a researcher uses a video camera to record

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Methods to Improve Reliability of Video Recorded Behavioral Data

Kim kopenhaver haidet.

School of Nursing, 307 Health & Human Development East, The Pennsylvania State University, University Park, PA 16802

Judith Tate

School of Nursing, University of Pittsburgh, Pittsburgh, PA

Dana Divirgilio-Thomas

School of Nursing, University of Pittsburgh

Ann Kolanowski

School of Nursing, The Pennsylvania State University, University Park, PA

Mary Beth Happ

Behavioral observation is a fundamental component of nursing practice and a primary source of clinical research data. The use of video technology in behavioral research offers important advantages to nurse scientists in assessing complex behaviors and relationships between behaviors. The appeal of using this method should be balanced, however, by an informed approach to reliability issues. In this paper, we focus on factors that influence reliability, such as the use of sensitizing sessions to minimize participant reactivity and the importance of training protocols for video coders. In addition, we discuss data quality, the selection and use of observational tools, calculating reliability coefficients, and coding considerations for special populations based on our collective experiences across three different populations and settings.

Behavioral observation is a fundamental component of nursing practice and a primary source of clinical research data. The use of video technology to capture behavioral observations is becoming more prevalent in nursing research because of the advantage it provides in the ability to replay and review observational data, the control of observer fatigue or drift, the ability to achieve levels of observation and analysis not afforded by real-time observations, and the relative ease of using modern sophisticated recording equipment ( Caldwell & Atwal, 2005 ; Heacock, Souder, & Chastain, 1996 ). The appeal of this method needs to be balanced, however, by an informed approach to reliability issues that include those universal to all observation methods and those unique to video recording.

In this paper, we describe techniques and strategies to ensure the reliability of behavioral data obtained via video recording. We emphasize non-technical factors influencing the reliability of such recordings. Our discussion is based on our collective experiences conducting the following studies involving three different subject populations and settings : Improving Communication with Non-speaking ICU Patients; Symptom Management ( PI: MBH )), Patient-Caregiver Communication, and ICU Outcomes ( PI: MBH ); Non-pharmacological Interventions for Behavioral Symptoms in Nursing Home Residents with Dementia ( PI: AK ); and Patterns of Stress Reactivity in Low Birth Weight Preterm Infants ( PI: KKH ).

Selecting Video Recording As a Method of Data Collection

The decision to use video recording for data collection should be informed by the purpose and aims of the study as well as the unit of behavior being measured ( Caldwell & Atwal, 2005 ). Video recordings are an excellent source of data that can be used to assess relationships between behaviors that occur in close temporal proximity to one another. For example, data from video recordings have demonstrated relationships among infant handling in the neonatal intensive care unit (NICU), facial expressions of the neonate, and simultaneously collected heart rate variability data ( Haidet, Susman, West, & Marks, 2005 ). Video recording as a means of data capture has also been used in a study focused on testing the relationship between morning-care processes and pain expression in nursing home residents with dementia ( Sloane et al., 2007 ). Video recording, however, may not be appropriate for all types of behavioral observations in clinical settings. The intrusiveness of the methodology may alter naturally occurring behaviors and, thus, limit its appropriateness in certain situations, such as interactions during emotionally-charged events or provision of direct care.

Video recording provides a high degree of reproducibility when measuring observations. Unlike real-time data collection, video recordings can be re-played any number of times. On the other hand, video recording provides only a window of what happens in real time and may lack important contextual data ( Latvala, Vuokila-Oikkonen, & Janhonen, 2000 ). Even with multiple, strategically-placed cameras, it may be difficult to capture the gestalt of a targeted behavioral observation, such as team collaboration during an emergency in a busy clinical setting.

The researcher needs to take all these factors into consideration when planning the study protocol. If the decision is in favor of using video recording for data collection, there are a number of ways to ensure the reliability of those data.

Methods To Improve Raw Data Quality

Participant reactivity is a persistent issue in any observational research, but the use of cameras means the presence of an additional “eye” ( Gross, 1991 ). Reactivity is defined as a response between the researcher and participant during data collection that affects the natural course of behavior as a result of being observed ( Paterson, 1994 ). The “Hawthorne effect” is a classic example of reactivity that can be decreased by exposing participants to longer periods of observation to acclimate them to the presence of the observer. This can be accomplished by conducting a sensitizing session or by analyzing data taped later in a predetermined “middle” segment of the session (e.g., after the first 3-5 minutes) ( Elder, 1999 ). For example, Happ, Sereika, Garrett, and Tate (2008) conducted a sensitizing session simply by collecting one more video recording of nurse-patient interactions in the ICU than necessary for measurement. The researchers did not use the first of five video-recorded sessions for outcome measurement. These strategies enable the participant to move beyond an initial period of self-consciousness and the observer to capture a more representative sample of behavior ( Rosenstein, 2002 ) Alternately, researchers might minimize participant reactivity by decreasing the presence of the additional “eye” through the use of a smaller or concealed camera (Gross).

Good planning in the observational protocol and training of research assistants are vital. Because of improvements in camera technology and the employment of trained video technicians as research assistants, we have minimal missing or lost data due to poor camera angle, camera malfunction, or poor picture quality. Skilled video technicians can be found within a university, particularly if the institution has film majors. Local technical or art schools may provide suitably skilled personnel. If trained video technicians are not available, even data collectors with limited or no videography skills can be trained to meet study standards because video technology has eliminated the need for advanced skills to obtain high quality data. Training unskilled video data collectors can be accomplished with a checklist of both operational and video quality standards prior to entering the field. We recommend a series of guided practice sessions with the camera and testing to predetermined performance criteria of all research assistants who will be responsible for operating the camera.

The length of video-recorded observation needed will vary depending on the behavioral unit, research questions, and associated measures. A reasonable representation of the behavior of interest can be obtained by sampling either by time or by event ( Casey, 2006 ). Selection of video segments for measurement should be guided by pragmatic and/or conceptually relevant criteria that can be consistently implemented across research participants and data samples. A predetermined time increment (e.g., beginning after the first 5 minutes of filming) or behavioral target (e.g., when dressing activities begin) can be used as the starting point for measurement. For example, surveillance video used to capture a naturally occurring event of interest will be longer than video recordings directed specifically to (and initiated with) a target event. A study examining gait among adults with Parkinson's disease video recorded subjects walking a predetermined distance. Length of video recordings varied by individual subject speed ( Dijkstra, Zijlstra, Scherder, & Kamsma, 2008 ). Measures can be applied to a time sample or series of time samples (e.g., 1 minute of video recording sampled every 10 minutes). Alternatively, measurement may target a specific event within a longer recording, such as a parent's arrival in the child's hospital room ( Dichter, 1999 ). There will likely be greater variability among surveillance video-recordings of hospitalized children's responses to parental visitation than in the preceding example of a planned test of gait.

Measures of human interaction and communication can be applied to relatively short, dense segments of videotaped observation. In a recent clinical trial, observations of nurse-patient interactions in the ICU were conducted systematically in the morning and afternoon based on the assumption that critical care nurses interact with patients on a regular basis ( Happ et al., 2008 ). This sampling structure also accounted for the likelihood that behaviors may change based on the time of day. Event sampling is appropriate for those phenomena that are less frequently observed, such as bathing procedures in nursing home facilities. Researchers need to carefully define the temporal and spatial boundaries of the phenomena of interest. Once the behavioral unit is established, the next step is to select appropriate measurement instruments.

Use of Observational Tools

Options for quantitative measurement of behaviors from observation span the full range of measurement levels, depending on the tool selected and the needs of the research. For example, researchers may choose to apply a nominal typology or categorization scheme such as “communication styles,” or an ordinal Likert-type scale to rate degree of success ( Happ et al., 2008 ). Behavioral measures can be as simple as frequency counts of the occurrence of a target behavior or a continuous measure of the duration of that behavior ( Roter, Larson, Fischer, Arnold, & Tulsky, 2000 ; Spencer, Coiera, Logan, 2003 ).

The first decision in selection of observational measures is whether the level of measurement requires a microanalytic or macroanalytic approach. Microanalytic approaches can produce fine discrimination between units of analysis and a variety of information, but are very time consuming, resource-intensive, and produce large datasets. Light (1988) recommended microanalysis for single-subject case designs or small-sample studies. Macroanalysis, or analysis of an entire interactional process as a whole, using global measures may be more appropriate for studies evaluating the effectiveness of an intervention.

Observational measures employ predetermined coding or rating schemes to quantify behavior ( Morrison, Phillips, & Chae 1990 ). Observational tools designed for use with video recording allow researchers to use manualized procedures and decision rules to assign codes to nonverbal behaviors, verbal expressions, or vocal utterances. For example, the Facial Action Coding System (FACS) is a microanalytic system for rating facial movements and emotional facial expressions ( Ekman, Friesen, & Hager, 2002 ). FACS coders receive specialized instruction involving 100 hours of training and practice with final competency test and certification as a trained coder ( Description of Facial Action Coding System [FACS], n.d. ). Computer software available for conducting microanalysis of video-recorded behavioral interactions include Canopus Procoder3™ (Thompson Grass Valley 2008, Inc., Burbank, CA), Noldus™ (Noldus Information Technologies, 2008, Wageningen, The Netherlands), Transana™ (Wisconsin Center for Education Research, University of Wisconsin-Madison), and Studiocode™ (Studiocode Business Group, Warriewood, Australia).

Observational checklist tools intended for use in direct observation can also be applied to video recordings, usually at the macroanalytic level. The QUALCARE Scale ( Phillips, Morrison, & Chae, 1990 ), the Discomfort Scale in Dementia of the Alzheimer's Type (DC-DAT; Hurley, Volicer, Hanrahan, Houde, & Volicer, 1992 ), the Pain Assessment in Advanced Dementia (PAINAD) scale ( Warden, Hurley, & Volicer, 2003 ), and the Premature Infant Pain Profile (PIPP) ( Stevens, Johnston, Petryshen, & Taddio, 1996 ) are examples of such observational checklists. The QUALCARE Scale was developed for direct measurement of physical, psychological, and environmental dimensions of caregiving for dependent older adults (Phillips et al.). The DC-DAT; measures discomfort (Hurley et al), and the PAINAD measures pain by observation of nonverbal patients with advanced Alzheimer's disease (Warden et al.).The PIPP was designed to measure the physiological and behavioral indicators of pain in premature infants (Stevens et al.). Video exemplars were used to train coders in the use of the DC-DAT (Hurley et al.), QUALCARE Scale ( Morrison et al., 1990 ), and the PIPP ( Stevens, 1999 ).

Not all direct observation tools can be used with video data. This is particularly true of instruments that require the observer to capture contextual observations. For example, in the Passivity in Dementia Scale (PDS), one subscale is: interaction with the environment ( Colling, 1999 ), but it is often impossible to see this interaction through the lens of a camera (i.e., resident responding to an off camera resident. One of us (A.K.) found that the PDS subscale had low reliability when used with video data in contrast to real-time observation. This experience highlights the importance of pilot testing instruments that will be used to code video data and to compute their reliability coefficients when used with video data so that appropriate selection of instruments can be made.

When measuring phenomena for which there are no established observational tools, researchers may choose to create adaptations of checklist tools that are typically applied by clinicians or self-rated by patients. For example, items from the revised Condensed-Memorial Symptom Assessment Scale (C-MSAS; Chang, Hwang, Feuerman, Kasimis, & Thaler, 2000 ) are currently being applied to video observations of nurse-patient communication in the ICU to measure symptom assessment and management (K24-NR010244). Nelson et al. (2001) revised the C-MSAS for use with nonspeaking critically ill patients. In this revision, patients endorse the presence or absence of each symptom on a checklist of 14 physical and emotional symptoms (e.g., shortness of breath, pain, thirst, worry) followed by rating the level of distress from each physical symptom or frequency of each emotional symptom experienced. In applying this symptom checklist to video observation of nurse-patient communication, only symptom presence as identified in the observed communication interaction is counted. Judgments about symptom distress or intensity are not made from the video recording. Although the previously established symptom constructs are foundational to the application of the C-MSAS checklist to video observation, new psychometric testing should be conducted for this application. In conjunction with employing appropriate observational tools for measurement, it is imperative to establish an effective training regimen for video coders.

Training, Re-training, and Retaining Video Coders

In order to achieve acceptable levels of reliability between coders, a standardized training procedure should be implemented to provide instruction on the use of instruments and the video coding process ( Castorr et al., 1990 ). We developed detailed training manuals that provide specific instructions for using each instrument. The training includes operational definitions of terms and specific interpretation of the behaviors to be rated. Training reduces variability in scoring by minimizing personal interpretation ( Curyto, Van Haitsma, & Vriesman, 2008 ; Washington & Moss, 1988 ) and the effects of prior experience with the study population ( Kaasa, Wessel, Darrah, & Bruera, 2000 ).

Some projects, do not require any specific credentials or experience for video coders. In fact, naïve coders are often the best research assistants because they may not have preconceived notions about which behaviors are “appropriate” in specific situations, and would, therefore, be more likely to note these behaviors. In other cases, clinical experience would be an asset, if not a necessity, when coding highly technical procedures ( Treloar et al., 2008 ). Regardless of background, research assistants need detailed study information to achieve reliability performance in the field ( Cambron & Evans, 2003 ). The content and length of training programs will vary according to the complexity of the protocol and the reliability of the instruments used to code data. In general, instruments with lower reliability coefficients will require that video coders have more training in their use before acceptable levels of reliability are achieved.

We begin our training by discussing the difference between an objective behavioral observation and a behavioral inference. We illustrate this critical difference by describing situations in which the context could influence coders' rating of behaviors: for example, inferring that a positive affective behavior is being expressed by a subject who is sitting next to individuals who are smiling and laughing, without noting the actual facial expression of the subject. Once this concept is grasped, coders commit to memory the behaviors and the definitions of behaviors that are included on each instrument they use. Additionally, we instruct our coders to access the data collection manuals we develop that describe in detail the behavioral units we are measuring, and we include short definitions of these behaviors on our data collection forms as memory prompts.

We then practice using each instrument with actual video data from the project. This is initially done in a group setting so we can discuss differences across coders. When coders feel confident in identifying behaviors, we ask them to rate video data individually and compare their ratings to a pre-established gold standard. Practice continues until coders achieve 80% agreement with the gold standard. The cut point for competency reliability (≥ .80) is commonly used as acceptable inter-rater reliability in research involving observational coding from videos ( de los Rios Castillo & Sosa, 2002 ; Morse, Beres, Spiers, Mayan, & Olson, 2003 ; Topf, 1986 ), however, some researchers use >70% inter-observer agreement as acceptable for new instruments ( Le May & Redfern, 1987 ).

Across all studies, we train video coders to avoid fatigue when coding tapes and to take frequent breaks to counteract drift. Coders are also asked to avoid coding tapes on days when they are emotionally upset or ill, as their own emotional state can influence the behavioral observations they make. To avoid intra-rating bias during video coding, we assign different coders to rate different behaviors in the same subject. Calibration reliability checks are completed on 10% of recordings throughout the project on each video coder. If reliability falls below 80%, retraining takes place. In one of our projects, routine booster sessions are conducted in which all raters are brought together when there is a lapse of 1 month or more in video coding because of slow enrollment. The purpose of these sessions is briefly to review behavioral definitions and their expression in order to prevent low reliability due to loss of rating skill.

Because the training and re-training of video coders is so intense in terms of time and labor, we minimize video coder attrition and subsequent fluctuations in reliability by compensating video coders for their training after 6 months of service to the project. We have found this to be a very effective and acceptable method for retention of our well-trained video coders. In addition to the establishment of an effective training protocol and acceptable levels of reliability between video coders, it is important to estimate reliability coefficients using standard methods.

Computation of Reliability Coefficients

In order to establish reliability of the instrument, it is important to measure internal consistency, and consistency of measures across time when rated by different individuals. Use of Cronbach's alpha allows the researcher to estimate the average correlation of each scale item with the other scale items used ( Kerlinger & Lee, 2000 ). The reliability of observation systems is most often defined as agreement among two or more independent raters of behavior, also known as inter-rater reliability (IRR). Kappa-type statistics, coefficient kappa (also known as Cohen's kappa) and intraclass correlation (ICC) are commonly used to quantify IRR ( Hulley, Cummings, Browner, Grady, & Newman, 2007 ).

Kappa is an estimation of the degree of consensus between raters. The calculated value will be in the range of -1 to +1 ( Landis & Koch, 1977 ). If the raters are in perfect agreement, kappa equals 1, in contrast to perfect disagreement between raters where kappa equals -1. A kappa of zero represents the same likelihood that the agreement between raters is equal to chance. An acceptable coefficient kappa (good agreement) would be a value above .75, moderate agreement is between .4 – .75, and low agreement is below .4 ( Bellieni et al., 2007 ; Laschinger, 1992 ). Kappa is calculated using the following equation:

Coefficient kappa allows for quantification of IRR as well as rater consistency, and provides a more precise determination of the reliability estimate than a simple correlation coefficient ( Hulley et al., 2007 ).

Reliable Video Coding in Special Populations

In addition to general issues for improving the reliability of video-recorded behavioral data, there are specific issues that relate to special populations.

Critically Ill Patients

There are special conditions to consider when coding data from critically ill and non-speaking participants. In the case of video-recorded communication interactions with intensive care unit (ICU) patients, several factors contribute to the complexity of the task.

An initial challenge of analyzing video recordings in critical care is to distinguish which of the participants is speaking due to camera angles that may obscure a visual record of the speaker, co-occurring conversations, and the effect of environmental noise. These environmental noises can be a radio, a television set, family members sitting in the room, clinicians outside of the room, equipment noises or monitor alarms. Yet, it is very important to listen for these environmental noises, even if this means listening to the audio several times and in smaller segments, because patients sometimes react to them. For example, a patient in the Study of Patient-Nurse Effectiveness with Assisted Communication Strategies (SPEACS) study reacted to a news current event on the television by mouthing words, but his conversational message was misinterpreted by the nurse who was concerned with the patient's immediate physical comfort needs. Data coders reviewed the video many times to accurately lip read the patient's message using the context of the background noise of the television news program to aid interpretation.

Critically ill patients tend to have oral endotracheal tubes or tracheostomy tubes preventing speech. The tube and fixation tape masks the participant's mouth movements, making it difficult to read the patient's lips. Beards can also contribute to the distortion of oral movements. Furthermore, when participants no longer have their own teeth or exhibit oral tremors, the enunciation of words may be affected, adding an additional layer of difficulty to the analysis.

In addition to mouthing words as a means of communicating, critically ill patients tend to gesture. The patient's ability to use upper motor movements is not always precise; much of the time, movements are gross. Patients' arms and hands can be swollen creating difficulty in deciphering the gestures. The coder must discern whether the patient is truly intending to use voluntary gestural communication or whether the movement is involuntary. The wrists of ICU patients may be restrained, which prevents, limits, or distorts the gesture for communication purposes. Furthermore, the position of the patient (in the bed or a bedside chair), blankets on the patient, and bed rails can also hinder or obstruct the patient's gestures.

Despite these barriers to the interpretation of patient communication in the ICU, videography offers a unique and powerful tool for recording communicative behaviors among nonspeaking critically ill patients. The ability to pause, replay, and study the interaction permits a level of understanding unavailable during real-time observation. We recommend the use of close-up camera shots and wide angle lenses, hand-held cameras to follow the action/ actors, and directional microphones to optimize the recording quality. Data collection protocols that reduce extraneous background noise before the start of recording sessions should be considered: for example, muting the television, closing the door, and posting a “video recording in progress” sign on the door to discourage interruptions. Finally, because critically ill patients can have dramatic fluctuations in illness severity, cognition, and attention, we recommend that researchers always consider measures of illness severity or acuity and delirium for use in describing the sample, eligibility screening, and/or as covariates.

Nursing Home Residents with Dementia

A second program of research focused on interventions for improving the behavioral symptoms of nursing home residents with dementia. Important outcomes in this work are displays of both positive and negative affect. A growing literature documents that persons with dementia experience a full range of emotions and not just the more negative ones of anger, anxiety or sadness ( Cotrell & Hooker, 2005 ; Hubbard, Downs, & Tester, 2003 ; Kolanowski, Hoffman, & Hofer, 2007 ). Video coders, however, often have trouble distinguishing normal age changes or changes brought on by chronic conditions, medications, or loss of dentition from facial, voice, and postural indicators of negative affect. It is essential that video coders be trained to differentiate, for example, the stooped posture resulting from degenerative arthritis from the stooped shoulders indicating depression; the wrinkled, loose skin around the eyes and horizontal furrows in the forehead due to gravitational effects of aging from the sagging facial muscles and horseshoe furrows in the forehead due to sadness; or, the loud, coarse voice quality due to hearing loss from the increased voice volume indicative of anger ( Ekman & Rosenberg, 1997 ). Although it seems intuitive that video recording coupled with blinded video coders would eliminate bias related to familiarity with the subject, the gain in objectivity may be at the expense of inaccuracy in rating. A baseline period is critical to help video coders differentiate the behavioral signs of affect from the age and disease changes typical in older adults with dementia.

Because fluctuations in affect and other behavioral outcomes are related to cognitive status, it is important to describe the sample along these characteristics. We have also found that variability in displayed negative affect (but not positive affect) increases with increasing severity of disease ( Kolanowski et al., 2007 ). This necessitates more intense measurement models (more frequent collection of data) accurately to capture the day-to-day variability in affect in this population.

Preterm Infants in Neonatal Intensive Care

A third program of research focused on the integration of biological and behavioral responses of preterm infants to environmental stressors associated with nursing care in the NICU. The goal is to investigate patterns of early stress response and stress recovery to determine the extent to which individual patterns of stress response are predictive of negative short-term health consequences. A growing literature supports the use of behavioral indicators for pain/stress measurement in preverbal infants and young children ( Ballantyne, Stevens, McAllister, Dionne, & Jack, 1999 ; Gibbins et al., 2008 ; Grunau & Oberlander, 1998 ; Hunt et al., 2004 ; Hunt et al., 2007 ; Peters et al., 2003 ).

The neonatal facial coding system (NFCS) is a behavioral rating scale with demonstrated clinical validity as a measure of pain/stress in infants ( Grunau & Oberlander, 1998 ). The measure consists of 9 distinct facial dimensions defined by upper and lower facial muscle activity. The activities include the following: brow bulge, eye squeeze, nasolabial furrow, open lips, vertical mouth stretch, horizontal mouth stretch, lip purse, taut tongue and chin quiver (Grunau & Oberlander). In the preterm infant, the lower facial muscles develop later than the upper facial muscles; there is a normal progression of development from head to toe. Thus, the preterm infant with limited lower facial muscle development most commonly displays only the upper facial activities (brow bulge, eye squeeze, and nasolabial furrow) during stress or pain evoking events ( Peters et al., 2003 ). Infant behavioral states (quiet sleep, active sleep, transition, quiet awake, active awake) as well as current level of illness are important covariates that may contribute to a diminution or robustness of behavioral responses. We recommend that researchers document behavioral state and acuity of illness at the time of observation.

The Premature Infant Pain Profile (PIPP) is a comprehensive pain/stress scale that includes both behavioral and physiological parameters and has established validity and reliability for use in preterm infants ( Bellieni et al., 2007 ; Stevens, 1999 ). The PIPP instrument consists of seven indicators, each scored on a 4-point scale. Indicators include the following: gestational age, behavioral state, heart rate and oxygen saturation responses, and upper facial behavioral responses (brow bulge, eye squeeze, nasolabial furrow; Stevens et al., 1996 ). To determine responses to stimuli, behaviors are recorded during baseline, stimulus, and recovery times. Reliability for facial coding can be established by determining the percentage of agreement per occurrence for each facial activity within 2-minute epochs ( Gibbins et al., 2008 ). Because gestational age is the important contextual indicator for rating distress in preterm infants, it is critical that video coders using PIPP are instructed on the key developmental differences between infants of varied gestational ages (Gibbins et al.). Attention should be directed at establishing intra- and inter-rater agreement for evaluating infants within each of the preterm gestational age categories under observational study (< 27 weeks, 27-29 weeks, 30-33 weeks and 34-36 weeks gestation).

The use of video technology in behavioral research offers important advantages to nurse scientists in assessing complex behaviors and relationships between behaviors. The reliability of data obtained using this method needs to be weighed against the reliability of using real-time observations. Video recordings of behavioral responses are preferable as they afford the rater with the ability to freeze frame and replay data for review and coding of behavior, thus, enhancing reliability. An additional advantage of video-recorded behavioral observations over real-time observations is the ability of the coder to take breaks as needed and minimize problems associated with observer fatigue and drift. Video-recorded behavioral data also facilitates independent rating of responses by a coder who can be blinded to the scoring of other coders, reducing intra-rater bias. In addition, video recording provides an alternate method to real-time observation when complex multi-level systems comprise the behavioral units, as with complicated physiological and behavioral scoring systems where managing all the data at once in real time would be difficult. Further, this method allows for the implementation of training of naïve observers without clinical expertise and, thus, facilitates rating to be done without preconceived judgment based solely on clinical knowledge.

The limitations associated with the use of video recording in behavioral studies include intrusiveness of the equipment, potential for higher participant reactivity, and potential loss of the larger environmental context outside the view of the lens. Researchers can minimize the difficulties associated with these limitations by using expert technicians to set up and manage the equipment and to employ additional strategies to capture the broader environmental context.

In summary, when the decision has been made to use video recording as a means to collect behavioral data, researchers need to give careful consideration to factors that enhance raw data quality, the selection of appropriate observational tools to use with video data, the development of a strong training program for video coders that includes special consideration of the population studied, and the methods used for calculation of reliability coefficients. An informed approach to video recording as a method of data collection will enhance the quality of data obtained and the findings that ultimately inform nursing practice.

Acknowledgments

Kim Kopenhaver Haidet was supported by a Johnson & Johnson Health Behaviors and Quality of Life: 2006-2007 grant, Autonomic and Behavioral Stress Responses in Low Birth Weight Preterm Infants. Ann Kolanowski was supported by a grant from the National Institute of Nursing Research (R01 NR008910), A Prescription For Enhancing Resident Quality of Life. Mary Beth Happ was supported by NINR (K24 NR010244) Symptom Management, Patient-Caregiver Communication, and Outcomes in ICU; NICHD (R01 HD042988) Improving Communication with NonSpeaking ICU Patients.

Contributor Information

Kim Kopenhaver Haidet, School of Nursing, 307 Health & Human Development East, The Pennsylvania State University, University Park, PA 16802.

Judith Tate, School of Nursing, University of Pittsburgh, Pittsburgh, PA.

Dana Divirgilio-Thomas, School of Nursing, University of Pittsburgh.

Ann Kolanowski, School of Nursing, The Pennsylvania State University, University Park, PA.

Mary Beth Happ, School of Nursing, University of Pittsburgh.

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Why use video in qualitative research.

10 min read If you or your team are running qualitative research, you may wonder if it’s useful to use video as a data source. We give you insights on the use of video in qualitative research and how best to use this data format.

What is video in qualitative research?

One of the major challenges and opportunities for organizations today is gaining a deeper and more authentic understanding of their customers.

From concerns and feedback to approval and advocacy, uncovering customer sentiment , needs and expectations is what will empower organizations to take the next step in designing, developing and improving experiences.

Most of this starts with qualitative research — e.g. focus groups, interviews, ethnographic studies — but the fundamental problem is that traditional approaches are sometimes restrictive, costly and difficult to scale.

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The challenges of traditional and disparate qualitative research

Slow: Long time to value while teams or agencies scope, recruit, design , build, execute, analyze & report out on studies. It also takes a long time to sift through qualitative data (e.g. video) and turn it into insights at scale.

Expensive: Qualitative methods of research can be costly, both when outsourcing to agencies or managing the process in-house. Organizations also have to factor in  participant management costs (travel, hotels, venues, etc.).

Siloed: Data and research tools are outdated, siloed and scattered across teams and vendors, resulting in valuable data loss; no centralized approach to executing studies and incorporating customer/market insights across teams to support multiple initiatives. It also makes it hard to perform meta-analysis (e.g. perform analysis across multiple studies & methods) .

Hard to scale: Requires a lot of manual analysis; lack of in-house research & insights expertise due to cost of hiring/maintaining internal teams; heavy reliance on expertise of consultants to deliver quality insights; data from studies only used for initial research question, when it could be used by other teams.

Of course, while some organizations are using qualitative research, most rely heavily on in-person methods, manual work and a high degree of expertise to run these studies, meaning it’s near impossible to execute on data without these three things.

Alternative options that many organizations rely on:

• Agencies: Services-dependent with either an in-person or digital collection process, usually performed in a moderation style. Are slow, expensive, and result in loss of IP. Recruitment for interviews and discussion/diary studies can take a couple weeks or more. Reporting takes another couple weeks.
• In-person: Results in loss of efficiency & IP; data can’t be leveraged in concert with quantitative data easily; data can’t be reused. Very labor intensive to review and analyze all content.
• Point solutions: Technology providers for collecting synchronous or asynchronous video feedback. Can be either moderated or unprompted. Disconnected from other research and teams that could benefit from data and insights.
• Traditional qualitative studies: Organizations typically depend on traditional studies like, focus groups, personal interviews, phone calls, etc.

The reality is that the world has changed: the pandemic, coupled with the emergence of widespread remote and hybrid working, has forced researchers to move their efforts online.

Consequently, many organizations have turned to video feedback, a cost-effective type of qualitative research that can support existing methods. And it’s paying massive dividends.

History of video in qualitative research

Video in qualitative research is nothing new, and organizations have been using this response-gathering method for years to collect more authentic data from customers at scale.

It’s long been viewed as a medium through which an audiovisual record of everyday events, such as people shopping or students learning, is preserved and later made available for closer scrutiny, replay and analysis. In simple terms, video offers a window through which researchers can view more authentic and specific situations, interactions and feedback to uncover deeper and more meaningful insights.

But with the rapid shift to digital, video has quickly become a popular type of supplementary qualitative research — and is often used to support existing qualitative efforts. Plus, the growing ubiquity of video recording technologies (Zoom, Skype, Teams), has made it even easier for researchers to  acquire necessary data in streamlined and more effective ways.

The reason it’s so effective is because it’s as simple as using a mobile phone to record audio and video with just a press of a button. Plus, the affordability of high-quality technical equipment, e.g. wearable microphones, and superior camera quality compared to other forms of digital recording has made video a strong supplementary component of qualitative research.

As a result, video provides respondents with the freedom and flexibility to respond at their leisure — removing any issue of time — and with smartphone technologies that are increasingly of high grade, helping to mitigate costs while increasing scale.

And the best part? Organizations can get more insightful and actionable insights from data around-the-clock, and that data can be utilized for as long as necessary.

Types of video qualitative research methods

In 2016, university researcher, Rebecca Whiting, in ‘ Who’s Behind the Lens?: A Reflexive Analysis of Roles in Participatory Video Research ’, described the four types of video research methods that are now available:

• “Participatory video research, which uses participant-generated videos, such as video diaries,
• Videography, which entails filming people in the field as a way to document their activities,
• Video content analysis, which involves analysis of material not recorded by the researcher, or
• Video elicitation, which uses footage (either created for this purpose by the researcher, or extant video) to prompt discussion.”

What’s increasingly clear is that as technology advances and cloud-based platforms provide video streaming and capture services that are increasingly accessible, researchers now have an always-on, scalable and highly effective way to capture feedback from diverse audiences.

With video feedback, researchers can get to the “why” far faster than ever before. The scope of video in qualitative research has never been wider. Let’s take a closer look at what it can do for organizations.

Scope of video in qualitative research

There are several benefits to using video feedback in qualitative research studies, namely:

• More verifiability and less researcher bias – If a video record exists of a conversation, it’s possible to review.
• More authentic responses – in an open-ended survey, some participants may feel able to describe their views better in film, than by translating it down into text. This often results in them providing more “content” (or insight) than they would have done via a traditional survey or interview.
• Perception of being faster – The perceived immediacy and speed of giving feedback by video may appeal to people with less time, who otherwise may not have responded to the survey.
• Language support – The option for people to answer in their own language can help participants open up about topics in a way that writing down their thoughts may not allow for.
• Provides visual cues – Videos can provide context and additional information if the participant provides visual cues like body language or showing something physical to the camera.
• Video has some benefits over audio recordings and field notes – A working paper from the National Center of Research Methods found three benefits: “1) its character as a real-time sequential record; 2) a fine-grained multimodal record; and 3) its durability, malleable, and share-ability.” [1]
• Empowers every team to carry out and act on insight — With video responses available for all teams to view and utilize, it becomes significantly easier for teams to act on insights and make critical changes to their experience initiatives.

How can Qualtrics help with video in qualitative research?

Qualtrics Video Feedback solution is a for-purpose benefit to the Qualtrics products that help businesses manage their video feedback and insights, right alongside traditional survey data types.

It brings insights to life by making it easy for respondents to deliver their thoughts and feelings through a medium they’re familiar and comfortable with, resulting in 6x more content than traditional open feedback.

As well as this, Qualtrics Video Feedback features built-in AI-powered analytics that enables researchers to analyze and pull sentence-level topics and sentiment from video responses to see exactly how respondents feel at scale.

Finally, customizable video editing allows researchers to compile and showcase the best clips to tell the story of the data, helping to deliver a more authentic narrative that lands with teams and key stakeholders.

Qualtrics Video Feedback provides:

• End-to-end research: Conduct all types of video feedback research with point-and-click question types, data collection, analytics and reporting — all in the same platform. This means it’s easy to compare and combine qualitative and quantitative research , as well as make insights available to all relevant stakeholders.
• Scalability and security: Easily empower teams outside the research department to conduct their own market research with intuitive tools, guided solutions, and center of excellence capabilities to overcome skill gaps and governance concerns.
• Better, faster insights: As respondents can deliver more authentic responses at their leisure (or at length), you can gather quality insights in hours and days, not weeks or months, and at an unmatched scale.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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• Methodology
• Open access
• Published: 28 July 2018

Research as storytelling: the use of video for mixed methods research

• Erica B. Walker   ORCID: orcid.org/0000-0001-9258-3036 1 &
• D. Matthew Boyer 2  

Video Journal of Education and Pedagogy volume  3 , Article number:  8 ( 2018 ) Cite this article

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Mixed methods research commonly uses video as a tool for collecting data and capturing reflections from participants, but it is less common to use video as a means for disseminating results. However, video can be a powerful way to share research findings with a broad audience especially when combining the traditions of ethnography, documentary filmmaking, and storytelling.

Our literature review focused on aspects relating to video within mixed methods research that applied to the perspective presented within this paper: the history, affordances and constraints of using video in research, the application of video within mixed methods design, and the traditions of research as storytelling. We constructed a Mind Map of the current literature to reveal convergent and divergent themes and found that current research focuses on four main properties in regards to video: video as a tool for storytelling/research, properties of the camera/video itself, how video impacts the person/researcher, and methods by which the researcher/viewer consumes video. Through this process, we found that little has been written about how video could be used as a vehicle to present findings of a study.

From this contextual framework and through examples from our own research, we present current and potential roles of video storytelling in mixed methods research. With digital technologies, video can be used within the context of research not only as data and a tool for analysis, but also to present findings and results in an engaging way.

Conclusions

In conclusion, previous research has focused on using video as a tool for data collection and analysis, but there are emerging opportunities for video to play an increased role in mixed methods research as a tool for the presentation of findings. By leveraging storytelling techniques used in documentary film, while staying true to the analytical methods of the research design, researchers can use video to effectively communicate implications of their work to an audience beyond academics and use video storytelling to disseminate findings to the public.

Using motion pictures to support ethnographic research began in the late nineteenth century when both fields were early in their development (Henley, 2010 ; “Using Film in Ethnographic Field Research, - The University of Manchester,” n.d ). While technologies have changed dramatically since the 1890s, researchers are still employing visual media to support social science research. Photographic imagery and video footage can be integral aspects of data collection, analysis, and reporting research studies. As digital cameras have improved in quality, size, and affordability, digital video has become an increasingly useful tool for researchers to gather data, aid in analysis, and present results.

Storytelling, however, has been around much longer than either video or ethnographic research. Using narrative devices to convey a message visually was a staple in the theater of early civilizations and remains an effective tool for engaging an audience today. Within the medium of video, storytelling techniques are an essential part of a documentary filmmaker’s craft. Storytelling can also be a means for researchers to document and present their findings. In addition, multimedia outputs allow for interactions beyond traditional, static text (R. Goldman, 2007 ; Tobin & Hsueh, 2007 ). Digital video as a vehicle to share research findings builds on the affordances of film, ethnography, and storytelling to create new avenues for communicating research (Heath, Hindmarsh, & Luff, 2010 ).

In this study, we look at the current literature regarding the use of video in research and explore how digital video affordances can be applied in the collection and analysis of quantitative and qualitative human subject data. We also investigate how video storytelling can be used for presenting research results. This creates a frame for how data collection and analysis can be crafted to maximize the potential use of video data to create an audiovisual narrative as part of the final deliverables from a study. As researchers we ask the question: have we leveraged the use of video to communicate our work to its fullest potential? By understanding the role of video storytelling, we consider additional ways that video can be used to not only collect and analyze data, but also to present research findings to a broader audience through engaging video storytelling. The intent of this study is to develop a frame that improves our understanding of the theoretical foundations and practical applications of using video in data collection, analysis, and the presentation of research findings.

Literature review

The review of relevant literature includes important aspects for situating this exploration of video research methods: the history, affordances and constraints of using video in research, the use of video in mixed methods design, and the traditions of research as storytelling. Although this overview provides an extensive foundation for understanding video research methods, this is not intended to serve as a meta-analysis of all publications related to video and research methods. Examples of prior work provide a conceptual and operational context for the role of video in mixed methods research and present theoretical and practical insights for engaging in similar studies. Within this context, we examine ethical and logistical/procedural concerns that arise in the design and application of video research methods, as well as the affordances and constraints of integrating video. In the following sections, the frame provided by the literature is used to view practical examples of research using video.

The history of using video in research is founded first in photography and next in film followed more recently, by digital video. All three tools provide the ability to create instant artifacts of a moment or period of time. These artifacts become data that can be analyzed at a later date, perhaps in a different place and by a different audience, giving researchers the chance to intricately and repeatedly examine the archive of information contained within. These records “enable access to the fine details of conduct and interaction that are unavailable to more traditional social science methods” (Heath et al., 2010 , p. 2).

In social science research, video has been used for a range of purposes and accompanies research observation in many situations. For example, in classroom research, video is used to record a teacher in practice and then used as a guide and prompt to interview the teacher as they reflect upon their practice (e.g. Tobin & Hsueh, 2007 ). Video captures events from a situated perspective, providing a record that “resists, at least in the first instance, reduction to categories or codes, and thus preserves the original record for repeated scrutiny” (Heath et al., 2010 , p. 6). In analysis, these audio-visual recordings allow the social science researcher the chance to reflect on their subjectivities throughout analysis and use the video as a microscope that “allow(s) actions to be observed in a detail not even accessible to the actors themselves” (Knoblauch & Tuma, 2011 , p. 417).

Examining the affordances and constraints of video in research provides a researcher the opportunity to examine the value of including video within a study . An affordance of video, when used in research, is that it allows the researcher to see an event through the camera lens either actively or passively and later share what they have seen, or more specifically, the way they saw it (Chalfen, 2011 ). Cameras can be used to capture an event in three different modes: Responsive, Interactive, and Constructive. Responsive mode is reactive. In this mode, the researcher captures and shows the viewer what is going on in front of the lens but does not directly interfere with the participants or events. Interactive mode puts the filmmaker into the storyline as a participant and allows the viewer to observe the interactions between the researcher and participant. One example of video captured in Interactive mode is an interview. In Constructive mode, the researcher reprocesses the recorded events to create an explicitly interpretive final product through the process of editing the video (MacDougall, 2011 ). All of these modes, in some way, frame or constrain what is captured and consequently shared with the audience.

Due to the complexity of the classroom-research setting, everything that happens during a study cannot be captured using video, observation, or any other medium. Video footage, like observation, is necessarily selective and has been stripped of the full context of the events, but it does provide a more stable tool for reflection than the ever-changing memories of the researcher and participants (Roth, 2007 ). Decisions regarding inclusion and exclusion are made by the researcher throughout the entire research process from the initial framing of the footage to the final edit of the video. Members of the research team should acknowledge how personal bias impacts these decisions and make their choices clear in the research protocol to ensure inclusivity (Miller & Zhou, 2007 ).

One affordance of video research is that analysis of footage can actually disrupt the initial assumptions of a study. Analysis of video can be standardized or even mechanized by seeking out predetermined codes, but it can also disclose the subjective by revealing the meaning behind actions and not just the actions themselves (S. Goldman & McDermott, 2007 ; Knoblauch & Tuma, 2011 ). However, when using subjective analysis the researcher needs to keep in mind that the footage only reveals parts of an event. Ideally, a research team has a member who acts as both a researcher and a filmmaker. That team member can provide an important link between the full context of the event and the narrower viewpoint revealed through the captured footage during the analysis phase.

Although many participants are initially camera-shy, they often find enjoyment from participating in a study that includes video (Tobin & Hsueh, 2007 ). Video research provides an opportunity for participants to observe themselves and even share their experience with others through viewing and sharing the videos. With increased accessibility of video content online and the ease of sharing videos digitally, it is vital from an ethical and moral perspective that participants understand the study release forms and how their image and words might continue to be used and disseminated for years after the study is completed.

Including video in a research study creates both affordances and constraints regarding the dissemination of results. Finding a journal for a video-based study can be difficult. Traditional journals rely heavily on static text and graphics, but newly-created media journals include rich and engaging data such as video and interactive, web-based visualizations (Heath et al., 2010 ). In addition, videos can provide opportunities for research results to reach a broader audience outside of the traditional research audience through online channels such as YouTube and Vimeo.

Use of mixed methods with video data collection and analysis can complement the design-based, iterative nature of research that includes human participants. Design-based video research allows for both qualitative and quantitative collection and analysis of data throughout the project, as various events are encapsulated for specific examination as well as analyzed comparatively for changes over time. Design research, in general, provides the structure for implementing work in practice and iterative refinement of design towards achieving research goals (Collins, Joseph, & Bielaczyc, 2004 ). Using an integrated mixed method design that cycles through qualitative and quantitative analyses as the project progresses gives researchers the opportunity to observe trends and patterns in qualitative data and quantitative frequencies as each round of analysis informs additional insights (Gliner et al., 2009 ). This integrated use also provides a structure for evaluating project fidelity in an ongoing basis through a range of data points and findings from analyses that are consistent across the project. The ability to revise procedures for data collection, systematic analysis, and presenting work does not change the data being collected, but gives researchers the opportunity to optimize procedural aspects throughout the process.

Research as storytelling refers to the narrative traditions that underpin the use of video methods to analyze in a chronological context and present findings in a story-like timeline. These traditions are evident in ethnographic research methods that journal lived experiences through a period of time and in portraiture methods that use both aesthetic and scientific language to construct a portrait (Barone & Eisner, 2012 ; Heider, 2009 ; Lawrence-Lightfoot, 2005 ; Lenette, Cox & Brough, 2013 ).

In existing research, there is also attention given to the use of film and video documentaries as sources of data (e.g. Chattoo & Das, 2014 ; Warmington, van Gorp & Grosvenor, 2011 ), however, our discussion here focuses on using media to capture information and communicate resulting narratives for research purposes. In our work, we promote a perspective on emergent storytelling that develops from data collection and analysis, allowing the research to drive the narrative, and situating it in the context from where data was collected. We rely on theories and practices of research and storytelling that leverage the affordances of participant observation and interview for the construction of narratives (Bailey & Tilley, 2002 ; de Carteret, 2008 ; de Jager, Fogarty & Tewson, 2017 ; Gallagher, 2011 ; Hancox, 2017 ; LeBaron, Jarzabkowski, Pratt & Fetzer, 2017 ; Lewis, 2011 ; Meadows, 2003 ).

The type of storytelling used with research is distinctly different from methods used with documentaries, primarily with the distinction that, while documentary filmmakers can edit their film to a predetermined narrative, research storytelling requires that the data be analyzed and reported within a different set of ethical standards (Dahlstrom, 2014 ; Koehler, 2012 ; Nichols, 2010 ). Although documentary and research storytelling use a similar audiovisual medium, creating a story for research purposes is ethically-bounded by expectations in social science communities for being trustworthy in reporting and analyzing data, especially related to human subjects. Given that researchers using video may not know what footage will be useful for future storytelling, they may need to design their data collection methods to allow for an abundance of video data, which can impact analysis timelines as well. We believe it important to note these differences in the construction of related types of stories to make overt the essential need for research to consider not only analysis but also creation of the reporting narrative when designing and implementing data collection methods.

This study uses existing literature as a frame for understanding and implementing video research methods, then employs this frame as perspective on our own work, illuminating issues related to the use of video in research. In particular, we focus on using video research storytelling techniques to design, implement, and communicate the findings of a research study, providing examples from Dr. Erica Walker’s professional experience as a documentary filmmaker as well as evidence from current and former academic studies. The intent is to improve understanding of the theoretical foundations and practical applications for video research methods and better define how those apply to the construction of story-based video output of research findings.

The study began with a systematic analysis of theories and practices, using interpretive analytic methods, with thematic coding of evidence for conceptual and operational aspects of designing and implementing video research methods. From this information, a frame was constructed that includes foundational aspects of using digital video in research as well as the practical aspects of using video to create narratives with the intent of presenting research findings. We used this frame to interpret aspects of our own video research, identifying evidence that exemplifies aspects of the frame we used.

A primary goal for the analysis of existing literature was to focus on evidentiary data that could provide examples that illuminate the concepts that underpin the understanding of how, when, and why video research methods are useful for a range of publishing and dissemination of transferable knowledge from research. This emphasis on communicating results in both theoretical and practical ways highlighted areas within the analysis for potential contextual similarities between our work and other projects. A central reason for interpreting findings and connecting them with evidence was the need to provide examples that could serve as potentially transferable findings for others using video with their research. Given the need for a fertile environment (Zhao & Frank, 2003 ) and attention to contextual differences to avoid lethal mutations (Brown & Campione, 1996 ), understand that these examples may not work for every situation, but the intent is to provide clear evidence of how video research methods can leverage storytelling to report research findings in a way that is consumable by a broader audience.

In the following section, we present findings from the review of research and practice, along with evidence from our work with video research, connecting the conceptual and operational frame to examples and teasing out aspects from existing literature.

Results and findings

When looking at the current literature regarding the use of video in research, we developed a Mind Map to categorize convergent and divergent themes in the current literature, see Fig.  1 . Although this is far from a complete meta-analysis on video research (notably absent is a comprehensive discussion of ethical concerns regarding video research), the Mind Map focuses on four main properties in regards to video: video as a tool for storytelling/research, properties of the camera/video itself, how video impacts the person/researcher, and methods by which the researcher/viewer consumes video.

Mind Map of current literature regarding the use of video in mixed methods research. Link to the fully interactive Mind Map- http://clemsongc.com/ebwalker/mindmap/

Video, when used as a tool for research, can document and share ethnographic, epistemic, and storytelling data to participants and to the research team (R. Goldman, 2007 ; Heath et al., 2010 ; Miller & Zhou, 2007 ; Tobin & Hsueh, 2007 ). Much of the research in this area focuses on the properties (both positive and negative) inherent in the camera itself such as how video footage can increase the ability to see and experience the world, but can also act as a selective lens that separates an event from its natural context (S. Goldman & McDermott, 2007 ; Jewitt, n.d .; Knoblauch & Tuma, 2011 ; MacDougall, 2011 ; Miller & Zhou, 2007 ; Roth, 2007 ; Sossi, 2013 ).

Some research speaks to the role of the video-researcher within the context of the study, likening a video researcher to a participant-observer in ethnographic research (Derry, 2007 ; Roth, 2007 ; Sossi, 2013 ). The final category of research within the Mind Map focuses on the process of converting the video from an observation to records to artifact to dataset to pattern (Barron, 2007 ; R. Goldman, 2007 ; Knoblauch & Tuma, 2011 ; Newbury, 2011 ). Through this process of conversion, the video footage itself becomes an integral part of both the data and findings.

The focus throughout current literature was on video as data and the role it plays in collection and analysis during a study, but little has been written about how video could be used as a vehicle to present findings of a study. Current literature also did not address whether video-data could be used as a tool to communicate the findings of the research to a broader audience.

In a recent two-year study, the research team led by Dr. Erica Walker collected several types of video footage with the embedded intent to use video as both data and for telling the story of the study and findings once concluded (Walker, 2016 ). The study focused on a multidisciplinary team that converted a higher education Engineering course from lecture-based to game-based learning using the Cognitive Apprenticeship educational framework. The research questions examined the impact that the intervention had on student learning of domain content and twenty-first Century Skills. Utilizing video as both a data source and a delivery method was built into the methodology from the beginning. Therefore, interviews were conducted with the researchers and instructors before, during, and after the study to document consistency and changes in thoughts and observations as the study progressed. At the conclusion of the study, student participants reflected on their experience directly through individual video interviews. In addition, every class was documented using two static cameras, placed at different angles and framing, and a mobile camera unit to capture closeup shots of student-instructor, student-student, and student-content interactions. This resulted in more than six-hundred minutes of interview footage and over five-thousand minutes of classroom footage collected for the study.

Video data can be analyzed through quantitative methods (frequencies and word maps) as well as qualitative methods (emergent coding and commonalities versus outliers). Ideally, both methods are used in tandem so that preliminary results can continue to inform the overall analysis as it progresses. In order to capitalize on both methods, each interview was transcribed. The researchers leveraged digital and analog methods of coding such as digital word-search alongside hand coding the printed transcripts. Transcriptions contained timecode notations throughout, so coded segments could quickly be located in the footage and added to a timeline creating preliminary edits.

There are many software workflows that allow researchers to code, notate timecode for analysis, and pre-edit footage. In the study, Opportunities for Innovation: Game-based Learning in an Engineering Senior Design Course, NVivo qualitative analysis software was used together with paper-based analog coding. In a current study, also based on a higher education curriculum intervention, we are digitally coding and pre-trimming the footage in Adobe Prelude in addition to analog coding on the printed transcripts. Both workflows offer advantages. NVivo has built-in tools to create frequency maps and export graphs and charts relevant to qualitative analysis whereas Adobe Prelude adds coding notes directly into the footage metadata and connects directly with Adobe Premiere video editing software, which streamlines the editing process.

From our experience with both workflows, Prelude works better for a research team that has multiple team members with more video experience because it aligns with video industry workflows, implements tools that filmmakers already use, and Adobe Team Projects allows for co-editing and coding from multiple off-site locations. On the other hand, NVivo works better for research teams where members have more separate roles. NVivo is a common qualitative-analysis software so team members more familiar with traditional qualitative research can focus on coding and those more familiar with video editing can edit based on those codes allowing each team member to work within more familiar software workflows.

In both of these studies, assessments regarding storytelling occurred in conjunction with data processing and analysis. As findings were revealed, appropriate clips were grouped into timelines and edited to produce a library of short, topic-driven videos posted online , see Fig.  2 . A collection of story-based, topic-driven videos can provide other practitioners and researchers a first-hand account of how a study was designed and conducted, what worked well, recommendations of what to do differently, participant perspectives, study findings, and suggestions for further research. In fact, the videos cover many of the same topics traditionally found in publications, but in a collection of short videos accessible to a broad audience online.

The YouTube channel created for Opportunities for Innovation: Game-based Learning in an Engineering Senior Design Course containing twenty-four short topical videos. Direct link- https://goo.gl/p8CBGG

By sharing the results of the study publicly online, conversations between practitioners and researchers can develop on a public stage. Research videos are easy to share across social media channels which can broaden the academic audience and potentially open doors for future research collaborations. As more journals move to accept multi-media studies, publicly posted videos provide additional ways to expose both academics and the general public to important study results and create easy access to related resources.

Video research as storytelling: The intersection and divergence of documentary filmmaking and video research

“Film and writing are such different modes of communication, filmmaking is not just a way of communicating the same kinds of knowledge that can be conveyed by an anthropological text. It is a way of creating different knowledge” (MacDougall, 2011 ).

When presenting research, choosing either mode of communication comes with affordances and constraints for the researcher, the participants, and the potential audience.

Many elements of documentary filmmaking, but not all, are relevant and appropriate when applied to gathering data and presenting results in video research. Documentary filmmakers have a specific angle on a story that they want to share with a broad audience. In many cases, they hope to incite action in viewers as a response to the story that unfolds on screen. In order to further their message, documentarians carefully consider the camera shots and interview clips that will convey the story clearly in a similar way to filmmakers in narrative genres. Decisions regarding what to capture and how to use the footage happen throughout the entire filmmaking process: prior to shooting footage (pre-production), while capturing footage (production), and during the editing phase (post-production).

Video researchers can employ many of the same technical skills from documentary filmmaking including interview techniques such as pre-written questions; camera skills such as framing, exposure, and lighting; and editing techniques that help draw a viewer through the storyline (Erickson, 2007 ; Tobin & Hsueh, 2007 ). In both documentary filmmaking and in video research, informed decisions are made about what footage to capture and how to employ editing techniques to produce a compelling final video.

Where video research diverges from documentary filmmaking is in how the researcher thinks about, captures, and processes the footage. Video researchers collect video as data in a more exploratory way whereas documentary filmmakers often look to capture preconceived video that will enable them to tell a specific story. For a documentary filmmaker, certain shots and interview responses are immediately discarded as they do not fit the intended narrative. For video researchers, all the video that is captured throughout a study is data and potentially part of the final research narrative. It is during the editing process (post-production) where the distinction between data and narrative becomes clear.

During post-production, video researchers are looking for clips that clearly reflect the emergent storylines seen in the collective data pool rather than the footage necessary to tell a predetermined story. Emergent storylines can be identified in several ways. Researchers look for divergent statements (where an interview subject makes unique observation different from other interviewees), convergent statements (where many different interviewees respond similarly), and unexpected statements (where something different from what was expected is revealed) (Knoblauch & Tuma, 2011 ).

When used thoughtfully, video research provides many sources of rich data. Examples include reflections of the experience, in the direct words of participants, that contain insights provided by body language and tone, an immersive glimpse into the research world as it unfolds, and the potential to capture footage throughout the entire research process rather than just during prescribed times. Video research becomes especially powerful when combined with qualitative and quantitative data from other sources because it can help reveal the context surrounding insights discovered during analysis.

We are not suggesting that video researchers should become documentary filmmakers, but researchers can learn from the stylistic approaches employed in documentary filmmaking. Video researchers implementing these tools can leverage the strengths of short-format video as a storytelling device to share findings with a more diverse audience, increase audience understanding and consumption of findings, and encourage a broader conversation around the research findings.

Implications for future work

As the development of digital media technologies continues to progress, we can expect new functionalities far exceeding current tools. These advancements will continue to expand opportunities for creating and sharing stories through video. By considering the role of video from the first stages of designing a study, researchers can employ methods that capitalize on these emerging technologies. Although they are still rapidly advancing, researchers can look for ways that augmented reality and virtual reality could change data analysis and reporting of research findings. Another emergent area is the use of machine learning and artificial intelligence to rapidly process video footage based on automated thematic coding. Continued advancements in this area could enable researchers to quickly quantify data points in large quantities of footage.

In addition to exploring new functionalities, researchers can still use current tools more effectively for capturing data, supporting analysis, and reporting findings. Mobile devices provide ready access to collect periodic video reflections from study participants and even create research vlogs (video blogs) to document and share ongoing studies as they progress. In addition, participant-created videos are rich artifacts for evaluating technical and conceptual knowledge as well as affective responses. Most importantly, as a community, researchers, designers, and documentarians can continue to take strengths from each field to further the reach of important research findings into the public sphere.

In conclusion, current research is focused on using video as a tool for data collection and analysis, but there are new, emerging opportunities for video to play an increased and diversified role in mixed methods research, especially as a tool for the presentation and consumption of findings. By leveraging the storytelling techniques used in documentary filmmaking, while staying true to the analytical methods of research design, researchers can use video to effectively communicate implications of their work to an audience beyond academia and leverage video storytelling to disseminate findings to the public.

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• Mixed methods
• Storytelling
• Video research

DIY Science Lab Video Recording

Preparation

Camera types.

Your videos can be recorded either through a built-in laptop camera, external webcam, cell phone camera, or DSLR. The minimum specs you will want to record your video are 1080p (meaning the screen size is 1080×1920 pixels, considered high definition) and 30fps (meaning 30 frames per second.) These are the standard guidelines for videos intended to be streamed/viewed online.

External Webcam

An external webcam might be the easiest choice for you to record your labs. They hook into your laptop but provide much higher quality picture than most built-in laptop cameras. Some webcams like the   Logitech Brio webcam  can record in 4K and can be mounted on a tripod as well as your laptop or desktop. When searching for the right external webcam, make sure it can record in at least 1080p at 30fps.

Smartphone (iPhone, Android) 

Most smartphones are equipped with very high-quality cameras in them. However, depending on the length of your lab, your phone might not have enough storage to record the entire session. For example, 1 hour of recorded video on an iPhone with the settings of 1080p at 30fps takes up 7.6 GB of data! Phones may also overheat if you are recording for long periods of time. Please research your phone brand/model and its video recording limitations before you film.

If you are using a smartphone, you will also need a tripod to put your smartphone on. We want a stable picture, and no one wants to hold a phone that long! A simple phone tripod such as the  amazon fugetek smartphone tripod  can either be placed on a tabletop or on the ground. Make sure your tripod is set up so that the smartphone is approximately eye-level with the person speaking.

How to use a smartphone or tablet as a document camera

• This  Youtube video  shows how to use a cell phone as a doc camera at home. Here is  another  one using Apple’s AirPlay. And, here’s how to  use the iPad as a doc camera.
• Here are  more options
• This is a stand  that can make an iPad or tablet as a doc camera, and there are other cheaper stands on Amazon for both  tablets  and  smartphones
• IP Webcam  for Android
• DroidCam  app for Android

A DSLR, such as a Canon 5D or Sony a7, is a wonderful tool for recording your lectures! If you want to invest in a DSLR, check out techradar’s list of the  best entry level DSLR cameras.  DSLR cameras can record crisp, high-definition pictures with true-to-life color.

If you are using a DSLR, you will also need a tripod to mount your camera on, as well as an SD card to record your video on to. There are many  affordable tripods  suitable for DSLRs- just make sure your camera fits within the weight limitations of the tripod. (Most handheld DSLR cameras fall below the 6.6lb weight limit for the tripod linked above.)

If you would like to record straight from the camera into your laptop, (especially if you are using YuJa to record your lectures)  you will need a  USB to HDMI capture card,  along with the appropriate usb and HDMI cables to hook your camera into your computer.

If you are not recording into your laptop, you will also need an  SD Card  to insert into the camera and record your video on. After filming, the card can be inserted into your computer to transfer your video. Make sure the card has enough storage space to record your lab, and is fast enough for the data to be written to the card. A good rule of thumb is to make sure your card holds at least 64gb of data, and has a speed of 90 MB/s.

Built-in Laptop Camera

If you do not have access to a webcam or another recording device, you can use the built-in camera in your laptop. Most built-in laptop cameras are lower definition, and picture quality will not be as good as using another method. Check to see what size your webcam records in, and if it is below 720p, it will not be a good solution for recording your lab videos. (We recommend filming in 1080p, but if no other option is available, 720 can be acceptable for your filming.)

Should I record a 4K video?

If you have the ability (and storage space) on your device to record in 4k, then by all means, do! You can upload your 4k video directly to YuJa. The higher definition you can record your lab, the clearer the picture will be for your students!

Audio recording

Just as important as it is for students to see you clearly, they need to hear you clearly as well! Most of the microphones built into your laptop, smartphone, or camera will not be sufficient for your videos- especially if you are talking while using a fume hood!

Audio recording for external webcams (external or built-in)

There are many good clip-on mics that can be plugged right into your computer via your USB port.  This USB microphone  has a 20-foot cord, meaning you can be up to 20 feet away from your camera/computer while recording. If you don’t want to be tethered to your computer, there are  wireless bluetooth options  to give you more freedom to walk around during your filming.

Audio recording for smartphones

There are many  wireless mics for smartphones  that can plug in to an audio jack, or connect to an app. If you are recording via smartphone, research which mic will work best for you—if you have a newer smartphone, you might need an  adapter  to plug your mic in.

Audio recording for DSLRs

There are a range of good, mid-priced mics for DSLRs, like this  Insignia lapel microphone . When researching mics, make sure that the audio jack is the same size as the input for your camera.

Make sure to test your camera and audio set up prior to your first shoot! You can do this anywhere, even in your own home. The better the understanding you have of your technology, the easier your filming will go.

How to set up for the best film shoot

If you are filming in a lab, most likely you are not going to have control over how the environment is set up. Make sure you have lots of lights turned on, to help avoid a dark or grainy picture in your camera. Also, if you have a choice of where you are working, make sure there are no windows behind you and your experiment. You want the majority of the light coming into the room facing pointed  toward you and away from the camera.  If you are working with a fume hood or in another situation where your back is turned to the camera, try to step away from the camera angle your body slightly towards the camera as you are filming to try to make sure you aren’t blocking the view of the experiment.

When setting up your camera, make sure it is at about eye level or higher with the person appearing on camera. If using a webcam attached to a laptop or a smartphone with a tabletop speaker, make sure you have everything on a high enough table that the camera can point slightly down at the experiment. Set up your camera far enough away that you have some wiggle room on either side of the picture—you don’t want to accidentally cut off your experiment!

Have at least one other person to help

You want to make sure you have at least one other person in the lab with you to monitor your recording device and, if possible, the audio through headphones. If you are using a fume hood or around particularly noisy lab equipment, you might instead want an additional person to narrate the experiment off camera. Have someone read a script or narrate what you are doing slightly farther back from the experiment, so that the noise is not as prominent. 

Recommended video length

If possible, think of breaking up your lab into several, smaller videos. Student attention spans can be limited! While planning your experiment, find natural breaking points to “chunk” your longer lab into several, smaller videos. 

After filming

Depending on the type of camera used, your video file may need to be converted to a readable format. If you video file ends in .mp4, .mov, .avi, or .m4a, you do not need to convert your video and you can skip the following step. Webcams, smart phones, and most DSLRs all record in either .mp4 or .mov; you will most likely only need to convert your video if you are recording on a high-quality cinema camera.

Converting to MP4 for easy distribution

If your video file ends in a more uncommon file type, such as .mxf,  you will want to convert it to an .mp4 or .mov to ensure student access. You can download the  free handbrake software , an open-source transcoding software. A quick-start guide to using handbrake can be found at:  https://handbrake.fr/docs/en/latest/introduction/quick-start.html .  When using handbrake, the default preset Fast 1080p30 will be the best choice for converting your videos.

Uploading to UCI Yuja

• Getting Started with Yuja
• Upload a Video to Yuja
• UCI Teach Anywhere
• TeachPrep @ UCI

Editing in Yuja

Are there certain parts of your video you would like cut out? For example, if you are setting water to boil, you might not want to make your students watch the process of boiling water. You can easily edit portions of your full video with the Yuja video editor.  Please watch this video  to learn more about editing videos through YuJa.

Sharing with your students

• Post and embed videos on Canvas
• Canvas Support hotline 855 -213 -7130

A Researcher Uses a Video Camera to Record Children as They

Question 104

A researcher uses a video camera to record children as they play on a school playground.The researcher plans to carefully watch the videotape to count the children's helping behaviors.This researcher is conducting a:

A) case study. B) systematic observation. C) standardized test. D) multisource study.

Correct Answer:

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Q87: The process by which some organisms become

Q99: While Piaget emphasized biological aspects of development,

Q100: Carlos is just starting to learn about

Q101: Dr.Sam collects data on how often strangers

Q102: Janice is studying how baby geese recognize

Q103: While ethology stresses biological factors in human

Q106: Kristina consistently studies and gets good grades.She

Q107: The textbook states that no single theory

Q108: An eclectic theoretical orientation assumes that: A)development is

Q109: Shawn's job requires lots of travel.He would

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Parent Control

• How to Get Notified When Someone is Online on Instagram (An Easy Guide)
• Is M4ufree Safe and Legal for Streaming?
• How to Find Who Viewed Your Facebook Profile
• What is Google Family Link: A Guide and Review for Parents
• How to Limit Time on YouTube: Ways for a Healthier Digital Life

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Snapchat Tricks: How to Save Pictures and Video without Alerting the Sender

Snapchat app is known for its disappearing messages, where photos and videos vanish after being viewed or expired. This feature ensures privacy and spontaneity, making it popular for sharing moments. However, sometimes, you might want to save snaps to your device without notifying the sender.

Whether it's a memorable photo or an important video you want to keep, finding ways to save these Snaps discreetly can be quite useful. So, we will explore several methods to save snaps without triggering the dreaded notification, helping you preserve your favourite moments while maintaining the app's intended privacy.

• Screen Mirroring the Screen to Another Device (Android)
• Build-in Screen Recording (Android & iPhone)
• Use Google Assistant (Android)
• Use Another Device's Camera
• Reply Pop-up Method
• Set YouTube Break Reminders
• 2. 3 Popular Methods That Don't Work Anymore

6 Proven Methods to Save Snap without Notification

Method 1. screen mirroring the screen to another device (android).

This method is ideal when you can access another device and want to save a Snap without the sender knowing. You can try installing and using AirDroid Parental Control to mirror your Android screen to another device seamlessly. While primarily designed for parents to monitor their children’s devices, this app can also be used to mirror your own device. Here are the steps:

• Get the AirDroid Parental Control app from the Google Play Store or App Store and install it on another mobile. You can also use the website version from webparent.airdroid.com on any device.
• Open the app and follow the setup instructions to connect your Android device to another device.

• Use the other device to record or take a screenshot of the mirrored screen. This way, the Snap is saved without triggering a notification to the sender.

The quality of the captured Snap may depend on the mirroring and recording capabilities of the second device.

Method 2. Build-in Screen Recording (Android & iPhone)

This method works well for quick and easy Snaps recording on Android and iPhone devices using the built-in screen recording feature. You should ensure enough storage space before recording. Besides, audio might not be recorded, depending on device settings. Here are the steps:

• Open Snapchat and view the Snap you want to save. The screen recording feature will capture everything displayed on the screen.
• Stop the recording and save the video to your device.

Method 3. Use Google Assistant (Android)

Google Assistant voice commands is ideal for quickly capturing a Snap on an Android device. You should make sure that Google Assistant has the necessary permissions (Settings > Apps > Default apps > Digital assistant app > make sure Use text from screen and Use screenshot are enabled) to take screenshots. Then follow the steps below:

• Say "Hey Google" or press and hold the home button to activate Google Assistant.

• Google Assistant will capture and save the screenshot to your device.

You may clear recent tasks to avoid accidentally capturing other content.

Method 4. Use Another Device's Camera

This method is simple and effective when you have another device available, such as a phone or camera. The quality of the captured Snap depends on the secondary device’s camera. Please follow the steps:

• Use your main device to view the Snap you want to save.
• Use a second device’s camera to take a photo or video of the Snap on your main device.

You’d better use a high-quality camera and ensure good lighting to improve capture quality. Besides, hold both devices steady to avoid blurry images.

Method 5. Reply Pop-up Method

This way requires quick reflexes to capture the Snap before it disappears. It is for quickly saving Snaps while replying to a message, particularly when multitasking. It may cause possible account restrictions from Snapchat if detected frequently. Please follow the steps below and use this method with caution.

• When you receive a Snap, open it and swipe up to reply.

Keep your device's screenshot function easily accessible and it will better if you practice the timing to capture the Snap efficiently.  However, the method might not work consistently across all devices and Snapchat versions.

Method 6. Save Snaps with the Snapchat Saver App

There are third-party apps which are designed to save Snaps without notifications. SnapSave is one popular app for this purpose. Please take care of potential security risks from entering Snapchat credentials into third-party apps. Here are the steps:

• Download and install SnapSave from the App Store or Google Play Store.
• Open SnapSave and log in using your Snapchat credentials.
• Use SnapSave to view and save Snaps without notifying the sender.

Some apps may not be available or functional in certain regions. Besides, Snapchat may periodically update to block third-party apps like SnapSave. So you’d better use reputable third-party apps with good reviews and frequent updates. Besides, you’d better regularly change your Snapchat password to mitigate security risks. It is also necessary to monitor your Snapchat account for any unusual activity after using third-party apps.

3 Popular Methods That Don't Work Anymore

Method 1. save snaps in airplane mode (android).

Snapchat has updated its app to detect offline actions and notify the sender once the device reconnects to the internet. Force-stopping the app and clearing the cache no longer prevent Snapchat from sending notifications about saved Snaps. We can’t make it with the steps below any more:

• Turn on Airplane Mode.
• Save Snaps.
• Force-stop Snapchat and clear the Snapchat cache.

Method 2. Using the Recents View (Android)

Snapchat has enhanced its detection mechanisms, making it capable of recognising when users access the recent app's view to capture Snaps. The app now immediately notifies the sender if a screenshot is taken or if the recent app's view is used.

Method 3. Save iPhone Snap with Mac QuickTime

Snapchat has implemented security measures to detect and block this method. QuickTime recording can no longer bypass Snapchat's screenshot detection. Here are the previous steps:

• Connect iPhone to Mac.
• Launch QuickTime and start a new movie recording.
• Select the iPhone as the recording device and view the Snap to capture it.

Final Thoughts: Discreetly Saving Snapchat Pictures

While Snapchat is designed to prioritise privacy, there are various methods you can use to save pictures without alerting the sender.

Whether you choose screen mirroring, built-in screen recording, using Google Assistant, or a third-party app like SnapSave, each approach has its own benefits and limitations. Always use these methods responsibly, considering the ethical implications and respecting the privacy of others.

By understanding and employing these techniques, you can preserve your favorite Snaps without compromising the intended privacy of the platform.

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TERMINUS Blog: Robot Meets Glacier

August 15, 2024

Ancient snow-capped peaks guard the entrance to the glacial valley. There’s less than half a mile of seawater between us and the glacier, with its river-like trail that weaves a jagged path to the mountains beyond.

I’m aboard the research vessel Celtic Explorer and I’m watching the robot submersible Nereid Under Ice (NUI) maneuvering just off the starboard bow, its bright orange and red bay doors peeping above the surface.

It’s 2 pm on day three of my assignment with the TERMINUS Greenland research expedition. In a few moments, NUI’s vertical thrusters will burst into life and carry it down to the murky deep.

The NUI team including its pilot (and his repurposed X Box controller) are topside in the Celtic Explorer’s science operations room. Their attention is fixed on the instrument panels in front of them, keeping an eye on the submersible’s position and making sure it’s in good shape. In water this cloudy, navigation is by sonar and echolocation, and thus, done very carefully.

NUI is the centerpiece of the TERMINUS Greenland project, a scientific underwater exploration of Greenland’s glaciers that’s led by Ginny Catania, professor at the Jackson School of Geosciences and the University of Texas Institute for Geophysics. The submersible is both a remotely operated vehicle, tethered to the ship by a slender fiber optic cable through which it is piloted, and an autonomous drone, able to follow a predetermined survey route before finding its way home.

“I see NUI as the star of this expedition,” Catania said. “The science we’ve done with NUI has been incredible. Just seeing it in action and the organization behind it has been a career highlight.”

NUI’s dives so far have revealed deep channels, overhangs and ice caverns along the glacial wall.

NUI expedition leader, Molly Curran of Woods Hole Oceanographic Institution, which developed and operates the submersible, later explained to me why NUI is so uniquely suited to this kind of environment.

The vehicle’s fiber optic cable allows it to be directly controlled from distances up to 8 km away and reach depths of at least 4 km. That’s ideal for navigating the jagged underwater face of a glacier from a safe distance. Conventional ROVs are unable to stray further than a few dozen meters while autonomous vehicles would almost certainly get lost in the glacier’s underwater ice caves.

“Only a hybrid vehicle allows us to get to these hard-to-reach places,” she said.

Two hours into the dive, NUI encounters just such a cavern and, for a while at least, the pilot allows it to drift into the icy chasm.

“Oh my god what is that?” says Catania. The video feed shows only turbid water, but the cavern’s walls are clear in the vehicle’s sonar. The other scientists crowding round the NUI team gasp in disbelief.

Over the course of the next hour and a half, NUI carefully makes its way along the length of the glacier, mapping it from top to bottom with a multibeam sonar. Multibeam sonar is typically used to create bathymetries of the seafloor but at the UT scientists’ request, the NUI team have jury-rigged the instrument to NUI’s side and pointed it at the glacier’s face.

By 5:30pm the multibeam survey is complete and the team have made history using it to map the face of a glacier.

Its primary mission complete, the NUI team direct the vehicle to its next objective. Catania and her team have selected four locations just meters from the glacier, where NUI will run its ocean sensors vertically from surface to seafloor. At the bottom NUI will use its robotic arm to push clear plastic tubes into the seafloor and retrieve foot-long sediment cores for later study.

At the first site, a wide-eyed tiny shrimp peers into NUI’s high-resolution camera as the pilot maneuvers a sediment core into place. There are no curious visitors at the second site, where sediment rivers pouring out from the glacier bring visibility to zero. The pilot works quickly before NUI’s instrument bay fill with sand.

Shortly after 7 pm, while on its way to the third site, NUI’s fiber optic tether breaks. The video feed cuts out and the pilot’s control is severed. The team quickly program a secondary science route and transmit it acoustically through the water. Without direct pilot control NUI is unable to gather more sediment cores but it’s still able to complete the rest of its science mission autonomously, before returning to the Celtic Explorer some three hours later.

Curran explains that breaking the fiber optic tether was always a risk in the fjord, where the movement of the glacier has left parts of the seafloor scattered with moraines. It’s likely that the cable caught on one of these shallow drifts and snapped. Catania and Curran have no complaints; the dive was an unmitigated success.

With NUI safely back on deck, the team will spend the following day servicing the vehicle, replacing the fiber optic tether, and prepping it for its next dive.

Catania still has her eye on returning to Kangerlussuup Sermia, the expedition’s first glacier. That glacier is much larger and more erratic than the current one and thus scientifically more important. NUI has already mapped that glacier’s seafloor and moraines. But they’ve yet to map it’s face with multibeam or take vertical measurements of the water column.

Both of those will be much harder at the larger glacier but the NUI team and the scientists of the TERMINUS expedition are eager to take on the task.

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Raygun becomes viral sensation during breaking performance at 2024 Paris Olympics: Social media reacts

Breaking , more commonly known as breakdancing, made its debut as an Olympic sport this week at the 2024 Paris Games , with 17 B-girls and 16 B-boys making their way to France with the hopes of securing a gold medal.

On the first day of competition, viewers from across the world were treated to a different kind of introduction — not to the sport itself, but one of its athletes.

Though she was a long way from winning a gold medal, likely no breaker Friday captured the imagination of the international audience more than Rachael Gunn, an Australian breaker who competes under the name “Raygun.”

REQUIRED READING: Follow USA TODAY's coverage of the 2024 Paris Olympics

Raygun went 0-3 in her head-to-head competitions Friday — falling to Logistx of the United States, Syssy of France and eventual silver medalist Nicka of Lithuania by a combined score of 54-0 — and failed to record a point across those three matches, but for what she lacked in smoothly executed moves, she made up for in the hearts she won over with her demeanor.

Raygun’s short-lived Olympic experience made her a celebrity, one who people became even more enamored with once they learned more about her.

The 36-year-old Gunn, who was one of the oldest qualifiers in the breaking competition, has a PhD in cultural studies and is a college professor at Macquarie University in Sydney. Her research focuses primarily on breaking, street dance and hip-hop culture while her work draws on “cultural theory, dance studies, popular music studies, media, and ethnography.”

“In 2023, many of my students didn’t believe me when I told them I was training to qualify for the Olympics, and were shocked when they checked Google and saw that I qualified,” Gunn said to CNBC earlier this month .

Unlike much of her competition in Paris, Gunn took up break dancing later in life. She didn’t enter her first battle until 2012.

On Friday, a person who began the day as a little-known academic ended it as a viral worldwide sensation.

Here’s a sampling of the reaction to Raygun and her performance:

2024 PARIS OLYMPICS: Meet the members of Team USA competing at the 2024 Paris Olympics

Social media reacts to Raygun’s breaking performance at 2024 Paris Olympics

I could live all my life and never come up with anything as funny as Raygun, the 36-year-old Australian Olympic breakdancer pic.twitter.com/1uPYBxIlh8 — mariah (@mariahkreutter) August 9, 2024

Give Raygun the gold right now #breakdancing pic.twitter.com/bMtAWEh3xo — n★ (@nichstarr) August 9, 2024

my five year old niece after she says “watch this!” : pic.twitter.com/KBAMSkgltj — alex (@alex_abads) August 9, 2024

I'd like to personally thank Raygun for making millions of people worldwide think "huh, maybe I can make the Olympics too" pic.twitter.com/p5QlUbkL2w — Bradford Pearson (@BradfordPearson) August 9, 2024

The Aussie B-Girl Raygun dressed as a school PE teach complete with cap while everyone else is dressed in funky breaking outfits has sent me. It looks like she’s giving her detention for inappropriate dress at school 🤣 #Olympics pic.twitter.com/lWVU3myu6C — Georgie Heath🎙️ (@GeorgieHeath27) August 9, 2024

There has not been an Olympic performance this dominant since Usain Bolt’s 100m sprint at Beijing in 2008. Honestly, the moment Raygun broke out her Kangaroo move this competition was over! Give her the #breakdancing gold 🥇 pic.twitter.com/6q8qAft1BX — Trapper Haskins (@TrapperHaskins) August 9, 2024

my dog on the lawn 30 seconds after i've finished bathing him pic.twitter.com/A5aqxIbV3H — David Mack (@davidmackau) August 9, 2024

My wife at 3AM: I think I heard one of the kids Me: No way, they are asleep *looks at baby monitor* pic.twitter.com/Ubhi6kY4w4 — Wes Blankenship (@Wes_nship) August 9, 2024

me tryna get the duvet off when i’m too hot at night #olympics pic.twitter.com/NM4Fb2MEmX — robyn (@robynjournalist) August 9, 2024

Raygun really hit them with the "Tyrannosaurus." pic.twitter.com/ZGCMjhzth9 — Mike Beauvais (@MikeBeauvais) August 9, 2024

Raygun (AUS) https://t.co/w2lxLRaW2x — Peter Nygaard (@RetepAdam) August 9, 2024

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Fact-Checking Claims About Tim Walz’s Record

Republicans have leveled inaccurate or misleading attacks on Mr. Walz’s response to protests in the summer of 2020, his positions on immigration and his role in the redesign of Minnesota’s flag.

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By Linda Qiu

Since Gov. Tim Walz of Minnesota was announced as the Democratic nominee for vice president, the Trump campaign and its allies have gone on the attack.

Mr. Walz, a former teacher and football coach from Nebraska who served in the National Guard, was elected to the U.S. House of Representatives in 2006 and then as Minnesota’s governor in 2018. His branding of former President Donald J. Trump as “weird” this year caught on among Democrats and helped catapult him into the national spotlight and to the top of Vice President Kamala Harris’s list of potential running mates.

The Republican accusations, which include questions over his military service , seem intended at undercutting a re-energized campaign after President Biden stepped aside and Ms. Harris emerged as his replacement at the top of the ticket. Mr. Trump and his allies have criticized, sometimes inaccurately, Mr. Walz’s handling of protests in his state, his immigration policies, his comments about a ladder factory and the redesign of his state’s flag.

Here’s a fact check of some claims.

What Was Said

“Because if we remember the rioting in the summer of 2020, Tim Walz was the guy who let rioters burn down Minneapolis.” — Senator JD Vance of Ohio, the Republican nominee for vice president, during a rally on Wednesday in Philadelphia

This is exaggerated. Mr. Walz has faced criticism for not quickly activating the National Guard to quell civil unrest in Minneapolis in the summer of 2020 after the murder of George Floyd by a police officer. But claims that he did not respond at all, or that the city burned down, are hyperbolic.

Mr. Floyd was murdered on May 25, 2020, and demonstrators took to the streets the next day . The protests intensified, with some vandalizing vehicles and setting fires. More than 700 state troopers and officers with the Minnesota Department of Natural Resources’ mobile response team were deployed on May 26 to help the city’s police officers, according to a 2022 independent assessment by the state’s Department of Public Safety of the response to the unrest.

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