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"An essential book linked to the LLUK Standards for teachers, trainers &​ tutors: a practical introduction to teaching &​ learning.This popular introductory textbook is ideal for anyone working or training to work in the lifelong learning sector. The new edition has been comprehensively revised to reflect recent developments in the sector and current research in learning and teaching. The book covers key topics such as reflective teaching, communication, learning theories, and assessment for learning. In addition there are new chapters on: Behaviour for learning; A curriculum for inclusive learning; The lifelong learning sector and Functional skills. This edition also includes more student journal extracts, case studies and developmental activities. Common elements of good practice in teaching and learning spanning the lifelong learning, further education and skills sector and are fully explored so that you will: Gain a thorough understanding of learners and their needs Understand the importance of effective communication Appreciate the role of reflective practice and continuing professional development Achieve a good grasp of theory and practice including methods of active learning and assessment for learning Teaching in the Lifelong Learning Sector is essential reading for those teaching or training to teach in further and higher education, adult and community learning, and work-based learning.This popular introductory textbook is ideal for anyone working or training to work in the lifelong learning sector. This new edition has been comprehensively revised to reflect recent developments in the sector and current research in learning and teaching."

"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."

"The basic concern of the volume is to determine the preconditions of personality development and to show their significance and their perspectives for educational science and for pedagogical practice. First, these basic preconditions of becoming oneself are collected in a single volume and discussed in terms of their significance for science and for educational practice. In all fundamental dimensions are understood as precondition of becoming oneself. "(BBildung" is here for the first time understood as the formation of the overall individual personality, which the OECD postulates to be the key qualification of the twenty-first century. From a pedagogical perspective, it is a matter of furthering the personality. It provides research with a new perspective, in that it makes the furthering of the overall personality the object of education.;The basic concern of the volume is to determine the preconditions of personality development and to show their significance and their perspectives for educational science and for pedagogical practice. First, these basic preconditions of becoming oneself are collected in a single volume and discussed in terms of their significance for science and for educational practice. In all fundamental dimensions are understood as precondition of becoming oneself. "(BBildung" is here for the first time understood as the formation of the overall individual personality, which the OECD postulates to be the key qualification of the twenty-first century. From a pedagogical perspective, it is a matter of furthering the personality. It provides research with a new perspective, in that it makes the furthering of the overall personality the object of education.;The basic concern of the volume is to determine the preconditions of personality development and to show their significance and their perspectives for educational science and for pedagogical practice. First, these basic preconditions of becoming oneself are collected in a single volume and discussed in terms of their significance for science and for educational practice. In all fundamental dimensions are understood as precondition of becoming oneself. "(BBildung" is here for the first time understood as the formation of the overall individual personality, which the OECD postulates to be the key qualification of the twenty-first century. From a pedagogical perspective, it is a matter of furthering the personality. It provides research with a new perspective, in that it makes the furthering of the overall personality the object of education."

"This book is concerned with the general issues of ageing, learning and education for the elderly and then with the more specific issues of why, how and what elders want to learn. This monograph consists of 10 chapters written by various internationally renowned researchers and scholar-practitioners in the field."

"Kemajuan teknologi memicu pertumbuhan industri teknologi dan mendorong masyarakat untuk menggunakan smartphone, terutama untuk berkomunikasi di media sosial. Media sosial merupakan tempat yang efektif untuk mencari berbagai informasi. Oleh karena itu, media sosial menyimpan banyak data, terutama data tekstual. Data tersebut muncul dari para pengguna yang jumlahnya meningkat pesat. Data tekstual bisa digunakan untuk analisis sentimen. Skripsi ini membahas analisis sentimen untuk melihat kecenderungan suatu informasi dari penulisnya. Analisis sentimen mengklasifikasikan data tekstual menjadi kelas sentimen positif dan negatif. CNN merupakan salah satu algoritma deep learning yang dapat mengklasifikasi data tekstual. Model dari algoritma CNN menunjukkan hasil yang cukup baik dalam mengkalsifikasi permasalahan analisis sentimen dengan bantuan lifelong learning. Lifelong learning merupakan machine learning yang menyerupai proses belajar pada otak manusia. Proses yang dijalankan yaitu dengan memanfaatkan hasil pembelajaran dari masa lalu untuk membantu pembelajaran pada masa depan. 4 dataset dengan domain yang berbeda, dijalankan menggunakan model CNN pada proses Lifelong learning dan menghasilkan akurasi yang meningkat, seiring dengan penambahan dataset pada proses training.

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Technological advances are fueling the growth of the technology industry and encouraging people to use smartphones, especially for surfing on social media. Social media is an effective tool to find information. Therefore, social media stores a lot of data, especially textual data. The data came from users whose numbers had increased rapidly. Textual data can be used for sentiment analysis. Sentiment analysis is conducted in this study to obtain the tendency of the authors about an article. Sentiment analysis classifies textual data into a class of positive and negative sentiments. CNN is one of the deep learning algorithms that can classify textual data into positive, negative and natural classes. The model of the CNN algorithm shows good results in classifying the problem of sentiment analysis with the help of lifelong learning. Lifelong learning is a machine learning that resembles the learning process in the human brain. The process that is carried out is by utilizing learning outcomes from the past to help learning in the future. 4 datasets with different domains had ran using the CNN model in the Lifelong learning process, and produced increased accuracy along with the addition of datasets in the training process."

"Studi ini mengkaji bagaimana kebijakan pembelajaran sepanjang hayat di Jepang dalam pendidikan khusus bagi lansia mendorong keterlibatan sosial dan berkontribusi terhadap peningkatan kesehatan, serta tantangan dalam implementasi kebijakannya. Dengan menggunakan pendekatan kualitatif, penelitian ini menganalisis proses kebijakan, mulai dari latar belakang dan implementasi (output kebijakan) hingga dampak serta tantangan yang dihadapi.Penelitian ini menggunakan teori dan konsep masyarakat lansia Jepang dari beban menjadi sumber daya manusia produktif yaitu kajian proses kebijakan, activity theory, perspektif institusional, healthy ageing dan lifelong learning, dan resosialisasi dalam second life stage.Temuan menunjukkan bahwa kebijakan ini terutama mendorong partisipasi sosial, kegiatan sukarela, dan pengembangan diri, bukan reintegrasi lansia ke pasar tenaga kerja. Meskipun beberapa program mendukung pemeliharaan kapasitas kerja dan re-employment pasca pensiun, universitas lansia lebih menekankan pada keterlibatan komunitas daripada kesiapan kerja. Pembelajaran di universitas lansia memberikan dampak positif bagi para peserta yaitu tercapai healthy ageing, kemampuan fungsional yang baik dan penuaan yang aktif.Namun, studi ini juga mengidentifikasi tantangan-tantangan utama, termasuk pelabelan usia yang berisiko menimbulkan ageism (diskriminasi usia), kesenjangan sosial ekonomi yang memengaruhi akses terhadap program, kesulitan dalam menyelaraskan kurikulum dengan tujuan pembelajaran non-industri, serta keterbatasan kesehatan lansia yang dapat menghambat efektivitas kebijakan dan menyebabkan investasi yang kurang efisien.

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This study examines how Japan's lifelong learning policy in special education for the elderly promotes social engagement and contributes to health outcomes, alongside challenges in policy implementation. Using a qualitative approach, the research analyzes the policy process, from its setting background and implementation (policy outputs) to its outcomes and encountered challenges.

This study explores the transformation of Japan’s aging population from being seen as a societal burden to becoming a valuable and productive segment of society. It draws on a range of theoretical and conceptual approaches, including policy process analysis, activity theory, institutional perspectives, healthy aging and lifelong learning, and the resocialization of older adults during the second stage of life.

Findings reveal that the policy primarily encourages social participation, volunteering, and self-enrichment, rather than reintegration into the labor market. While some programs support the maintenance of work capacity and post-retirement employment, elderly universities emphasize community engagement over job readiness. As a result, participants experience improved functional health and active aging.

However, the study identifies key challenges, including age-based labeling that risks ageism, socio-economic disparities affecting program access, difficulties aligning curricula with non-industrial learning goals, and health-related constraints that may hinder policy efficacy and lead to inefficient investment."

"Skripsi ini menjelaskan tentang proses berkembangnya literasi informasi pada diri pustakawan rujukan. Penelitian ini menggunakan metode life history yang bertujuan untuk mengidentifikasi perkembangan kemampuan literasi informasi dan strategi yang dilakukan untuk mengembangkan literasi informasi tersebut. Hasil penelitian ini menyatakan bahwa proses perkembangan kemampuan literasi informasi pada diri pustakawan rujukan ini terjadi berdasarkan pengalaman hidup, mulai dari masa kecil sampai menjadi seorang pustakawan rujukan. Selain itu, untuk perkembangan literasi informasi sesorang harus memahami konsep literasi informasi agar menjadi pembelajar yang mandiri. Strategi yang digunakan untuk mengembangkan dan menerapkan literasi informasi adalah dengan memiliki penguasaan terhadap hasil olahan dari kemampuan berpikir, sharing, proaktif, dan memberikan pelatihan-pelatihan literasi informasi kepada orang lain.

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This undergraduate thesis describes about the development of information literacy within the reference librarian itself. This study uses a life history method to identify the development of information literacy skills and the strategy to develop it. The results suggest that the development process of information literacy skills in self-reference librarian occurred by life experiences, since they were a kid until they become a reference librarian. In addition, to develop their information literacy, they must understand the concept of information literacy in order to be independent learners. The strategy that used to develop and apply information literacy are to have a good understanding toward the result of thinking skills, sharing, being proactive and providing information literacy trainings to others."