Estilos de aprendizagem

Outro exemplo de pessoas que agem segundo seus estilos de aprendizado diz respeito à diferença entre os modos de pensamento intuitivo e reflexivo. Aqui está um problema simples para ilustrar a diferença: “Um vaso pequeno segura uma bola branca e nove bolas vermelhas. Um vaso grande contém 10 bolas brancas e 91 vermelhas. De qual vaso você deve selecionar aleatoriamente uma bola, se deseja obter uma branca? ”O pensamento intuitivo é rápido e usa associações simples na memória para gerar uma resposta, o que o levaria a selecionar o vaso grande. Esse vaso tem mais bolas brancas, então você tem mais chances de conseguir uma branca. O modo reflexivo de pensar é mais lento e depende de um processamento mais profundo e analítico das informações disponíveis. Isso o levaria a calcular a probabilidade de desenhar uma bola branca de cada vaso e, finalmente, a resposta correta, o vaso menor.

Todo mundo usa os dois modos de pensar em momentos diferentes, mas os indivíduos são tendenciosos para começar com um ou outro tipo de processamento, especialmente se nada no ambiente (como instruções ou limite de tempo) os empurrar para um ou outro. Mas a maioria dos problemas não estão abertos a soluções igualmente boas por meio de qualquer tipo de processamento. Os problemas de probabilidade (como o exemplo do vaso) são melhor resolvidos por meio da reflexão, mesmo que seu viés seja direcionado à intuição. Os problemas de criatividade que se beneficiam da livre associação são mais bem resolvidos pela intuição, não pela reflexão. Os dados mostram que as pessoas têm alguma propensão a usar um ou outro modo de pensar, mas as pessoas ficariam melhor se não o fizessem; em vez disso, eles devem usar o modo de pensar que melhor se ajusta à tarefa em questão.

order of growth

Scientific method

The very same approach that scientists use to understand the natural world is effective for studying the running time of programs:
■ Observe some feature of the natural world, generally with precise measurements.
■ Hypothesize a model that is consistent with the observations.
■ Predict events using the hypothesis.
■ Verify the predictions by making further observations.
■ Validate by repeating until the hypothesis and observations agree.
One of the key tenets of the scientific method is that the experiments w

Who would be a poor man If he had a rich man in his hand?

the lord of the flies

Bot

Solaris - Andrei Tarkovsky

Bloom’s Taxonomy

6 WAYS MACHINE LEARNING WILL REVOLUTIONIZE THE EDUCATION SECTOR

1. Increasing efficiency. Machine learning in the form of artificial intelligence has the potential to make educators more efficient by completing tasks such as classroom management, scheduling, etc. In turn, educators are free to focus on tasks that cannot be achieved by AI, and that require a human touch.
2. Learning analytics. Machine learning in the form of learning analytics can help teachers gain insight into data that cannot be gleaned by using the human brain. In this capacity, computers can perform deep dives into data, sifting through millions of pieces of content, and making connections and conclusions that positively impact the teaching and learning process.
3. Predicative analytics. Machine learning in the form of predictive analytics can make conclusions about things that may happen in the future. For instance, using a data set of middle school students’ cumulative records, predictive analytics can tell us which ones are more likely to drop out because of academic failure or even their predicated score on a standardized exam, such as the ACT or SAT.
4. Adaptive learning. Machine learning in the form of adaptive learning can be used to remediate struggling students or challenge gifted ones. Adaptive learning is a technology-based or online educational system that analyzes a student’s performance in real time and modifies teaching methods and the curriculum based on that data. Think AI meets dedicated math tutor meets personalized engagement.
5. Personalized learning. Machine learning in the form of personalized learning could be used to give each student an individualized educational experience. Personalized learning is an educational model where students guide their own learning, going at their own pace and, in some cases, making their own decisions about what to learn. Ideally, in a classroom using personalized learning, students choose what they’re interested in, and teachers fit the curriculum and standards to the students’ interests.
6. Assessment. Machine learning in the form of artificial intelligence can be used to grade student assignments and exams more accurately than a human can. It may require some input from a human being, but the results will have higher validity and reliability.

optimisation method

Perceptron

IA Architecture

The Five Cogs of Innovation.

Innovation Adoption Curve.

Seven Project Dimensions and Examples of Some Metrics

Three maturity levels versus seven authority levels.

16 basic desires guide nearly all human behaviors

The Structure-Behavior Model of systems.

seventh son of a seventh son

Boa sorte

Jaspion 3D

Habitus