Improving Student’s Motivation To Learning Math By Cooperative Learning Technique Make A Match

This study aims to enhance student’s motivation to learn mathematics by cooperative learning techniques make a match. This study is classroom action research who carried out collaboratively between researchers and mathematics teacher of class experiment and assisted by three observers at each meeting. Subjects were students of class VIII C SMP Negeri 14 Yogyakarta on scholl year 2008/2009, which consists of 35 persons with a heterogeneous capabilities. Research carried out in 2 cycles. Cycle 1 consisted of 4 meetings, and every meeting there which lasted for 2 x 40 minutes and some are held for 1 x 40 minutes. Cycle 2 consists of 2 meetings with each meeting lasted 2 x 40 minutes. The techniques to collect the data are done through observation, interview, questionnaire, and documentation. The results showed an increase in student’s motivation to learn mathematics after given action in the form of cooperative learning techniques make a match. In general, the implementation phase of learning is discussion on group using worksheet, the explanation the results of discussion by students, critism of the results of discussions, the game looking for a partner and asked questions among students, and group awards. Keyword: motivation to learning, cooperative learning technique make a match

DIFFERENCE IN STUDY RESULTS OF STUDENT USING TEAM ASSISTED INDIVIDUALIZATION AND MAKE A MATCH COOPERATIVE LEARNING METHODS IN KKPI SUBJECT IN SMKN 1 PANDAK

This research was intended to study difference in study result between student using team assisted individualization and that using of Make a Match cooperative learning models in KKPI subject. It was experimental research, using quasi experiment. Sample was 62 students in grade XII, majors of BB and TPHP in SMKN 1 Pandak. Data was collected using objective test to measure student cognitive capability. Analysis prerequisite test included normality test, homogeneity test and two-mean differential test. Data analysis was carried out by calculating different gain score for each student in sampling group. The results indicated that there is different learning result between student using TAI cooperative learning model and student using Make-a-Match cooperative learning model. It was indicated with average gain and posttest of greater experiment class of 86.58 and 86.32 for posttest and 0.65 and 0.62 for gain compared with control class of 81.68 and 81.68 for posttest and 0.52 and 0.49 for average gain. It indicted that TAI type cooperative learning model is more suitable for learning in KKPI in SMKN 1 Pandak Keywords: learning result, TAI learning model, Make a Match learning mode

Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method

Learning to match names across languages

We report on research on matching names in different scripts across languages. We explore two trainable approaches based on comparing pronunciations. The first, a cross-lingual approach, uses an automatic name-matching program that exploits rules based on phonological comparisons of the two languages carried out by humans. The second, monolingual approach, relies only on automatic comparison of the phonological representations of each pair. Alignments produced by each approach are fed to a machine learning algorithm. Results show that the monolingual approach results in machine-learning based comparison of person-names in English and Chinese at an accuracy of over 97.0 F-measure.

Comparison of Gaussian ARTMAP and the EM Algorithm

Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses Gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of sufficient complexity to solve a problem it is trained on. GAM's representation is a Gaussian mixture model of the input space, with learned mappings from the mixture components to output classes. We show a close relationship between GAM and the well-known Expectation-Maximization (EM) approach to mixture-modeling. GAM outperforms an EM classification algorithm on a classification benchmark, thereby demonstrating the advantage of the ART match criterion for regulating learning, and the ARTMAP match tracking operation for incorporate environmental feedback in supervised learning situations.Office of Naval Research (N00014-95-1-0409