Çoklu zeka kuramı destekli kubaşık öğrenme yönteminin ilköğretim dördüncü sınıf öğrencilerin matematik dersindeki akademik başarılarına ve kalıcılığa etkisi

TEZ6856Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2007.Kaynakça (s.93-104) var.xv, 136 s. ; 29 cm.In the present experimental study, to compare the effects of cooperative learning method supported by mutliple intelligence theory (CLMI) on elementary school fourth grade students' academic achievement and retention toward mathematics course were investigated. The study, which lasted sixteen consecutive weeks, was conducted at a public elemetary school in the district of Seyhan - Adana in 2005 - 2006 academic year. The participants of the study were 150 students that they were divided in two experimental and two control groups. The group which was taught by using CLMI in the preceding academic year (2004 - 2005 academic year) was choosen as the first experimental groups. The other experimental and two control groups were selected randomly among three classes of the school.Bu deneysel çalışmada; çoklu zeka destekli kubaşık öğrenme yönteminin ilköğretim dördüncü sınıf öğrencilerinin matematik dersindeki akademik başarılarına ve kalıcılığa etkisi olup olmadığı araştırılmıştır. Bu çalışma, 2005-2006 öğretim yılının birinci yarıyılında Adana İli Seyhan İlçesindeki bir resmi ilköğretim okulunda yapılmıştır. Araştırma, iki deney ve iki kontrol grubunda bulunan toplam 150 öğrenci üzerinde yürütülmüştür. Çalışma, 16 haftalık bir süreci kapsamıştır. Araştırmanın, 1. deney grubunu, bir önceki dönem (2004 - 2005 öğretim yılının ikinci yarısında) toplam 9 hafta boyunca, matematik derslerini araştırmacı tarafından çoklu zeka destekli kubaşık öğrenme yöntemine göre işleyen grup oluşturmuştur. 2. deney ve kontrol grupları rasgele belirlenmiştir. Dersler, deney grubunda çoklu zeka kuramı destekli kubaşık öğrenme yöntemine göre, kontrol gruplarında ise 2005-2006 Matematik öğretim programı doğrultusunda yapılan öğretime göre işlenmiştir. Akademik başarıyı ölçmek için, veri toplama aracı olarak, araştırmacılar tarafından geliştirilmiş bir ""Matematik Başarı Testi"" kullanılmıştır.Bu çalışma Ç.Ü. Bilimsel Araştırma Projeleri Birimi Tarafından Desteklenmiştir. Proje No

Named entity recognition from scratch on social media

With the extensive amount of textual data flowing through social media platforms, the interest in Information Extraction (IE) on such textual data has increased. Named Entity Recognition (NER) is one of the basic problems of IE. State-of-the-art solutions for NER face an adaptation problem to informal texts from social media platforms. In this study, we addressed this generalization problem with the NLP from scratch idea that has been shown to be successful for several NLP tasks on formal text. Experimental results have shown that word embeddings can be successfully used for NER on informal text

Improving Efficiency of Sequence Mining by Combining First Occurrence Forest (FOF) Strategy and Sibling Principle

Sequential pattern mining is one of the basic problems in data mining and it has many applications in web mining. The WAP-Tree (Web Access Pattern Tree) data structure provides a compact representation of single-item sequence databases. WAP-Tree based algorithms have shown notable execution time and memory consumption performance on mining single-item sequence databases. We propose a new algorithm FOF-SP, a WAP-Tree based algorithm which combines an early prunning strategy called "Sibling Principle" from the literature and FOF (First Occurrence Forest) strategy. Experimental results revealed that FOF-SP finds patterns faster than previous WAP-Tree based algorithms PLWAP and FOF. Moreover, FOF-SP can mine patterns faster than PrefixSpan and as fast as LAPIN on real sequence databases from web usage mining and bioinformatics.Sequential pattern mining is one of the basic problems in data mining and it has many applications in web mining. The WAP-Tree (Web Access Pattern Tree) data structure provides a compact representation of single-item sequence databases. WAP-Tree based algorithms have shown notable execution time and memory consumption performance on mining single-item sequence databases. We propose a new algorithm FOF-SP, a WAP-Tree based algorithm which combines an early prunning strategy called "Sibling Principle" from the literature and FOF (First Occurrence Forest) strategy. Experimental results revealed that FOF-SP finds patterns faster than previous WAP-Tree based algorithms PLWAP and FOF. Moreover, FOF-SP can mine patterns faster than PrefixSpan and as fast as LAPIN on real sequence databases from web usage mining and bioinformatics

TOPONYM RECOGNITION ON TURKISH TWEETS

In recent years, Twitter has become a popular platform for following and spreading trends, news and ideas all over the world. Geographical scope of tweets is crucial to many tasks like disaster management, event tracking and information retrieval. First step for assigning a geographical location to a tweet is toponym recognition. Toponym Recognition (Geoparsing) is identification of toponyms (place names) in a text. In this study, we investigated performance of three existing approaches for toponym recognition on Turkish tweets. We conducted experiments for measuring performance of the existing approaches on a sample data set. Best results have been obtained with the NER algorithm by Kucuk et.al.. However, we observed that existing NER algorithms for Turkish neglect the syntactic and semantic features of text

Toponym recognition on Turkish tweets

In recent years, Twitter has become a popular platform for following and spreading trends, news and ideas all over the world. Geographical scope of tweets is crucial to many tasks like disaster management, event tracking and information retrieval. First step for assigning a geographical location to a tweet is toponym recognition. Toponym Recognition (Geoparsing) is identification of toponyms (place names) in a text. In this study, we investigated performance of three existing approaches for toponym recognition on Turkish tweets. We conducted experiments for measuring performance of the existing approaches on a sample data set. Best results have been obtained with the NER algorithm by Kucuk et.al.. However, we observed that existing NER algorithms for Turkish neglect the syntactic and semantic features of text

Utilizing Word Embeddings for Result Diversification in Tweet Search

The performance of result diversification for tweet search suffers from the well-known vocabulary mismatch problem, as tweets are too short and usually informal. As a remedy, we propose to adopt a query and tweet expansion strategy that utilizes automatically-generated word embeddings. Our experiments using state-of-the-art diversification methods on the Tweets2013 corpus reveal encouraging results for expanding queries and/or tweets based on the word embeddings to improve the diversification performance in tweet search. We further show that the expansions based on the word embeddings may serve as useful as those based on a manually constructed knowledge base, namely, ConceptNet

Türkiye'nin çukurova bölgesinde non-hodgkin lenfoma olgularının tedavi sonuçları

Amaç: Bizim amacımız, çukurova bölgesindeki NHL.lı çocuk hastalarının klinikopatolojik karakteristiklerini ve prognostic bulgularını değerlendirmektir. Gereç ve Yöntemler: Histopatolojik olarak, 24 hasta Burkitt lenfoma, 12 hasta (%31,6) Burkitt dışı hasta ve 2 hasta ise büyük hücreli NHL idi. Hastaların evresi Murphy.nin NHL sınıflamasına göre yapıldı. Buna göre; bir hasta evre I, 8 hasta evre II, 18 hasta evre III ve 11 hasta ise evre IV idi. Hastalara BFM-90 ve LSA2L2 tedavi protokolü uygulandı. Bulgular: NHL.lı hastaların ortalama yaşı 75,3±41,5 ay idi. Hastaların 12.si (%31,6) kız hasta ve 26.sı (%68,4) ise erkek hasta idi. 5 yıllık yaşam olasılığı %71 olarak saptandı. Histopatolojik tipine göre yaşam olasılığı; T hücreli lenfoblastik lenfomada %93, Burkitt lenfomada %56 ve Burkitt dışı büyük hücreli lenfomada ise %50 olarak saptandı. Sonuç: Kliniğimiz tarafından takip edilen NHL.lı hastaların prognozu, histopatolojik tip ve evreye göre değişmekteydi. Bizim olgularımızın büyük kısmı Burkitt ya da evre III-IV lenfoma olmasına rağmen, tedavi protokollerine aldığımız cevap literatürle benzerdi.Purpose: We aimed to evaluate the clinicopathological characteristics and prognostic features of non-Hodgkin lymphoma (NHL) in pediatric patients in Çukurova Region, Turkey. Material and Methods: Histopathologically, 24 (63.2%) patients were diagnosed as Burkitt lymphoma, 12 (31.6%) patients were diagnosed as T-cell lymphoblastic lymphoma and 2 (5.2%) patients were diagnosed as diffuse large cell lymphoma. Patients were staged according to Murphy’s classification in children. Treatment protocols of BFM-90 and LSA2L2 were applied to patients. Results: While mean age of 38 patients with NHL was 75.3±41.5 months , 12 (31.6%) patients were female and 26 (68.4%) patients were male. One (2.6%) patient was evaluated as stage I, 8 (21.1%) patients were as stage II, 18 (47.4%) patients were as stage III and, 11 (28.9 %) patients were as stage IV. Overall survival for 5 years was found as 71%. When overall survival were estimated based on histopathological study, 93%, 56% and %50 were found for T-cell lymphoblastic lymphoma, Burkitt lenfoma and diffuse large cell group, respectively. Conclusion: The prognosis of NHL cases followed by our clinic varied according to the stage and histopathological type. Although most of our cases were Burkitt or stage III-IV lymphomas, the clinical response to the treatment protocols were similar to the literature