Empowering students starts with empowering their minds! #Active_Learning

Despite the popularity of #active_learning, @SChewPsych isn't having it: "Active learning is a mostly meaningless educational buzzword." In brief: the phrase is so broad and squishy as to have almost no usefulness in psychology research. Chew proposes other, better ways to think about effective teaching. If you want to understand his argument more fully, read more here: bit.ly/4kA4GtF

#Highcited #highviewed #review 📝A Survey on #Active_Learning: State-of-the-Art, Practical Challenges and Research Directions 🔎 Article Views: 12310; Citations: 45 📍 buff.ly/3RBm03M #query_strategy; #supervised_learning; #AI @MDPIOpenAccess @ComSciMath_Mdpi

I had the opportunity to talk in a workshop on #Active_Learning for my colleagues, organised by ALiTE, #SQU. It was an amazing experience! 🙌 exploring #AL strategies aimed at making learning more engaging and student-centered.

At IPS @cambridgewing , we nurture #confident communicators through #interactive discussions and dynamic #language learning. #CIE @RitaSingh0210 @IprmGrp @akmittals @ipsindirapuram @Anupam_Sharmaa @active_learning @CambridgeInt @GEMReport @HTEduNews

Spelling bee competition Final round #Manarat_Qaisumah students Mr. Ahmed Sakr #Active_learning

🔵grade one elementary stage 🔵Alphabet song 🔵Reading CvC words #Challenge_time_for_reading 🔵Mr:Reda Fayez - English teacher #Manarat_AlQaisuma_Schools #Active_Learning

Spelling bee competition among #Manarat_Qaisumah students Elementary and intermediate stages Final round coming soon Mr. Ahmed Sakr #Active_learning

#MS_DEED Level 2 workshop @MSFDA_Official @IISERPune Day 4 dedicated to #active_learning strategies to get students #involved and #engaged.. exploring the joy of learning! With @ScienceActivity and lab sessions for all subjects… to be continued

Thank you all for having me 😊

Setting goals as a staff with @Miss_Burns15 to share and discuss the rationale of a collaborative Enquiry Process. Always looking to plan ahead for our pupils @GIC_Glasgow @ActiveLearnUK @active_learning

Our 3rd & 5th classes did a river study last Friday in the Liffey ⁦@PDSTPrimarySTEM⁩ ⁦@active_learning⁩

Just a bored guy on chatgpt import cv2 import dlib import imutils import numpy as np import pandas as pd import tensorflow as tf from sklearn.preprocessing import StandardScaler from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import ResNet50 from tensorflow.keras.callbacks import LearningRateScheduler from tensorflow.keras.regularizers import l2 from tensorflow.keras.optimizers import Adam from sklearn.model_selection import GridSearchCV from sklearn.metrics import classification_report from sklearn.utils import shuffle from sklearn.model_selection import train_test_split from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.layers import Embedding, LSTM, Dense, Input from tensorflow.keras.models import Model from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.utils import plot_model from tensorflow.keras.layers import Concatenate, Conv2D, MaxPooling2D, GlobalMaxPooling2D, Dropout from tensorflow.keras.layers import TimeDistributed, Bidirectional, GlobalAveragePooling1D from tensorflow.keras.layers import SpatialDropout1D, BatchNormalization, Flatten from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint from tensorflow.keras.layers import Attention from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Embedding, Dropout from tensorflow.keras.initializers import Constant from tensorflow.keras.layers import GRU, Masking from tensorflow.keras.utils import to_categorical from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.callbacks import ReduceLROnPlateau from tensorflow.keras.layers import LSTM, GRU, Dense, Dropout, Input, Embedding, Masking, Concatenate from tensorflow.keras.utils import to_categorical from tensorflow.keras.models import Model from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint from tensorflow.keras.layers import Add, Multiply, Lambda, Activation from tensorflow.keras.layers import GlobalMaxPooling1D, GlobalAveragePooling1D from tensorflow.keras.layers import Conv1D, BatchNormalization, TimeDistributed from tensorflow.keras.models import load_model from sklearn.metrics import roc_auc_score from sklearn.utils import shuffle from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.layers import Embedding, LSTM, Dense, Input, Bidirectional, Dropout from tensorflow.keras.models import Model from tensorflow.keras.optimizers import Adam from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint from tensorflow.keras.utils import plot_model from tensorflow.keras.metrics import AUC from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Embedding, Dropout from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.initializers import Constant from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, GRU, Dense, Dropout, Input, Embedding, Masking from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.layers import Attention from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Embedding, Dropout from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.initializers import Constant from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, GRU, Dense, Dropout, Input, Embedding, Masking from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint # Facial Recognition def detect_faces(image): # ... # Face detection logic goes here # ... return faces # Speech to Text def transcribe_speech(audio_file): # ... # Speech-to-text transcription logic goes here # ... return text # Data Augmentation def augment_data(images, labels): # ... # Data augmentation logic goes here # ... return augmented_images, augmented_labels # Pretrained Models and Transfer Learning def load_pretrained_model(): # ... # Pretrained model loading logic goes here # ... return model # Advanced Attention Mechanisms def advanced_attention(inputs): # ... # Advanced attention mechanism logic goes here # ... return attention_output # Ensemble Models def create_ensemble(models): # ... # Ensemble model creation logic goes here # ... return ensemble_model # Learning Rate Scheduling def learning_rate_schedule(epoch): # ... # Learning rate scheduling logic goes here # ... return learning_rate # Regularization Techniques def apply_regularization(model): # ... # Regularization techniques logic goes here # ... return regularized_model # Hyperparameter Tuning def tune_hyperparameters(X, y): # ... # Hyperparameter tuning logic goes here # ... return best_params, best_model # Pretrained Embeddings def load_pretrained_embeddings(): # ... # Pretrained embeddings loading logic goes here # ... return embeddings # Multimodal Approaches def multimodal_integration(text_features, image_features): # ... # Multimodal integration logic goes here # ... return fused_features # Error Analysis and Debugging def analyze_errors(predictions, labels): # ... # Error analysis and debugging logic goes here # ... return error_analysis_results # Reinforcement Learning def reinforcement_learning(): # ... # Reinforcement learning logic goes here # ... return trained_policy # Generative Adversarial Networks (GANs) def train_gan(): # ... # GAN training logic goes here # ... return generator, discriminator # Natural Language Processing (NLP) Techniques def apply_nlp_techniques(text): # ... # NLP techniques logic goes here # ... return processed_text # Graph Neural Networks def train_gnn(): # ... # GNN training logic goes here # ... return gnn_model # Time Series Analysis def analyze_time_series(time_series): # ... # Time series analysis logic goes here # ... return analysis_results # Anomaly Detection def detect_anomalies(data): # ... # Anomaly detection logic goes here # ... return anomalies # Active Learning def active_learning(X, y): # ... # Active learning logic goes here # ... return updated_X, updated_y # Model Compression def compress_model(model): # ... # Model compression logic goes here # ... return compressed_model # Online Learning def online_learning(data): # ... # Online learning logic goes here # ... return updated_model # AutoML def auto_ml(data): # ... # AutoML logic goes here # ... return best_model def main(): # ... Code for your specific task ... if __name__ == '__main__': main()

Is there any way to avoid forgetting previous knowledge in #active_learning? or models 'should' forget their previous knowledge? #RadAIchat

[Unit 8 Period 1 Lesson] #Amazing_Animals 🐝 [تفعيل تقنية #الواقع_المعزز في مبحث اللغة الإنجليزية].🐙 تفعيل الواقع المعزز في درس #حيوانات_مدهشة لطالبات الصف الحادي عشر. #متعة_التعليم_الهادف 🐙 #Teacher_Munira #Active_learning #Augmented_Reality #Palestine

#Active_learning has now become a fad. Almost all colleges and universities want their faculty to adopt active learning. However, I think most of what happens in our classrooms is more of 'active teaching' and 'active testing' than actual active learning! What do you think?

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Excited to join other @IHSchools leaders as we embark on the journey to become @iste certified. Looking forward to enhancing my strategies to engage students and teachers in #realworld, #authentic, #active_learning with technology. @Primary_PrinIH @IHPSRuebusch #ihpromise

self_taught = { 'passive_learning': ['Google', 'YT', 'Udemy,' 'StackOverflow', 'Medium', 'Kaggle'], 'active_learning': ['Docs', 'OJTs', 'Projects', 'A lot of failures'] } #python #programming #100daysofcode #CodeNewbie #100daysofcodechallenge #Datascience #MachineLearning