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This paper builds on the requirements of listening and writing, conducts a de-tailed analysis on demands and a feasibility study, and designs a set of intelligent English listening and writing training system. This system carries out an analysis on demands from three main aspects—the orchestration system before the test, the mock test subsystem dur...
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... overall system architecture is illustrated by figure 1. The front UI interface is displayed as three module entries by means of WPF design. ...
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... Literature [10] designs an intelligent English listening and writing training system from the requirements of English listening and writing, which includes an orchestration system, a simulation test subsystem and a performance management subsystem, describes the functions of the university English listening and speech simulation test system, and realizes the intelligent training of English listening ability and writing ability. Literature [11], on the basis of an in-depth understanding of wireless communication algorithms, introduces gas pedals to further improve the speed of digital signal processing and combines virtual reality technology to construct an English listening teaching mode based on a wireless communication microprocessor combined with a virtual environment, which effectively improves the English listening level. ...
The training of college students’ English listening and speaking skills needs to be individualized scientifically and strategically in order to get a qualitative improvement. In this paper, we first analyze the correlation between students’ listening and speaking skill levels by using the improved maximum information coefficient (MIC) with the aid of an online platform. The students’ listening ability is assessed by the plain Bayesian algorithm, trained by the neural network to reduce the error, and the similarity between the feature vector and each pattern is calculated so that the element that obtains the highest value is used as the candidate mapping. Next, the schema mapping of data distribution (SMDD) method is combined to obtain the best matching schema. Finally, the effectiveness of English listening and speaking training with the Smart Teaching Platform was investigated through controlled experiments. The results show that in the post-test speech scores, the experimental group’s increase in speech scores was 2.322, while the control group’s increase was only 0.652. The effectiveness of the English listening and speaking ability training strategy is evident due to the obvious difference. This study proposes rational suggestions for improving college students’ English listening and speaking abilities.
... How to accurately obtain the information users need most from the massive information sources is a common goal pursued by researchers in the information age. In recent years, the emergence of question-and-answer systems has gradually attracted the attention of scholars [7]. e design of the system makes a hierarchical description of the system's overall architecture, achievement management architecture, and orchestration system architecture and demonstrates database design and interface design of this system. ...
Intelligent communication processing in English aims to obtain effective information from unstructured text data using various text processing techniques. Text vector representation and text similarity calculation are important fundamental tasks in the whole field of natural language processing. In response to the shortcomings of existing sentence vector representation models and the singularity of text similarity algorithms, improved models and algorithms are proposed based on a thorough study of related domain technologies. This paper presents an in-depth and comprehensive study of text vectorization representation and text similarity calculation algorithms in the field of natural language processing. The existing text vectorized representation models and text similarity computation algorithms are described, and their shortcomings are summarized to provide a basis for the background and significance of this paper, as well as to provide ideas for improvement directions. It is experimentally verified that the sentence vector model proposed in this paper achieves higher accuracy than the SIF sentence vector model for text classification tasks. In the task of text similarity computation, it achieves better results in three evaluation metrics: accuracy, recall, and F1 value. The algorithm also improves the computational efficiency of the model to a certain extent by removing feature words with low feature contribution. The algorithm first improves the deficiencies of the traditional word-shift distance algorithm by defining multifeature fusion weights and realizes a text similarity calculation algorithm based on multifeature weighted fusion with better similarity calculation results. Then, a linear weighting model is constructed to further combine the similarity calculation results of the hierarchical pooled IIG-SIF sentence vectors to realize the multimodel fusion text similarity calculation algorithm.
... MYCIN kesinlik faktörü öğrenme seviyesini belirlemek için, öğrencinin soruyu cevaplamak için harcadığı zamana göre bulanık mantık karar sistemi kullanılmaktadır. Önerilen ZÖS, öğrenci hatalarını tanımlar ve hemen geri bildirimde bulunmaktadır [9].CuiLi (2018), Bu çalışmada dinleme ve yazma gereklilikleri üzerine kurulmuştur. Talepler üzerine detaylı bir analiz ve fizibilite çalışmasını yapabilmektedir. ...
Zeki ve Uyarlanabilir e-Öğrenme Sistemleri; öğrenenlerin kişilik özelliklerine, geçmiş öğrenme deneyimlerine, bilgi düzeylerine, ilgi alanlarına ve tercihlerine göre farklı öğrenme yaşantıları sunarak kişiselleştirilmiş e-öğrenme ortamları sunan sistemlerdir. Bu sistemler, yeni web teknolojilerinin yanı sıra kullandıkları yapay zekâ algoritmaları sayesinde öğrenenlere daha etkili bir öğrenme ortamı sunmaktadır.
Bu bildirinin amacı; Zeki ve Uyarlanabilir e-Öğrenme Sistemlerinin Yabancı Dil Öğrenimine katkılarını incelemektir. Amaç doğrultusunda bildirinin ilk bölümünde Zeki ve Uyarlanabilir e-Öğrenme Sistemlerinin genel yapısı ve bu sistemlerin dil öğrenimine katkıları literatürden derlenen uygulamaların ışığında açıklanacaktır. Sonraki bölümde, Zeki ve Uyarlanabilir bir Öğrenme Sistemi olan TÜRKZÖS (Türkçe için Zeki Öğretim Sistemi) tanıtılacaktır. TÜRKZÖS; Yapay Sinir Ağları ve Bulanık Mantık gibi Yapay Zekâ yöntemleri ve Web Yazılım Teknolojileri kullanılarak programlanmış bir e-öğrenme sistemidir. Bu sistem, Türkçenin yabancı dil olarak öğretimi amacıyla kullanılabilecek özelliklere sahiptir. Yabancı Dil alan uzmanları ve öğretim teknolojisi uzmanları eşliğinde hazırlanmış öğrenme
materyallerinin kişiselleştirilmiş e-öğrenme ortamında sunulabilmesi ve bu materyallerin öğrenenler
tarafından izlenebilmesini sağlar. Bildirimizde, TÜRKZÖS’ün teknik özelliklerinin yanı sıra,
öğrenenler ve öğretenler açısından sunduğu olanaklar açıklanacaktır. Öğretenler açısından; e- öğrenme materyallerinin TÜRKZÖS ortamına yüklenme süreci, sisteme kayıtlı öğrencilerin sistem üzerinden izlenebilmesi hususlarında sunulan olanaklar ve aşamaları kısaca anlatılacaktır. Öğrenenler açısından e-öğrenme materyallerinin izlenme süreci ile ilgili aşamalar ve TÜRKZÖS’ün öğrenenlerin bilgi seviyesi ve öğrenme ihtiyaçlarına göre kişiselleştirilmiş içerikleri sunma özelliği örnek uygulamalar eşliğinde gösterilecektir. Uygulamalar, Yabancı Dil olarak Türkçe öğretim yöntemlerine göre A1 ve A2 temel düzeyinde okuma, yazma ve dinleme etkinliklerini içeren örneklerle sunulacaktır. Konulara dayalı metinler, bilgi, açıklamalar ve alıştırmalar gibi örnek (anlama, konuşma, yazma) uygulamalara yer verilecektir.
... The five major factors are key determinants to effective ICT adoption in Nigeria that can facilitate effective learning [35][36][37][38][39]. However, government and top management of schools [40] are required to drive the ICT adoption by development of policies [41] and strategies for implementation [42][43]. ...
This paper addressed the factors affecting ICT adoption in a survey of 300 public and private secondary school students in Ogun state, Nigeria. Likert Scale self-designed questionnaire was the instrument of data collection. Data collected were analysed using frequency, scores, percentage scores, total weighted values and factor analysis. The ungrouped factors obtained using the mean weighted values were grouped into five dimensions using the factor analysis. The research showed that five factors are responsible for ICT adoption as perceived by the students. The factors are: assistance or support factors, availability factors, infrastructure factors, learning tools factors and cognitive factors. These factors can be studied in details and addressed to improve the level of ICT adoption in Nigeria.
