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Hey Guys, can you guys let me know what you think of my cover letter for Wells Fargo? I have an interview scheduled for Tuesday morning! Any advice would be appreciated! Thanks. November 2, 2010 Human Resources Wells Fargo Bank 2030 Main Street, 11th Floor Suite Irvine, CA Dear Human... show more Hey Guys, can you guys let me know what you think of my cover letter for Wells Fargo? I have an interview scheduled for Tuesday morning! Any advice would be appreciated! Thanks.

November 2, 2010



Human Resources

Wells Fargo Bank

2030 Main Street, 11th Floor Suite

Irvine, CA



Dear Human Resources:

I am writing in response to the phone call I received from the Human Resources department on Saturday, October 23, 2010. I was initially referred to you by Christopher Tran, a current employee for Wells Fargo, who informed me you are actively seeking to hire an experienced and customer oriented individual as a bank teller for your Fullerton branch. I believe my educational background and skills would be an asset to Wells Fargo.

Customer Service is a key component of many businesses and corporations. From my perspective, customer service means, putting the customer first. Moreover, customer service means attending to the customers’ needs in the most efficient, productive, and friendly manner. It’s twice as hard to attract a new customer as it is to maintain an existing one. Unfortunately, many businesses often overlook this fact. Delivering high-quality, responsive service is vital in banking, and that’s exactly what I’ll deliver if offered employment.

As a member of the College of Business and Economics at California State University, Fullerton and International Business Fraternity of Delta Sigma Pi, I have obtained a solid academic education and learned the value of excellent customer service. During my employment at Universal Studios Hollywood, I had the opportunity to relate theory to practice in a real world environment. Moreover, I have learned how to deal with a wide variety of people, from the pleasant customer to the irate. In every case, I assess their needs and how the company can address them most effectively. The vast majority of the customers I have interacted with have walked away gratified.

If you’re looking for an experienced professional to provide superior service and to promote customer satisfaction, I am fully confident that I will meet, if not, exceed expectations. Thank you for your time and consideration to discuss my qualifications for employment at Wells Fargo.

Sincerely,

Roshawn Kintaudi

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1. Introduction

Online social spaces have become increasingly popular in the past few years and are receiving the attention of researchers in the educational field as possible supports for formal learning or opportunities of informal learning [1,2,3,4]. Their learning potential, however, cannot be taken for granted just because learning has long been recognized as social in nature. Social spaces can widely differ as concerns aims, operation and internal structure, and all of these factors affect their usability and affordances. It is therefore necessary to investigate the potential of different types of social spaces in order to highlight their possible contribution to improve and innovate education.

In this paper, we take into consideration a kind of social space that has so far been scarcely considered in the educational field, that is, question answering (QA), with the aim to understand if informal learning opportunities are actually provided by such online environments.

Social QA services have been widely developing in the past decade, with the mission to be places where everybody can contribute what they know, because everybody, not only teachers and experts, has some knowledge to share [5]. Such services are also viewed as an expression of the collective intelligence of all of their users [6]. QA spaces, and in particular Yahoo! Answers (YA), which is currently one of the largest and most visited, are extremely popular, with hundreds of thousands of users and new questions every month [7]. They have become prominent places for online information seeking, especially since answered questions remain available in the website’s database (for everybody, not only for community members) and can be retrieved also through search engines.

This exploratory study aims to shed light on the learning potential of QA spaces by building a descriptive picture of the kinds of information exchange (from the learning point of view) that actually take place in it and of the possible learning-oriented attitudes showed by its users. To this end, we selected and analyzed a small corpus of posts in the Languages section of the Italian chapter of YA. We choose to concentrate on one topic, because it is recognized [8] that there are wide differences among users’ involvement and behavior in different content categories, and hence, the average outcomes of a transversal analysis would likely fail to faithfully mirror the real situation in any category. We chose the Languages section for our analysis, because languages are a study subject, but they are also used in everyday life to communicate in the current globalized world. This fact provides the opportunity for YA users to ask both academic and very practical questions, leaving aside the kind of vague, opinion-oriented questions (usually called “factoids” in the literature [9]) that are often diffused within other topics.

In the next section, we present a concise review of previous studies on QA spaces; then, we describe and discuss the organization and outcomes of our study.

2. QA Spaces in the Literature

A number of studies have been produced in the last decade, investigating QA systems from different points of view, analyzing a variety of aspects and characterizing them mainly as information seeking devices, social spaces and technological environments. Several interesting examples are reported below that can give an overall picture of the current research trends in this field.

Patterns of interaction are the focus of a study by Adamic, Zhang, Bakshy and Ackerman [5], who try to understand knowledge sharing activity within YA across its categories. Starting from the observation that some users focus only on specific categories, while others like to move across several ones, they map related categories, define an “entropy” of users’ interests and combine these attributes to predict the choice of best answers.

Microcollaboration is investigated by Gazan [10], who shows how QA users sometimes engage in episodes of collaborative information seeking; this author spots social capital and affective factors as key elements apt at predicting the formation of such microcollaboration teams.

Knowledge sharing continuance is the focus of Jin, Zhou, Lee and Cheung [7], who propose a model to predict its occurrence, based on ex-post users’ satisfaction and knowledge self-efficacy.

User satisfaction and effectiveness are also investigated by Shah [11], who concludes that YA is a very effective platform for QA, because posted questions receive a very fast answer, even though really satisfactory answers usually take longer to be posted, depending on the question’s difficulty.

User motivations and expectations are investigated by Choi, Kitzi and Shah [12] based on gratification theory. Their findings highlight the importance of understanding the interrelationship among these aspects.

Sentiment analysis in questions and answers is carried out by Kucuktunc, Cambazoglu, Weber and Ferhatosmanoglu [13], who found that best answers differ from other answers, as concerns the sentiment they express, and predict the attitude that a question will provoke in answerers.

Best answer selection criteria are investigated by Kim, Oh and Oh [14], who detect seven value categories of various natures at the origin of users’ choices.

Technical tools to automate some function within QA spaces include proposals like: identifying authoritative actors [15], learning to recognize reliable users and content [16], personalizing the interaction with the system based on user’s interests [17] and a multi-channel recommender system for associating questions with potential answerers [18].

Two interesting reviews are also worth mentioning.

Shah, Oh and Oh [19], after reviewing the literature on online QA services, draw a research agenda for investigating information seeking behaviors in such settings; they identify three main areas of interest: users (including needs, tasks, expectations and motivation), information (including quality of questions and credibility of answers) and technology (including user interface, usability and business model), as well as some intermediate areas rising from the intersections of the main ones: collective knowledge, usage pattern/behavior and devices/policies related to user-generated content.

Gazan [20], after reviewing the current literature, identifies as major threads of QA research: question and answers classifications, quality assessment, user satisfaction, reward structures, motivation for participation, operationalization of trust and expertise. Directions for future research are also identified as extensions of these threads.

A point that emerges from this concise literature review is that the potential value of QA spaces in education is never addressed. Learning may be mentioned as a consequence of information seeking, but is never the focus of any of these studies. This is nevertheless an important issue to investigate, both to bring extra value to QA spaces and to foster innovation in the educational field by understanding, and exploiting, the learning potential of available resources. For this reason, we have conceived of this exploratory study, with the aim to stimulate the development of research studies on the considered topic.

3. Methodology of Our Study

We extracted a corpus of 500 questions and related answers from the Languages section of YA Italy, posted and resolved within a few days in December 2013. We limited our choice to “closed” questions, that is, questions whose authors had already chosen the best answer, in order to work on a consolidated situation.

As pointed out in the previous section, current studies in this field concentrate on a variety of aspects apt at investigating social and information retrieval issues, without considering the learning potential. None of the classification approaches described in the literature, therefore, appeared suitable to support our analysis, and we needed to work out our own approach. Repeated explorations of YA Languages, carried out before the selection of the corpus to analyze, led us to spot a number of aspects as relevant to highlight users’ learning orientation: types of questions, context/motivation and answers received as concerns the number, pertinence, correctness and richness from a language learning point of view. We defined each of them carefully (see the details in the next section), so as to avoid doubts and inconsistencies during post classification. Our choice to base this exploratory study on these aspects is due to four main reasons: (1) all of them contribute in some way to shed light on users’ orientation to learning; (2) these pieces of information are usually found in the posts themselves and, hence, can be acquired straightforwardly without requiring inferences on our part; (3) these are the only learning-related clues that we have detected by analyzing the posts; and (4) they are sufficient to draw a meaningful picture of the language learning potential of the considered QA space.

Posts’ analysis was carried out by the two researchers involved in this study, who independently read and classified 20% of the selected posts, in order to check their inter-coder agreement before processing the whole corpus. Thanks to the preparatory work on aspect definition, five of the six classification aspects of our choice (types of questions, as well as the number, pertinence, correctness and richness of answers) were simply acquired from the posts, which easily led to complete coders’ agreement on those variables. Some difficulties, on the other hand, arose as concerns context/motivation. For this variable, some explicit references present in most of the posts allowed us to split them into four groups (school, leisure, study and work); in a few cases, however, contexts or reasons motivating the questions were not explicitly mentioned, nor was it easy to determine, based on the overall content of such posts, if their authors were asking for suggestions and explanations while engaged in a formal learning context (which we classified as study) or were pursuing a free personal interest (which we classified as leisure). For this reason, we decided to introduce a fifth group, “study or leisure”, including questions clearly showing a learning interest, but whose motivation for asking was not explicitly mentioned. Introducing such a mixed group does not bias the correctness of our approach, because our focus is on users’ orientation to/interest in learning, and this was always evident for the posts included in this group, independently of the reasons that motivated the questions. With the introduction of this fifth group, it was possible to achieve complete agreement between the two coders also on this aspect. Once inter-coder agreement was verified, the analysis of the remaining posts was split between the two researchers.

The data collection was carried out by means of content analysis [21,22], a research methodology often applied to investigate aspects of learning of both a cognitive and affective nature, especially in online environments, which extensively use written communication. Post analysis was performed manually, because orientation to learning is a “latent” variable, i.e., it cannot be associated with the use of particular expressions or constructs, hence excluding the possibility to apply automated procedures.

We also included in our analysis a list and the frequency of the addressed languages, even though these data do not directly contribute to determine users’ orientation to learning, in order to draw a more expressive picture of the corpus analyzed. This information, moreover, contributes to highlighting what a variety of language-related questions can actually receive a (serious) answer in the considered QA space, hence widening its boundaries as a potential learning place.

On the other hand, it was not possible to analyze contributors’ characteristics, because they use rather anonymous nicknames (as suggested by Yahoo! itself to protect privacy), mostly without explicit contextual features about themselves. We can only mention, therefore, that questions appeared to be mostly asked by different people, while only about 6% of answerers were frequent (and often appreciated) contributors. Finding a high number of different authors should not be surprising (even though the YA web site lists numerous answerers with a very high number of posts) being in line with the findings of other studies: Jin and colleagues [7], for instance, acknowledge a high turnover in online communities, pointing out that many people post a contribution just once, even though they continue for a while to visit the community’s space, reading other members’ posts.

4. Exploring the Languages Section of Yahoo! Answers Italy

4.1. Languages Addressed

Fourteen modern languages were addressed in the posts analyzed, plus three, classical, ancient languages (Latin, Sanskrit, Ancient Greek), a few special cases (Italian dialects, sign language, Elvish), five different combinations of two or three modern languages, together with general linguistic questions. We labelled as spam a small percentage of off-topic questions and messages posted twice. These data are summarized in Table 1 and Figure 1.

Table 1. Languages addressed in the considered posts.

Language%Language%
English45.0Sanskrit0.4
German14.6Romanian0.4
Italian10.8Portuguese0.4
Spanish5.6Ancient Greek0.4
French3.8Swedish0.2
Japanese3.4Hindi0.2
Russian1.2more than 1 language1.2
Latin1.0particular cases1.2
Chinese0.8general/linguistics5.4
Albanian0.8
Arabic0.6spam/double2.6

Figure 1. Occurrences of languages in the considered posts.

Figure 1. Occurrences of languages in the considered posts.

The most numerous group by far is represented by questions about the English language. This is likely due to the current wide diffusion of English, both as an object of study and a cultural means to communicate online or to understand cartoons, popular music and fiction.

The national language—Italian—is the third most frequently mentioned in the posts analyzed; most questions concerned meaning of words or grammar rules and were submitted by native speakers, while only about 13% were submitted by foreigners trying to get in touch with native Italian speakers.

4.2. Types of Questions

We classified the posts analyzed into three groups, which we labelled suggestions/opinions, information and tasks. Requests of the type “suggestions/opinions” concern activities and strategies for improving language learning aspects (e.g., memorization or listening understanding) or help to choose a language to study (“which one is more useful or more interesting?”), references of books and websites to practice, as well as schools or websites for studying a (scarcely diffused) language (e.g., Hindi) or to earn a certification (e.g., Cambridge English language certification) and websites to meet native-speaker conversation mates or to find au-pair accommodations.

We classified as “information” questions concerning the meaning of single words or short sentences, correct spelling or pronunciation, form and use of idiomatic expressions, different shades of meaning between similar words or expressions and grammar explanations; all requests in which the emphasis is more on acquiring knowledge and building meanings than on obtaining material help with translations or other tasks (even though a translation or task may also be involved in the request).

Finally, we included among “tasks” all other questions, which essentially consisted of translations to or from languages, execution or correction of grammar and lexicon exercises and even the composition of letters or short essays.

Tasks constituted the largest group, followed by slightly less frequent information questions and a much lower amount of suggestions/opinions, as shown in Figure 2.

Figure 2. Types of questions in the analyzed set.

Figure 2. Types of questions in the analyzed set.

4.3. Contexts and Motivations of the Analyzed Questions