Different people may have a different learning styles and it is important to provide the most suitable content and course materials for learning. However, determining the learning style may be difficult due to limited information about the learner and lack of a learner profile. The learner has to complete a questionnaire form based on educational theory in order to determine the learning style. Moreover, it is necessary to know the learner's study record related to other factors such as demographic factors. Therefore, an enhanced model to identify learning style is needed. This research aims to address the problem of identifying the learning style for a new student. A learning style classification model was proposed and ensemble classification techniques were implemented. The results showed that the proposed ensemble classification techniques performed better compared to other classification techniques used in the experiment.
One of the most powerful internet communication channels is email. As employees and their clients communicate primarily via email, much crucial business data is conveyed via email content. Where businesses are understandably concerned, they need a sophisticated workflow management system to manage their transactions. A workflow management system should also be able to classify any incoming emails into suitable categories. Previous research has implemented a system to categorize emails based on the words found in email messages. Two parameters affected the accuracy of the program, namely the number of words in a database compared with sample emails, and an acceptable percentage for classifying emails. As the volume of email has become larger and more sophisticated, this research classifies email messages into a larger number of categories and changes a parameter that affects the accuracy of the program. The first parameter, namely the number of words in a database compared with sample emails, remains unchanged, while the second parameter is changed from an acceptable percentage to the number of matching words. The empirical results suggest that the number of words in a database compared with sample emails is 11, and the number of matching words to categorize emails is 7. When these settings are applied to categorize 12,465 emails, the accuracy of this experiment is approximately 65.3%. The optimal number of words that yields high accuracy levels lies between 11 and 13, while the number of matching words lies between 6 and 8.
Email is one of the most powerful tools for communication. Many businesses use email as the main channel for communication, so it is possible that substantial data are included in email content. In order to help businesses grow faster, a workflow management system may be required. The data gathered from email content might be a robust source for a workflow management system. This research proposes an email extraction system to extract data from any incoming emails into suitable database fields. The database, which is created by the program, has been planned for the implementation of a workflow management system. The research is presented in three phases: (1) define suitable criteria to extract data; (2) implement a program to extract data, and store them in a database; and (3) implement a program for validating data in a database. Four criteria are applied for an email extraction system. The first criterion is to select contact information at the end of the email content; the second criterion is to select specified keywords, such as tel, email, and mobile; the third criterion is to select unique names, which start with a capital letter, such as the names of people, places, and corporates; the fourth criterion is to select special texts, such as Co. Ltd, .com, and www. The empirical results suggest that when all four criteria are considered, the accuracy of a program and percentage of blank fields are at an acceptable level compared with the results from other criteria. When four criteria are applied to extract 7,340 emails in English, the accuracy of this experiment is approximately 68.66%, while the percentage of blank fields in a database is approximately 68.05. The database created by the experiment can be applied in a workflow management system. KeywordsBusiness operations, startup business, import/export industry, email, business data, workflow management system, business transactions, migrating, email extraction system. Revised: May 2017 * The authors would like to express their gratitude to the Executive Vice-President of Finish International Freight Co. Ltd, as well as another two anonymous companies which cannot be mentioned because of confidentiality. The companies provided very useful information and insights to conduct this research. Thanks also to Khun Natthicha Phonjan and Khun Sariporn Plipon, who assisted greatly to edit and verify the accuracy of the program. It is appreciated that the business data provided by the three selected businesses are sensitive, and will not be disclosed or used for any purpose other than the research for the paper. The authors are also grateful for the helpful comments and suggestions of Chia-Lin Chang. Corresponding author: Takorn Prexawanprasut (takorn.pre@dpu.ac.th) 1 JEL Classification AbstractEmail is one of the most powerful tools for communication. Many businesses use email as the main channel for communication, so it is possible that substantial data are included in email content. In order to help businesses grow faster, a workflow management system ma...
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