Learning does not happen in a vacuum. It is influenced by social dynamics, most notably between students and their peers.

college students engage with and consume more content than at any time in history. It just so happens that this content is delivered by a streaming service, video game or social media platform, not by a college instructor.

Student engagement is one of the strongest leading indicators we have of positive learning outcomes. Consequently, when students are disengaged, they are less likely to achieve their learning goals.

“Protracted immaturity and dependence on paternal care is not an unfortunate byproduct of our evolution but instead a highly adaptive trait of our species, which has enabled human infants to efficiently organize attention to social agents and learn efficiently from social output

the beauty of perceptual immaturity in altricial species is that it makes learning easier by reducing the complexity of the world

Deep Learning (DL) A Technique for Implementing Machine LearningSubfield of ML that uses specialized techniques involving multi-layer (2+) artificial neural networksLayering allows cascaded learning and abstraction levels (e.g. line -> shape -> object -> scene)Computationally intensive enabled by clouds, GPUs, and specialized HW such as FPGAs, TPUs, etc.

Minimum sample size for external validation of a clinicalprediction model with a binary outcome

Adaptive sample size determination for thedevelopment of clinical prediction models

Chatti notes that Connectivism misses some concepts, which are crucial for learning, such as reflection, learning from failures, error detection and correction, and inquiry. He introduces the Learning as a Network (LaaN) theory which builds upon connectivism, complexity theory, and double-loop learning. LaaN starts from the learner and views learning as the continuous creation of a personal knowledge network (PKN).[18]

The promise of using machine learning on your own notes to connect with external sources is not new. Andromeda Yelton’s HAMLET is six years old.

MACHINE LEARNING FOR STOCK PRICES FORECASTING

The few notes I did refer back to frequently where checklists, self-written instructions to complete regular tasks, lists (reading lists, watchlists, etc.) or recipes. Funnily enough the ROI on these notes was a lot higher than all the permanent/evergreen/zettel notes I had written.

Stop to think about "normal app" as like desktop app. Android isn't a desktop platform, there is no such this. A "normal" mobile app let the system control the lifecycle, not the dev. The system expect that, the users expect that. All you need to do is change your mindset and learn how to build on it. Don't try to clone a desktop app on mobile. Everything is completely different including UI/UX.

Training language models to follow instructionswith human feedback

at the targeted level

The goal of this progression is to build up gradually, with each step building on the previous one,

progression starts with Beginning, where the student tries to write a conclusion

44:19 - [Claudia] The classification is anything but indifferent.44:24 The manner of shelving the books44:26 is meant to impart certain suggestions to the reader,44:30 who, looking on the shelves for one book,44:33 is attracted by the kindred ones next to it,44:36 glances at the sections above and below,44:39 and finds himself involved in a new trend of thought44:43 which may lend to additional interests44:46 to the one he was pursuing.

You cannot claim a credit for education expenses paid with tax-free funds. You must reduce the amount of expenses paid with tax-free grants, scholarships and fellowships and other tax-free education help.

One way to weed those out is to begin with the most basic question we can formulate. Conceptual artist Jonathon Keats calls these “naive questions.” Geochemist Hope Jahren calls them “curiosity questions.” Whatever the label, they are, in essence, the kind of question a child could come up with.Progressing from such questions requires us to dig deeper and slow down our thinking — which, in turn, may reveal to us unknown unknowns or information we may have missed last time we explored the topic.

We can be bolder about asking questions in public and encouraging others to pursue their curiosity, too. In that encouragement, we help create an environment where those around us feel safe from the shame and humiliation they may feel in revealing a lack of knowledge about a subject, which can round back to us.

Many people, myself included, can find asking questions to be daunting. It fills us with worry and self-doubt, as though the act of being inquisitive is an all-too-public admission of our ignorance. Unfortunately, this can also lead us to find solace in answers — no matter how shaky our understanding of the facts may be — rather than risk looking stupid in front of others or even to ourselves.

8 Overtuigingen funest voor leren leren #1 Leren leren verloopt impliciet De eerste overtuiging die leraren kunnen hebben, is dat leren leren impliciet wordt aangeleerd en dus de informatieoverdracht niet expliciet hoeft te zijn. Leren leren wordt vanzelf aangeleerd als gevolg van ervaringen, dus expliciete instructie is niet nodig, is dan de gedachte. Deze gedachte gaat geheel voorbij aan wat we inmiddels uit onderwijsonderzoek weten over het cruciale belang van expliciete strategie-instructie. #2 Anders van aard De tweede overtuiging is dat kennis van leerstrategieën anders van aard is dan kennis van de leerstof. Voor leraren met deze overtuiging is kennis van leren leren eenduidig en niet problematisch, waardoor ze het onnodig vinden om er relatief veel aandacht aan te besteden. Terwijl we weten dat leren leren best een complexe aangelegenheid is. Zo identificeerden Pressley en Afflerbach maar liefst meer dan 150 verschillende leerstrategieën die mensen gebruiken tijdens het lezen. Als we verwachten dat leerlingen gedegen kennis hebben van vakken als geschiedenis of natuurkunde, waarom dan niet kennis van hoe ze moeten leren? #3 Komt niet vaak van pas Kennis over leren leren wordt toch niet zo vaak gebruikt dus hoef je er ook niet veel aandacht aan te besteden tijdens de les, is de derde overtuiging volgens Lawson cum suis. De gedachte is dat je als leraar je daarom maar beter kan richten op het onderwijzen van de leerstof. Maar als je leerlingen eens vraagt om hardop na te denken terwijl ze bijvoorbeeld de hoek van een driehoek proberen te berekenen, dan zal je horen dat ze voortdurend verschillende soorten strategieën inzetten; met de antwoorden krijg je bovendien meer inzicht in welke strategieën je leerlingen gebruiken. Zo leer je hoe je leerlingen leren. #4 Gewoon ervaren in de praktijk De vierde overtuiging is dat de kennis die leerlingen nodig hebben om leren leren aan te leren, vooral praktisch moet zijn en niet theoretisch. De gedachte is dat leerlingen leren leren aanleren door gewoon aan de slag te gaan, door te ervaren in de praktijk. Deze gedachte komt voort uit wat leraren in het verleden hebben ervaren, toen ze zelf op school zaten. #5 Onzeker over de materie De vijfde overtuiging is een belangrijke, namelijk dat leraren niet zeker weten of ze kunnen lesgeven over leren leren. Twee zaken zijn hier van belang: het oordeel van leraren over hun eigen kennis van leren leren en het vertrouwen dat ze nodig hebben om de instructie te kunnen geven. Dat laatste gaat over de self-efficacy van leraren. Self-efficacy van leraren gaat over de mate waarin zij zichzelf in staat achten om complexe taken uit te voeren, zoals het geven van strategie-instructie. Self-efficacy is een sterke motivator voor het instructiegedrag van leraren. #6 Is aan de leerlingen zelf Leren leren moet aan leerlingen worden overgelaten, is de zesde overtuiging. Leraren gaan ervan uit dat de verantwoordelijkheid en het initiatief voor leren leren bij de leerling liggen, terwijl kennis van de verschillende strategieën de leerling juist in staat stelt om verantwoordelijkheid en initiatief te nemen. Het middel wordt hier verward met het doel van leren leren. #7 Lage verwachtingen De zevende overtuiging is dat leren leren slechts is weggelegd voor enkele leerlingen, meestal de goed presterende leerlingen. De leraar kan de leerlingen die moeilijk meekomen in de klas, maar beter niet lastigvallen met instructie in leren leren, is de overtuiging. Er zijn bizar veel studies beschikbaar die laten zien dat expliciete instructie juist ook voor deze groep leerlingen de voorkeur geniet. Daarnaast laat onderzoek van Jeltsen Peeters cum suis onder 127 basisschoolleerkrachten in Vlaanderen zien dat de leraar met deze overtuiging laag presterende leerlingen zelfs opzadelt met een dubbel nadeel. Niet alleen hebben deze leerlingen al te maken met de nodige uitdagingen door de beperkte mate waarin ze in staat zijn om te leren, bovendien krijgen ze vanuit deze overtuiging minder mogelijkheden om leerstrategieën aan te leren. Extra inzet is dus geboden op het terrein van strategie-instructie. #8 Leren leren is niet aan te leren De achtste overtuiging is dat leren leren waarschijnlijk niet is aan te leren. Sommige leraren en onderzoekers denken dat leren leren niet te onderwijzen valt en dus geen onderdeel kan en hoeft te zijn van een expliciete instructie. John Sweller en Fred Paas verwoorden dit heel helder: “Self-regulated learning is likely to be a biologically primary skill and so unteachable.” Deze onderzoekers baseren zich op het werk van de Amerikaanse hoogleraar David Geary. In het artikel ‘An evolutionarily informed education science’ bekijkt Geary het leren van kinderen door een evolutionaire bril. Sommige dingen leren zij vanzelf. Ze leren bijvoorbeeld lopen met vallen en opstaan, ze leren luisteren en spreken in een moedertaal en ze leren hoe ze met anderen omgaan. Volgens Geary zijn dit vormen van leren die evolutionair zijn ingebakken, omdat deze noodzakelijk zijn gebleken om te overleven. Deze automatische processen noemt Geary ‘primair leren’. Daarnaast bestaat er ‘secundair leren’. Daarbij gaat het om het verwerven van kennis (en vaardigheden) die evolutionair gezien veel jonger is en die van generatie op generatie wordt overgedragen. Denk aan leren lezen en schrijven. Secundaire kennis is van belang om goed te functioneren in de huidige maatschappij. Jammer genoeg gaat secundair leren niet vanzelf, maar kost dat moeite, en volgens Geary is daar expliciete instructie door de leraar bij nodig. Of zoals Kirschner, Claessens en Raaijmakers (2018, p. 21) het stellen: “Het verwerven van kennis op school gaat dus niet vanzelf.” En dat geldt ook voor het aanleren van leren leren; dat hebben de vele meta-analyses en interventiestudies ons inmiddels wel geleerd.

one must also submit to the discipline provided by imitationand practice.

they weresensible enough to recognize that one does not acquire a skill simply bystudying rules; one must also submit to the discipline provided by imitationand practice. And they recognized too that in order to derive the maximumbenefit from precept, imitation, and practice, the student had to be firedwith a desire to learn as much as his natural endowments permitted.

“Hij kwam tot de conclusie dat leerlingen drie keer op een andere manier met de leerstof in aanraking moesten komen om de materie te begrijpen”

‘socially biased individual learning’

the problems inherent in assuming any simple individual/social learning distinction are already well understood by some researchers working on cultural evolution.

The problem with this way of defining things is that we ignore the fact that, even when acting in a manner that appears to involve no direct interaction with other creatures, organisms nonetheless develop and learn in environments that have been affected by the prior actions of their conspecifics (and not just their conspecifics). This is precisely the sort of phenomenon stressed by the proponents of the niche-construction approach to evolution, and it is also stressed by developmental systems theorists [40,41]. Organisms grow in environments that have been constructed by the actions of previous generations: in that way, what an organism learns can be profoundly affected and enhanced by the collective activities of individuals it may never meet. In other words, we should not assume that there is any good distinction between individual learning and what we might call ‘social transmission’. The latter can be achieved via the former.

Instead of wasting energy in the vain attempt to hold mentally slow and defective children up to a level of progress which is normal to the average child, it will be wiser to take account of the inequalities of children in original endowment and to differentiate the course of study in such a way that each child will be allowed to progress at the rate which is normal to him, whether that rate be rapid or slow.

If we assume students want to learn - and I do - we should show our interest in their learning, rather than their performance.

Learning has to be knowledge. 00:10:07 And learning has to be based on understanding. And what you understand you can absorb, internalize and it becomes knowledge. What you know, you don't forget. You can block something that you know, but not forget.

Ritual practice embeds tacit knowledge. Its bodily actions enact meaning and operationalize values. The bodily motions of ritual actions, such as physically sharing drinks and food, and giving gifts, matters because of the reciprocal ideomotor effects of unconscious priming (Kahneman 2011:53). As Lakoff explains, there are connections between metaphoric meanings and bodily actions such that metaphoric associations are embedded in the structures of our brains. Compartmentalism, or “biconceptualism” in his terms, is physical in our brains, and frame shifts can be triggered through bodily movement with priming effects. “Going through the motions” of ritual will have some effects even for those who start off feeling silly for doing it.

interpretive drift is largely unconscious, not articulated, but brought on through practice (Luhrmann 1989:316). It involves more than a shift in the language people use (Luhrmann 1989:315, 321). It is not just cognitive, not just a new interpretive framework, but a shift in ontology and habitus, though Luhrmann uses the term “interpretive” drift. It is an acculturative process of change, but not an entirely passive internalization of culture. It is an interactive, though not necessarily conscious ongoing collaboration. We do this partly through imitation, but also growing skills in ourselves, as Michael Polanyi describes of tacit learning of personal knowledge.

Haluza-Delay’s description of informal and incidental learning sounds much like Michael Polanyi’s (1974) “practical knowledge.” Haluza-Delay discusses it as tacit learning, a term Polanyi introduced. From Polanyi’s description, much of tacit learning is initially conscious, but subsides into subsidiary awareness. People learn values in this fashion, but core values are picked up through imitation without conscious awareness.

we have turned to machine learning, an ingenious way of disclaiming responsibility for anything. Machine learning is like money laundering for bias. It's a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. The numbers don't lie.

The state of current technology greatly impacts our ability to manipulate information, which in turn exerts influence on our ability to develop new ideas and technologies. Tools designed to enable networked thinking are a step in the direction of Douglas Engelbart’s vision of augmenting the human intellect, resulting in “more-rapid comprehension, better comprehension, the possibility of gaining a useful degree of comprehension in a situation that previously was too complex, speedier solutions, better solutions, and the possibility of finding solutions to problems that before seemed insolvable.”

Divergence and emergence allow networked thinkers to uncover non-obvious interconnections and explore second-order consequences of seemingly isolated phenomena. Because it relies on undirected exploration, networked thinking allows us to go beyond common sense solutions.

For instance, we used to think that the main cause of obesity was a poor diet at an individual level, leading to treatments focused on the individual. However, taking a networked thinking approach in a 32-year-long study with over 12,000 people led researchers to discover that the participants’ personal network had a great impact on their likelihood to be obese. “Discernible clusters of obese persons were present in the network at all time points,” write the researchers.

Networked thinking is an explorative approach to problem-solving, whose aim is to consider the complex interactions between nodes and connections in a given problem space. Instead of considering a particular problem in isolation to discover a pre-existing solution, networked thinking encourages non-linear, second-order reflection in order to let a new idea emerge.

asks for the Minecraft domain.

When you call 'foo' in Ruby, what you're actually doing is sending a message to its owner: "please call your method 'foo'". You just can't get a direct hold on functions in Ruby in the way you can in Python; they're slippery and elusive. You can only see them as though shadows on a cave wall; you can only reference them through strings/symbols that happen to be their name. Try and think of every method call 'object.foo(args)' you do in Ruby as the equivalent of this in Python: 'object.getattribute('foo')(args)'.

100% human-authored

RSFCR can directlymodel non-linear effects and interactions to performaccurate prediction without making any prior assump-tions about the underlying data.

The aims of this manuscript can be summarised as:(i) examination of extensions of PLANNCR method(PLANNCR extended) for the development and vali-dation of prognostic clinical prediction models withcompeting events, (ii) systematic evaluation of model-predictive performance for ML techniques (PLANNCRoriginal, PLANNCR extended, RSFCR) and SM (cause-specific Cox, Fine-Gray) regarding discrimination andcalibration, (iii) investigation of the potential role ofML in contrast to conventional regression methods forCRs in non-complex eSTS data (small/medium samplesize, low dimensional setting), (iv) practical utility of themethods for prediction

Nowadays, there is a growing interest in applyingmachine learning (ML) for prediction (diagnosis or prog-nosis) of clinical outcomes [12, 13] which has sparked adebate regarding the added value of ML techniques ver-sus SM in the medical field. Criticism is attributed toML prediction models. Despite no assumptions aboutthe data structure are made, and being able to naturallyincorporate interactions between predictive features,they are prone to overfitting of the training data andthey lack extensive assessment of predictive accuracy(i.e., absence of calibration curves) [14, 15]. On the otherhand, traditional regression methods are consideredstraightforward to use and harder to overfit. That beingsaid, they do make certain (usually strong) assumptionssuch as the proportional hazards over time for the Coxmodel, and require manual pre-specification of interac-tion terms.

In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statisticalmodels (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recentlythere is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also beenextended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM forCRs within non-complex data (small/medium sample size, low dimensional setting).

who believed that parents, caregivers, peers, and the culture at large are responsible for developing higher-order functions.

Collecting does not transform us and always postpones learning and transformation to the future. Collecting creates debt that we promise to pay back in some future that never arrives.

Remember that life in a Zettelkasten is supposed to be fun. It is a joyful experience to work with it when it works back with you. Life in Zettelkasten is more like dance than a factory.

this is different than simply copying someone else's behavior.

it is time to actually perform the behavior you observed.

Retention can be affected by a number of factors, but the ability to pull up information later and act on it

Definition 3.2 (simple reward machine).

e thenshow that an RM can be interpreted as specifying a single reward function over a largerstate space, and consider types of reward functions that can be expressed using RMs

However, an agent that hadaccess to the specification of the reward function might be able to use such information tolearn optimal policies faster.

Reward Machines: Exploiting Reward FunctionStructure in Reinforcement Learning

Using Reward Machines for High-Level Task Specificationand Decomposition in Reinforcement Learning

“Writing a thesis,”Eco wrote, “requires a student to organize ideas and data, towork methodically, and to build an ‘object’ that in principlewill serve others. In reality, the research experience mattersmore than the topic.”

But the book’s enduring appeal—the reason it might interest someone whose life no longer demands the writing of anything longer than an e-mail—has little to do with the rigors of undergraduate honors requirements. Instead, it’s about what, in Eco’s rhapsodic and often funny book, the thesis represents: a magical process of self-realization, a kind of careful, curious engagement with the world that need not end in one’s early twenties.

around that same time i got a call from my daughter you know leave it to your kids and she said you know mom it's 00:03:48 just that all the problems we're dealing with in the world right now are insidious and um you know it came up last night siva was talking about the insidiousness 00:04:01 of the facebook problem and and this was an unlocker for me of what what does it mean for something to be insidious so i looked it up and i started to 00:04:14 explore and it turns out that insidious is defined and i think this is from the you know the oxford on the internet not the original but um that there's proceeding in a gradual 00:04:27 subtle way but with very harmful effects in other words there's something that's that's gathering combining in an unseen way that's leading to danger

This points to perhaps the most dangerous pitfall of note-taking. It’s very tempting to convince yourself you are learning just because you are writing down - in the sense of passively recording - what someone else says or writes.

student outcomes, including learning, persistence, or attitudes.

Zettelkasten can be described as a collection of conceptual maps in a written format.

“The evolutionary goal of altricial species is not to become highly competent as quickly as possible but rather to excel at learning over time.”

extended altriciality creates opportunities for sophisticated social learning within the parent-offspring system.

Founder of StudyWand.com here, who received a 15k grant to develop an AI generating flashcard app in 2020 after an earlier prototype.We've found students more consistently study ready-made cards that are at desirable difficulty (they get about 80% correct) and which are segmented by topic (e.g. semantic grouping of flashcards to tackle "one lesson at a time" like Duolingo). Students would prefer to use pre-made flashcards by other students in their class, then AI flashcards, then create and use their own.There is limited evidence by Roediger and Karpicke who are the forefathers of retrieval practise that creating cards is also important. Frank Leeming (2002 study Exam-a-day) also showed that motivation when studying is peaked when you ask just a few questions a day, but every working day.Now one of the vital benefits of retrieval practise with AI over creating your own cards is foresight bias - not mentioned yet in this thread - the fact that particularly in some subjects like Physics, students don't know what they don't know (watch this amazing Veritasium video, it also explains why misconceptions are so handy for learning physics): https://www.youtube.com/watch?v=eVtCO84MDj8 - basically, if you use AI quizzes (or any prepared subject-specific right/wrong system), you learn quickly where your knowledge sits and what to focus on, and reduce your exam stress. If you just sit their making quizzes, firstly you make questions on things you already know, you overestimate how much you can learn, and you consolidate on your existing strengths, and avoid identifying your own knowledge gaps until later on, which is less effective.--To quote from my dissertation experiment on background reading for retrieval practise, the end is about foresight bias a little: Retrieval practice – typically, quizzing - is an exceedingly effective studying mechanism (Roediger & Karpicke, 2006; Roediger & Butler 2011; Bae, Therriault & Redifer, 2017, see Binks 2018 for a review), although underutilized relative to recorded merit, with students vastly preferring to read content (Karpicke & Butler, 2009; Toppino and Cohen, 2009). Notably mature students do engage in practice quizzes more than younger students (Tullis & Maddox, 2020). Undertaking a Quiz (Retrieval practice) can enhance test scores significantly, including web-based quizzes (Daniel & Broida, 2017). Roediger & Karpicke (2006) analysed whether students who solely read content would score differently to students who took a practice quiz, one week after a 5-minute learning session. Students retained information to a higher level in memory after a week with the quiz (56% retained), versus without (42%), despite having read the content less (average 3.4 times) than the control, read-only group (14.2 times). Participants subjectively report preference for regular Quizzing (Leeming, 2002) over final exams, when assessed with the quiz results, with 81% and 83% of participants in two intervention classes recommending Leemings “Exam-a-day” procedure for the next semester, which runs against intuition that students might biases against more exams/quizzes (due to Test Anxiety). Retrieval Practice may increase performance via increasing cognitive load which is generally correlated with score outcomes in (multimedia) learning (Muller et al, 2008). Without adequate alternative stimuli, volume of content could influence results, thus differentiated conditions to control for this possible confound are required when exploring retrieval practice effects (as seen in Renkl 2010 and implemented in Methods). Retrieval practice in middle and high school students can reduce Test Anxiety, when operationalised by “nervousness” (Agarwal et al 2014), though presently no research appears to have analysed the influence of retrieval practice on university students’ Test Anxiety. Quizzing can alleviate foresight bias – overestimation of required studying time – in terms of students appropriately assigning a greater, more realistic study time plan (Soderstrom & Bjork, 2014). Despite the underutilization noted by Karpicke and Butler (2009), quizzing is becoming more common in burgeoning eLearning courses, supported by the research (i.e. Johnson & Johnson, 2006; Leeming, 2002; Glass et al. 2008) demonstrating efficacy in real exam performance.

There’s a holy trinity in machine learning: models, data, and compute. Models are algorithms that take inputs and produce outputs. Data refers to the examples the algorithms are trained on. To learn something, there must be enough data with enough richness that the algorithms can produce useful output. Models must be flexible enough to capture the complexity in the data. And finally, there has to be enough computing power to run the algorithms.

个人学习可能取决于他人行为的主张突出了将学习环境视为一个涉及多个互动参与者的系统的重要性

Carers in this position need the support of a caring community to sustain them”

rule-based algorithms like decision trees are not affected by feature scaling.

The best way to learn common table expressions is through practice. I recommend LearnSQL.com's interactive Recursive Queries course. It contains over 100 exercises that teach CTEs starting with the basics and progressing to advanced topics like recursive common table expressions.

We can have a machine learning model which gives more than 90% accuracy for classification tasks but fails to recognize some classes properly due to imbalanced data or the model is actually detecting features that do not make sense to be used to predict a particular class.