118 research outputs found

    TRACKING AKTIVITAS BELAJAR SISWA DALAM E-LEARNING

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    Abstrak : E-learning merupakan pembelajaran berbantuan teknologi informasi dan komunikasi. Wujudnya mencakup sejumlah aplikasi dan proses, termasuk pembelajaran berbasis komputer, pembelajaran berbasis web, virtual classrooms dan digital collaboration. Implementasinya dapat berwujud web-based, web-distributed atau web-capable untuk tujuan pendidikan. Guna meningkatkan efektivitas belajar para siswa pengguna e-learning maka perlu dilakukan tracking aktivitas belajar mereka. Tracking dalam e-learning dapat menggunakan cookie. Dengan tracking dapat diketahui berapa banyak para siswa login dan berapa lama mereka mengakses untuk belajar serta materi apa saja yang dipelajari. Informasi ini diperlukan untuk meningkatkan performa sistem e-learning dalam mendukung pencapaian prestasi belajar siswa. Kata kunci : E-learning, tracking, cookie, aktivitas belaja

    Manganese and carbon lines as temperature indicators

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    Selected hyperfine structure broadened lines of various excitations and the carbon line at 5380 Å are tested as temperature diagnostics for photospheric heterogeneities. This is done by comparing the observed center-to-limb variation of the equivalent widths of these lines with predictions by several proposed models of homogeneous type. Model 10 (Elste, 1968), which also explains the limb-darkening of the continuum in the same spectral range, provides a better basis for the analysis of heterogeneities at different photospheric levels than other recent reference models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43705/1/11207_2004_Article_BF00155340.pd

    The protocols for the 10/66 dementia research group population-based research programme

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    BACKGROUND: Latin America, China and India are experiencing unprecedentedly rapid demographic ageing with an increasing number of people with dementia. The 10/66 Dementia Research Group's title refers to the 66% of people with dementia that live in developing countries and the less than one tenth of population-based research carried out in those settings. This paper describes the protocols for the 10/66 population-based and intervention studies that aim to redress this imbalance. METHODS/DESIGN: Cross-sectional comprehensive one phase surveys have been conducted of all residents aged 65 and over of geographically defined catchment areas in ten low and middle income countries (India, China, Nigeria, Cuba, Dominican Republic, Brazil, Venezuela, Mexico, Peru and Argentina), with a sample size of between 1000 and 3000 (generally 2000). Each of the studies uses the same core minimum data set with cross-culturally validated assessments (dementia diagnosis and subtypes, mental disorders, physical health, anthropometry, demographics, extensive non communicable disease risk factor questionnaires, disability/functioning, health service utilisation, care arrangements and caregiver strain). Nested within the population based studies is a randomised controlled trial of a caregiver intervention for people with dementia and their families (ISRCTN41039907; ISRCTN41062011; ISRCTN95135433; ISRCTN66355402; ISRCTN93378627; ISRCTN94921815). A follow up of 2.5 to 3.5 years will be conducted in 7 countries (China, Cuba, Dominican Republic, Venezuela, Mexico, Peru and Argentina) to assess risk factors for incident dementia, stroke and all cause and cause-specific mortality; verbal autopsy will be used to identify causes of death. DISCUSSION: The 10/66 DRG baseline population-based studies are nearly complete. The incidence phase will be completed in 2009. All investigators are committed to establish an anonymised file sharing archive with monitored public access. Our aim is to create an evidence base to empower advocacy, raise awareness about dementia, and ensure that the health and social care needs of older people are anticipated and met

    Repurposing NGO data for better research outcomes: A scoping review of the use and secondary analysis of NGO data in health policy and systems research

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    Background Non-government organisations (NGOs) collect and generate vast amounts of potentially rich data, most of which are not used for research purposes. Secondary analysis of NGO data (their use and analysis in a study for which they were not originally collected) presents an important but largely unrealised opportunity to provide new research insights in critical areas including the evaluation of health policy and programmes. Methods A scoping review of the published literature was performed to identify the extent to which secondary analysis of NGO data has been used in health policy and systems research (HPSR). A tiered analytic approach provided a comprehensive overview and descriptive analyses of the studies which: 1) used data produced or collected by or about NGOs; 2) performed secondary analysis of the NGO data (beyond use of an NGO report as a supporting reference); 3) used NGO-collected clinical data. Results Of the 156 studies which performed secondary analysis of NGO-produced or collected data, 64% (n=100) used NGO-produced reports (e.g. to critique NGO activities and as a contextual reference) and 8% (n=13) analysed NGO-collected clinical data.. Of the studies, 55% investigated service delivery research topics, with 48% undertaken in developing countries and 17% in both developing and developed. NGO-collected clinical data enabled HPSR within marginalised groups (e.g. migrants, people in conflict-affected areas), with some limitations such as inconsistencies and missing data. Conclusion We found evidence that NGO-collected and produced data are most commonly perceived as a source of supporting evidence for HPSR and not as primary source data. However, these data can facilitate research in under-researched marginalised groups and in contexts that are hard to reach by academics, such as conflict-affected areas. NGO–academic collaboration could help address issues of NGO data quality to facilitate their more widespread use in research. Their use could enable relevant and timely research in the areas of health policy, programme evaluation and advocacy to improve health and reduce health inequalities, especially in marginalised groups and developing countries

    Naskah Awal Prosiding (Cover, Editor, Susunan Panitia, Kata Pengantar, Susunan Acara, Jadwal Presentasi Makalah Paralel, Daftar Isi, Daftar Pemakalah)

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    Cover, Editor, Susunan Panitia, Kata Pengantar, Susunan Acara, Jadwal Presentasi Makalah Paralel, Daftar Isi, Daftar Pemakala

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    Editorial Board :Soepomo Poedjosoedarmo (Gajah Mada University)Sudaryanto (Widya Dharma University)Inyo Yos Fernandes (Gajah Mada University)Kisyani Laksono (UNNESA)Riyadi Santosa (Sebelas Maret University)Edi Subroto (Sebelas Maret University)Sri Samiati Tarjana (Sebelas Maret University) Editor –In – Chief :Sumarlam Secretary :Djatmika Executive Editor :Tri Wiratno Staff :Arif Choirul Bashir Cover Designer :Sofyan Hadinat

    FMIPA UNS Gelar Workshop Statistika

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    Jurusan Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Sebelas Maret (FMIPA UNS) menggelar Workshop Statistika “Time Series dan Model Spatio-Temporal”, Senin - Selasa (1-2/4) di ruang Sidang Gedung C FMIPA UNS. Workshop ini mengundang Dr. Suharyono, M.Sc dari Laboratorium Statistik Bisnis dan Ekonomi Jurusan Statistika FMIPA Institut Teknologi Surabaya (ITS) yang sekaligus Ketua Program Pascasarjana Jurusan Statistika di universitas tersebut. Ketua Jurusan Matematika Ir. Irwan Susanto, S.Si, DEA mengatakan bahwa workshop ini merupakan rangkaian workshop yang telah diadakan oleh jurusan Matematika sebelumnya, diantaranya workshop bidang analisis, aljabar, dan terapan. Sementara itu, Dekan FMIPA UNS Prof. Ir. Ari Handono Ramelan, M.Sc., Ph.D dalam sambutannya berharap agar workshop ini dapat meningkatkan kinerja Jurusan Matematika terutama untuk persiapan akreditasi. Disamping itu dengan ilmu yang didapat dapat ditularkan kepada orang lain, terutama kepada mahasiswa. Workshop yang digelar dua hari ini diikuti oleh dosen, alumni, dan mahasiswa. Di sana mereka mendapatkan materi tentang Time Series dan Model Spatio-Temporal yang meliputi : Time Series Forecasting, Peramalan Time Series: Naive Model, Model ARIMA Box-Jenkins untuk data time series nonstationer, Two Lebels Regression Modelling of Trading Day and Holiday Effects for Forecasting Retail Data, A Novel Weighted Fuzzy Time Series for Forecasting Seasonal Data, A New Hybrid Approach Based on Winter’s Method & Weighted Fuzzy Time Series dan Application of Spatial Time Series Models: VARIMA-STAR-GSTAR
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