PENDAMPINGAN PEMBUATAN FOTO UNTUK GURU SD KECAMATAN SUKAMAKMUR KABUPATEN BOGOR

The low student learning outcomes of SDN Sukamakmur Bogor District based on the analysis conducted because teachers still use the traditional approach, marked by still dominant activity while students are only as listeners and recipients of information. students only listen to the teacher's explanation and take notes or summarize the important things from the material presented. Therefore it is necessary to strive for the use of media and appropriate in the learning process, an interesting atmosphere so that students are more active in learning so that students' learning activities and outcomes increase. The approach applied to the Community Service Program is a model of empowerment through mentoring with the following steps: 1) Preparation Phase; 2) Assessment Phase; 3) Alternative Planning Phase of Programs or Activities; 4) Formulation Phase of the Action Plan; 5) Stage of Implementation (Implementation) of the Program or Activity; 6) Evaluation Phase; and 7) Termination Phase. This Community Service is carried out as an empowerment of elementary school teachers in the District of Sukamakmur, Bogor Regency, producing Photo Media that meets theoretically feasible criteria; meet the proper criteria in terms of format, content and appearance; have high applicability or are worthy of learning

Supporting mediated peer-evaluation to grade answers to open-ended questions

We show an approach to semi-automatic grading of answers given by students to open ended questions (open answers). We use both peer-evaluation and teacher evaluation. A learner is modeled by her Knowledge and her assessments quality (Judgment). The data generated by the peer- and teacher- evaluations, and by the learner models is represented by a Bayesian Network, in which the grades of the answers, and the elements of the learner models, are variables, with values in a probability distribution. The initial state of the network is determined by the peer-assessment data. Then, each teacher’s grading of an answer triggers evidence propagation in the network. The framework is implemented in a web-based system. We present also an experimental activity, set to verify the effectiveness of the approach, in terms of correctness of system grading, amount of required teacher's work, and correlation of system outputs with teacher’s grades and student’s final exam grade

Towards a quantitative evaluation of the relationship between the domain knowledge and the ability to assess peer work

In this work we present the preliminary results provided by the statistical modeling of the cognitive relationship between the knowledge about a topic a the ability to assess peer achievements on the same topic. Our starting point is Bloom's taxonomy of educational objectives in the cognitive domain, and our outcomes confirm the hypothesized ranking. A further consideration that can be derived is that meta-cognitive abilities (e.g., assessment) require deeper domain knowledge

Recall termination in free recall

Although much is known about the dynamics of\ud memory search in the free recall task, relatively little is\ud known about the factors related to recall termination. Rean-\ud alyzing individual trial data from 14 prior studies (1,079\ud participants in 28,015 trials) and defining termination as\ud occurring when a final response is followed by a long\ud nonresponse interval, we observed that termination proba-\ud bility increased throughout the recall period and that retriev-\ud al was more likely to terminate following an error than\ud following a correct response. Among errors, termination\ud probability was higher following prior-list intrusions and\ud repetitions than following extralist intrusions. To verify that\ud this pattern of results can be seen in a single study, we report\ud a new experiment in which 80 participants contributed recall\ud data from a total of 9,122 trials. This experiment replicated\ud the pattern observed in the aggregate analysis of the prior\ud studies.\u

Multi-resolution Tensor Learning for Large-Scale Spatial Data

High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models. MMT leverages the property that spatial data can be viewed at multiple resolutions, which are related by coarsening and finegraining from one resolution to another. Using this property, MMT learns a tensor model by starting from a coarse resolution and iteratively increasing the model complexity. In order to not "over-train" on coarse resolution models, we investigate an information-theoretic fine-graining criterion to decide when to transition into higher-resolution models. We provide both theoretical and empirical evidence for the advantages of this approach. When applied to two real-world large-scale spatial datasets for basketball player and animal behavior modeling, our approach demonstrate 3 key benefits: 1) it efficiently captures higher-order interactions (i.e., tensor latent factors), 2) it is orders of magnitude faster than fixed resolution learning and scales to very fine-grained spatial resolutions, and 3) it reliably yields accurate and interpretable models

A Framework for Genetic Algorithms Based on Hadoop

Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in many real-world applications. The sequential execution of GAs requires considerable computational power both in time and resources. Nevertheless, GAs are naturally parallel and accessing a parallel platform such as Cloud is easy and cheap. Apache Hadoop is one of the common services that can be used for parallel applications. However, using Hadoop to develop a parallel version of GAs is not simple without facing its inner workings. Even though some sequential frameworks for GAs already exist, there is no framework supporting the development of GA applications that can be executed in parallel. In this paper is described a framework for parallel GAs on the Hadoop platform, following the paradigm of MapReduce. The main purpose of this framework is to allow the user to focus on the aspects of GA that are specific to the problem to be addressed, being sure that this task is going to be correctly executed on the Cloud with a good performance. The framework has been also exploited to develop an application for Feature Subset Selection problem. A preliminary analysis of the performance of the developed GA application has been performed using three datasets and shown very promising performance

Free Search of real value or how to make computers think

This book introduces in detail Free Search - a novel advanced method for search and optimisation. It also deals with some essential questions that have been raised in a strong debate following the publication of this method in journal and conference papers. In the light of this debate, Free Search deserves serious attention, as it appears to be superior to other competitive methods in the context of the experimental results obtained. This superiority is not only quantitative in terms of the actual optimal value found but also qualitative in terms of independence from initial conditions and adaptation capabilities in an unknown environment