The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform
Jigar Jadav - One of the best experts on this subject based on the ideXlab platform.
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correlation discovery between high school student Web queries and their grade point average
IEEE Annual Computing and Communication Workshop and Conference, 2017Co-Authors: Jigar Jadav, Greg Goldberg, Dawn Lindelin, Andrew Preciado, Charles C Tappert, Andrew Burke, Michael KollmerAbstract:In recent years, the K-12 learning space has been utilizing mobile devices to supplement student learning. To support the funding of school-issued mobile devices, the schools are interested in determining the degree to which the students use these devices for school-related activities. This study examined high school student Web queries performed on school issued iPads using anonymized data from Web Filter logs. These Web queries were first classified as either school-related or non-school-related using an earlier-developed algorithm. The classification results were then used to examine whether a correlation exists between Web queries, especially the school-related ones, and the student's Grade Point Average. Regression analysis found a highly significant correlation (p < 0.00005) between the percentage of school-related Web queries and the student's Grade Point Average.
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CCWC - Correlation discovery between high school Student Web Queries and their Grade Point Average
2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 2017Co-Authors: Jigar Jadav, Greg Goldberg, Dawn Lindelin, Andrew Preciado, Charles C Tappert, Andrew Burke, Michael KollmerAbstract:In recent years, the K-12 learning space has been utilizing mobile devices to supplement student learning. To support the funding of school-issued mobile devices, the schools are interested in determining the degree to which the students use these devices for school-related activities. This study examined high school student Web queries performed on school issued iPads using anonymized data from Web Filter logs. These Web queries were first classified as either school-related or non-school-related using an earlier-developed algorithm. The classification results were then used to examine whether a correlation exists between Web queries, especially the school-related ones, and the student's Grade Point Average. Regression analysis found a highly significant correlation (p < 0.00005) between the percentage of school-related Web queries and the student's Grade Point Average.
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using text analysis on Web Filter data to explore k 12 student learning behavior
Ubiquitous Computing, 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
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UEMCON - Using text analysis on Web Filter data to explore K-12 student learning behavior
2016 IEEE 7th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
Michael Kollmer - One of the best experts on this subject based on the ideXlab platform.
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correlation discovery between high school student Web queries and their grade point average
IEEE Annual Computing and Communication Workshop and Conference, 2017Co-Authors: Jigar Jadav, Greg Goldberg, Dawn Lindelin, Andrew Preciado, Charles C Tappert, Andrew Burke, Michael KollmerAbstract:In recent years, the K-12 learning space has been utilizing mobile devices to supplement student learning. To support the funding of school-issued mobile devices, the schools are interested in determining the degree to which the students use these devices for school-related activities. This study examined high school student Web queries performed on school issued iPads using anonymized data from Web Filter logs. These Web queries were first classified as either school-related or non-school-related using an earlier-developed algorithm. The classification results were then used to examine whether a correlation exists between Web queries, especially the school-related ones, and the student's Grade Point Average. Regression analysis found a highly significant correlation (p < 0.00005) between the percentage of school-related Web queries and the student's Grade Point Average.
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CCWC - Correlation discovery between high school Student Web Queries and their Grade Point Average
2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 2017Co-Authors: Jigar Jadav, Greg Goldberg, Dawn Lindelin, Andrew Preciado, Charles C Tappert, Andrew Burke, Michael KollmerAbstract:In recent years, the K-12 learning space has been utilizing mobile devices to supplement student learning. To support the funding of school-issued mobile devices, the schools are interested in determining the degree to which the students use these devices for school-related activities. This study examined high school student Web queries performed on school issued iPads using anonymized data from Web Filter logs. These Web queries were first classified as either school-related or non-school-related using an earlier-developed algorithm. The classification results were then used to examine whether a correlation exists between Web queries, especially the school-related ones, and the student's Grade Point Average. Regression analysis found a highly significant correlation (p < 0.00005) between the percentage of school-related Web queries and the student's Grade Point Average.
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using text analysis on Web Filter data to explore k 12 student learning behavior
Ubiquitous Computing, 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
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UEMCON - Using text analysis on Web Filter data to explore K-12 student learning behavior
2016 IEEE 7th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
Andrew Burke - One of the best experts on this subject based on the ideXlab platform.
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correlation discovery between high school student Web queries and their grade point average
IEEE Annual Computing and Communication Workshop and Conference, 2017Co-Authors: Jigar Jadav, Greg Goldberg, Dawn Lindelin, Andrew Preciado, Charles C Tappert, Andrew Burke, Michael KollmerAbstract:In recent years, the K-12 learning space has been utilizing mobile devices to supplement student learning. To support the funding of school-issued mobile devices, the schools are interested in determining the degree to which the students use these devices for school-related activities. This study examined high school student Web queries performed on school issued iPads using anonymized data from Web Filter logs. These Web queries were first classified as either school-related or non-school-related using an earlier-developed algorithm. The classification results were then used to examine whether a correlation exists between Web queries, especially the school-related ones, and the student's Grade Point Average. Regression analysis found a highly significant correlation (p < 0.00005) between the percentage of school-related Web queries and the student's Grade Point Average.
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CCWC - Correlation discovery between high school Student Web Queries and their Grade Point Average
2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 2017Co-Authors: Jigar Jadav, Greg Goldberg, Dawn Lindelin, Andrew Preciado, Charles C Tappert, Andrew Burke, Michael KollmerAbstract:In recent years, the K-12 learning space has been utilizing mobile devices to supplement student learning. To support the funding of school-issued mobile devices, the schools are interested in determining the degree to which the students use these devices for school-related activities. This study examined high school student Web queries performed on school issued iPads using anonymized data from Web Filter logs. These Web queries were first classified as either school-related or non-school-related using an earlier-developed algorithm. The classification results were then used to examine whether a correlation exists between Web queries, especially the school-related ones, and the student's Grade Point Average. Regression analysis found a highly significant correlation (p < 0.00005) between the percentage of school-related Web queries and the student's Grade Point Average.
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using text analysis on Web Filter data to explore k 12 student learning behavior
Ubiquitous Computing, 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
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UEMCON - Using text analysis on Web Filter data to explore K-12 student learning behavior
2016 IEEE 7th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
Charles C Tappert - One of the best experts on this subject based on the ideXlab platform.
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correlation discovery between high school student Web queries and their grade point average
IEEE Annual Computing and Communication Workshop and Conference, 2017Co-Authors: Jigar Jadav, Greg Goldberg, Dawn Lindelin, Andrew Preciado, Charles C Tappert, Andrew Burke, Michael KollmerAbstract:In recent years, the K-12 learning space has been utilizing mobile devices to supplement student learning. To support the funding of school-issued mobile devices, the schools are interested in determining the degree to which the students use these devices for school-related activities. This study examined high school student Web queries performed on school issued iPads using anonymized data from Web Filter logs. These Web queries were first classified as either school-related or non-school-related using an earlier-developed algorithm. The classification results were then used to examine whether a correlation exists between Web queries, especially the school-related ones, and the student's Grade Point Average. Regression analysis found a highly significant correlation (p < 0.00005) between the percentage of school-related Web queries and the student's Grade Point Average.
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CCWC - Correlation discovery between high school Student Web Queries and their Grade Point Average
2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 2017Co-Authors: Jigar Jadav, Greg Goldberg, Dawn Lindelin, Andrew Preciado, Charles C Tappert, Andrew Burke, Michael KollmerAbstract:In recent years, the K-12 learning space has been utilizing mobile devices to supplement student learning. To support the funding of school-issued mobile devices, the schools are interested in determining the degree to which the students use these devices for school-related activities. This study examined high school student Web queries performed on school issued iPads using anonymized data from Web Filter logs. These Web queries were first classified as either school-related or non-school-related using an earlier-developed algorithm. The classification results were then used to examine whether a correlation exists between Web queries, especially the school-related ones, and the student's Grade Point Average. Regression analysis found a highly significant correlation (p < 0.00005) between the percentage of school-related Web queries and the student's Grade Point Average.
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using text analysis on Web Filter data to explore k 12 student learning behavior
Ubiquitous Computing, 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
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UEMCON - Using text analysis on Web Filter data to explore K-12 student learning behavior
2016 IEEE 7th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
Pratik Dhiman - One of the best experts on this subject based on the ideXlab platform.
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using text analysis on Web Filter data to explore k 12 student learning behavior
Ubiquitous Computing, 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
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UEMCON - Using text analysis on Web Filter data to explore K-12 student learning behavior
2016 IEEE 7th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2016Co-Authors: Jigar Jadav, Charles C Tappert, Michael Kollmer, Andrew Burke, Pratik DhimanAbstract:The K-12 learning space is evolving in both the United States and internationally. Students are given increasingly frequent access to the internet through various platforms such as desktop computers, laptops, tablets, and other mobile devices. Some schools are distributing mobile devices to students in order to facilitate the integration of technology in the classroom. These devices have a Web Filter installed on them to Filter inappropriate content irrespective to the Wi-Fi network to which they are connected. These Web Filters collect logs of student activities on the internet. To date, however, this data has not been systematically analyzed. In this paper we explore data collected from K-12 student generated search queries on different search engines to gain insight into student learning behavior. Term frequency analysis is used to discover the relationship between effective and inappropriate usage of mobile devices in school.
