People learn whenever and wherever possible, and whatever they like or encounter--Mathematics, Drama, Art, Languages, Physics, Philosophy, and so on. With the bursting of knowledge, evaluation of one's possession of knowledge becomes increasingly difficult. There are a lot of demands to evaluate one's understanding of a piece of knowledge. Assessment of understanding of knowledge is conventionally through tests or interviews, but they have some limitations such as low-efficiency and not-comprehensive. This paper proposes a method called Understanding Tree to estimate one's understanding of knowledge, by keeping track of his her learning activities. It overcomes some limitations of traditional methods, hence complements traditional methods.
Expertise is one's expert skill or knowledge in a particular field. Expertise finding is the use of tools for finding and assessing individual expertise @cite_16 @cite_19 @cite_30 . As an important link of knowledge sharing, expertise finding has been heavily studied in many research communities @cite_12 @cite_17 @cite_20 @cite_14 @cite_3 @cite_29 . Many sources of data have been exploited to assess an individual's expertise, such as one's publications, documents, emails, web search behavior, other people's recommendations, social media etc. Knowledge Model and Understanding Tree provide a new source of data to analyze one's expertise -- one's learning history about a subject, which is more comprehensive and straightforward than other data sources, because one's expertise is mainly obtained through learning (Including Informal Learning", which occurs through the experience of day-to-day situations, such as a casual conversation, play, exploring, etc.)
@cite_30 @cite_14 @cite_29 @cite_3 @cite_19 @cite_16 @cite_20 @cite_12 @cite_17
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The automatic search for knowledgeable people in the scope of an organization is a key function which makes modern Enterprise search systems commercially successful and socially demanded. A number of effective approaches to expert finding were recently proposed in academic publications. Although, most of them use reasonably defined measures of personal expertise, they often limit themselves to rather unrealistic and sometimes oversimplified principles. In this thesis, we explore several ways to go beyond state-of-the-art assumptions used in research on expert finding and propose several novel solutions for this and related tasks. First, we describe measures of expertise that do not assume independent occurrence of terms and persons in a document what makes them perform better than the measures based on independence of all entities in a document. One of these measures makes persons central to the process of terms generation in a document. Another one assumes that the position of the person’s mention in a document with respect to the positions of query terms indicates the relation of the person to the document’s relevant content. Second, we find the ways to use not only direct expertise evidence for a person concentrated within the document space of the person’s current employer and only within those organizational documents that mention the person. We successfully utilize the predicting potential of additional indirect expertise evidence publicly available on the Web and in the organizational documents implicitly related to a person. Finally, besides the expert finding methods we proposed, we also demonstrate solutions for the tasks from related domains. In one case, we use several algorithms of multi-step relevance propagation to search for typed entities in Wikipedia. In another case, we suggest generic methods for placing photos uploaded to Flickr on the World map using language models of locations built entirely on the annotations provided by users with a few task specific extensions. Expert finding is a task of finding knowledgeable people on a given topic. State-of-the-art expertise retrieval algorithms identify matching experts based on analysis of textual content of documents experts are associated with. While powerful, these models ignore social structure that might be available. In this paper, we develop a Bayesian hierarchical model for expert finding that accounts for both social relationships and content. The model assumes that social links are determined by expertise similarity between candidates. We demonstrate the improved retrieval performance of our model over the baseline on a realistic data set. One of the key challenges for users of social media is judging the topical expertise of other users in order to select trustful information sources about specific topics and to judge credibility of content produced by others. In this paper, we explore the usefulness of different types of user-related data for making sense about the topical expertise of Twitter users. Types of user-related data include messages a user authored or re-published, biographical information a user published on his her profile page and information about user lists to which a user belongs. We conducted a user study that explores how useful different types of data are for informing human's expertise judgements. We then used topic modeling based on different types of data to build and assess computational expertise models of Twitter users. We use We follow directories as a proxy measurement for perceived expertise in this assessment. Our findings show that different types of user-related data indeed differ substantially in their ability to inform computational expertise models and humans's expertise judgements. Tweets and retweets a#x2014; which are often used in literature for gauging the expertise area of users a#x2014; are surprisingly useless for inferring the expertise topics of their authors and are outperformed by other types of user-related data such as information about users' list memberships. Our results have implications for algorithms, user interfaces and methods that focus on capturing expertise of social media users. Recently expertise retrieval has received increasing interests in both academia and industry. Finding experts with demonstrated expertise for a given query is a nontrivial task especially from a large-scale Web 2.0 systems, such as question answering and bibliography data, where users are actively publishing useful content online, interacting with each other, and forming social networks in various ways, leading to heterogeneous networks in addition to the large amounts of textual content information. Many approaches have been proposed and shown to be useful for expertise ranking. However, most of these methods only consider the textual documents while ignoring heterogeneous network structures or can merely integrate with one additional kind of information. None of them can fully exploit the characteristics of heterogeneous networks. In this paper, we propose a joint regularization framework to enhance expertise retrieval by modeling heterogeneous networks as regularization constraints on top of document-centric model. We argue that multi-typed linking edges reveal valuable information which should be treated differently. Motivated by this intuition, we formulate three hypotheses to capture unique characteristics for different graphs, and mathematically model those hypotheses jointly with the document and other information. To illustrate our methodology, we apply the framework to expert finding applications using a bibliography dataset with 1.1 million papers and 0.7 million authors. The experimental results show that our proposed approach can achieve significantly better results than the baseline and other enhanced models. Locating the expertise necessary to solve difficult problems is a nuanced social and collaborative problem. In organizations, some people assist others in locating expertise by making referrals. People who make referrals fill key organizational roles that have been identified by CSCW and affiliated research. Expertise locating systems are not designed to replace people who fill these key organizational roles. Instead, expertise locating systems attempt to decrease workload and support people who have no other options. Recommendation systems are collaborative software that can be applied to expertise locating. This work describes a general recommendation architecture that is grounded in a field study of expertise locating. Our expertise recommendation system details the work necessary to fit expertise recommendation to a work setting. The architecture and implementation begin to tease apart the technical aspects of providing good recommendations from social and collaborative concerns. Expertise retrieval, whose task is to suggest people with relevant expertise on the topic of interest, has received increasing interest in recent years. One of the issues is that previous algorithms mainly consider the documents associated with the experts while ignoring the community information that is affiliated with the documents and the experts. Motivated by the observation that communities could provide valuable insight and distinctive information, we investigate and develop two community-aware strategies to enhance expertise retrieval. We first propose a new smoothing method using the community context for statistical language modeling, which is employed to identify the most relevant documents so as to boost the performance of expertise retrieval in the document-based model. Furthermore, we propose a query-sensitive AuthorRank to model the authors' authorities based on the community coauthorship networks and develop an adaptive ranking refinement method to enhance expertise retrieval. Experimental results demonstrate the effectiveness and robustness of both community-aware strategies. Moreover, the improvements made in the enhanced models are significant and consistent. With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures. In an expert search task, the users’ need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users’ query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query, called the Voting Model for Expert Search. In the Voting Model, we see the problem of ranking experts as a voting problem. We model the voting problem using 12 various voting techniques, which are inspired from the data fusion field. We investigate the effectiveness of the Voting Model and the associated voting techniques across a range of document weighting models, in the context of the TREC 2005 and TREC 2006 Enterprise tracks. The evaluation results show that the voting paradigm is very effective, without using any query or collection-specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the voting techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the voting techniques for the proposed approach is stable on a given task regardless of the used weighting models, suggesting that some of the proposed voting techniques will always perform better than other voting techniques. Visual expertise has been explored in numerous professions, but research on teachers’ vision remains limited. Teachers’ visual expertise is an important professional skill, particularly the ability to simultaneously perceive and interpret classroom situations for effective classroom management. This skill is complex and relies on an awareness of classroom events. Using eye tracking measurements and verbal think aloud, we investigated differences in how expert and novice teachers perceive problematic classroom scenes. Sixty-seven teachers participated, 35 experienced secondary school teachers (experts) and 32 teachers-in-training (novices). Participants viewed videos of authentic lessons and their eye movements were recorded as they verbalized thoughts about what they had seen in the lesson and how it was relevant to classroom management. Two different types of videos were viewed: lesson fragments showing (1) multiple events depicting disengaged students with no overt disruptions and (2) multiple events that included a prominent disruptive event affecting the class. Analysis of eye movements showed that novices’ viewing was more dispersed whereas experts’ was more focused. Irrespective of the video type, expert teachers focused their attention on areas where relevant information was available, while novice teachers’ attention was more scattered across the classroom. Experts’ perception appears to be more knowledge-driven whereas novices’ appears more image-driven. Experts monitored more areas than novices, while novices skipped more areas than experts. Word usage also differed, showing that expertise was associated with a higher frequency of words referencing cognition, perception, and events than novices.