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"Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. --"

"This Infoline defines action learning as a group effort that involves solving real world problems through the use of acquired learning and implementing systems-wide solutions. It discusses why this process works and the benefits to organizations that use this technique. It features brief case studies from Whirlpool, National Semiconductor, General Electric, and Cigna International Property and Casualty Corporation."

"The WLP Scorecard provides a structure for establishing goals, compiling information, tracking progress, quantifying effectiveness, and articulating results. Use WLP Scorecard: Why Learning Matters to quickly and easily navigate through the complex scorecard implementation and measurement processes."

"The objectives of this research are to 1) find out the profile of student’s learningmotivation in Physics class measured by aspects such as Attention, Relevation, Confidence,and Satisfaction; and 2) measure the percentage of each learning motivation aspect suchas Attention, Relevation, Confidence, and Satisfaction contributed to students’ learningmotivation in Physics class. The sample of this research is 90 students from XI class ofScience Program. The data was collected through questionnaire and observation. Thequestionnaire was analyzed using descriptive quantitatively while the observation sheetwas analyzed qualitatively. The results show that: 1) the student’s learning motivationcan be categorized into high, average, and low; and 2) the percentage of each learningmotivation aspects are Attention (59,86%), Relevance (57,08%), Confidence (55,28%),and Satisfaction (60,14%). To conclude, the average level of students’ learning motivationfor Physics is in the medium and low category caused by the lack of students’ interest inlearning Physics."

"The advanced development of information and communication technologybrings a significant impact on the way of how human learn. The e-learning orInternet-based and online learning has been widely used in a variety of learning activities as a suplement to the conventional in-class learning, flipped classroom etc. The term e-learning refers to learning activities conducted by the use ofInternet. Universitas Terbuka (UT) utilizes e-learning program in the form of online tutorial. The online tutorial is basically a form of learning support provided by UT to facilitate student to learn. The online tutorial consists of three major learning activities including reading, discussion and assignment. During online tutorial student must complete the assigned activities and interaction with peers, tutor and digital learning resources. Online tutorial can be seen as an instrumental tool that provide an opportunity for students to expandhis/her knowledge and skills beyond the provided printed and digital module. Tutors should be able to design online learning programs that can be used to facilitate students in expanding their knowledge and skills. The purpose of this study is to elaborate conceptually the use of constructivism learning theory in online tutorial of the open and distance learning higher institution."

"Perkembangan pesat teknologi telah memberikan akses kepada masyarakat untuk mengemukakan opini dan evaluasi pribadi di media sosial dan berbagai penjuru dunia digital. Hal ini menjadi pemicu berkembangnya ilmu analisis sentimen atau sering disebut juga opinion mining yang merupakan pengaplikasian dari ilmu machine learning. Umumnya, metode machine learning mempelajari satu domain untuk menghasilkan suatu model, tetapi dengan pengembangan lanjut dihasilkan lifelong learning dimana pembelajaran model berlangsung secara kontinu menggunakan berbagai source domain. Pada tahun 2022, Osmardifa melakukan penelitan mengenai perbandingan kinerja model Bidirectional Encoding Representation from Transformers (BERT) terhadap kinerja model Convolutional Neural Network (CNN) dan model Long Short-Term Memory (LSTM) untuk lifelong learning. Namun, dari perbandingan kinerja tersebut hanya menggunakan satu kombinasi urutan domain dari total 120 kombinasi dari urutan 5 source domain. Dalam skripsi ini, kombinasi semua kombinasi urutan source domain menggunakan dataset penelitian Osmardifa disimulasikan untuk mengukur kinerja model menggunakan urutan pembelajaran yang berbeda dari simulasi yang dijalankan Osmardifa. Hasil simulasi urutan source domain lainnya menggunakan metode BERT menunjukkan banyak kombinasi urutan source domain yang menghasilkan kinerja lebih baik dibandingkan penelitian sebelumnya. Didapat bahwa urutan pembelajaran Capres – Jenius – Shopback – Ecom- Grab menghasilkan akurasi tertinggi 82,49% untuk retain of knowledge bagi source domain yang menggunakan dataset Capres sebagai Source Domain 1 dan urutan Capres – Jenius – Grab – Ecom – Shopback menghasilkan akurasi tertinggi 91,32% untuk transfer of knowledge. Hasil ini menunjukkan kenaikan sebesar 1,53% dan 1,72% dibandingkan simulasi awal yang dilakukan oleh Osmardifa. Analisis lanjut dilaksanakan untuk melihat apakah ada pola atau alasan yang dapat menjelaskan perbedaan kinerja pada model ketika urutan source domain digantikan akan tetapi tidak ditemukan pola atau atau alasan tersebut tidak ditemukan pada penelitian.

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Technological advancements have given the public more of an opportunity to share opinions and personal evaluations within public spaces through social media and other domains on the internet.This phenomenon sparked an interest to develop a field of study under machine learning called opinion mining which specializes in analyzing sentiments found within texts. Generally, machine learning models have one domain or dataset which is used to develop the model, however with further developments a lifelong learning was developed which aims to develop models through continual learning with multiple domains or datasets. In 2022, Osmardifa underwent a study to compare the results of the Bidirectional Encoding Representations from Transfomers (BERT) model with the Convolutional Neural Network (CNN) model and the Long Short-Term Memory (LSTM) model when all of the above are used for lifelong learning. However, the comparison that was used within the study only used one combination of the sequence of source domains available using 5 source domains when there are in fact 120 possible sequences of source domains when using 5 source domains. Therefore, this study aims to further analyze the accuracy of the model in Osmardifa’s research when tested and trained using the other 120 possible learning orders of the model. Further simulations on the previously unused sequences using the BERT model showed better results than the sequence of source domains that was used in previous studies. The Capres – Jenius – Shopback – Ecom- Grab sequence showed the best resulting accuracy for the retain of knowledge tests which used the Capres dataset as the first source domain (Source Domain 1), said sequence of source domains had a final accuracy of 82.49% which is a 1.53% increase compared to previous results. The transfer of knowledge tests also showed that the Capres – Jenius – Grab – Ecom – Shopback sequence gave the best overall results with a final accuracy of 91.32% which is an increase of 1.72% compared to the previous study. Further analysis on the results of the simulations were done to check whether or not there was an underlying pattern or reason for this difference in accuracy, however no conclusive pattern or reasons were found."

"Terdapat berbagai penelitian yang menyebutkan bahwa pembelajaran secara kolaboratif menunjukkan hasil yang sangat positif yakni meningkatnya hasil proses belajar, meningkatnya performa tim yang terkait dengan pemahaman suatu pengetahuan. Metodologi yang dilakukan dalam penelitian ini terdiri dari beberapa tahapan diantaranya tahap identifikasi dan analisis data, perancangan desain sistem, tahap implementasi, dan tahap evaluasi. Sistem yang dikembangkan dalam penelitian ini berdasarkan pada knowledge construction learning environment yang bertujuan untuk menyediakan sarana bagi para peserta ajar untuk mengartikulasikan pendapat mereka, mendiskusikan ide mereka kepada orang lain, untuk membedakan perspektif yang mereka miliki, mengadopsi ide-ide dari orang lain, mengklarifikasi terjadinya ketidaksepahaman, menegosiasikan pemahaman dan merumuskan pengetahuan agar menjadi produk yang bernilai. Untuk mencapai tujuan-tujuan tersebut, penulis akan merancang sebuah sistem pembelajaran kolaboratif online yang menerapkan pendekatan knowledge construction dalam melakukan kolaborasi antar-Peserta.

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There were several researches that stated collaborative learning approach shows a very positive result in the improvement of study result and in the enhancement of team performance related to the understanding of knowledge. The research methodology that is being implemented in this thesis consists of identification, data analysis, system design, implementation, and evaluation stage. The system that is being developed in this research based on a knowledge construction learning environment that aimed to provide students the facility for articulating, discussing ideas to others, distinguishing perspectives, adopting ideas from others, clarifying any misunderstanding, negotiating for an agreement, and constructing knowledge to formulate valuable learning output. To achieve those goals, the author has designed a learning system which provides collaborative tools based on the knowledge construction approach. The knowledge construction approach comprises six learning phases, which are the Articulation, Clarification, Argumentation, Negotiation, and Integration phase. Each of the phases provides collaborative tools which facilitate the learning process of the student."

"ABSTRAKSkripsi ini membahas rancang bangun dan analisis performa adaptive learning untuk homeschooling tingkat Sekolah Dasar (SD) dengan menggunakan metode VAK Learning Style. Skripsi ini bertujuan untuk merancang, mengimplementasikan, dan menganalisa performa sistem homeschooling adaptive e-learning berbasiskan PHP dan MySQL. Pengujian performa sistem terdiri dari pengukuran kecepatan dan survei. Hasil pengukuran dengan 50 users pada JMeter menunjukkan kecepatan yang baik yaitu rata-rata akses log in 138.3ms, kuis VAK 158.36ms, pre-test 114ms, materi video 143.72ms. Hasil surveimenggunakan kuesioner menunjukkan bahwa sistem masuk ke dalam grade 3 (skala 1-4), yaitu ?baik?, dengan nilai rata-rata kuesioner teacher dan kuesioner student sebesar 3.34.

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ABSTRACTThis thesis discusses development and performance analysis of adaptive learning for homeschooling elementary school (SD) using VAK learning style method. This thesis aims to design, implement, and analyze system performance of homeschooling adaptive e-learning based on PHP and MySQL . Performance testing is consisted of speed measurement and survey. Measurement results with 50 users on JMeter showed good speed with average access of log in 138.3ms,

VAK quiz 158.36ms, pre-test 114ms, video material 143.72ms. Survey results using questionnaire showed that system goes into grade 3 (scale 1-4), which is 'good', with average value of teacher and student questionnaire at 3.34."