COMUNICAÇÃO E MARKETING NA EDUCAÇÃO SUPERIOR: uma análise dos conteúdos comunicacionais online de instituições de ensino privadas

O presente contexto mercadológico da educação superior, onde a concorrência é cada vez mais acirrada, tem levado as instituições de ensino a estabelecer um processo de gestão de comunicação e marketing mais estratégico e competitivo, buscando alcançar uma posição diferenciada em relação à concorrência, a fim de conquistar seus públicos de interesse. Este trabalho contemplou a aplicação dos objetivos de comunicação no mercado de ensino superior, analisando as formas pelas quais as instituições vêm estabelecendo os processos comunicacionais com seus públicos-alvo, estando direcionado para as Universidades privadas brasileiras. A pesquisa se apóia em: revisão bibliográfica, entrevistas em profundidade com gestores de comunicação e marketing do setor pesquisado e análise de conteúdo de peças de comunicação em mídia online. Inicialmente foi elaborado um relato acerca do contexto atual do mercado de ensino superior no Brasil: sua evolução e caracterização. Em seguida, definiu-se marketing aplicado ao segmento de educação superior: conceitos e o papel designado a ele. Posteriormente relacionou-se comunicação mercadológica com o serviço de educação superior e sua aplicabilidade neste setor. Depois, foram realizadas entrevistas em profundidade - semiestruturadas, com gestores de comunicação e marketing de duas instituições, localizadas na cidade de São Paulo (Insper e Universidade São Judas Tadeu) com posicionamentos antagônicos e classificações distintas quanto à sua imagem para o mercado -, com a finalidade de conhecer suas visões e opiniões sobre o mercado e as ações de comunicação de marketing que vêm adotando. Finalmente, foi elaborada análise de contéudo, comparando anúncios (peças publicitárias) em mídia online das duas IES estudadas. Todos os procedimentos da análise de contéudo foram estabelecidos e categorizados com base nos objetivos de comunicação definidos por Yanaze (2011).

Content filtering and enrichment using triplets for text generation

Existe una cantidad enorme de información en Internet acerca de incontables temas, y cada día esta información se expande más y más. En teoría, los programas informáticos podrían beneficiarse de esta gran cantidad de información disponible para establecer nuevas conexiones entre conceptos, pero esta información a menudo aparece en formatos no estructurados como texto en lenguaje natural. Por esta razón, es muy importante conseguir obtener automáticamente información de fuentes de diferentes tipos, procesarla, filtrarla y enriquecerla, para lograr maximizar el conocimiento que podemos obtener de Internet. Este proyecto consta de dos partes diferentes. En la primera se explora el filtrado de información. La entrada del sistema consiste en una serie de tripletas proporcionadas por la Universidad de Coimbra (ellos obtuvieron las tripletas mediante un proceso de extracción de información a partir de texto en lenguaje natural). Sin embargo, debido a la complejidad de la tarea de extracción, algunas de las tripletas son de dudosa calidad y necesitan pasar por un proceso de filtrado. Dadas estas tripletas acerca de un tema concreto, la entrada será estudiada para averiguar qué información es relevante al tema y qué información debe ser descartada. Para ello, la entrada será comparada con una fuente de conocimiento online. En la segunda parte de este proyecto, se explora el enriquecimiento de información. Se emplean diferentes fuentes de texto online escritas en lenguaje natural (en inglés) y se extrae información de ellas que pueda ser relevante al tema especificado. Algunas de estas fuentes de conocimiento están escritas en inglés común, y otras están escritas en inglés simple, un subconjunto controlado del lenguaje que consta de vocabulario reducido y estructuras sintácticas más simples. Se estudia cómo esto afecta a la calidad de las tripletas extraídas, y si la información obtenida de fuentes escritas en inglés simple es de una calidad superior a aquella extraída de fuentes en inglés común.

Computer Assisted Content Analysis of the print press coverarge of corruption in Hungary

Coverage of corruption in the Hungarian media was analyzed using four online news portals. Three of them, Magyar Nemzet Online (short name: MNO, web: mno.hu), Népszava (web: nepszava.hu) and Heti Világgazdaság (web: hvg.hu) are also available as newspapers but the content of these papers is different from the online form to a certain extent. The news portal Origo (web: origo.hu) has no print version.

Effective video content distribution by combining TCP with adaptive FEC coding

This paper will look at the benefits and limitations of content distribution using Forward Error Correction (FEC) in conjunction with the Transmission Control Protocol (TCP). FEC can be used to reduce the number of retransmissions which would usually result from a lost packet. The requirement for TCP to deal with any losses is then greatly reduced. There are however side-effects to using FEC as a countermeasure to packet loss: an additional requirement for bandwidth. When applications such as real-time video conferencing are needed, delay must be kept to a minimum, and retransmissions are certainly not desirable. A balance, therefore, between additional bandwidth and delay due to retransmissions must be struck. Our results show that the throughput of data can be significantly improved when packet loss occurs using a combination of FEC and TCP, compared to relying solely on TCP for retransmissions. Furthermore, a case study applies the result to demonstrate the achievable improvements in the quality of streaming video perceived by end users.

Service users experiences of a therapeutic group programme in an acute psychiatric inpatient unit.

Psychiatric nurses have been facilitating therapeutic groups in acute psychiatric inpatient units for many years; however, there is a lack of nursing research related to this important aspect of care. This paper reports the findings of a study which aimed to gain an understanding of service users' experiences in relation to therapeutic group activities in an acute inpatient unit. A qualitative descriptive study was undertaken with eight service users in one acute psychiatric inpatient unit in Ireland. Data were collected using in-depth semi-structured interviews and analysed using Burnard's method of thematic content analysis. Several themes emerged from the findings which are presented in this paper.

Adaptive Brain-Computer Interface Systems For Communication in People with Severe Neuromuscular Disabilities

Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

Online Journal Titles Usability Test - June 2016

Emily Daly and Gordon Chadwick conducted a think-aloud usability study on June 20, 2016 in the Perkins Library at Duke University. The study tested users’ perceptions of a new search results page for online journal titles.

"I Use Weed for My ADHD": A Qualitative Analysis of Online Forum Discussions on Cannabis Use and ADHD.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a risk factor for problematic cannabis use. However, clinical and anecdotal evidence suggest an increasingly popular perception that cannabis is therapeutic for ADHD, including via online resources. Given that the Internet is increasingly utilized as a source of healthcare information and may influence perceptions, we conducted a qualitative analysis of online forum discussions, also referred to as threads, on the effects of cannabis on ADHD to systematically characterize the content patients and caregivers may encounter about ADHD and cannabis. METHODS: A total of 268 separate forum threads were identified. Twenty percent (20%) were randomly selected, which yielded 55 separate forum threads (mean number of individual posts per forum thread = 17.53) scored by three raters (Cohen's kappa = 0.74). A final sample of 401 posts in these forum threads received at least one endorsement on predetermined topics following qualitative coding procedures. RESULTS: Twenty-five (25%) percent of individual posts indicated that cannabis is therapeutic for ADHD, as opposed to 8% that it is harmful, 5% that it is both therapeutic and harmful, and 2% that it has no effect on ADHD. This pattern was generally consistent when the year of each post was considered. The greater endorsement of therapeutic versus harmful effects of cannabis did not generalize to mood, other (non-ADHD) psychiatric conditions, or overall domains of daily life. Additional themes emerged (e.g., cannabis being considered sanctioned by healthcare providers). CONCLUSIONS: Despite that there are no clinical recommendations or systematic research supporting the beneficial effects of cannabis use for ADHD, online discussions indicate that cannabis is considered therapeutic for ADHD-this is the first study to identify such a trend. This type of online information could shape ADHD patient and caregiver perceptions, and influence cannabis use and clinical care.

HomeBank: An Online Repository of Daylong Child-Centered Audio Recordings.

HomeBank is introduced here. It is a public, permanent, extensible, online database of daylong audio recorded in naturalistic environments. HomeBank serves two primary purposes. First, it is a repository for raw audio and associated files: one database requires special permissions, and another redacted database allows unrestricted public access. Associated files include metadata such as participant demographics and clinical diagnostics, automated annotations, and human-generated transcriptions and annotations. Many recordings use the child-perspective LENA recorders (LENA Research Foundation, Boulder, Colorado, United States), but various recordings and metadata can be accommodated. The HomeBank database can have both vetted and unvetted recordings, with different levels of accessibility. Additionally, HomeBank is an open repository for processing and analysis tools for HomeBank or similar data sets. HomeBank is flexible for users and contributors, making primary data available to researchers, especially those in child development, linguistics, and audio engineering. HomeBank facilitates researchers' access to large-scale data and tools, linking the acoustic, auditory, and linguistic characteristics of children's environments with a variety of variables including socioeconomic status, family characteristics, language trajectories, and disorders. Automated processing applied to daylong home audio recordings is now becoming widely used in early intervention initiatives, helping parents to provide richer speech input to at-risk children.

Social-Cognitive Determinants of Success in Online Communities for Weight Management

Background: Online communities may be an effective, convenient, and relatively inexpensive intervention platform for individuals seeking assistance with weight management. Recent research suggests that these communities may be as effective as in-person treatments for weight management; however, very little is known about the characteristics that predict weight loss amongst those using an online community. Methods: Within a social-cognitive framework, we sought to identify the psychosocial characteristics that are associated with successful weight management for users of MyFitnessPal, a popular online community for weight management. We recruited participants who were new to the online community and asked them to complete 2 surveys (one at baseline and one 3 months later) that assessed various psychosocial constructs as well as self-reported height and weight. Results: Participants in our sample reported losing, on average, 4.55 kg during the 3-month time period. We found that engaging in weight control behaviors (e.g., monitoring food intake, weighing oneself, etc.) fully mediated the relationship between several of our variables of interest (i.e., baseline self-efficacy and perceived social support within the community) and weight loss. We also found that participants who expected to lose more weight at baseline were significantly more likely to have lost more weight at follow-up. Conclusions: On average, participants in our study lost a clinically meaningful amount of weight. Predictors of weight loss within this community included perceived support within the community (mediated by weight control behaviors), baseline self-efficacy (mediated by weight control behaviors), and baseline outcome expectations. Results of this study can ultimately serve to inform the design of future eHealth interventions for weight management.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.