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

Principles of instruction

• Begin a lesson with a short review of previous learning.
• Present new material in small steps with student practice after each step.
• Limit the amount of material students receive at one time.
• Give clear and detailed instructions and explanations.
• Ask a large number of questions and check for understanding.
• Provide a high level of active practice for all students.
• Guide students as they begin to practice.
• Think aloud and model steps.
• Provide models of worked-out problems.
• Ask students to explain what they had learned.
• Check the responses of all students.
• Provide systematic feedback and corrections.
• Use more time to provide explanations.
• Provide many examples.
• Re-teach material when necessary.
• Prepare students for independent practice.
• Monitor students when they begin independent practice.

How we learned?

References

Week 1. Busting myths

Coffield, F., Moseley, D., Hall, E. & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review (Report no. 041543). Learning and Skills Research Centre, London.

Dekker, S., Lee, N. C., Howard-Jones, P. A. & Jolles, J. (2012). Neuromyths in education: Prevalence and predictors of misconceptions among teachers. Frontiers in Psychology, 3.

Howard-Jones, P. A. (2010). Introducing Neuroeducational Research: Neuroscience, Education and the Brain from Contexts to Practice. Routledge.

Howard-Jones, P. A. (2014). Neuroscience and education: myths and messages. Nature Reviews Neuroscience 15, 817-824.

Royal Society (2011). Brain Waves Module 2: Neuroscience:implications for education and lifelong learning. Royal Society, London.

Week 2. Engagement for learning

Adcock, R. A. (2006). Reward-motivated learning: mesolimbic activation precedes memory formation. Neuron, 50, 507-517.

Beilock, S. L., Gunderson, E. A., Ramirez, G. & Levine, S. C. (2010). Female teachers’ math anxiety affects girls’ math achievement. Proceedings of the National Academy of Science U.S.A., 107, 1860-1863.

Dawes, L., Littleton, K., Mercer N., Wegwrif, R., Warwick, P. (n.d.). Thinking Together in the Primary Classroom. UK: The Open University. Centre for Research in Education and Educational Technology.

Fales, C. L., Becerril, K. E., Luking, K. R. & Barch, D. M. (2010). Emotional-stimulus processing in trait anxiety is modulated by stimulus valence during neuroimaging. Cogn. Emot., 24, 200-222.

Farooqi, I. S. et al. (2007). Leptin regulates striatal regions and human eating Behavior. Science 317, 1355-1355.

Koepp, M. J. et al. (1998). Evidence for striatal dopamine release during a video game. . Nature 393, 266-268.

Filimon, F., Nelson, J. D., Hagler, D. J. & Sereno, M. I. (2007). Human cortical representations for reaching: Mirror neurons for execution, observation, and imagery. NeuroImage 37, 1315-1328.

Furukawa, E. et al. (2014). Abnormal Striatal BOLD Responses to Reward Anticipation and Reward Delivery in ADHD. Plos One 9, 9.

Gaastra, G. F., Groen, Y., Tucha, L. & Tucha, O. (2016). The Effects of Classroom Interventions on Off-Task and Disruptive Classroom Behavior in Children with Symptoms of Attention-Deficit/Hyperactivity Disorder: A Meta-Analytic Review. Plos One 11, 19.

Howard-Jones, P. A. (2014). Neuroscience and education: myths and messages. Nature Reviews Neuroscience 15, 817-824.

Howard-Jones, P. A., Jay, T., Mason, A. & Jones, H. (2016). Gamification of Learning Deactivates the Default Mode Network. Frontiers in Psychology 6, 16.

Izuma, K., Saito, D. N. & Sadato, N. (2008). Processing of social and monetary rewards in the human striatum. Neuron 58, 284-294.

Ker, H. W. (2016). The impacts of student-, teacher- and school-level factors on mathematics achievement: an exploratory comparative investigation of Singaporean students and the USA students. Educational Psychology 36, 254-276.

Knutson, B., Adams, C. M., Fong, G. W. & Hommer, D. (2001). Anticipation of monetary reward selectively recruits nucleus accumbens. Journal of Neuroscience 21, 1-5.

Kratzig, G. P. & Arbuthnott, K. D. (2006). Perceptual learning style and learning proficiency: A test of the hypothesis. Journal of Educational Psychology 98, 238-246.

Littleton, Karen; Mercer, Neil; Dawes, Lyn; Wegerif, Rupert; Rowe, Denise and Sams, Claire (2005). Talking and thinking together at Key Stage 1. Early Years: An International Journal of Research and Development, 25(2) 167 -182.

Rizzolatti, G. & Craighero, L. (2004). The mirror neuron system. Annual Review of Neuroscience 27, 169-192.

Schilbach, L. et al. (2010). Minds Made for Sharing: Initiating Joint Attention Recruits Reward-related Neurocircuitry. Journal of Cognitive Neuroscience 22, 2702-2715.

Schomaker, J. & Meeter, M. (2015). Short- and long-lasting consequences of novelty, deviance and surprise on brain and cognition. Neurosci. Biobehav. Rev. 55, 268-279.

van Duijvenvoorde, A. C. K., Zanolie, K., Rombouts, S., Raijmakers, M. E. J. & Crone, E. A. (2008). Evaluating the negative or valuing the positive? Neural mechanisms supporting feedback-based learning across development. Journal of Neuroscience 28, 9495-9503.

Week 3. Building knowledge and understanding

Brod, G., Werkle-Bergner, M. & Shing, Y. L. (2013). The influence of prior knowledge on memory: a developmental cognitive neuroscience perspective. Front. Behav. Neurosci. 7, 13.

Buchweitz, A., Mason, R. A., Tomitch, L. M. B. & Just, M. A. (2009). Brain activation for reading and listening comprehension: An fMRI study of modality effects and individual differences in language comprehension. Psychology & neuroscience 2, 111-123.

Butler, A. J., James, T. W., & James, K. H. (2011). Enhanced Multisensory Integration and Motor Reactivation after Active Motor Learning of Audiovisual Associations. Journal of Cognitive Neuroscience, 23(11), 3515-3528.

Coffield, F., Moseley, D., Hall, E. & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review (Report no. 041543). Learning and Skills Research Centre, London.

Fischer, U., Moeller, K., Bientzle, M., Cress, U. & Nuerk, H.-C. (2011). Sensori-motor spatial training of number magnitude representation. Psychon. Bull. Rev. 18, 177–183.

Horvath, J. C. (2014). The Neuroscience of PowerPoint (TM). Mind Brain and Education 8, 137-143.

Johnson-Glenberg, M. C., Birchfield, D. A., Tolentino, L. & Koziupa, T. (2014) Collaborative embodied learning in mixed reality motion-capture environments: two science studies. Journal of Educational Psychology 106, 86–104.

Kontra, C., Lyons, D. J., Fischer, S. M. & Beilock, S. L. (2015). Physical Experience Enhances Science Learning. Psychological Science 26, 737-749.

Pickering, S.J. (2006), Working Memory and Education. London, Elsevier Academic Press.

Shing, Y. L. & Brod, G. (2016). Effects of Prior Knowledge on Memory: Implications for Education. Mind, Brain, and Education 10, 153-161.

Wais, P. E. & Gazzaley, A. (2014). Distractibility during retrieval of long-term memory: domain-general interference, neural networks and increased susceptibility in normal aging. Frontiers in Psychology 5, 12.

Week 4. Consolidation of learning

Dworak, M., Schierl, T., Bruns, T., & Struder, H. K. (2007). Impact of singular excessive computer game and television exposure on sleep patterns and memory performance of school-aged children. Pediatrics, 120(5), 978-985.

Maquet, P. et al. (2000). Experience dependent changes in cerebral activation during human REM sleep. Nature Neuroscience 3, 831-836.

McDaniel, M. A., Roediger, H. L. & McDermott, K. B. (2007). Generalizing test-enhanced learning from the laboratory to the classroom. Psychon. Bull. Rev. 14, 200-206.

Roediger, H. L. & Karpicke, J. D. (2006). Test-enhanced learning - Taking memory tests improves long-term retention. Psychological Science 17, 249-255.

Roseshine, B. (2010). Principles of Instruction. International Academy of Education, UNESCO.

Wirebring, L. K. et al. (2015). Lesser Neural Pattern Similarity across Repeated Tests Is Associated with Better Long-Term Memory Retention. Journal of Neuroscience 35, 9595-9602.

Week 5. The science of learning in your classroom

Blackwell, L. S., Trzesniewski, K. H. & Dweck, C. S. (2007). Implicit theories of intelligence predict achievment across an adolescent transition: A longtitudinal study and an intervention. Child Development 78, 246-263.

Dekker, S. & Jolles, J. (2015). Teaching About “Brain and Learning” in High School Biology Classes: Effects on Teachers’ Knowledge and Students’ Theory of Intelligence. Frontiers in Psychology 6, 8.

Maguire, E. A. et al. (2000). Navigation related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences (USA) 97, 4398-4403. Martensson, J. et al. (2012). Growth of language-related brain areas after foreign language learning. Neuroimage 63, 240-244.

Paunesku, D. et al. (2015). Mind-Set Interventions Are a Scalable Treatment for Academic Underachievement. Psychological Science 26, 784-793.

Supekar, K. et al. (2013). Neural predictors of individual differences in response to math tutoring in primary-grade school children. Proc. Natl. Acad. Sci. U. S. A. 110, 8230-8235..

Wenger, E. & Lovden, M. (2016). The Learning Hippocampus: Education and Experience-Dependent Plasticity. Mind Brain and Education 10, 171-183.

© National STEM Learning Centre

How we learned?

Situações interessantes

Sorria...

Reasoning Mode

Dog Whistle

Academic word list

The Academic Word List (AWL) was developed by Averil Coxhead at the School of Linguistics and Applied Language Studies at Victoria University of Wellington, New Zealand. The list contains 570 word families which were selected because they appear with great frequency in a broad range of academic texts. The list does not include words that are in the most frequent 2000 words of English (the General Service List), thus making it specific to academic contexts. The AWL was primarily made so that it could be used by teachers as part of a programme preparing learners for tertiary level study or used by students working alone to learn the words most needed to study at colleges and universities. The 570 words are divided into 10 Groups. The Groups are ordered such that the words in the first Group are the most frequent words and those in the last Group are the least frequent.

Group 1

sector • available • financial • process • individual • specific • principle • estimate • variables • method • data • research • contract • environment • export • source • assessment • policy • identified • create • derived • factors • procedure • definition • assume • theory • benefit • evidence • established • authority • major • issues • labour • occur • economic • involved • percent • interpretation • consistent • income • structure • legal • concept • formula • section • required • constitutional • analysis • distribution • function • area • approach • role • legislation • indicate • response • period • context • significant • similar •

Group 2

community • resident • range • construction • strategies • elements • previous • conclusion • security • aspects • acquisition • features • text • commission • regulations • computer • items • consumer • achieve • final • positive • evaluation • assistance • normal • relevant • distinction • region • traditional • impact • consequences • chapter • equation • appropriate • resources • participation • survey • potential • cultural • transfer • select • credit • affect • categories • perceived • sought • focus • purchase • injury • site • journal • primary • complex • institute • investment • administration • maintenance • design • obtained • restricted • conduct •

Group 3

comments • convention • published • framework • implies • negative • dominant • illustrated • outcomes • constant • shift • deduction • ensure • specified • justification • funds • reliance • physical • partnership • location • link • coordination • alternative • initial • validity • task • techniques • excluded • consent • proportion • demonstrate • reaction • criteria • minorities • technology • philosophy • removed • sex • compensation • sequence • corresponding • maximum • circumstances • instance • considerable • sufficient • corporate • interaction • contribution • immigration • component • constraints • technical • emphasis • scheme • layer • volume • document • registered • core •

Group 4

overall • emerged • regime • implementation • project • hence • occupational • internal • goals • retained • sum • integration • mechanism • parallel • imposed • despite • job • parameters • approximate • label • concentration • principal • series • predicted • summary • attitudes • undertaken • cycle • communication • ethnic • hypothesis • professional • status • conference • attributed • annual • obvious • error • implications • apparent • commitment • subsequent • debate • dimensions • promote • statistics • option • domestic • output • access • code • investigation • phase • prior • granted • stress • civil • contrast • resolution • adequate

Group 5

alter • stability • energy • aware • licence • enforcement • draft • styles • precise • medical • pursue • symbolic • marginal • capacity • generation • exposure • decline • academic • modified • external • psychology • fundamental • adjustment • ratio • whereas • enable • version • perspective • contact • network • facilitate • welfare • transition • amendment • logic • rejected • expansion • clause • prime • target • objective • sustainable • equivalent • liberal • notion • substitution • generated • trend • revenue • compounds • evolution • conflict • image • discretion • entities • orientation • consultation • mental • monitoring • challenge •

Group 6

intelligence • transformation • presumption • acknowledged • utility • furthermore • accurate • diversity • attached • recovery • assigned • tapes • motivation • bond • edition • nevertheless • transport • cited • fees • scope • enhanced • incorporated • instructions • subsidiary • input • abstract • ministry • capable • expert • preceding • display • incentive • inhibition • trace • ignored • incidence • estate • cooperative • revealed • index • lecture • discrimination • overseas • explicit • aggregate • gender • underlying • brief • domain • rational • minimum • interval • neutral • migration • flexibility • federal • author • initiatives • allocation • exceed •

Group 7

intervention • confirmed • definite • classical • chemical • voluntary • release • visible • finite • publication • channel • file • thesis • equipment • disposal • solely • deny • identical • submitted • grade • phenomenon • paradigm • ultimately • extract • survive • converted • transmission • global • inferred • guarantee • advocate • dynamic • simulation • topic • insert • reverse • decades • comprise • hierarchical • unique • comprehensive • couple • mode • differentiation • eliminate • priority • empirical • ideology • somewhat • aid • foundation • adults • adaptation • quotation • contrary • media • successive • innovation • prohibited • isolated •

Group 8

highlighted • eventually • inspection • termination • displacement • arbitrary • reinforced • denote • offset • exploitation • detected • abandon • random • revision • virtually • uniform • predominantly • thereby • implicit • tension • ambiguous • vehicle • clarity • conformity • contemporary • automatically • accumulation • appendix • widespread • infrastructure • deviation • fluctuations • restore • guidelines • commodity • minimises • practitioners • radical • plus • visual • chart • appreciation • prospect • dramatic • contradiction • currency • inevitably • complement • accompany • paragraph • induced • schedule • intensity • crucial • via • exhibit • bias • manipulation • theme • nuclear •

Group 9

bulk • behalf • unified • commenced • erosion • anticipated • minimal • ceases • vision • mutual • norms • intermediate • manual • supplementary • incompatible • concurrent • ethical • preliminary • integral • conversely • relaxed • confined • accommodation • temporary • distorted • passive • subordinate • analogous • military • scenario • revolution • diminished • coherence • suspended • mature • assurance • rigid • controversy • sphere • mediation • format • trigger • qualitative • portion • medium • coincide • violation • device • insights • refine • devoted • team • overlap • attained • restraints • inherent • route • protocol • founded • duration •

Group 10

whereby • inclination • encountered • convinced • assembly • albeit • enormous • reluctant • posed • persistent • undergo • notwithstanding • straightforward • panel • odd • intrinsic • compiled • adjacent • integrity • forthcoming • conceived • ongoing • so-called • likewise • nonetheless • levy • invoked • colleagues • depression • collapse •

Poverty

A Coerência Mítica

Capitanias Hereditárias

Science, hard x soft

Juspion 3D - Daileon Assebly V2 - Trailer

Strategy

Park bench all day long

Childhood's Memories

 

Equity

Idiocracy x Windows

Medindo a Disponibilidade

Excelente e divertido documentário sobre o inconsciente - National Geographic

How to Reset the MySQL Root Password on Ubuntu

In this article we will reset the MySQL root password in Ubuntu by starting MySQL with the --skip-grant-tables option.

1. Confirm MySQL version

Firstly, you must confirm which version of MySQL on Ubuntu you are running as commands will be different.

mysql -V

mysql Ver 14.14 Distrib 5.7.25, for Linux (x86_64) using EditLine wrapper

Keep note of your “Distrib”. In the above example, we are on MySQL 5.7. Keep note of this for later.

2. Restart MySQL with skip-grant-table

In order to skip the grant tables and reset the root password, we must first stop the MySQL service.

sudo /etc/init.d/mysql stop

Ensure the directory /var/run/mysqld exists and correct owner set.

sudo mkdir /var/run/mysqld

sudo chown mysql /var/run/mysqld

Now start MySQL with the --skip-grant-tables option. The & is required here.

sudo mysqld_safe --skip-grant-tables&

You should see something similar:

[1] 1283
user@server:~$ 2019-02-12T11:15:59.872516Z mysqld_safe Logging to syslog.
2019-02-12T11:15:59.879527Z mysqld_safe Logging to '/var/log/mysql/error.log'.
2019-02-12T11:15:59.922502Z mysqld_safe Starting mysqld daemon with databases from /var/lib/mysql

You may need to press ENTER to return to the Linux BASH prompt.

3. Change MySQL Root Password

You can now log in to the MySQL root account without a password.

sudo mysql --user=root mysql

Once logged in, you will see the mysql> prompt.
For MySQL 5.7 or above on Ubuntu, run this command to change the root password. Replace your_password_here with your own. (Generate a strong password here)

mysql> update user set authentication_string=PASSWORD('your_password_here') where user='root';

For MySQL 5.6 or below on Ubuntu, run this command to change the root password. Replace your_password_here with your own. (Generate a strong password here)

mysql> update user set Password=PASSWORD('your_password_here') where user='root';

When resetting a root password, you must also change the auth plugin to mysql_native_password

mysql> update user set plugin="mysql_native_password" where User='root';

Flush privileges.

mysql>  flush privileges;

Exit MySQL.

mysql> exit

Make sure all MySQL processes are stopped before starting the service again.

sudo killall -u mysql

Start MySQL again.

sudo /etc/init.d/mysql start

4. Test New Password

Log in to MySQL again and you should now be prompted for a password.

sudo mysql -p -u root

Enter your password. If correct, you should see

Welcome to the MySQL monitor. Commands end with ; or \g.
Your MySQL connection id is 6
Server version: 5.7.25-0ubuntu0.18.04.2 (Ubuntu)

You’re all done!

Source: https://devanswers.co/how-to-reset-mysql-root-password-ubuntu/

Scene of Memory

CMMI

Senoidal

Organizando Objetivos

Objetivo: oferecer uma ótima experiência de aprendizagem.
Para quem: você, que chegou até aqui.
Restrição: tempo.
Variável sensível (sob controle): preço.
Variável perigosa (incontrolável?): atenção.

Uma breve história do intrigante número e

Para apresentar o e, vamos supor uma situação bastante hipotética.

Imagine que um banco pague juros de 100% ao ano. Eu não falei que era uma situação hipotética? Mesmo assim, vamos fazer de conta que existe um banco com essa maravilhosa generosidade.

Após um ano, teríamos o montante de R$ 2,00 para cada R$ 1,00 aplicado.

E se, com uma generosidade inexplicável, os juros fossem creditados semestralmente, ao final de um ano teríamos R$ 2,25. Um sonho!

A expressão para esse cálculo é a seguinte:

(1 + 1/n)n = (1 + 1/2)2 = 2,25

Para o crédito ser trimestral, temos n = 4 e o resultado é 2,44141.

         Vejamos alguns resultados para diversos valores de n na tabela abaixo.

n	(1 + 1/n)n
1	2
2	2,25
3	2,37037
4	2,44141
5	2,48832
10	2,59374
50	2,69159
100	2,70481
1.000	2,71692
10.000	2,71815
100.000	2,71827
1.000.000	2,71828
10.000.000	2,71828

A “loucura total” seria calcular quanto seria o resultado para o crédito instantâneo, ou seja, com n tendendo ao infinito. Esse limite é um número irracional e transcendental chamado número e (número de Euler).

Um número é irracional quando não pode ser colocado na forma a/b com a e b inteiros. É transcendental quando não pode ser resultado de uma equação polinomial com coeficientes inteiros do tipo: axn + bxn-1 + ... + z = 0

Em termos matemáticos:

e = 2,71828182845904523536028747135266... E nunca termina.

Quem efetivamente calculou o número e foi Leonhard Euler, e dizem que a designação decorre da inicial de seu sobrenome, mas também existe a versão de que o e se deva à inicial de “exponencial”.

Esse número é a base dos logaritmos neperianos.

E se aquele banco generoso quisesse creditar juros instantâneos à sua aplicação de R$1,00, a 100% ao ano, você teria ao final de um ano o valor de:

R$ 2,71828182845904523536028747135266...

Ou, o que é mais provável, R$ 2,71, deixando aquela montanha de decimais ao banco. Afinal, esse banco merece!

Vamos, pois, ficar atentos à publicidade. Quem sabe não aparece um banco assim?

e=2,71828182845904523536028747135266249775724709369995957496696762772407663035354759451382178525166427...

Existem, entre outros, dois especialmente intrigantes: p e i.

p = 3,14159... Também transcendental.

i = Ö-1 porque i foi o símbolo adotado por Euler para a raiz quadrada de -1.

E olha só o que o Euler também conseguiu – uma correlação entre eles:

eiπ + 1= 0

         Mas isso já é coisa para os efetivamente matemáticos...

1984

O objetivo da classe dominante é deixar como esta.
O objetivo da classe média é derrubar a dominante.
O objetivo da classe inferior é criar uma sociedade onde todas as classes são iguais.

The mostly complete chart of Neural Networks

Education x Real Life

Data Science Mindset

The world is opening up with possibilities for people who are quantitatively minded and interested in putting their brains to work to solve the world’s problems.

The data scientist is involved in every part of this process

The data science process

A bunch of continuous density functions (aka probability distributions)

Simplicity

Bloom’s Taxonomy of Educational Objectives: Cognitive Domain

Bloom’s Taxonomy

Learning Styles

Problemas de pesquisa na internet

A Model of Learning Styles

NoSQL taxonomy

Some difference?

Abstraction

Classification of Data Structure

the trade-off between time and space

In computer science curricula, a common theme is the trade-off between time and space. In order to have a fast-running pro- gram, you may need to use more memory space. On the other hand, in order to conserve memory space, you might need to settle for slower code.