The Experts below are selected from a list of 183 Experts worldwide ranked by ideXlab platform
Patricia S. Moyer-packenham - One of the best experts on this subject based on the ideXlab platform.
-
Affordance Access Matters: Preschool Children’s Learning Progressions While Interacting with Touch-Screen Mathematics Apps
Technology Knowledge and Learning, 2017Co-Authors: Emma P. Bullock, Jessica F. Shumway, Christina M. Watts, Patricia S. Moyer-packenhamAbstract:The purpose of this study was to contribute to the research on mathematics app use by very young children, and specifically mathematics Apps for touch-screen mobile devices that contain virtual manipulatives. The study used a convergent parallel mixed methods design, in which quantitative and qualitative data were collected in parallel, analyzed separately, and then merged. During the study, 35 children, ages 3–4, interacted with four touch-screen mathematics Apps on iPad devices during one-on one clinical interviews while learning seriation and counting. Researchers administered pre and post assessments of learning during the interviews. Each interview was videotaped using a wall-mounted camera and a GoPro camera to provide different views of the interview. Videos were analyzed to examine children’s learning progressions, access of affordances, and patterns of behavior while interacting with the mathematics Apps. The results suggest that different affordances of the Individual Apps were perceived in different ways, depending on the age of the child, and that these perceptions were observable in young children’s patterns of behavior. Implications are discussed for iPad app use in young children’s educational settings.
-
Affordance Access Matters: Preschool Children's Learning Progressions While Interacting with Touch-Screen Mathematics Apps.
Technology Knowledge and Learning, 2017Co-Authors: Emma P. Bullock, Jessica F. Shumway, Christina M. Watts, Patricia S. Moyer-packenhamAbstract:The purpose of this study was to contribute to the research on mathematics app use by very young children, and specifically mathematics Apps for touch-screen mobile devices that contain virtual manipulatives. The study used a convergent parallel mixed methods design, in which quantitative and qualitative data were collected in parallel, analyzed separately, and then merged. During the study, 35 children, ages 3–4, interacted with four touch-screen mathematics Apps on iPad devices during one-on one clinical interviews while learning seriation and counting. Researchers administered pre and post assessments of learning during the interviews. Each interview was videotaped using a wall-mounted camera and a GoPro camera to provide different views of the interview. Videos were analyzed to examine children’s learning progressions, access of affordances, and patterns of behavior while interacting with the mathematics Apps. The results suggest that different affordances of the Individual Apps were perceived in different ways, depending on the age of the child, and that these perceptions were observable in young children’s patterns of behavior. Implications are discussed for iPad app use in young children’s educational settings.
Feng Feng - One of the best experts on this subject based on the ideXlab platform.
-
PRADA: Prioritizing Android Devices for Apps by Mining Large-Scale Usage Data
2016 IEEE ACM 38th International Conference on Software Engineering (ICSE), 2016Co-Authors: Xuan Lu, Huoran Li, Gang Huang, Feng FengAbstract:Selecting and prioritizing major device models are critical for mobile app developers to select testbeds and optimize resources such as marketing and quality-assurance resources. The heavily fragmented distribution of Android devices makes it challenging to select a few major device models out of thousands of models available on the market. Currently app developers usually rely on some reported or estimated general market share of device models. However, these estimates can be quite inaccurate, and more problematically, can be irrelevant to the particular app under consideration. To address this issue, we propose PRADA, the first approach to prioritizing Android device models for Individual Apps, based on mining large-scale usage data. PRADA adapts the concept of operational profiling (popularly used in software reliability engineering) for mobile Apps - the usage of an app on a specific device model reflects the importance of that device model for the app. PRADA includes a collaborative filtering technique to predict the usage of an app on different device models, even if the app is entirely new (without its actual usage in the market yet), based on the usage data of a large collection of Apps. We empirically demonstrate the effectiveness of PRADA over two popular app categories, i.e., Game and Media, covering over 3.86 million users and 14,000 device models collected through a leading Android management app in China.
-
ICSE - PRADA: prioritizing android devices for Apps by mining large-scale usage data
Proceedings of the 38th International Conference on Software Engineering - ICSE '16, 2016Co-Authors: Xuan Lu, Huoran Li, Gang Huang, Feng FengAbstract:Selecting and prioritizing major device models are critical for mobile app developers to select testbeds and optimize resources such as marketing and quality-assurance resources. The heavily fragmented distribution of Android devices makes it challenging to select a few major device models out of thousands of models available on the market. Currently app developers usually rely on some reported or estimated general market share of device models. However, these estimates can be quite inaccurate, and more problematically, can be irrelevant to the particular app under consideration. To address this issue, we propose PRADA, the first approach to prioritizing Android device models for Individual Apps, based on mining large-scale usage data. PRADA adapts the concept of operational profiling (popularly used in software reliability engineering) for mobile Apps -- the usage of an app on a specific device model reflects the importance of that device model for the app. PRADA includes a collaborative filtering technique to predict the usage of an app on different device models, even if the app is entirely new (without its actual usage in the market yet), based on the usage data of a large collection of Apps. We empirically demonstrate the effectiveness of PRADA over two popular app categories, i.e., Game and Media, covering over 3.86 million users and 14,000 device models collected through a leading Android management app in China.
Emma P. Bullock - One of the best experts on this subject based on the ideXlab platform.
-
Affordance Access Matters: Preschool Children’s Learning Progressions While Interacting with Touch-Screen Mathematics Apps
Technology Knowledge and Learning, 2017Co-Authors: Emma P. Bullock, Jessica F. Shumway, Christina M. Watts, Patricia S. Moyer-packenhamAbstract:The purpose of this study was to contribute to the research on mathematics app use by very young children, and specifically mathematics Apps for touch-screen mobile devices that contain virtual manipulatives. The study used a convergent parallel mixed methods design, in which quantitative and qualitative data were collected in parallel, analyzed separately, and then merged. During the study, 35 children, ages 3–4, interacted with four touch-screen mathematics Apps on iPad devices during one-on one clinical interviews while learning seriation and counting. Researchers administered pre and post assessments of learning during the interviews. Each interview was videotaped using a wall-mounted camera and a GoPro camera to provide different views of the interview. Videos were analyzed to examine children’s learning progressions, access of affordances, and patterns of behavior while interacting with the mathematics Apps. The results suggest that different affordances of the Individual Apps were perceived in different ways, depending on the age of the child, and that these perceptions were observable in young children’s patterns of behavior. Implications are discussed for iPad app use in young children’s educational settings.
-
Affordance Access Matters: Preschool Children's Learning Progressions While Interacting with Touch-Screen Mathematics Apps.
Technology Knowledge and Learning, 2017Co-Authors: Emma P. Bullock, Jessica F. Shumway, Christina M. Watts, Patricia S. Moyer-packenhamAbstract:The purpose of this study was to contribute to the research on mathematics app use by very young children, and specifically mathematics Apps for touch-screen mobile devices that contain virtual manipulatives. The study used a convergent parallel mixed methods design, in which quantitative and qualitative data were collected in parallel, analyzed separately, and then merged. During the study, 35 children, ages 3–4, interacted with four touch-screen mathematics Apps on iPad devices during one-on one clinical interviews while learning seriation and counting. Researchers administered pre and post assessments of learning during the interviews. Each interview was videotaped using a wall-mounted camera and a GoPro camera to provide different views of the interview. Videos were analyzed to examine children’s learning progressions, access of affordances, and patterns of behavior while interacting with the mathematics Apps. The results suggest that different affordances of the Individual Apps were perceived in different ways, depending on the age of the child, and that these perceptions were observable in young children’s patterns of behavior. Implications are discussed for iPad app use in young children’s educational settings.
Xuan Lu - One of the best experts on this subject based on the ideXlab platform.
-
PRADA: Prioritizing Android Devices for Apps by Mining Large-Scale Usage Data
2016 IEEE ACM 38th International Conference on Software Engineering (ICSE), 2016Co-Authors: Xuan Lu, Huoran Li, Gang Huang, Feng FengAbstract:Selecting and prioritizing major device models are critical for mobile app developers to select testbeds and optimize resources such as marketing and quality-assurance resources. The heavily fragmented distribution of Android devices makes it challenging to select a few major device models out of thousands of models available on the market. Currently app developers usually rely on some reported or estimated general market share of device models. However, these estimates can be quite inaccurate, and more problematically, can be irrelevant to the particular app under consideration. To address this issue, we propose PRADA, the first approach to prioritizing Android device models for Individual Apps, based on mining large-scale usage data. PRADA adapts the concept of operational profiling (popularly used in software reliability engineering) for mobile Apps - the usage of an app on a specific device model reflects the importance of that device model for the app. PRADA includes a collaborative filtering technique to predict the usage of an app on different device models, even if the app is entirely new (without its actual usage in the market yet), based on the usage data of a large collection of Apps. We empirically demonstrate the effectiveness of PRADA over two popular app categories, i.e., Game and Media, covering over 3.86 million users and 14,000 device models collected through a leading Android management app in China.
-
ICSE - PRADA: prioritizing android devices for Apps by mining large-scale usage data
Proceedings of the 38th International Conference on Software Engineering - ICSE '16, 2016Co-Authors: Xuan Lu, Huoran Li, Gang Huang, Feng FengAbstract:Selecting and prioritizing major device models are critical for mobile app developers to select testbeds and optimize resources such as marketing and quality-assurance resources. The heavily fragmented distribution of Android devices makes it challenging to select a few major device models out of thousands of models available on the market. Currently app developers usually rely on some reported or estimated general market share of device models. However, these estimates can be quite inaccurate, and more problematically, can be irrelevant to the particular app under consideration. To address this issue, we propose PRADA, the first approach to prioritizing Android device models for Individual Apps, based on mining large-scale usage data. PRADA adapts the concept of operational profiling (popularly used in software reliability engineering) for mobile Apps -- the usage of an app on a specific device model reflects the importance of that device model for the app. PRADA includes a collaborative filtering technique to predict the usage of an app on different device models, even if the app is entirely new (without its actual usage in the market yet), based on the usage data of a large collection of Apps. We empirically demonstrate the effectiveness of PRADA over two popular app categories, i.e., Game and Media, covering over 3.86 million users and 14,000 device models collected through a leading Android management app in China.
Jessica F. Shumway - One of the best experts on this subject based on the ideXlab platform.
-
Affordance Access Matters: Preschool Children’s Learning Progressions While Interacting with Touch-Screen Mathematics Apps
Technology Knowledge and Learning, 2017Co-Authors: Emma P. Bullock, Jessica F. Shumway, Christina M. Watts, Patricia S. Moyer-packenhamAbstract:The purpose of this study was to contribute to the research on mathematics app use by very young children, and specifically mathematics Apps for touch-screen mobile devices that contain virtual manipulatives. The study used a convergent parallel mixed methods design, in which quantitative and qualitative data were collected in parallel, analyzed separately, and then merged. During the study, 35 children, ages 3–4, interacted with four touch-screen mathematics Apps on iPad devices during one-on one clinical interviews while learning seriation and counting. Researchers administered pre and post assessments of learning during the interviews. Each interview was videotaped using a wall-mounted camera and a GoPro camera to provide different views of the interview. Videos were analyzed to examine children’s learning progressions, access of affordances, and patterns of behavior while interacting with the mathematics Apps. The results suggest that different affordances of the Individual Apps were perceived in different ways, depending on the age of the child, and that these perceptions were observable in young children’s patterns of behavior. Implications are discussed for iPad app use in young children’s educational settings.
-
Affordance Access Matters: Preschool Children's Learning Progressions While Interacting with Touch-Screen Mathematics Apps.
Technology Knowledge and Learning, 2017Co-Authors: Emma P. Bullock, Jessica F. Shumway, Christina M. Watts, Patricia S. Moyer-packenhamAbstract:The purpose of this study was to contribute to the research on mathematics app use by very young children, and specifically mathematics Apps for touch-screen mobile devices that contain virtual manipulatives. The study used a convergent parallel mixed methods design, in which quantitative and qualitative data were collected in parallel, analyzed separately, and then merged. During the study, 35 children, ages 3–4, interacted with four touch-screen mathematics Apps on iPad devices during one-on one clinical interviews while learning seriation and counting. Researchers administered pre and post assessments of learning during the interviews. Each interview was videotaped using a wall-mounted camera and a GoPro camera to provide different views of the interview. Videos were analyzed to examine children’s learning progressions, access of affordances, and patterns of behavior while interacting with the mathematics Apps. The results suggest that different affordances of the Individual Apps were perceived in different ways, depending on the age of the child, and that these perceptions were observable in young children’s patterns of behavior. Implications are discussed for iPad app use in young children’s educational settings.
