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Abstrak :
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.

Abstrak :
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. ......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.

Abstrak :
[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. , 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. ]

Abstrak :
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.

Abstrak :
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.

Abstrak :
ABSTRAK
Tesis ini membahas nilai yang terkandung dalam kegiatan literasi informasi. Bagaimana nilai dalam penguasaan literasi informasi akan memberikan dampak kepada kehidupan seseorang. Adapaun tujuannya adalah mengintepretasikan pandangan ahli dan praktisi mengenai nilai yang terkandung pada program literasi informasi yang ada di Indonesia. Pendekatan yang digunakan dalam penelitian ini adalah pendekatan kualitatif dengan metode fenomenografi. Pengumpulan data dilakukan menggunakan observasi informan dan wawancara secara online dan offline serta melakukan kajian dokumentasi. Hasil penelitian menunjukkan bahwa nilai yang dikandung dalam literasi informasi berada dalam konsepsi kearifan wisdom . Penelitian ini terbatas pada studi yang dilakukan pada ahli dan praktisi di Universitas Indonesia, Universitas Atmajaya, dan Universitas Pelita Harapan sebagai rujukan program literasi informasi di perpustakaan akademik lainnya yang setara.

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ABSTRACT
The purpose of this research is to describe the results of a study examining the perception of LIS academic staff and LIS practitioner on the value of information literacy during university Education. It also attempts to describe the impact of field experiences and mentorship programs of Information Literacy. The study was carried out using a qualitative method and phenomenography approach. The methods using interview, observation and document analysis. Informan using snowball technic. Results reveal that the value of field experiences and mentorship of Information Literacy programs during university education, shows wisdom for Academic staffs and academic practioners considered. The study is limited to the Universitas Indonesia, Universitas Atmadjaya, and Universitas Pelita Harapan thus generalization to other academic libraries is premature at this stage.

Abstrak :
Pesatnya perkembangan teknologi saat ini menjadi salah satu faktor berkembangnya media sosial. Pengguna media sosial khususnya di Indonesia sudah tidak diragukan lagi jumlahnya. Dari tingginya tingkat penggunaan media sosial, penelitian terkait data pada media sosial kerap dilakukan. Penelitian yang populer dilakukan adalah analisis sentimen. Analisis sentimen adalah kegiatan untuk mengklasifikasikan sentimen data tekstual ke dalam kelas positif atau negatif. Metode yang kerap digunakan adalah metode berbasis machine learning yaitu Convolutional Neural Network (CNN) dan Long-short Term Memory (LSTM). Metode CNN sudah terbukti baik digunakan untuk data tekstual. Adapun model gabungan yaitu LSTM-CNN yang sudah terbukti memberikan hasil lebih baik dibanding model CNN. Selanjutnya akan dilakukan analisis sentimen menggunakan model LSTM-CNN. Namun, metode berbasis machine learning hanya efektif digunakan pada satu domain saja. Berdasarkan hal tersebut, dikembangkanlah lifelong learning. Lifelong learning adalah metode dalam machine learning yang menerapkan pembelajaran berkelanjutan terhadap lebih dari satu domain. Lifelong learning pada machine learning meniru bagaimana manusia mempelajari sesuatu berdasarkan apa yang sudah dipelajari selanjutnya. Pada skripsi ini, akan dilakukan penelitian model LSTM-CNN untuk permasalahan lifelong learning analisis sentimen terhadap lima data berbahasa Indonesia. Lima data set tersebut akan digunakan sebagai data pembelajaran secara berkelanjutan terhadap suatu model LSTM-CNN. Evaluasi model akan dilakukan pada setiap proses pembelajaran yang dilakukan. Hasil yang diperoleh adalah perkembangan akurasi pada setiap proses pembelajaran terhadap suatu data set.

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The rapid development of technology is currently one of the factors in the development of social media. There are no doubt about the number of social media users, especially in Indonesia. From the high level of use of social media, research related to data on social media is often done. Popular research is sentiment analysis. Sentiment analysis is an activity to classify textual data sentiments into positive or negative classes. The method often used is machine learning-based methods, namely Convolutional Neural Network (CNN) and Long-short Term Memory (LSTM). The CNN method has been proven good for textual data. The combined model is LSTM-CNN which has been proven to provide better results than the CNN model. Then sentiment analysis will be performed using the LSTM-CNN model. However, machine learning based methods are only effective in one domain. Based on this, lifelong learning was developed. Lifelong learning is a method in machine learning that applies continuous learning to more than one domain. Lifelong learning in machine learning mimics how humans learn something based on what has been learned next. In this thesis, LSTM-CNN model research will be conducted for the problem of lifelong learning sentiment analysis of five Indonesian-language data. The five data sets will be used as continuous learning data on an LSTM-CNN model. Evaluation of the model will be carried out in each learning process that is carried out. The results obtained are the development of accuracy in each learning process of a data set.