PEMBENTUKAN KARAKTER SISWA MELALUI PROGRAM LIMA S (SENYUM, SAPA, SALAM, SOPAN, SANTUN) DI SMA NEGERI 1 SIDOARJO

AbstrakKrisis moral yang dialami bangsa Indonesia saat ini sangat memprihatinkan. Krisis moral ini bukan lagi menjadi sebuah permasalahan sederhana dan memiliki dampak serius di kalangan para peserta didik. Perilaku-perilaku yang mencerminkan adanya krisis moral tersebut sudah mengarah pada rendahnya perilaku kesopanan pada diri siswa. Tujuan peneletian yaitu mendeskripsikan proses pembentukan karakter siswa melalui program Lima S (Senyum, Sapa, Salam, Sopan, Santun) di SMA Negeri 1 Sidoarjo dan guna mengetahui perubahan perilaku siswa setelah melaksanakan kegiatan yang ada dalam Program Lima S (Senyum, Sapa, Salam, Sopan, Santun). Teori yang digunakan dalam penelitian ini adalah teori belajar observasional Albert Bandura dan Teori Perilaku dari Thomas Lickona. Metode yang digunakan dalam penelitian ini dengan pendekatan deskriptif kualitatif. Lokasi penelitian ini berada di SMA Negeri 1 Sidoarjo. Tehnik pengumpulan data menggunakan observasi, wawancara mendalam, dan dokumentasi. Tehnik analisis data langkah-langkahnya adalah mengolah pengumpulan data, penyajian data,reduksi data, dan penarikan kesimpulan. Berdasarkan data di lapangan dan hasil analisis data, hasil penelitian ini menunjukkan proses pembentukan karakter siswa SMA Negeri 1 Sidoarjo melalui Program Lima S (Senyum, Sapa, Salam, Sopan, Santun). Proses pembentukan karakter siswa terjadi pada saat siswa melaksanakan (a) kegiatan tata tertib dan tata krama, (b) kegiatan pengembangan diri (c) kegiatan pembelajaran. Perubahan karakter yang terjadi setelah siswa melaksanakan Program Lima S(Senyum, Sapa, Salam, Sopan, Santun) adalah (1) religius, (2) disiplin, (3) mandiri, (4) bertanggung jawab, (5) peduli sosial, (6) menghargai prestasi, (7) kreatif, (8) bersahabat/ komunikatif, (8) cinta damai, (9) cinta tanah air, (10) peduli lingkungan.Kata Kunci : Program Lima S, KarakterAbstractMoral crisis experienced by Indonesia at this time is very concern. Moral crisis is not a simple problem and have a serious impact on the learners. Behaviors that reflect a moral crisis that has led to the lack of courtesy on the student's behavior. The purpose of this research to describe of character formation process of students through the Program Five S (Smile, Scold, Greetings, Polite, Courteous) in Senior High School 1 Sidoarjo and behavioral changes in students after carrying out activities in the Program Five S (Smile, Scold , Greetings, Polite, Courteous) in Senior High School 1 Sidoarjo With the aim to describe the process of character formation of students through the Five S (Smile, Scold, Greetings, Polite, Courteous) in Senior High School 1 Sidoarjo and to determine changes in student behavior after carrying out activities in the Program Five S (Smile, Scold, Regards, Polite, Courteous). The theory used in research is Albert Bandura's observational learning and Behavioral Theory of Thomas Lickona.The methods used in research with the approach qualitative. Location of the research in Senior High School 1 Sidoarjo. data collection techniques used observation, in-depth interviewies, and documentation. Technical analysis of the data processing steps are collection data, presentation data, reduction data, and conclusion. Based on field data and the result of data analysis, the results of this study demonstrate the process of character formation of students of Senior High School 1 Sidoarjo through Program Five S(Smile, Scold, Greetings, Polite, Courteous). The process of character formation of students occurs when students carry out (a) the activities of the discipline and manners, (b) self-development activities (c) learn activities. Character changes that occur after students take five courses of S(Smile, Sapa, Greetings, Polite, Courteous) is (1)religious, (2) discipline, (3) independent, (4) be responsible, (5)social care, (6) appreciate the achievement, (7) creative, (8) friends / communicative, (8) love peace, (9) patriotism, (10)environmental careKey Words : Five S program, Character

Screening the Psycho-Dynamics of Learning to Teach: A Study of Depression in Teacher Education

Screening the Psycho-Dynamics of Learning to Teach is a psychoanalytic study about the status of depression in teacher education. How do films that depict depressed teachers and students offer educationalists a resource for working through depression in pedagogy? I suggest that the interminable process of learning to teach requires teachers to encounter loss, vulnerability, and sadness. Yet, the ubiquity of these emotional conditions means that depression, as a psychical defense against strong emotions, pervades the profession of teaching and prevents teachers and learners from thinking creatively. With the problem of the teachers depression in mind, I turn to three recent films about depressed educational subjects, Monsieur Lazhar (2011), Half Nelson (2006), and Mona Lisa Smile (2004) to examine both how popular representations of education depict depression in teaching and how these representations may be used as a resource for making significance of the extraordinary and mundane emotional conflicts of learning to teach. I frame my discussion of depression using the psychoanalytic theories of the dead mother (Green, 1980) and the dead teacher (Farley, 2014) in order to think about how new teachers (lost) desire affects teaching and learning relations. In each chapter, I analyze one film using one psychoanalytic concept that is relevant to pedagogy: transference in Half Nelson, identification in Mona Lisa Smile, and melancholia in Monsieur Lazhar. Alternatively, these chapters each analyze one depressed figure who haunts the scene of education: the teacher in Half Nelson who is in transference with a caring student repeats the unconscious fantasy of the emotionally dead mother; the new teacher in Mona Lisa Smile identifies with feminist historical fantasies in order to sustain her teaching desire for the depressed student(s); and, the depressed teacher in Monsieur Lazhar finds a surviving maternal teacher through whom he learns to symbolize and mourn his losses in teaching. The final chapter turns from visual analysis of the films to a discussion of the films as sites of viewer pedagogy. I suggest finally that viewer emotional responses to the films often repeat the psychodynamics of pedagogy represented on screen. Film pedagogy thus creates a space for viewers to remember, repeat, and work through the emotional conflicts of teaching and learning

Principals training in school improvement practices

La experiencia que se presenta forma parte del Programa de Mejora Escolar para la Mejora de los Aprendizajes (GEMA), UNICEF Argentina.Gestión Escolar para la Mejora de los Aprendizajes (GEMA) es un programa específicamente diseñado para contribuir a la mejora de los aprendizajes de los estudiantes a través del fortalecimiento de la conducción y gestión del director de la escuela. Aborda el ejercicio de la función directiva a través de su focalización en los aprendizajes, la fortaleza de la conducción y la gestión estratégica. Los directivos que han participado del Programa aplican la propuesta de GEMA en sus escuelas, y han fortalecido sus prácticas específicas para intervenir y sostener procesos que redunden en potenciales mejorasSchool Management for Improvement Learning (SMILe) is a program specially designed to contribute to improving student learning by strengthening the leadership and management abilities of a school´s principal. GEMA addresses the exercise of the management function through its focus on learning, the strength of leadership and the strategic management. Principals who have participated in the Program, apply GEMA proposal in their schools and have strengthened their specific practices to intervene and sustain processes that lead to potential improvement

Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

Smile detection in the wild based on transfer learning

Smile detection from unconstrained facial images is a specialized and challenging problem. As one of the most informative expressions, smiles convey basic underlying emotions, such as happiness and satisfaction, which lead to multiple applications, e.g., human behavior analysis and interactive controlling. Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems. To leverage the large amount of labeled data from face recognition datasets and to alleviate overfitting on smile detection, an efficient transfer learning-based smile detection approach is proposed in this paper. Unlike previous works which use either hand-engineered features or train deep convolutional networks from scratch, a well-trained deep face recognition model is explored and fine-tuned for smile detection in the wild. Three different models are built as a result of fine-tuning the face recognition model with different inputs, including aligned, unaligned and grayscale images generated from the GENKI-4K dataset. Experiments show that the proposed approach achieves improved state-of-the-art performance. Robustness of the model to noise and blur artifacts is also evaluated in this paper

Online Reciprocal Recommendation with Theoretical Performance Guarantees

A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clearvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines

Every Smile is Unique: Landmark-Guided Diverse Smile Generation

Each smile is unique: one person surely smiles in different ways (e.g., closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video generation problem, we propose a novel deep learning architecture named Conditional Multi-Mode Network (CMM-Net). To better encode the dynamics of facial expressions, CMM-Net explicitly exploits facial landmarks for generating smile sequences. Specifically, a variational auto-encoder is used to learn a facial landmark embedding. This single embedding is then exploited by a conditional recurrent network which generates a landmark embedding sequence conditioned on a specific expression (e.g., spontaneous smile). Next, the generated landmark embeddings are fed into a multi-mode recurrent landmark generator, producing a set of landmark sequences still associated to the given smile class but clearly distinct from each other. Finally, these landmark sequences are translated into face videos. Our experimental results demonstrate the effectiveness of our CMM-Net in generating realistic videos of multiple smile expressions.Comment: Accepted as a poster in Conference on Computer Vision and Pattern Recognition (CVPR), 201