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COAR Access Rights: metadata only accessSubject: topic modeling

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    Article
    Citation - WoS: 53
    Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
    (Athabasca Univ Press, 2021) Gurcan, Fatih; Ozyurt, Ozcan; Cagiltay, Nergiz Ercil
    E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning studies is critical for a deeper comprehension of the field. There are many studies that provide such a picture of the e-learning field, but the limitation is that they do not examine the field as a whole. This study aimed to investigate the emerging trends in the e-learning field by implementing a topic modeling analysis based on latent Dirichlet allocation (LDA) on 41,925 peer-reviewed journal articles published between 2000 and 2019. The analysis revealed 16 topics reflecting emerging trends and developments in the e-learning field. Among these, the topics "MOOC," "learning assessment," and "elearning systems" were found to be key topics in the field, with a consistently high volume. In addition, the topics of "learning algorithms," "learning factors," and "adaptive learning" were observed to have the highest overall acceleration, with the first two identified as having a higher acceleration in recent years. Going by these results, it is concluded that the next decade of e-learning studies will focus on learning factors and algorithms, which will possibly create a baseline for more individualized and adaptive mobile platforms. In other words, after a certain maturity level is reached by better understanding the learning process through these identified learning factors and algorithms, the next generation of e-learning systems will be built on individualized and adaptive learning environments. These insights could be useful for e-learning communities to improve their research efforts and their applications in the field accordingly.
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    Conference Object
    Citation - WoS: 5
    Citation - Scopus: 7
    VISUAL AND TEXTUAL FEATURE FUSION FOR AUTOMATIC CUSTOMS TARIFF CLASSIFICATION
    (Ieee, 2015) Turhan, Bilgehan; Akar, Gozde B.; Turhan, Cigdem; Yuksel, Cihan
    The Harmonized Tariff Schedule for the classification of goods is a major determinant of customs duties and taxes. The basic HS Code is 6 digits long but can be extended according to the needs of the countries such as application of custom duties based on details of the product. Finding the correct, consistent, legally defensible HS Code is at the heart of Import Compliance. However finding the best code can be complicated, especially in the case of specialized products. In this paper, we propose an automatic HS code detection system based on visual properties of the product together with textual analysis of its labels/explanations. The proposed system first uses morphological parsing in order to extract roots of the words occurring in the textual phrases. Processed text information is further processed by the topic modeling module of the system to find the best matching HS Code definitions within the system. The result of the topic modeling is used to trigger visual search based on quantized local features. The proposed algorithm is evaluated using a database of 4494 Binding Tariffs published in 2014 by the European Union. The results show that accuracy rate above 80 % can be achieved for 4-digit HS Codes.