Education
The dataset file contains all the relevant data for this paper, including original text data, labels, and statistical information, which is utilized for training, testing, and validation of the proposed models or arguments. Additionally, there is a question bank file that comprises all test questions, filtered test data, and annotated result data after testing. This data is used to evaluate the performance of the models or methods proposed in the paper.
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The dataset file contains all the relevant data for this paper, including original text data, labels, and statistical information, which is utilized for training, testing, and validation of the proposed models or arguments. Additionally, there is a question bank file that comprises all test questions, filtered test data, and annotated result data after testing. This data is used to evaluate the performance of the models or methods proposed in the paper.
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RMUTT-DLD is an aggregated collection of data that encompasses details derived from the IC3 digital literacy certification program conducted at Rajamangala University of Technology Thanyaburi (RMUTT) in Thailand spanning from 2016 to 2023. The expanded dataset includes demographic details, academic records, and certification results, offering a holistic perspective on the progression of students' digital literacy over a period of time. The dataset has the flexibility to be imported into diverse applications, enabling its utilization for various purposes.
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Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory
By Juliet Corbin & Anselm Strauss
Publisher: SAGE Publications, Inc.
Publication year: 2008
Print ISBN:9781412906449
Online ISBN:9781452230153
Keywords: culture wars, death, emotion, enemies, pain, Vietnam, war
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Transcribing a theoretical or conceptual paper allows readers to quickly read and reference the spoken material. Transcriptions further benefit deaf and hard-of-hearing people and others who need written translations. Transcribing the content makes it more accessible and valuable for theoretical and conceptual paper writers.
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Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
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Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
- Categories:
Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19”, Proceedings of the 25th International Conference on Human-Computer Interaction (HCII 2023), Copenhagen, Denmark, July 23-28, 2023 (Accepted for Publication)
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
- Categories:
Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
- Categories: