or political require members to participate for the community to have value and meaning. Without participation the community becomes non-existent
Digital Citizenship:
similarly require participation and society has a role to play in preparing youth to participate in these communities in meaningful, responsible and caring ways
103
Citizenship vs. Digital Citizenship: education
Citizenship
Attaining high moral principles in community interactions does not occur automatically. It requires guidance, typically from an elder’s (e.g. teacher’s) hand.
Digital Citizenship:
even greater need and often is even more challenging to guide, given its sometimes abstract nature.
104
Citizenship vs. Digital Citizenship: ever-evolving and thus requires ongoing conversation and debate
Citizenship
What is appropriate in one time-frame or culture is not necessarily appropriate in the next.
Digital Citizenship:
as a relatively new form of citizenship, will require ample conversation – especially as society works to educate youth in this new realm.
105
Citizenship vs. Digital Citizenship: must be inclusive
Citizenship
Society cannot afford to regress to earlier historical models where one culture or community had greater citizenship rights than others
Digital Citizenship:
further shrinking the globe, creating new relationships, meaning and communications. Equality and equity will need to be watchwords in this new citizenship form.
106
Citizenship vs. Digital Citizenship: has close linkage with media advancement
Citizenship
Changes in media have resulted in changes of community and community relationships.
Digital Citizenship:
Digital communities are only possible through media forms that have enabled their creation.
107
Citizenship vs. Digital Citizenship: intimately tied to community
Citizenship
Citizenship does not reside in a vacuum – it must have a community
Digital Citizenship:
has reformulated the reach and nature of communities. Such communities can now be multi-cultural, global, highly-focused and longtailed
108
Using others' works without permission is discouraged unless ?
allowed by the Copyright Ac
109
Fair dealings Guidelines (7)
Educational staff in nonprofit institutions can use short excerpts from copyrighted works for research, study, criticism, review, news reporting, education, satire, and parody.
Copying short excerpts under Fair Dealing for news, criticism, or review should credit the source, including the author or creator's name if available.
One short excerpt from a copyrighted work can be shared with each student enrolled in a class:
as a handout;
on a password-protected learning system;
as part of a course pack.
A short excerpt is defined as:
up to 10 percent of a copyright-protected work;
one chapter from a book;
a single article from a periodical;
an entire artistic work from a work containing other artistic works;
an entire newspaper article or page;
an entire single poem or musical score from a work containing other poems or musical scores;
an entire entry from an encyclopedia, annotated bibliography, dictionary, or similar reference work.
Copying or communicating multiple short excerpts from the same copyrighted work with the intention of reproducing or transmitting the majority of the entire work is not allowed.
Exceeding the limits outlined in the Fair Dealing Guidelines may be reviewed by a supervisor or designated person from the educational institution. The evaluation will consider all relevant circumstances to determine if the copying or communication is allowed under fair dealing.
Any fee imposed by the educational institution for copying or communicating a short excerpt from a copyrighted work should only aim to cover the institution's costs, including overhead expenses.
110
CMEC
Council of Ministers in Education, Canada
111
what do CMEC do
Goal is to balance rights of creators with the rights of educators
Created guidelines for school boards on copyright laws.
School boards use these guidelines and create their own copyright rules that may be more restrictive
112
what should principles should check with teachers at least once a year
COPYRIGHT COMPLIANCE CHECKLIST
113
The technological origins of student-focused AIED can be traced back to
Sidney Pressey's mechanized multiple choice machine
B. F. Skinner's "teaching machine"
114
form of Generative AI
ChatGPT
DALL-E 2
115
Face to face teaching
Usually used as the counterpoint to Online learning
Could be called direct instruction, traditional teaching, “lecturing”, etc.
116
Learning Management System
Online and blended learning both need this
software application that is used to administer, track, report, and deliver training
ex; eclass, google classroom, classdojo
117
Common LMS Functions;
Tracking attendance
Recording marks and calculating Averages
Depot for assignment submission
Database for activities, documents, and media
118
Blended Learning;
It is a teaching approach where both traditional and face-to-face instructional time and online or computer-mediated activities are integrated
119
synchronous
learning is defined as a learning event where the learner and instructor are in the same place at the same time
120
Asynchronous
are still instructor-facilitated but are not conducted in real-time. The student and teacher can engage in course-related activities at their convenience.
121
what is lms good for
- assessment
paperless classroom
coding tools
ai tools for teachers
122
Which of the following statements is a key lecture takeaway concerning the state of blended learning
Blended Learning programs are often supplemental in nature
123
Concerns & Limitations of Blended Learning
Budget and infrastructure
Technology failure
Burn out
Plagiarism and copyright
Privacy and security
124
Concerns & Limitations of Blended Learning:Budget and infrastructure
Technology can be costly (especially for initial set up)
125
Concerns & Limitations of Blended Learning: Technology failure
Matter of when
Need backup lessons if system is down
126
Concerns & Limitations of Blended Learning: Burn out
Takes lots of planning
Clear vision of how and why
127
Concerns & Limitations of Blended Learning: Plagiarism and copyright
All info and images need to be free of copyright laws
128
Concerns & Limitations of Blended Learning: Privacy and security
Need a licensed program with the school - consent from school board and parents
Takes personal info of students that is confidential
129
blended learning models
Face to Face
best for traditional teaching style.
Closest model to lecture style
Online instruction provides supplemental resources
Online instruction used in conjunction with Face to Face
130
blended learning models
Self Blend
Students opt to take online courses outside of school time
Allows students to take subjects that their school may not offer during the day
Mostly in high school
Assists in honing digital literacy skills
Student is main driver - take courses bc of interest or lack of district resources
Seen in rural communities
131
blended learning models
rotation
Students rotate on a fixed schedule between online and self paced activities and face-to-face learning
Most in-between (50%) of face to face and online because it involves a split between two and can be remote and onsite
132
blended learning models
flex
Asynchronous learning or at student's pace
Assignments are completed independently on computers
Teachers supervise everything and provide one-on-one or group instruction when needed
133
blended learning models
online driver
Most reliant on technology compared to other models
Teacher takes on facilitator role
Students can decide where they would like to learn
All instruction is completed through virtual channels
May only meet instructor in-person during their exams - all other meetings done via web-conferencing
134
Reasons to blend classrooms
Increase student motivation and engagement
Immediate feedback
reduce operational costs after setup
personalize learning
enhance competency based learning
135
Of the eight different ways (categories) games & learning are related, which of the following four were the main focus of the lecture
Learning FROM Education games
Learning FROM PLAYING games
Learning FROM game design principles
Learning WITH game creation
136
According to Dr. Kapp, conflict is a challenge provided by:
a meaningful opponent
137
Digital games include:
Video games, computer games, console games, tablet games, smartphone games
138
The use of digital games can be controversial in K-12 for many reasons
Time away from other things
Inappropriate materials’
Excessive screen time
Over-stimulation (addiction)
Games vs reality
Violent material
139
Learning with entertainment games
Educational games repurposed in an educational setting
Commercial off the shelf games (COTS) that have been repurposed
Advantages- motivational, engaging
Disadvantages - cost barrier
140
Learning FROM Education games (Edu-tainment games)
Games developed especially to teach something (game was the teacher)
Eg, Math blaster, Oregon Trail, Prodigy, Crystal Island
Advantages -
Disadvantages - not engaging, not very good, rote learning, drill and practice
141
Learning FROM PLAYING games
Analysis of informal learning that takes place during the playing of games for fun
Eg. memory skills, perceptual skills, attention skills, reasoning
Eg. Wii
Serious Games
Games meant to give you an experience
Developed for educational purposes
Ex. Medical games for doctors and nurses for fun and proper procedure
Flight simulators
Constructivist games
142
Learning INSPIRED by Games
Using games in the context of understanding learning and problem-solving
Eg. sliding-puzzle, towers of Hanoi, Chess
143
Learn ABOUT games
Games as cultural artifacts and studying them in that context , much like we do with books or other literature and media
E.g. video game based on a movie that was based on a book
U of A course Understanding Video Games
Indigenous Video Games - Never Alone (game)
Some connections between storytelling and the game
ATCG - Indigenous-produced video games or Indigenous concepts
144
Learning FROM game design principles
How game design principles might be applied in learning situations
Eg, leveling up, badges,
Good digital games incorporate good learning principles
Reward system of wanting to have/do “one more turn”
145
Learning WITHIN game communities
Groups and communities that form both online and in the real world. These can result in communities of practice focused around a game, that facilitate collaboration communication and support learning
Advantages - want to be there
Disadvantages - tough to supervise and assess, games not designed for education ex. Roblox (spending of real $$)
146
Learning WITH game creation
Learning process that takes place in the construction of games (design, development building)
Building a digital game, involves the development of computational thinking skills, but can also be used to teach subject area content.
Advantages - actively working with concepts and building knowledge, fun, powerfu
147
Gamification -
the incorporation of game principles into learning environments
Use of game design elements in non-game context
148
Principles of massive multiplayer online gaming as a guide for the development of training and education.
Achievement
Interaction -
multiple routes -
Practice -
Probing -
Challenge
149
Principles of massive multiplayer online gaming: Achievement
- Learners are continuously rewarded for skill mastery and advancing knowledge. Rewarding learners with achievements allows them to experience a sense of competence.
150
Principles of massive multiplayer online gaming: Interaction
Learners grow through interactions with others, including technology. By collaborating with peers, learners can learn from each other and extend their knowledge
151
Principles of massive multiplayer online gaming: multiple routes
Learners have more than one way to progress and learn. Choices support autonomy, which plays an essential role in promoting student motivation and engagement, highly engaged students perform better
152
Principles of massive multiplayer online gaming: Practice
Learners spend time practicing in an interesting context. challenge and encourage learners to actively and repeatedly practice new concepts and skills within a safe context for learning
153
Principles of massive multiplayer online gaming: Probing
- Learners engage in cycles of inquiring, hypothesis building, and “doing”. a gamified learning environment should allow learners to frequently test their hypothesis, learn from the results, and build new hypotheses to test later
154
Principles of massive multiplayer online gaming: Challenge
The game should push learners outside of their current comfort zone in an attainable manner. The ideal challenge should match learners’ abilities to accomplish the tasks while providing motivational tension
155
five-course elements that incorporate the aforementioned principles of gamification:
Leveling up
badges and awards
mastery focused
quests
boss level
156
five-course elements that incorporate the aforementioned principles of gamification: Leveling Up
earning a new status in the course for each level. With each new level, students face challenges that become increasingly more difficult and develop deeper expertise within a specific trend.
students progressed to higher levels; they repeatedly practiced previously acquired skills in new and novel situations.
students gained experience points for successfully completing a task rather than earning a grade
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five-course elements that incorporate the aforementioned principles of gamification: Badges and Awards
achieve awards for their skill mastery or advancement of knowledge. At the conclusion of each level, projects that both met and exceeded the requirements were eligible for awards for excellence. Badges were awarded when students contributed positively to the course outside of the required activities.
students were pushed outside of their comfort zones in order to attain new skills or knowledge without the threat of grade loss for failure
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five-course elements that incorporate the aforementioned principles of gamification: Mastery-Focused
resubmit work if instructor feedback indicated that they failed to meet the requirements of the tasks associated with each level. This placed the focus of the course on achievement and challenge - experience was only earned if and when a project met the specified requirements, but students could resubmit until they earned or reached their own desired level of experience
Replay option
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five-course elements that incorporate the aforementioned principles of gamification: Quests
learners regularly worked in small groups to share recent technology discoveries and applications in various contexts. They then practiced applying their new knowledge by brainstorming a learning activity around their group discussion
quests occurred during each synchronous class meeting, providing students with an opportunity to engage in repeated cycles of forming a hypothesis about a new technology, presenting it to others, and revising it based on feedback
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five-course elements that incorporate the aforementioned principles of gamification: A Boss Level
final challenge that requires students to use their recently acquired skills to defeat some sort of boss, often as a team that combines a variety of unique skills
Students designed content in a context appropriate for their professional goals and tested it with members of the target audience. Teamwork was strongly encouraged but not required
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Game Elements used in Gamification
Goals & rules
- Purpose of activity
- Helps focus the activity
- Also used to measure success
Conflict, competition, cooperation
- Goldilocks zone in terms of difficulty
Points, badges, leaderboards
- Encouragement and feedback
- Leaderboards can also be used to
foster competition
- But don't rely on just rewards
Feedback
- Should be regular and frequent
- Like it should be in learning
Levels
- Shows progression
- A way to scaffold
Story
- Shows relevance of task, brings
meaning and connection to activity
Curve of interest
- Attract attention and sustain it
throughout
Aesthetics
- Pretty games are better, feels better to play
- User satisfaction
Time
- Motivates players into action
Replay
- A chance to fail
- Encourages exploration and curiosity
- More satisfying than winning on the first attempt
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Motivation
Self-determination theory
To be moved to do something
163
Extrinsic Motivation
Separable outcome
To obtain or avoid something
Ex. dog walking for money
164
Intrinsic Motivation
Inherent satisfaction
The activity itself is enjoyable
Ex. dog walking cause you like dogs
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