An official website of the United States government
Abstract Video advertisements, either through television or the Internet, play an essential role in modern political campaigns. For over two decades, researchers have studied television video ads by analyzing the hand-coded data from the Wisconsin Advertising Project and its successor, the Wesleyan Media Project (WMP). Unfortunately, manually coding more than a hundred of variables, such as issue mentions, opponent appearance, and negativity, for many videos is a laborious and expensive process. We propose to automatically code campaign advertisement videos. Applying state-of-the-art machine learning methods, we extract various audio and image features from each video file. We show that our machine coding is comparable to human coding for many variables of the WMP datasets. Since many candidates make their advertisement videos available on the Internet, automated coding can dramatically improve the efficiency and scope of campaign advertisement research. Open-source software package is available for implementing the proposed methodology.
more »
« less
Addressing the Challenges of Online Video Analysis in Qualitative Studies: A Worked Example from Computational Thinking Research
In this paper, we share our approach and the process for qualitative analysis of online video data recorded during an after-school robotics program that emphasized computational thinking (CT). Online research strategies may be necessary for various reasons such as when working with a geographically distributed research team, when conducting research with students in an online program, or when resources are inaccessible due to campus closures like those experienced during the COVID-19 pandemic. We followed a three-stage process during qualitative analysis of the videos that included planning and setup, online analysis of videos, and structural coding of memos to explore patterns across the data. Analysis was conducted with a combination of technologies including Google Drive for collaborative coding online and NVivo to collate and summarize findings. The methods and process we describe are readily applicable to other research studies that include video as part of the data set.
more »
« less
- Award ID(s):
- 1640228
- PAR ID:
- 10283782
- Date Published:
- Journal Name:
- The Qualitative Report
- ISSN:
- 2160-3715
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes; participant age range = 4 months–3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing “LEFT” versus “RIGHT” and “ON” versus “OFF” looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos; held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance); and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research.more » « less
-
Online lecture videos are increasingly important e-learning materials for students. Automated content extraction from lecture videos facilitates information retrieval applications that improve access to the lecture material. A significant number of lecture videos include the speaker in the image. Speakers perform various semantically meaningful actions during the process of teaching. Among all the movements of the speaker, key actions such as writing or erasing potentially indicate important features directly related to the lecture content. In this paper, we present a methodology for lecture video content extraction using the speaker actions. Each lecture video is divided into small temporal units called action segments. Using a pose estimator, body and hands skeleton data are extracted and used to compute motion-based features describing each action segment. Then, the dominant speaker action of each of these segments is classified using Random forests and the motion-based features. With the temporal and spatial range of these actions, we implement an alternative way to draw key-frames of handwritten content from the video. In addition, for our fixed camera videos, we also use the skeleton data to compute a mask of the speaker writing locations for the subtraction of the background noise from the binarized key-frames. Our method has been tested on a publicly available lecture video dataset, and it shows reasonable recall and precision results, with a very good compression ratio which is better than previous methods based on content analysis.more » « less
-
In educational research, user-simulation interaction is gaining importance as it provides key insights into the effectiveness of simulation-based learning and immersive technologies. A common approach to study user-simulation interaction involves manually analyzing participant interaction in real-time or via video recordings, which is a tedious process. Surveys/questionnaires are also commonly used but are open to subjectivity and only provide qualitative data. The tool proposed in this paper, which we call Environmental Detection for User-Simulation Interaction Measurement (EDUSIM), is a publicly available video analytics tool that receives screen-recorded video input from participants interacting with a simulated environment and outputs statistical data related to time spent in pre-defined areas of interest within the simulation model. The proposed tool utilizes machine learning, namely multi-classification Convolutional Neural Networks, to provide an efficient, automated process for extracting such navigation data. EDUSIM also implements a binary classification model to flag imperfect input video data such as video frames that are outside the specified simulation environment. To assess the efficacy of the tool, we implement a set of immersive simulation-based learning (ISBL) modules in an undergraduate database course, where learners record their screens as they interact with a simulation to complete their ISBL assignments. We then use the EDUSIM tool to analyze the videos collected and compare the tool’s outputs with the expected results obtained by manually analyzing the videos.more » « less
-
Many online learning platforms and MOOCs incorporate some amount of video-based content into their platform, but there are few randomized controlled experiments that evaluate the effective- ness of the different methods of video integration. Given the large amount of publicly available educational videos, an investigation into this content’s impact on students could help lead to more ef- fective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learn- ing platform that allowed students to request skill-related videos while completing their online middle-school mathematics assign- ments. A total of 18,535 students participated in two large-scale randomized controlled experiments related to providing students with publicly available educational videos. The first experiment investigated the effect of providing students with the opportunity to request these videos, and the second experiment investigated the effect of using a multi-armed bandit algorithm to recommend relevant videos. Additionally, this work investigated which features of the videos were significantly predictive of students’ performance and which features could be used to personalize students’ learning. Ultimately, students were mostly disinterested in the skill-related videos, preferring instead to use the platforms existing problem- specific support, and there was no statistically significant findings in either experiment. Additionally, while no video features were significantly predictive of students’ performance, two video fea- tures had significant qualitative interactions with students’ prior knowledge, which showed that different content creators were more effective for different groups of students. These findings can be used to inform the design of future video-based features within online learning platforms and the creation of different educational videos specifically targeting higher or lower knowledge students. The data and code used in this work is hosted by the Open Science Foundation.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.46743/2160-3715/2021.4734
