20 things to do - История изменений

Patarakin в 12:29, 23 декабря 2025

2025-12-23T12:29:35Z

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 {{Book
-|Description=The concept of "Twenty Things to Do with a Computer," originally presented by Seymour Papert and Cynthia Solomon in 1971, highlights various engaging activities that can be accomplished through programming.
+|Description=The concept of "Twenty Things to Do with a Computer," Пейперт и Соломон в 1971 году: Создать робота черепаху, Запрограммировать черепаху нарисовать человека, Изучить биологию робота-черепахи, Сделать экранную черепаху, Играть в Spacewar, Изучить с черепахой дифференциальную геометрию, Рисовать спирали, Нарисовать сердце (и научиться искать ошибки), Выращивать цветы, Сделать фильм, Сделать музыкальную шкатулку и программировать мелодию, Экспериментировать с музыкальными эффектами, Компьютеризировать конструктор и построить башню блоков, Сделать грандиозное световое шоу, Писать стихи, '''Попробовать программированное обучение''', Изучать физики на кончиках пальцев, Объяснить себя при помощи программы, Играть в куклы, Запустить рекурсию и придумать ещё 20 способов
 |Field_of_knowledge=Информатика, Педагогика
 |launch year=1971

Patarakin в 06:28, 10 октября 2025

2025-10-10T06:28:19Z

← Предыдущая версия		Версия от 09:28, 10 октября 2025
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	== 1971 ==		== 1971 ==
	# Make a Turtle		# Make a Turtle
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	# '''Weird Recipes''' - Tseng and colleagues (2023) show how youth can use classifiers to build an application that provides recipe recommendations. Using libraries such as nanoGPT, could enable learners to play with language models, modify training data sets, and change parameters to create recipes.		# '''Weird Recipes''' - Tseng and colleagues (2023) show how youth can use classifiers to build an application that provides recipe recommendations. Using libraries such as nanoGPT, could enable learners to play with language models, modify training data sets, and change parameters to create recipes.
	# '''Dance Game Dance''' in AI/ML education has been growing over the past few years with projects that have learners partner to dance with AI agents (Long et al., 2020) and have learners create choreographies and animations (Castro et al., 2022; Jordan et al., 2021. Building on these efforts, children could design their own dance games similar to Just Dance, where they train models to recognize players’ moves (sequences of poses) using either pose recognition or data from wearable sensors and compare these to predetermined choreographies or where based on a set of basic moves the system generates possible choreographies that match music rhythms.		# '''Dance Game Dance''' in AI/ML education has been growing over the past few years with projects that have learners partner to dance with AI agents (Long et al., 2020) and have learners create choreographies and animations (Castro et al., 2022; Jordan et al., 2021. Building on these efforts, children could design their own dance games similar to Just Dance, where they train models to recognize players’ moves (sequences of poses) using either pose recognition or data from wearable sensors and compare these to predetermined choreographies or where based on a set of basic moves the system generates possible choreographies that match music rhythms.
	# '''Poetry Generator''' Papert and Solomon (#15, 1971) explored how creating sentence generators may support learners to better understand language structures and computing. With current off-the-shelf AI/ML libraries, learners can generate poetry while exploring more sophisticated natural language processing techniques. From simple Markov chains to training models using nanoGPT, youth can create synthetic text including poetry, short stories, and music lyrics.		# '''Poetry Generator''' [[Papert]] and Solomon (#15, 1971) explored how creating sentence generators may support learners to better understand language structures and computing. With current off-the-shelf AI/ML libraries, learners can generate poetry while exploring more sophisticated natural language processing techniques. From simple Markov chains to training models using nanoGPT, youth can create synthetic text including poetry, short stories, and music lyrics.
	# '''Sports Training App''' - Similarly, to #4, there’s a lot of promise for young people to learn about AI/ML while engaging with sports (Jordan et al., 2021; Kumar & Worsley, 2023; Zimmermann-Niefield; 2019).		# '''Sports Training App''' - Similarly, to #4, there’s a lot of promise for young people to learn about AI/ML while engaging with sports (Jordan et al., 2021; Kumar & Worsley, 2023; Zimmermann-Niefield; 2019).
	# '''Music Generator''' Papert & Solomon (# 11, 1971) suggested creating a music box with programmed tunes. Later in #12, they present a project that creates random songs. Today, children could build data sets of tunes and use them to train a model that generates music, these “compositions” could follow the traditions of aleatoric and minimalist music or even explore noise music. Here, like in #2 learners could engage in discussions about creativity in humans and machines and copyrigh		# '''Music Generator''' Papert & Solomon (# 11, 1971) suggested creating a music box with programmed tunes. Later in #12, they present a project that creates random songs. Today, children could build data sets of tunes and use them to train a model that generates music, these “compositions” could follow the traditions of aleatoric and minimalist music or even explore noise music. Here, like in #2 learners could engage in discussions about creativity in humans and machines and copyrigh

Patarakin в 06:27, 10 октября 2025

2025-10-10T06:27:17Z

← Предыдущая версия		Версия от 09:27, 10 октября 2025
Строка 3:		Строка 3:
	# Make a Turtle		# Make a Turtle
	# Program the Turtle to Draw a Man		# Program the Turtle to Draw a Man
	# [[Turtle]] Biology		# Turtle Biology
	# Make a Display Turtle		# Make a Display Turtle
	# Play Spacewar		# Play Spacewar

Patarakin в 06:25, 10 октября 2025

2025-10-10T06:25:47Z

← Предыдущая версия		Версия от 09:25, 10 октября 2025
Строка 3:		Строка 3:
	# Make a Turtle		# Make a Turtle
	# Program the Turtle to Draw a Man		# Program the Turtle to Draw a Man
	# Turtle Biology		# [[Turtle]] Biology
	# Make a Display Turtle		# Make a Display Turtle
	# Play Spacewar		# Play Spacewar

Patarakin в 05:16, 24 сентября 2024

2024-09-24T05:16:24Z

← Предыдущая версия		Версия от 08:16, 24 сентября 2024
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	# Make a Super Light Show		# Make a Super Light Show
	# Write Concrete Poetry		# Write Concrete Poetry
	# Try C.A.I. and Psychology		# Try C.A.I. (Computer-Assisted Instruction) and Psychology
	# Physics in the Finger-Tips		# Physics in the Finger-Tips
	# Explain Yourself		# Explain Yourself
Строка 42:		Строка 42:
	# Make a Super Light Show		# Make a Super Light Show
	# Write Concrete Poetry		# Write Concrete Poetry
	# Try C.A.I. and Psychology		# Try C.A.I. (Computer-Assisted Instruction) and Psychology
	# Physics in the Finger-Tips		# Physics in the Finger-Tips
	# Explain Yourself		# Explain Yourself

Patarakin в 05:12, 24 сентября 2024

2024-09-24T05:12:28Z

← Предыдущая версия		Версия от 08:12, 24 сентября 2024
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	# Make a Super Light Show		# Make a Super Light Show
	# Write Concrete Poetry		# Write Concrete Poetry
	# Try C.A.1. and Psychology		# Try C.A.I. and Psychology
	# Physics in the Finger-Tips		# Physics in the Finger-Tips
	# Explain Yourself		# Explain Yourself
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	\|Environment=Logo		\|Environment=Logo
	}}		}}

	== 1971 ==		== 1971 ==
	# Make a Turtle		# Make a Turtle
Строка 43:		Строка 42:
	# Make a Super Light Show		# Make a Super Light Show
	# Write Concrete Poetry		# Write Concrete Poetry
	# Try C.A.1. and Psychology		# Try C.A.I. and Psychology
	# Physics in the Finger-Tips		# Physics in the Finger-Tips
	# Explain Yourself		# Explain Yourself

Patarakin в 16:10, 22 сентября 2024

2024-09-22T16:10:49Z

@@ Строка 26: / Строка 26: @@
 |Environment=Logo
 }}
 == 2024 ==

Patarakin в 12:32, 22 сентября 2024

2024-09-22T12:32:19Z

@@ Строка 1: / Строка 1: @@
 {{Book
 |Description=The concept of "Twenty Things to Do with a Computer," originally presented by Seymour Papert and Cynthia Solomon in 1971, highlights various engaging activities that can be accomplished through programming.
 |Field_of_knowledge=Информатика, Педагогика
 |launch year=1971

Patarakin: /* 2024 */

2024-09-22T08:46:27Z

2024

← Предыдущая версия		Версия от 11:46, 22 сентября 2024
Строка 21:		Строка 21:
	# '''Adaptable Interactive Stories''' At Fablearn/Constructionism 2023, Rita Freudenberg suggested that children could “create interactive stories [or games], where the storyline not just depends on a deliberate action by the player, but the presented story options change based on previous decisions”. Rita explained learners would have to decide on what factors would influence the development of the narrative and how transparent these are to users.		# '''Adaptable Interactive Stories''' At Fablearn/Constructionism 2023, Rita Freudenberg suggested that children could “create interactive stories [or games], where the storyline not just depends on a deliberate action by the player, but the presented story options change based on previous decisions”. Rita explained learners would have to decide on what factors would influence the development of the narrative and how transparent these are to users.
	# '''Artificial Tutors for Peers''' Similar to #9, learners could create a system that interacts with junior students. Fablearn/Constructionism 2023 participant Daniel Agostini argued that this could support learners to "learn by teaching" an AI/ML system.		# '''Artificial Tutors for Peers''' Similar to #9, learners could create a system that interacts with junior students. Fablearn/Constructionism 2023 participant Daniel Agostini argued that this could support learners to "learn by teaching" an AI/ML system.
	#14 Modeling Climate and Carbon Emissions		#1 '''Modeling Climate and Carbon Emissions''' Students could create models using publicly available climate data to recognize patterns and make predictions. This suggestion by an anonymous participant at Fablearn/Constructionism 2023, highlights how such an activity can engage learners in thinking about AI/ML in a highly relevant area of concern that is socio-technical and involves thinking about complexity.
	Students could create models using publicly available climate data to recognize patterns and make predictions. This suggestion by an anonymous participant at Fablearn/Constructionism 2023, highlights how such an activity can engage learners in thinking about AI/ML in a highly relevant area of concern that is socio-technical and involves thinking about complexity.
	# '''Artificial Co-Learners''' - Eisenberg and colleagues also propose having learners create artificial co-learners. This, they describe, would involve having youth “design models of students (rather than teachers) that would accompany children in learning new material and articulate (perhaps simplistic, but concrete) strategies for thinking about the material.		# '''Artificial Co-Learners''' - Eisenberg and colleagues also propose having learners create artificial co-learners. This, they describe, would involve having youth “design models of students (rather than teachers) that would accompany children in learning new material and articulate (perhaps simplistic, but concrete) strategies for thinking about the material.
	#16 '''Role-Playing Games''' Students could create games based on text inputs. We build on this idea of Fablearn/Constructionism 2023 participant Ken Kahn by proposing that learners could create roleplaying games, for instance building datasets of role-playing game scripts (such as Dungeon and Dragons campaigns), training models (see #1) and then generating new games.		# '''Role-Playing Games''' Students could create games based on text inputs. We build on this idea of Fablearn/Constructionism 2023 participant Ken Kahn by proposing that learners could create roleplaying games, for instance building datasets of role-playing game scripts (such as Dungeon and Dragons campaigns), training models (see #1) and then generating new games.
	# 17 '''Personal Assistants''' During Fablearn/Constructionism 2023 Nicolás Acosta and Glenn Boustead suggested that learners could build personal assistants. Acosta and Boustead explained this could involve creating a voice assistant to control music, lights, and engage people in wellness activities (e.g., meditation, reflection).		# '''Personal Assistants''' During Fablearn/Constructionism 2023 Nicolás Acosta and Glenn Boustead suggested that learners could build personal assistants. Acosta and Boustead explained this could involve creating a voice assistant to control music, lights, and engage people in wellness activities (e.g., meditation, reflection).
	#18 Tamagotchi Learners could create virtual pets that can speak and listen, imagining for example that someone might create a “tamagotchi with sass.”		# Tamagotchi Learners could create virtual pets that can speak and listen, imagining for example that someone might create a “tamagotchi with sass.”
	# 19 Workout App During Fablearn/Cons		# '''Workout App''' During Fablearn/Cons
	#20 Create a constructionist activities generator		# '''Create a constructionist activities generator''' - Create an application that uses AI/ML to generate more constructionist activities.
	Create an application that uses AI/ML to generate more constructionist activities.

Patarakin: /* 2024 */

2024-09-22T08:44:44Z

2024

← Предыдущая версия		Версия от 11:44, 22 сентября 2024
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	# '''Face Filter''' - Young people interact with AI/ML powered face filters when using social media (SnapChat, TikTok) with their friends. Creating filters can involve them in learning about AI/ML in two ways: exploring how to recognize facial features and body parts and using generative models to create images to transform what is captured or superposed over the camera feed.		# '''Face Filter''' - Young people interact with AI/ML powered face filters when using social media (SnapChat, TikTok) with their friends. Creating filters can involve them in learning about AI/ML in two ways: exploring how to recognize facial features and body parts and using generative models to create images to transform what is captured or superposed over the camera feed.
	# '''Weird Recipes''' - Tseng and colleagues (2023) show how youth can use classifiers to build an application that provides recipe recommendations. Using libraries such as nanoGPT, could enable learners to play with language models, modify training data sets, and change parameters to create recipes.		# '''Weird Recipes''' - Tseng and colleagues (2023) show how youth can use classifiers to build an application that provides recipe recommendations. Using libraries such as nanoGPT, could enable learners to play with language models, modify training data sets, and change parameters to create recipes.
	# 4 '''Dance Game Dance''' in AI/ML education has been growing over the past few years with projects that have learners partner to dance with AI agents (Long et al., 2020) and have learners create choreographies and animations (Castro et al., 2022; Jordan et al., 2021. Building on these efforts, children could design their own dance games similar to Just Dance, where they train models to recognize players’ moves (sequences of poses) using either pose recognition or data from wearable sensors and compare these to predetermined choreographies or where based on a set of basic moves the system generates possible choreographies that match music rhythms.		# '''Dance Game Dance''' in AI/ML education has been growing over the past few years with projects that have learners partner to dance with AI agents (Long et al., 2020) and have learners create choreographies and animations (Castro et al., 2022; Jordan et al., 2021. Building on these efforts, children could design their own dance games similar to Just Dance, where they train models to recognize players’ moves (sequences of poses) using either pose recognition or data from wearable sensors and compare these to predetermined choreographies or where based on a set of basic moves the system generates possible choreographies that match music rhythms.
	# '''Poetry Generator''' Papert and Solomon (#15, 1971) explored how creating sentence generators may support learners to better understand language structures and computing. With current off-the-shelf AI/ML libraries, learners can generate poetry while exploring more sophisticated natural language processing techniques. From simple Markov chains to training models using nanoGPT, youth can create synthetic text including poetry, short stories, and music lyrics.		# '''Poetry Generator''' Papert and Solomon (#15, 1971) explored how creating sentence generators may support learners to better understand language structures and computing. With current off-the-shelf AI/ML libraries, learners can generate poetry while exploring more sophisticated natural language processing techniques. From simple Markov chains to training models using nanoGPT, youth can create synthetic text including poetry, short stories, and music lyrics.
	# '''Sports Training App''' - Similarly, to #4, there’s a lot of promise for young people to learn about AI/ML while engaging with sports (Jordan et al., 2021; Kumar & Worsley, 2023; Zimmermann-Niefield; 2019).		# '''Sports Training App''' - Similarly, to #4, there’s a lot of promise for young people to learn about AI/ML while engaging with sports (Jordan et al., 2021; Kumar & Worsley, 2023; Zimmermann-Niefield; 2019).
Строка 23:		Строка 23:
	#14 Modeling Climate and Carbon Emissions		#14 Modeling Climate and Carbon Emissions
	Students could create models using publicly available climate data to recognize patterns and make predictions. This suggestion by an anonymous participant at Fablearn/Constructionism 2023, highlights how such an activity can engage learners in thinking about AI/ML in a highly relevant area of concern that is socio-technical and involves thinking about complexity.		Students could create models using publicly available climate data to recognize patterns and make predictions. This suggestion by an anonymous participant at Fablearn/Constructionism 2023, highlights how such an activity can engage learners in thinking about AI/ML in a highly relevant area of concern that is socio-technical and involves thinking about complexity.
	# 15 Artificial Co-Learners		# '''Artificial Co-Learners''' - Eisenberg and colleagues also propose having learners create artificial co-learners. This, they describe, would involve having youth “design models of students (rather than teachers) that would accompany children in learning new material and articulate (perhaps simplistic, but concrete) strategies for thinking about the material.
	Eisenberg and colleagues also propose having learners create artificial co-learners. This, they describe, would involve having youth “design models of students (rather than teachers) that would accompany children in learning new material and articulate (perhaps simplistic, but concrete) strategies for thinking about the material.		#16 '''Role-Playing Games''' Students could create games based on text inputs. We build on this idea of Fablearn/Constructionism 2023 participant Ken Kahn by proposing that learners could create roleplaying games, for instance building datasets of role-playing game scripts (such as Dungeon and Dragons campaigns), training models (see #1) and then generating new games.
	#16 Role-Playing Games		# 17 '''Personal Assistants''' During Fablearn/Constructionism 2023 Nicolás Acosta and Glenn Boustead suggested that learners could build personal assistants. Acosta and Boustead explained this could involve creating a voice assistant to control music, lights, and engage people in wellness activities (e.g., meditation, reflection).
	Students could create games based on text inputs. We build on this idea of Fablearn/Constructionism 2023 participant Ken Kahn by proposing that learners could create roleplaying games, for instance building datasets of role-playing game scripts (such as Dungeon and Dragons campaigns), training models (see #1) and then generating new games.
	# 17 Personal Assistants
	During Fablearn/Constructionism 2023 Nicolás Acosta and Glenn Boustead suggested that learners could build personal assistants. Acosta and Boustead explained this could involve creating a voice assistant to control music, lights, and engage people in wellness activities (e.g., meditation, reflection).
	#18 Tamagotchi Learners could create virtual pets that can speak and listen, imagining for example that someone might create a “tamagotchi with sass.”		#18 Tamagotchi Learners could create virtual pets that can speak and listen, imagining for example that someone might create a “tamagotchi with sass.”
	# 19 Workout App During Fablearn/Cons		# 19 Workout App During Fablearn/Cons
	#20 Create a constructionist activities generator		#20 Create a constructionist activities generator
	Create an application that uses AI/ML to generate more constructionist activities.]		Create an application that uses AI/ML to generate more constructionist activities.