Text Feedback Research Articles

A Review of VoiceThread as a Tool for Advertising Education

IntroductionThis review highlights how a relatively novel online platform - VoiceThread - can serve several roles in the advertising classroom for not only content delivery, but for submitting work, receiving feedback in a variety of formats, and engaging in dynamic discussions. While primarily a tool for asynchronous discussions, VoiceThread can be used to achieve a range of student learning outcomes. The following emphasizes the unique opportunities the platform holds for advertising education specifically by allowing for submission of visual, textual and audio content, as well as visual, textual and audio feedback from both peers and instructors. While there are some technological and student considerations which must be weighed by instructors, VoiceThread is generally flexible enough to accommodate a range of learning activities and student needs.VoiceThread OverviewThe VoiceThread platform is a cloud application for conducting asynchronous, media-rich online communication and discussions. A VoiceThread is an interactive, multimedia slideshow in which the creator (instructor or student) posts a slideshow (of one or more slides, or files) and shares it with a group for review or discussion. When creating a VoiceThread post a variety of media can be uploaded as the primary content: video files, documents, PowerPoint presentations, webcam videos and links to online media provided by VoiceThread partners such as the New York Public Library. A single VoiceThread may contain a mix of these media to communicate information with others. Documents and PowerPoint slides conveniently upload with each page or slide displaying as a single VoiceThread slide. The creator can then use the VoiceThread Comment features to narrate the slides or provide instructions for discussion or collaboration.Once the VoiceThread is created, the owner can then share the VoiceThread with others by making it publicly available or privately sharing with specific VoiceThread users (generally students). Members of this group can then create comments on the VoiceThread via typed text, video or audio responses.The real power of VoiceThread is in the ability to construct an interactive activity focused around the central media that allow for peerto-peer interaction and collaboration without requiring the instructor and students to be active at the same time. It offers flexibility in format to users - if they do not have a webcam, they can still contribute via audio-only or text comments. The video comment option in VoiceThread provides an opportunity for nonverbal cues to enhance users' spoken responses and foster greater social presence. In addition VoiceThread now offers threaded comments, so users can reply directly to another person's comment. The opportunity for rich and varied feedback is particularly valuable, as it has been linked to greater student learning (see Yuan & Kim, 2015).VoiceThread offers both free and paid accounts. The free account is very limited in functionality, especially storage and sharing, but enables instructors to use the platform to determine how VoiceThread can enhance the social presence in a course and engage students in more focused, meaningful discussions. The paid instructor accounts are affordable and students do not have to pay to comment on VoiceThreads that are shared with them. VoiceThread also offers a site license option, which integrates in many learning management sites (LMS) to increase capabilities further and provide single sign-on and automated class group creation and management. Users can visit voicethread.com to register for a free account and browse publicly available VoiceThreads for a wide range of working examples, or click the Products tab to purchase a particular license. While large group pricing requires contacting a representative, Table 1 below reflects costs described on voicethread.com.Applications of VoiceThread in the Advertising ClassroomWhile at its core VoiceThread is a simple tool that allows for asynchronous conversations about a media text (e. …

Using video modeling with voiceover instruction plus feedback to train implementation of stimulus preference assessments

Behavior analysts frequently use stimulus preference assessments to identify putative reinforcers for consumers with autism spectrum disorder. The current study evaluated the effect of video modeling with voiceover instruction and on-screen text (VMVOT) and performance feedback to train staff to implement the multiple-stimulus-without-replacement, paired-stimulus, and single-stimulus preference assessments. Generalization probes with a larger stimulus array and with an actual consumer were conducted. The results indicated that VMVOT may be a useful prelude to in-vivo training approaches, as all staff mastered the preference assessments and demonstrated high levels of generalized responding. This outcome is discussed in light of previous staff training studies and avenues for future research.

A learning automata framework based on relevance feedback for content-based image retrieval

The need for efficient image browsing and searching motivates the use of Content-Based Image Retrieval (CBIR) systems. However, they suffer from a big gap between high-level image semantics and low-level features. So, a learning process to reduce the gap seems quite useful. This paper presents a novel Learning Automata (LA)-based approach to improve the CBIR systems. Distributed Learning Automata (DLA) is used in this work to learn the relevant images from textual query feedbacks of the users. Subsequently, the retrieved images are ranked according to the learning outcome and similarity measure. In this study, the similarity between images is evaluated based on two color descriptors: the global color histogram and local color auto-correlogram. A thorough observation and comparison of these color descriptors performances are performed with different color spaces and also with various similarity measures. Experimental results on two publicly available databases demonstrate that the performance of the proposed CBIR system after each round is improved and the system could retrieve images compatible with the users’ perception.

Student Perceptions of Video Communication in an Online Sport and Recreation Studies Graduate Course

This qualitative case study examined student perceptions of video communication with their instructor in an online research and writing course for sport and recreation graduate students. All students participated in two personalized Skype video calls with the instructor and received two video and text feedback critiques of their written projects. Eight students were interviewed following the course. Despite minor technological and scheduling concerns, students found that their Skype calls helped form a relationship with their instructor and improved their confidence in the course. Students found that video feedback recordings on their written projects were elaborate and friendly, while text feedback comments tended to be more convenient, efficient, and concise. However, all students reported that the advantages of video feedback outweighed the advantages of text. The article concludes with recommendations for future research and for online instructors who wish to effectively blend these forms of communication.

A machine learning approach to product review disambiguation based on function, form and behavior classification

Online product reviews have been shown to be a viable source of information for helping customers make informed purchasing decisions. In many cases, users of online shopping platforms have the ability to rate products on a numerical scale, and also provide textual feedback pertaining to a purchased product. Beyond using online product review platforms as customer decision support systems, this information rich data source could also aid designers seeking to increase the chances of their products being successful in the market through a deeper understanding of market needs. However, the increasing size and complexity of products on the market makes manual analysis of such data challenging. Information obtained from such sources, if not mined correctly, risks misrepresenting a product's true success/failure (e.g., a customer leaves a one star rating because of the slow shipping service of a product, not necessarily that he/she dislikes the product). The objective of this paper is three fold: i) to propose a machine learning approach that disambiguates online customer review feedback by classifying them into one of three direct product characteristics (i.e., form, function or behavior) and two indirect product characteristics (i.e., service and other), ii) to discover the machine learning algorithm that yields the highest and most generalizable results in achieving objective i) and iii) to quantify the correlation between product ratings and direct and indirect product characteristics. A case study involving review data for products mined from e-commerce websites is presented to demonstrate the validity of the proposed method. A multilayered (i.e., k-fold and leave one out) validation approach is presented to explore the generalizability of the proposed method. The resulting machine learning model achieved classification accuracies of 82.44% for within product classification, 80.84% for across product classification, 79.03% for across product type classification and 80.64% for across product domain classification. Furthermore, it was determined that the form of a product had the highest Pearson Correlation Coefficient relating to a product's star rating, with a value of 0.934. The scientific contributions of this work have the potential to transform the manner in which both product designers and customers incorporate product reviews into their decision making processes by quantifying the relationship between product reviews and product characteristics.

Prompting MEd students to engage with academia and the professional world through feedback

Little research has been conducted on writing-related issues in Master's level postgraduate professional development (PPD) programs. In such contexts lecturers' responses to student writing make a particularly worthwhile subject of research, as they reveal assumptions about valid knowledge surrounding the relationship between academia and the professional world. In this paper we report a study of a corpus of feedback texts gathered in a Master of Education (MEd) program at a university in Hong Kong. Employing a form of combined thematic and enumerative content analysis, we demonstrate how lecturers prompted students to engage with academia and the professional world at the levels of feedback points and of individual feedback texts, and how a host of lemmas prominent at the level of the corpus participated in constructing the two categories of feedback points that performed different feedback acts. In addition to having methodological and pedagogical implications, our study adds to the current literature on feedback research while aiming to inspire more research on academic writing in PPD contexts.

Monitoring oral anti-cancer therapy adherence in cancer patients using web based application guided communication compared with nurse counseling in a community cancer center.

88 Background: Smart phone web based applications (apps) can be effective communication tools for monitoring compliance and increasing adherence to oral anti-cancer therapy in the outpatient setting. Methods: Cancer patients age 18-90 treated with various oral anti-cancer therapies were recruited to this prospective, 3 month observational trial at our large community cancer center to determine feasibility of using an app for oral anti-cancer therapy compliance. Enrolled patients are randomized to either Arm 1 App arm and nurse counseling or Arm 2 nurse counseling only. The free apps MyMeds and Medisafe incorporate medication reminders and record doses. Patients complete the validated RAND 36- item quality of life (QOL) questionnaire and a free text feedback. Results: 91 patients were screened between 11/2015 to 9/2016 (45 declined enrollment due to various factors). 46 patients were randomized with 25 to Arm 1 and 21 to Arm 2. Demographics included 10 men (various tumor types but mostly multiple myeloma) and 36 women; 22 with breast cancer. MyMeds app lacked the necessary real-time communication so patients were switched to the MediSafe app which features email alerts. 15 patients have completed the study through Month 3. Four patients on Arm 1 missing their monthly visit or were non-compliant with the app. 23 patients remain active in the study. Differences are a higher QOL score in Arm 1 vs Arm 2 in measured domains of Energy/Fatigue (56% in Arm 1 compared to 44% for Arm 2) and Pain (72% in Arm 1 compared to 58% in Arm 2). Patients randomized to the app found the electronic alerts very helpful. Conclusions: Our limited pilot study demonstrated the potential of smart device apps as effective communication tools for improving patients’ adherence to oral anti-cancer therapy and QOL. Ultimately, convenient ways to monitor patients’ compliance with anti-cancer therapy will impact survival and facilitate provider-patient communication.

An analysis of instructor social presence in online text and asynchronous video feedback comments

Online and blended instructors are increasingly providing student feedback via asynchronous video, and students have reported in previous research that they are better able to perceive their instructors' social presence in video as compared to text. However, research is lacking that examines actual feedback comments for indicators of social presence. We addressed this gap by coding for indicators of social presence in 422 text and asynchronous video feedback comments provided to preservice teachers in three blended and online courses. Minimal differences were found in the frequency of social presence indicators between text and video feedback. However, we warn against interpreting this finding too simplistically. While text and video feedback had similar numbers of indicators, the indicators in video feedback may have had a larger impact on social presence due to the richness of the medium. Further research is needed in order to understand how text and video feedback promote social presence in online courses.

Automated Assessment of the Quality of Peer Reviews using Natural Language Processing Techniques

A review is textual feedback provided by a reviewer to the author of a submitted version. Peer reviews are used in academic publishing and in education to assess student work. While reviews are important to e-commerce sites like Amazon and e-bay, which use them to assess the quality of products and services, our work focuses on academic reviewing. We seek to help reviewers improve the quality of their reviews. One way to measure review quality is through metareview or review of reviews. We develop an automated metareview software that provides rapid feedback to reviewers on their assessment of authors’ submissions. To measure review quality, we employ metrics such as: review content type, review relevance, review’s coverage of a submission, review tone, review volume and review plagiarism (from the submission or from other reviews). We use natural language processing and machine-learning techniques to calculate these metrics. We summarize results from experiments to evaluate our review quality metrics: review content, relevance and coverage, and a study to analyze user perceptions of importance and usefulness of these metrics. Our approaches were evaluated on data from Expertiza and the Scaffolded Writing and Rewriting in the Discipline (SWoRD) project, which are two collaborative web-based learning applications.

Exploring patient perceptions of healthcare service quality through analysis of unstructured feedback

Mechanisms for collecting unstructured feedback (i.e., text comments) from patients of healthcare providers have become commonplace, but analysis techniques to examine such feedback have not been frequently applied in this domain. To fill this gap, we apply a text mining methodology to a large set of textual feedback of physicians by their patients and relate the textual commentary to their numeric ratings. While perceptions of healthcare service quality in the form of numeric ratings are easy to aggregate, freeform textual commentary presents more challenges to extracting useful information. Our methodology explores aggregation of the textual commentary using a topic analysis procedure (i.e., latent Dirichlet allocation) and a sentiment tool (i.e., Diction). We then explore how the extracted topic areas and expressed sentiments relate to the physicians’ quantitative ratings of service quality from both patients and other physicians. We analyze 23,537 numeric ratings plus textual feedback provided by patients of 3,712 physicians who have also been recommended by other physicians, and determine process quality satisfaction is an important driver of patient perceived quality, whereas clinical quality better reflects physician perceived quality. Our findings lead us to suggest that to maximize the usefulness of online reviews of physicians, potential patients should parse them for particular quality elements they wish to assess and interpret them within the scope of those quality elements.

Writing, Calculating and Peer Feedback in a Mathematically-oriented Course for Process Engineers: Raising Motivation and Initiating Processes of Thinking and Learning

Writing assignments can be seen as an important component of learning processes. Especially in the fields of engineering and sciences, writing assignments have the potential to consolidate subject-specific skills and to enhance motivation for solving technical problems. This paper introduces readers to a revised course structure that aims to strengthen motivation and mathematical understanding through written peer feedback based on mathematical exercises with written elements. The assignment was developed for the course Computational Fluid Dynamics in Process Engineering , a mathematically-oriented course for Master students of theoretical mechanical engineering and process engineering. Since the learning content was perceived as complex, students seemed to lack motivation in preparing for the course with the provided exercises. This paper suggests – based on the collected data, consisting of answers to mathematical problems, feedback texts, evaluation results, teachers’ observation, and examination results – that the introduced assignment enhances students’ understanding and has a positive impact on students’ motivation to solve the mathematical exercises.

A Textual Feedback Tool for Empowering Participants in Usability and UX Evaluations

ABSTRACTThe usability movement has historically always sought to empower end-users of computers so that they understand what is happening and can control the outcome. In this article, we develop and evaluate a “Textual Feedback” tool for usability and user experience (UX) evaluation that can be used to empower well-educated but low-status users in UX evaluations in countries and contexts with high power distances. The proposed tool contributes to the Human–Computer Interaction (HCI) community’s pool of localized UX evaluation tools. We evaluate the tool with 40 users from two socio-economic groups in real-life UX usability evaluations settings in Malaysia. The results indicate that the Textual Feedback tool may help participants to give their thoughts in UX evaluation in high power distance contexts. In particular, the Textual Feedback tool helps high status females and low status males express more UX problems than they can with traditional concurrent think aloud (CTA) alone. We found that classic concurrent think aloud UX evaluation works fine in high power contexts, but only with the addition of Textual Feedback to mitigate the effects of socio-economic status in certain user groups. We suggest that future research on UX evaluation look more into how to empower certain user groups, such as low status female users, in UX evaluations done in high power distance contexts.

Navigating Return to Work and Breastfeeding in a Hospital with a Comprehensive Employee Lactation Program.

The Surgeon General's Call to Action to Support Breastfeeding details the need for comprehensive employer lactation support programs. Our institution has an extensive employee lactation program, and our breastfeeding initiation and continuation rates are statistically significantly higher than state and national data, with more than 20% of our employees breastfeeding for more than 1 year. The objective of this research was complete secondary data analysis of qualitative data collected as part of a larger study on breastfeeding outcomes. In the larger study, 545 women who returned to work full or part time completed an online survey with the ability to provide free text qualitative data and feedback regarding their experiences with breastfeeding after return to work. Qualitative data were pulled from the online survey platform. The responses to these questions were analyzed using conventional content analysis by the research team (2 PhD-prepared nurse researchers trained and experienced in qualitative methodologies and 1 research assistant) in order to complete a thematic analysis of the survey data. Analysis of the data yielded 5 major themes: (1) positive reflections, (2) nonsupportive environment/work culture, (3) supportive environment/work culture, (4) accessibility of resources, and (5) internal barriers. The themes that emerged from this research clearly indicate that even in a hospital with an extensive employee lactation program, women have varied experiences-some more positive than others. Returning to work while breastfeeding requires time and commitment of the mother, and a supportive employee lactation program may ease that transition of return to work.

Development and Usability of REACH: A Tailored Theory-Based Text Messaging Intervention for Disadvantaged Adults With Type 2 Diabetes.

BackgroundAmong adults with type 2 diabetes mellitus (T2DM), adherence to recommended self-care activities is suboptimal, especially among racial and ethnic minorities with low income. Self-care nonadherence is associated with having worse glycemic control and diabetes complications. Text messaging interventions are improving the self-care of adults with T2DM, but few have been tested with disadvantaged populations.ObjectiveTo develop Rapid Education/Encouragement And Communications for Health (REACH), a tailored, text messaging intervention to support the self-care adherence of disadvantaged patients with T2DM, based on the Information-Motivation-Behavioral skills model. We then tested REACH’s usability to make improvements before evaluating its effects.MethodsWe developed REACH’s content and functionality using an empirical and theory-based approach, findings from a previously pilot-tested intervention, and the expertise of our interdisciplinary research team. We recruited 36 adults with T2DM from Federally Qualified Health Centers to participate in 1 of 3 rounds of usability testing. For 2 weeks, participants received daily text messages assessing and promoting self-care, including tailored messages addressing users’ unique barriers to adherence, and weekly text messages with adherence feedback. We analyzed quantitative and qualitative user feedback and system-collected data to improve REACH.ResultsParticipants were, on average, 52.4 (SD 9.5) years old, 56% (20/36) female, 63% (22/35) were a racial or ethnic minority, and 67% (22/33) had an income less than US $35,000. About half were taking insulin, and average hemoglobin A1c level was 8.2% (SD 2.2%). We identified issues (eg, user concerns with message phrasing, technical restrictions with responding to assessment messages) and made improvements between testing rounds. Overall, participants favorably rated the ease of understanding (mean 9.6, SD 0.7) and helpfulness (mean 9.3, SD 1.4) of self-care promoting text messages on a scale of 1-10, responded to 96% of assessment text messages, and rated the helpfulness of feedback text messages 8.5 (SD 2.7) on a scale of 1-10. User feedback led to refining our study enrollment process so that users understood the flexibility in message timing and that computers, not people, send the messages. Furthermore, research assistants’ feedback on the enrollment process helped improve participants’ engagement with study procedures.ConclusionsTesting technology-delivered interventions with disadvantaged adults revealed preferences and concerns unique to this population. Through iterative testing and multiple data sources, we identified and responded to users’ intervention preferences, technical issues, and shortcomings in our research procedures.

Opinion Sentiment Analysis

<span>An automatic system to analyze the opinions/feedbacks of the people working in any organization is proposed. The proposed system does two major jobs: the first part extracts the most relevant/important aspects from the textual feedbacks provided by clients/managers/co-workers for all the employees having similar job profile and the second part measures the degree of positivity or negativity for all the extracted aspects for every employee and then an overall performance of everyone is evaluated. We have implemented the system to study the feedback of teachers. The proposed system is flexible and versatile than the existing feedback evaluation system of teachers where students evaluate the teachers on the predefined aspects decided by experienced and senior teachers. Our system, Faculty Evaluation System (FES) identifies strengths and weaknesses of teachers on all aspects which are important to students. This information may be used by higher authorities of the institute in identifying suitable faculty members for different academic and administrative activities of the institute in addition to the teaching responsibility.</span>

The psychophysiology of approach, avoidance and conflict personality traits

Behavior analysts frequently use stimulus preference assessments to identify putative reinforcers for consumers with autism spectrum disorder. The current study evaluated the effect of video modeling with voiceover instruction and on-screen text (VMVOT) and performance feedback to train staff to implement the multiple-stimulus-without-replacement, paired-stimulus, and single-stimulus preference assessments. Generalization probes with a larger stimulus array and with an actual consumer were conducted. The results indicated that VMVOT may be a useful prelude to in-vivo training approaches, as all staff mastered the preference assessments and demonstrated high levels of generalized responding. This outcome is discussed in light of previous staff training studies and avenues for future research.

Brand associations in the higher education sector: The difference between shared and owned associations

This article evaluates brand associations in higher education. It examines the brand associations of healthy and unhealthy universities. This article also compares the students’ shared brand associations with universities’ owned communicated brand associations across healthy and unhealthy universities. A multi-pronged methodology is used. First, a new behavioural long-term-oriented measure of brand health is applied to classify universities in England into healthy and unhealthy. Second, undertaking a content analysis methodology, Leximancer is used to data mine textual feedback from an online community of university graduates. Leximancer is also used to analyse the brand associations communicated on universities’ own Websites and compare it with students’ perceived brand associations across healthy and unhealthy universities. The results showed healthy universities communicate themes such as opportunities and support services, whereas unhealthy brands mainly focus on facilities and cognitive benefits. In addition, the analysis revealed that students in healthy universities mainly associate themselves to university-related experiences, while in unhealthy universities, students associate themselves to the city and country-related experiences. The methods described enable managers in the higher education sector to communicate a more distinguished brand associations and to create a better alignment between owned brand association and shared brand association.

Abstract P134: One Year Follow-up of the mActive Randomized Clinical mHealth Trial of Physical Activity

Background: Novel mobile health (mHealth) technologies have transformed behavioral change research. Leveraging digital activity tracking technology, the mActive randomized clinical mHealth trial of cardiology patients showed that automated text message coaching increased short-term physical activity by 25%. In this one-year follow-up study, we aimed to understand the post-trial behavior of participants and examine their long-term perspectives. Methods: After trial completion, participants retained their digital activity tracking device (Fitbug) and their ongoing behavior was no longer directed by the research team. At one year, 34 of 48 participants answered a Google survey with questions about ongoing activity tracking usage and preferences regarding activity tracking devices and text messages. A proportion for each response was calculated. During focus groups (n=18), open questions were asked to promote discussion with themes assessed in a qualitative analysis. Results: On the survey (Table), 50% (17 of 34) reported still having the mActive pedometer and 27% continued activity tracking after the trial. Among those no longer tracking activity, 71% reported wanting to resume doing so; there was not a strong preference towards any particular activity tracking device. Preferences regarding text feedback were varied with most preferring 1-2 messages per day with a mix of positive and negative messages. During focus groups, participants emphasized a desire for feedback through personalized real-time text messages, and also expressed interest in having activity data synchronize with their electronic medical record to review it with their physician. Conclusions: After the trial, 1/3 of mActive participants continued to track activity. Integration of activity data with the electronic medical record and long-term delivery of real-time feedback could increase the desirability of long-term activity tracking. Future studies may address whether these capabilities aid in long-term engagement and behavioral change.

A chronic care ostomy self-management program for cancer survivors.

Individuals with ostomies experience extensive changes in health-related quality of life (HRQOL) and daily routine. Patients and families are typically forced to use trial and error to improve self-management. This is a longitudinal one-group design pilot study of a five-session ostomy self-care curriculum based on the Chronic Care Model to improve HRQOL and self-management for cancer survivors with ostomies. Participants were surveyed to evaluate each session. Multiple instruments were administered to examine outcomes at baseline, post-intervention, and at 6-month follow-up (Patient Activation Measure, self-efficacy, Hospital Anxiety and Depression Scale, Ways of Coping, Group Health Association of America Satisfaction with ostomy care survey, and the City of Hope Quality of Life Ostomy). Changes from pre-intervention to post-intervention and pre-intervention to follow-up were evaluated with paired t-tests. Text responses were coded and evaluated for important themes and recommendations. Thirty-eight subjects participated in the study. Most had a history of rectal cancer (60.5%) or bladder cancer (28.9%). Participants rated the overall program high (4.4-4.8 on 5-point scale). Text feedback indicated that participants enjoyed the group forums, wanted more participants, and more hands-on training. Scores on multiple surveys were shown to be improved and sustained, including patient activation (p = 0.0004), self-efficacy (p = 0.006), total HRQOL (p = 0.01), physical well-being (p = 0.005), and social well-being (p = 0.002). Survivor anxiety was significantly reduced by follow-up (p = 0.047). This self-management ostomy program can help cancer survivors with ostomies adapt to their stoma. Initiating this program in the community setting would be beneficial to many cancer survivors.

Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

The end of the course evaluation has become an integral part of education management in almost every academic institution. The existing automated evaluation method primarily employs the Likert scale based quantitative scores provided by students about the delivery of the course and the knowledge of the instructor. The feedback is subsequently used to improve the quality of the teaching and often for the annual appraisal process. In addition to the Likert scale questions, the evaluation form typically contains open-ended questions where students can write general comments/feedback that might not be covered by the fixed questions. The textual feedback, however, is usually provided to teachers and administration and due to its nonquantitative nature is frequently not processed to gain more insight. This paper aims to address this aspect by applying several text analytics methods on students’ feedback. The paper not only presents a sentiment analysis based metric, which is shown to be highly correlated with the aggregated Likert scale scores, but also provides new insight into a teacher’s performance with the help of tag clouds, sentiment score, and other frequency-based filters.