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.

Reward Machines: Exploiting Reward FunctionStructure in Reinforcement Learning

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.

I highly recommend Advent of Code to anyone looking to sharpen (or re-sharpen) their programming skills.

Dans le zero-shot learning, le modèle doit être capable de généraliser ce qu'il a appris sur des exemples précédents pour effectuer une tâche sur laquelle il n'a jamais été entraîné. Cela signifie que le modèle doit être capable de transférer ses connaissances acquises sur une tâche donnée à une nouvelle tâche, sans avoir besoin d'exemples d'entraînement spécifiques pour cette nouvelle tâche.

My goal was simply to scale this ladder over time. I worked the list 5 people at a time, starting at the bottom. I engaged relentlessly with those accounts until they noticed me and began engaging back.

Don’t try to convince everyone that what you say, feel, think, or have done is better than everyone else.

The collocation results can be used to correct the sensor data to more closely match thedata from the reference instrument. This correction process helps account for known biasand unknown interferences from weather and other pollutants and is typically done bydeveloping an algorithm. An algorithm can be a simple equation or more sophisticatedprocess (e.g., set of rules, machine learning) that is applied to the sensor data. This sectionfurther discusses the process of correcting sensor data

Learning Partners

Emergent abilities are not present in small models but can be observed in large models.

Whether you want to call them mottos, memes, or manifestos, words can be the building blocks of how we think and transmit ideas. You can also gauge how well someone is grasping your concepts—or at least making an effort to—by the language they’re responding to you with as well.

À la fin de ce cours, vous serez capable de :analyser un besoin de formation ;designer un dispositif pertinent ;développer ce dispositif en équipe ;implémenter la formation ;évaluer l’action de formation.

Discussion-Based Active Learning Strategies

partnerships, networking, and revenue generation such as donations, memberships, pay what you want, and crowdfunding

not really about the content of the sessions. Or anything you take from it. The most important thing are the relationships, the connections you gain from sharing the things you're passionate about with the people who are interested in it, the momentum you build from working on your project in preparation for a session

🌟 Highlight words as they are spoken (karaoke anybody?). 🌟 Navigate video by clicking on words. 🌟 Share snippets of text (with video attached!). 🌟 Repurpose by remixing using the text as a base and reference.

A blog post is a very long and complex search query to find fascinating people and make them route interesting stuff to your inbox.

Annotations are the first step of getting useful insights into my notes. This makes it a prerequisite to be able to capture annotations in my note making tool Obsidian, otherwise Hypothes.is is just another silo you’re wasting time on. Luckily h. isn’t meant as a silo and has an API. Using the API and the Hypothes.is-to-Obsidian plugin all my annotations are available to me locally.

In the same category of integrating h. into my pkm workflows, falls the interaction between h. and Zotero, especially now that Zotero has its own storage of annotations of PDFs in my library. It might be of interest to be able to share those annotations, for a more complete overview of what I’m annotating. Either directly from Zotero, or by way of my notes in Obsidian (Zotero annotatins end up there in the end)

Whatever your thing is, make the thing you wish you had found when you were learning. Don’t judge your results by “claps” or retweets or stars or upvotes - just talk to yourself from 3 months ago

a habit of creating learning exhaust:

Our kids have lost so much—family members, connections to friends and teachers, emotional well-being, and for many, financial stability at home. And, of course, they’ve lost some of their academic progress. The pressure to measure—and remediate—this “learning loss” is intense; many advocates for educational equity are rightly focused on getting students back on track. But I am concerned about how this growing narrative of loss will affect our students, emotionally and academically. Research shows a direct connection between a student’s mindset and academic success.

“Our kids have lost so much—family members, connections to friends and teachers, emotional well-being, and for many, financial stability at home,” the article begins, sifting through a now-familiar inventory of devastation, before turning to a problem of a different order. “And of course, they’ve lost some of their academic progress.”

As one expert reminded, “Bereavement is the No. 1 predictor of poor school outcomes.

Analysts have labelled this as “learning loss,” and many have blamed school closures and remote instruction in the course of the past two years as the culprit. Essentially, schools serving largely Black and Latino populations were more likely to turn to remote teaching.

Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

Meta-analysis statistical procedures provide a measure of the difference between two groups thatis expressed in quantitative units that are comparable across studies

Quadrants I and II: The average student’s scores on basic skills assessments increase by21 percentiles when engaged in non-interactive, multimodal learning (includes using textwith visuals, text with audio, watching and listening to animations or lectures that effectivelyuse visuals, etc.) in comparison to traditional, single-mode learning. When that situationshifts from non-interactive to interactive, multimedia learning (such as engagement insimulations, modeling, and real-world experiences – most often in collaborative teams orgroups), results are not quite as high, with average gains at 9 percentiles. While notstatistically significant, these results are still positive.

Multimodal Learning Through Media:What the Research Says

Scaffolding is the act of providing learners with assistance or support to perform a taskbeyond their own reach if pursued independently when “unassisted.”

Schemas are chunks of multiple individual units of memory that are linked into a system ofunderstanding

Learning is defined to be “storage of automated schema in long-term memory.

e argue that mutual learningwould benefit sentiment classification since it enriches theinformation required for the training of the sentiment clas-sifier (e.g., when the word “incredible” is used to describe“acting” or “movie”, the polarity should be positive)

. However, such a framework is not applicablehere since the learned latent topic representations in topicmodels can not be shared directly with word or sentencerepresentations learned in classifiers, due to their differentinherent meanings

“The metaphor is that the machine understands what I’m saying and so I’m going to interpret the machine’s responses in that context.”

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Teachers are actually managing something far more important than test scores. They're managing, massaging, inspiring, reinforcing and jollying along the only thing that helps a kid learn, which is the energy and trust in the classroom. Good teachers do it instinctively and constantly, though it's exhausting and painstaking to do. This is the one thing teachers don't get rewarded for or credit for. They care enough to manage the waves of excitement and wonder and fatigue and frustration in their classrooms. They manage the waves and let the particles take care of themselves.

Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence.

Weare on record as holding that unlimited educational opportunity-or, speaking practically, educational opportunity thatis limited only by individual desire, ability, and need-is themost valuable service that society can provide for its members.

understanding mental models of learning will make it easier to think about learning problems.

Technology like this, which lets you “talk” to people who’ve died, has been a mainstay of science fiction for decades. It’s an idea that’s been peddled by charlatans and spiritualists for centuries. But now it’s becoming a reality—and an increasingly accessible one, thanks to advances in AI and voice technology. 

Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic

Putting transformative learning theory into practice

Experiential Learning Theory as a Guide for Experiential Educators in Higher Education

How should the higher education workforce adapt to advancements in technology for teaching and learning?

Peer-to-peer Teaching in Higher Education: A Critical Literature Review

Technology-enhanced learning and teaching in higher education: what is ‘enhanced’ and how do we know? A critical literature review

By becoming a hybrid you can choose how you want to be unique. Countless unique combinations are available to you.

Being U-shaped requires bravery, because it’s so unusual. U-shaped people tend to be subjected to greater skepticism, because no one else really understands what they alone can see.

T-shaped. They tend to be natural leaders because they understand how different responsibilities overlap, and how to construct effective teams and processes.

The U-shaped path means developing skills that are not often found together. Like engineering and dancing, or singing and design.

The T-shaped hybrid path is one that many curious people follow. You grow your skillset and experience in areas that are adjacent to your dominant expertise. For example engineering and design, or singing and dancing.

The first interaction with a programming language should be what it can do for you, rather than an exhaustive glossary of what it is.

Our job is to make sure we promote this sense of learning — and learning starts with a question, as we like to say. We are the workflow folks, in that we are enabling just-in-time learning when people truly have an issue and want to get unstuck through Stack Overflow.

The student-centered mindset has led to a dumbing-down of curricula and a constant pressure on educators to motivate students, rather than a pressure on students to take ownership of their own success and failure.

en 2018 et 2019, Ubisoft a ajouté à chaque jeu un mode éliminant toutes les missions et tous lescombats, appelé « Discovery Tours », dans le but que les professeurs d’Histoire dans les écoles secondairespuissent utiliser ces jeux dans leurs cours.

Pourtant, cesmêmes chercheurs accordent toute leur attention aux jeux vidéo sérieux qui, ils le reconnaissent eux-mêmes,sont souvent « peu motivants », « amusants » et « divertissants » (c’est-à-dire, offrant un engagement et uneimmersion faibles), contrairement aux jeux vidéo commerciaux ou basés sur le divertissement (Davidson, 2008;Gee, 2008; Granic et coll., 2014; Hamari et Koivisto, 2015).

si l’onsuppose qu’une part importante de nos étudiants ont une motivation intrinsèque pour jouer aux jeux vidéopendant leur temps libre, il vaut la peine d’étudier si cette même motivation et ce même engagement peuventêtre transférés dans le contexte d’un cours, et si ce type d’expérience peut aboutir à des apprentissagessignificatifs

si l’onsuppose qu’une part importante de nos étudiants ont une motivation intrinsèque pour jouer aux jeux vidéopendant leur temps libre, il vaut la peine d’étudier si cette même motivation et ce même engagement peuventêtre transférés dans le contexte d’un cours, et si ce type d’expérience peut aboutir à des apprentissagessignificatifs

notre recherche a pour objectif de mesurer l’impact de ces jeux vidéo sur l’engagement général,l’apprentissage, l’empathie et le raisonnement critique des étudiants dans un cours en Humanities de niveaucégep et dans un cours du programme de techniques d’éducation spécialisée (TES) de niveau cégep,respectivement.

La volonté d’étudier les jeux vidéo basés sur le divertissement en particulier est motivée par le fait que lesétudiants sont largement plus susceptibles de jouer à ces jeux vidéo en dehors de la salle de classe, précisémentparce qu’ils sont plus populaires, captivants et immersifs, dès leur conception

les jeux vidéo basés sur le divertissement offrent des expériences similaires aux jeux vidéo sérieux, et qu’il estdonc probable qu’ils mènent à un apprentissage significatif

générer des résultats d’apprentissage significatif sous la formed’empathie en utilisant un jeu vidéo basé sur le divertissement, Never Alone (Kisima Inŋitchuŋa)(Upper One Games, 2015), dans le cours « Interactions and Cultural Communities » /« Interactions et communautés culturelles » (351-CC1-AS) du programme de Techniquesd’éducation spécialisée

générer des résultats d’apprentissage significatif sous la forme depensée critique en utilisant un jeu vidéo basé sur le divertissement, Portal (2007), dans le cours« Knowledge » (345-101-MQ) du programme Humanities 101.

peu de recherches ont été faites sur l’utilisation des jeux vidéo basés sur le divertissement et surleur capacité éventuelle à mener à des résultats d’apprentissage semblables à ceux des jeux vidéo sérieux.

There's no market for a machine-learning autopilot, or content moderation algorithm, or loan officer, if all it does is cough up a recommendation for a human to evaluate. Either that system will work so poorly that it gets thrown away, or it works so well that the inattentive human just button-mashes "OK" every time a dialog box appears.

For the sole true end of educationis simply this: to teach men how to learn for themselves; and whateverinstruction fails to do this is effort spent in vain.

We have lostthe tools of learning—the axe and the wedge, the hammer and the saw, thechisel and the plane—that were so adaptable to all tasks. Instead of them, wehave merely a set of complicated jigs, each of which will do but one task andno more, and in using which eye and hand receive no training, so that no manever sees the work as a whole or “looks to the end of the work.”

For the tools of learning are the same, in any and everysubject; and the person who knows how to use them will, at any age, get themastery of a new subject in half the time and with a quarter of the effortexpended by the person who has not the tools at his command.

modern education concentrates onteaching subjects, leaving the method of thinking, arguing, and expressingone’s conclusions to be picked up by the scholar as he goes along;

Thewhole of the Trivium was in fact intended to teach the pupil the proper use ofthe tools of learning, before he began to apply them to “subjects” at all

Is it not the great defect of our education to-day (—a defect traceablethrough all the disquieting symptoms of trouble that I have mentioned—)that although we often succeed in teaching our pupils “subjects,” we faillamentably on the whole in teaching them how to think? They learneverything, except the art of learning.

she says in contrast to e.g. reading, we don’t know much of anything about teaching programming. There’s no body of work

Czech teacherComenius (1592–1670). He championed universal education, which hepromoted in his Didactica magna, arguing for the commonality of education—itwas for everyone, including, shockingly, females.

William Petty, a doctor in Cromwell’s army in 1647, noted that ‘...we seechildren do delight in drums, pipes, fiddles, guns made of elder sticks, andbellowes noses, piped keys, etc., painting flags and ensigns with elder-berriesand corn poppy, making ships with paper, and setting even nut-shells aswimming, handling the tools of workmen as soon as they turn their backs, andtrying to work themselves’ (reported in the Harleian Miscellany, 1810).

Elizabeth I’s tutor, Roger Ascham (1515–68), promotedlearning-by-doing in The Scholemaster: ‘Bring not up your children in learningby compulsion and feare,’ he said, ‘but by playing and pleasure.’

‘Now, all this study of reckoning and geometry...must be presented to them while still young, not inthe form of compulsory instruction.’ ‘Why so?’ ‘Because,’ said I, ‘a free soul ought not to pursueany study slavishly; for while bodily labours performed under constraint do not harm the body,nothing that is learned under compulsion stays with the mind.’ ‘True,’ he said. ‘Do not, then, myfriend, keep children to their studies by compulsion but by play.’The Republic, 536d–e; 537a

but then relocate and roam around freely the rest of the time

in a matter of seconds

so that what students did in class meetings simulated the actual working practices of real mathematicians

worldviews

Thus, syllablessuch as ab, ac, ad, ib, ic were practiced for the sake of masteryof the language. When a child could name all of a determinednumber of combinations, he was said to know his ABC's.

new technologies leveraging techniques like spaced repetition mean it's much easier to remember what you learn

In 2019 the UK school inspection body Ofsted went further than this and changed their definition of ”learning” itself to “an alteration in long-term memory”.

to get faster at learning you must get more efficient at moving things into your long-term memory, i.e. stop forgetting things you learn. The less you forget the more you'll understand and the faster you'll learn.

To learn more than 4 new concepts we must move some of them into our long-term memory before learning the rest.

You can only understand something new if understanding it requires combining less than 4 new pieces of information.

our working memory has a maximum capacity of roughly 4. When reading about quantum mechanics we encounter new Concept 1 and store it in our working memory. Then when learning about Concept 1 we encounter Concepts 2, 3, and 4 and our working memory becomes full. We then cannot understand Concept 5.

why is there so little correlation between students’ performance in their physics courses and their ability to do physics research?

Cognitive-science research shows that people improve learning efficiency by practicing the set of specific cognitive tasks required for their area of expertise.11. K. A. Ericsson, R. T. Krampe, C. Tesch-Römer, Psych. Rev. 100, 363 (1993); https://doi.org/10.1037/0033-295X.100.3.363A. Ericsson, R. Pool, Peak: Secrets from the New Science of Expertise, HarperOne (2017). Although that approach is based on learning research, it is uncoincidentally quite similar to the ideal master–apprentice method for traditionally teaching a craft (see figure 1).

Fourth, students seemed reluctant to come to the office seeking help. They also failed to use with great frequency the able and congenial peer tutor assigned to the course. I suspect there is an increased factor of intimidation associated with being lost in the material and asking a much older male professor for help.

I had readBalzac off and on during the forties, and been much takenwith his self-appointed task of " c o v e r i n g " all the majorclasses and types in the society of the era he wished tomake his own.