Question: Do students use retrieval methods when studying independently as part of their metacognitive learning strategies?

This study explores the intriguing phenomenon where retrieval-induced forgetting plays a significant role in long-term memory dynamics. Across three experiments, participants were presented with eight categories (for instance, 'Fruit') and subsequently underwent a series of retrieval tests, focusing on half the members of half these categories (e.g., 'Fruit Or____'). The results revealed that recalling specific items led to a lasting impairment in the recall of unpracticed members within the same category when tested at a later time. Further investigations in Experiments 2 and 3 elucidated two key characteristics of this retrieval-induced forgetting. Firstly, the observed forgetting persisted even when output interference was accounted for, indicating that a retrieval-based suppression mechanism remains active for at least 20 minutes. Secondly, this impairment was notably confined to high-frequency items, with low-frequency items demonstrating minimal impact, despite the presence of strong, practiced competitors that could potentially hinder access to these less common items. These findings underscore the critical role of suppression in retrieval inhibition models and implicate the retrieval process as a significant factor in everyday forgetting, suggesting that the very act of remembering can paradoxically lead to forgetting.

Previous research has shown that spacing of information (over time) leads to better learning of product information. We develop a theoretical framework to describe how massed or spaced learning schedules interact with different learning styles to influence product usage proficiency. The core finding is that with experiential learning, proficiency in a product usage task is better under massed conditions, whereas with verbal learning, spacing works better. This effect is demonstrated for usage proficiency assessed via speed as well as quality of use. Further, massed learning also results in better usage proficiency on transfer tasks, for both experiential and verbal learning. We also find that massed learning in experiential learning conditions leads not only to better usage proficiency but also to positive perceptions of the product. Overall, the pattern of results is consistent with a conceptual mapping account, with massed experiences leading to a superior mental model of usage and thus to better usage proficiency.

For 120 years, cognitive psychologists have sought general laws of learning and memory. In this review I conclude that none has stood the test of time. No empirical law withstands manipulation across the four sets of factors that Jenkins (1979) identified as critical to memory experiments: types of subjects, kinds of events to be remembered, manipulation of encoding conditions, and variations in test conditions. Another factor affecting many phenomena is whether a manipulation of conditions occurs in randomized, within-subjects designs rather than between-subjects (or within-subject, blocked) designs. The fact that simple laws do not hold reveals the complex, interactive nature of memory phenomena. Nonetheless, the science of memory is robust, with most findings easily replicated under the same conditions as originally used, but when other variables are manipulated, effects may disappear or reverse. These same points are probably true of psychological research in most, if not all, domains.

A mong earthly organisms, humans have a unique propensity to introspect or look inward into the contents of their own minds, and to share those observations with others. With the ability to introspect comes the palpable feeling of " knowing, " of being objective or certain, of being mentally in control of one's thoughts, aware of the causes of one's thoughts, feelings, and actions, and of making decisions deliberately and rationally. Among the noteworthy discoveries of 20th century psychology was a challenge posed to this assumption of rationality. From the groundbreaking theorizing of Herbert Simon (1955) and the mind-boggling problems posed by Kahneman, Slovik, and Tversky (1982) to striking demonstrations of illusions of control (Wegner, 2002), the paucity of introspection (Nisbett and Wilson, 1977), and the automaticity of everyday thought (Bargh, 1997), psychologists have shown the frailties of the minds of their species. As psychologists have come to grips with the limits of the mind, there has been an increased interest in measuring aspects of thinking and feeling that may not be easily accessed or available to consciousness. Innovations in measurement have been undertaken with the purpose of bringing under scrutiny new forms of cogni-tion and emotion that were previously undiscovered and especially by asking if traditional concepts such as attitude and preference, belief and stereotype, self-concept and self-esteem can be rethought based on what the new measures reveal. These newer measures do not require introspection on the part of the subject. For many constructs this is considered a valuable, if not essential, feature of measurement; for others, avoiding introspection is greeted with suspicion and skepticism. For example, one approach to measuring math ability would be to ask " how good are you at math? " whereas an alternative approach is to infer math ability via a performance on a math skills test. The former requires introspection to assess the relevant construct, the latter does not. And yet, the latter is accepted

Structured Abstract Purpose – Designing a model and tools that are (a) capable of representing and handling personal knowledge in different degrees of structuredness and formalisation and (b) usable and extensible by end-users. Design/methodology/approach – This paper presents the result of analysing literature and various data-models and formalism used to structure information on the desktop. Findings – The unified data model (CDS) is capable of representing structures from various information tools, e. g. documents, file system, hypertext, tagging, and mind maps. The five knowledge axes of CDS are: identity, order, hierarchy, annotation and linking. Research limitations / implications – The CDS model is based on text. Extensions for multimedia annotations have not been investigated. Practical implications – Future PKM tools should take the mentioned shortcoming of existing PKM tools into account. Implementing the CDS model can be a way to make PKM tools interoperable. Originality/value – This paper presents research combining cognitive psychology, personal knowledge management and semantic web technologies. The CDS model shows a way to let end-users work on different levels of granularity and different levels of formality in one environment. Introduction " The most important contribution of management in the 20th century was to increase manual worker productivity fifty-fold. The most important contribution of management in the 21st century will be to increase knowledge worker productivity – hopefully by the same percentage. [...] The methods, however, are totally different from those that increased the productivity of manual workers. " Drucker (1999a, p. 79) What might these methods be? The field of knowledge management investigates since 1995 (Stankosky, 2005) how people and knowledge work together. North (2007) defines knowledge work as work based on knowledge with an immaterial result; value creation is based on processing, generating and communicating knowledge. Polanyi (1966) makes a distinction between explicit knowledge encoded in artefacts such as books or web pages, and tacit knowledge which resides in the individual. The SECI-model of Nonaka (1994) describes knowledge transfers between humans and artefacts. The field of knowledge management has focused on knowledge transfer between people, either via socialisation or via externalised artefacts. The high expectations of central enterprise knowledge repositories have often not been met (Braganza and Mollenkramer, 2002). The following wave of expert finders and corporate white pages focused mostly on connecting the right people and let them communicate. Today, knowledge workers are flooded with information (Alvarado et al., 2003). The field of Personal …

Research into the functioning of the human brain, particularly during the past decade, has greatly enhanced our understanding of cognitive behaviours which are fundamental to education: learning, memory, intelligence, emotion. Here, we argue the case that research findings from cognitive neuroscience hold implications for educational practice. In doing so we advance a bio-psycho-social position that welcomes multi-disciplinary perspectives on current educational challenges. We provide some examples of research implications which support conventional pedagogic wisdom, and others which are novel and perhaps counter-intuitive. As an example, we take a model of adaptive plasticity that relies on stimulus reinforcement and examine possible implications for pedagogy and curriculum depth. In doing so, we reject some popular but over-simplistic applications of neuroscience to education. In sum, the education profession could benefit from embracing rather than ignoring cognitive neuroscience. Moreover, educationists should be actively contributing to the research agenda of future brain research.

Feedback has been studied as a strategy for promoting energy conservation for more than 30 years, with studies reporting widely varying results. Literature reviews have suggested that the effectiveness of feedback depends on both how and to whom it is provided; yet variations in both the type of feedback provided and the study methodology have made it difficult for conclusions to be drawn. The current article analyzes past theoretical and empirical research on both feedback and proenvironmental behavior to identify unresolved issues, and utilizes a meta-analysis of 42 feedback studies published between 1976 and 2010 to test a set of hypotheses about when and how feedback about energy usage is most effective. Results indicate that feedback is effective overall, r = .071, p < .001, but with significant variation in effects (r varied from -.080 to .480). Several treatment variables were found to moderate this relationship, including frequency, medium, comparison message, duration, and combination with other interventions (e.g., goal, incentive). Overall, results provide further evidence of feedback as a promising strategy to promote energy conservation and suggest areas in which future research should focus to explore how and for whom feedback is most effective.

Probabilistic category learning is often assumed to be an incrementally learned cognitive skill, dependent on nondeclarative memory systems. One paradigm in particular, the weather prediction task, has been used in over half a dozen neuropsychological and neuroimaging studies to date. Because of the growing interest in using this task and others like it as behavioral tools for studying the cognitive neuroscience of cognitive skill learning, it becomes especially important to understand how subjects solve this kind of task and whether all subjects learn it in the same way. We present here new experimental and theoretical analyses of the weather prediction task that indicate that there are at least three different strategies that describe how subjects learn this task. (1) An optimal multi-cue strategy, in which they respond to each pattern on the basis of associations of all four cues with each outcome; (2) a one-cue strategy, in which they respond on the basis of presence or absence of a single cue, disregarding all other cues; or (3) a singleton strategy, in which they learn only about the four patterns that have only one cue present and all others absent. This variability in how subjects approach this task may have important implications for interpreting how different brain regions are involved in probabilistic category learning.

This paper presents a system that works as an assistant for students in their learning process. The assistant system has two main parts: an Android application and a server platform. The Android application is a chatterbot (i.e., an agent intended to conduct a conversation in natural language with a human being) based on AIML, one of the more successful languages for developing conversational agents. The chatterbot acts as an intermediation agent between a student and the server platform. The server platform contains four repositories and a recommender (which are part of a bigger professor assessment system). The final objective of the assistant system is to make a student able to carry out several actions related to his/her learning and assessment processes, such as: to consult exam questions from a repository, to receive recommendations about learning material, to ask questions about a course, and to check his/her own assessed exams. These actions are carried out through and Android application using a natural language interface (by voice or typing). The purpose of this development is to facilitate the access to this information through a friendly interface.

In this paper, we present a novel technique for assessing the learning of computational thinking in the early childhood classroom. Students in three second grade classrooms learned foundational computational thinking concepts using ScratchJr and applied what they learned to creating animated collages, stories, and games. They then conducted artifact-based video interviews with each other in pairs using their iPad cameras. As discussed in the results, this technique can show a broad range of what young children learn about computational thinking in classroom interventions using ScratchJr than more traditional assessment techniques. It simultaneously provides a developmentally appropriate educational activity (i.e. peer interviews) for early childhood classrooms.

Augmented reality (AR) is an educational medium increasingly accessible to young users such as elementary school and high school students. Although previous research has shown that AR systems have the potential to improve student learning, the educational community remains unclear regarding the educational usefulness of AR and regarding contexts in which this technology is more effective than other educational mediums. This paper addresses these topics by analyzing 26 publications that have previously compared student learning in AR versus non-AR applications. It identifies a list of positive and negative impacts of AR experiences on student learning and highlights factors that are potentially underlying these effects. This set of factors is argued to cause differences in educational effectiveness between AR and other media. Furthermore, based on the analysis, the paper presents a heuristic questionnaire generated for judging the educational potential of AR experiences.


Question: Do students employ retrieval strategies during independent study sessions, according to metacognitive research on learning?

This paper conducts a comprehensive meta-analysis to explore the distributed practice effect in verbal recall tasks, focusing on temporal variables often overlooked in prior studies. A total of 839 evaluations from 317 experiments across 184 articles were analyzed. The study investigates the impact of spacing (comparing consecutive massed learning sessions to spaced learning sessions) and lag (assessing less spaced versus more spaced learning sessions) on recall performance. Additionally, the expanding interstudy interval (ISI) effect is examined. The findings suggest that the optimal ISI for maximizing retention varies with the retention interval; as the retention interval lengthens, the ISI that yields the highest retention also increases. The paper highlights the need for further research and discusses the theoretical implications of these findings on learning and memory.

Question: How does the implementation of metacognitive strategies influence student learning outcomes in higher education?

Documents: The role of metacognition in learning and achievement: A review of the literature.
The present review explores the role of metacognition in learning and academic achievement, considering metacognition as a critical component of self-regulated learning. A comprehensive literature review was conducted, covering studies from various educational levels and subject areas. The analysis reveals that metacognitive strategies, such as planning, monitoring, and evaluating one's own learning, significantly contribute to improved academic performance. The review also discusses the importance of teaching metacognitive skills to students to enhance their learning processes and outcomes. Key findings include the positive correlation between metacognitive awareness and academic achievement, the role of metacognition in fostering deep learning, and the need for educators to integrate metacognitive instruction into their teaching practices.

Similar Documents: This review article examines the pivotal role of metacognition in learning and academic success, positioning metacognition as a central element of self-regulated learning. By synthesizing findings from a wide range of studies across different educational contexts and disciplines, the review underscores the impact of metacognitive strategies on learning outcomes. Strategies like planning, monitoring, and assessing one's learning are shown to be crucial for enhancing academic performance. The review emphasizes the significance of teaching these metacognitive skills to students to optimize their learning experiences and outcomes. Key insights from the literature include the strong association between metacognitive awareness and academic achievement, the role of metacognition in promoting deep learning, and the imperative for educators to incorporate metacognitive instruction into their teaching methodologies.

This study examines the impact on student performance after interactive and non-interactive tutorials using a 2×2 treatment-control design. In an undergraduate management course, a control group watched a video tutorial while the treatment group received the same content using a dynamic tutorial. Both groups received the same quiz questions. Using effect size to determine magnitude of change, it was found that those in the treatment condition performed better than those in the control condition. Students were able to take the quiz up to two times. When examining for change in performance from attempt one to attempt two, the treatment group showed a greater magnitude of change. Students who consistently performed lowest on the quizzes outperformed all students in learning gains.

Team-based learning (TBL) is an active learning method developed to help students achieve course objectives while learning how to function in teams. Many faculty members have adopted TBL because it is a unique teaching method, but evidence about its effectiveness is unclear. Seventeen original studies on TBL are presented in this systematic review of research. The studies include descriptive, explanatory, and experimental research published from 2003 to 2011 in the nursing, medical, education, and business literature. Generally, students are satisfied with TBL and student engagement is higher in TBL classes. Evidence also exists that students in TBL classes score higher on examinations. However, further high-quality experimental studies are needed to confirm that TBL positively affects examination scores and other learning outcomes and to determine whether TBL produces students who have the ability to function well in groups.

Gamifying learning activities can be beneficial as it can better engage students and result in improved learning. However, incorporating game elements into learning activities can be difficult because it requires an appropriate mix of science, art, and experience. Design science research can help to address this issue. In particular, its search process for determining an appropriate solution for a given problem is useful as it allows for a number of iterative cycles over which the solution is incrementally refined, and ultimately resulting in a successful implementation. Our objective in this study is to develop a non-discipline-specific instantiation that can be embedded into a learning activity to motivate students and improve the quality of their learning. We provide a detailed explanation of how we designed and developed a gamified multiple choice quiz software tool over multiple iterations. The tool was trialled in three undergraduate IT-related courses and evaluated using a questionnaire survey. Results showed that the tool was well received as 76 per cent of students believed it was effective for learning and would for it to be used in their other courses.

This paper, investigates how academic performance of students evolves over the years in their studies during a study programme. To determine typical progression patterns over the years, students are described by a 4 tuple (e.g. x1, x2, x3, x4), these being the clusters' mean to which a student belongs to in each year of the degree. For this purpose, two consecutive cohorts have been analyzed using X-means clustering. Interestingly the patterns found in both cohorts show that a substantial number of students stay in the same kind of groups during their studies.

Recent policy initiatives in England have focused on promoting ‘interactive’ teaching in schools, with a clear expectation that this will lead to improvements in learning. This expectation is based on the perceived success of such approaches in other parts of the world.At the same time, there has been a large investment in Information and Communication Technology (ICT) resources, and particularly in interactive whiteboard technology. This paper explores the idea of interactive teaching in relation to the interactive technology which might be used to support it. It explains the development of a framework for the detailed analysis of teaching and learning in activity settings which is designed to represent the features and relationships involved in interactivity. When applied to a case study of interactive teaching during a lesson involving a variety of technology-based activities, the framework reveals a confusion of purpose in students’ use of an ICT resource that limits the potential for learning when students are working independently. Discussion of relationships between technical and pedagogical interactivity points a way forward concerning greater focus on learning goals during activity in order to enable learners to be more autonomous in exploiting ICT’s affordances, and the conclusion identifies the variables and issues which need to be considered in future research which will illuminate this path.

Emotions are related with learning processes, and physiological signals can be used to detect them. In this work, it is presented a model for the dynamic personalization of a learning environment. In the model, a specific combination of emotions and cognition processes are connected and integrated with the concept of ‘flow’, since it may provide a way to improve learning abilities. Physiological signals can be used to relate temporal emotions and subject's learning processes; the cardiac pulse is a reliable signal that carries useful information about the subject's emotional condition, which is detected using a classroom chair adapted with non invasive Electro-Mechanical Film (EMFi) sensors and an acquisition system that generates a ballistocardiogram (BCG), which is analyzed by a developed algorithm to obtain cardiac pulse statistics. A study including different data obtained from the chair's sensors was carried out and interpreted in terms of emotions, which together with a cognitive model is used for the personalization of content in a learning scenario. Such model establishes a relation between the learner's knowledge, the content of a course or a learning activity and the emotional condition of each student.

Recent advances in technology and in ideology have unlocked entirely new directions for education research. Mounting pressure from increasing tuition costs and free, online course offerings is opening discussion and catalyzing change in the physical classroom. The flipped classroom is at the center of this discussion. The flipped classroom is a new pedagogical method, which employs asynchronous video lectures and practice problems as homework, and active, group-based problem solving activities in the classroom. It represents a unique combination of learning theories once thought to be incompatible—active, problem-based learning activities founded upon a constructivist ideology and instructional lectures derived from direct instruction methods founded upon behaviorist principles. This paper provides a comprehensive survey of prior and ongoing research of the flipped classroom. Studies are characterized on several dimensions. Among others, these include the type of in-class and out-of-class activities, the measures used to evaluate the study, and methodological characteristics for each study. Results of this survey show that most studies conducted to date explore student perceptions and use single-group study designs. Reports of student perceptions of the flipped classroom are somewhat mixed, but are generally positive overall. Students tend to prefer in-person lectures to video lectures, but prefer interactive classroom activities over lectures. Anecdotal evidence suggests that student learning is improved for the flipped compared to traditional classroom. However, there is very little work investigating student learning outcomes objectively. We recommend for future work studies investigating of objective learning outcomes using controlled experimental or quasi-experimental designs. We also recommend that researchers carefully consider the theoretical framework used to guide the design of in-class activities. 1 The Rise of the Flipped Classroom There are two related movements that are combining to change the face of education. The first of these is a technological movement. This technological movement has enabled the amplification and duplication of information at an extremely low-cost. It started with the printing press in the 1400s, and has continued at an ever-increasing rate. The electronic telegraph came in the 1830s, wireless radio in the late 1800s and early 1900s, television in the 1920s, computers in the 1940s, the internet in the 1960s, and the world-wide web in the 1990s. As these technologies have been adopted, the ideas that have been spread through their channels have enabled a second movement. Whereas the technological movement sought to overcome real physical barriers to the free and open flow of information, this ideological movement seeks to remove the artificial, man-made barriers. This is epitomized in the free software movement (see, e.g., Stallman and Lessig [67]), although this movement is certainly not limited to software. A good example of this can be seen from the encyclopedia. Encyclopedia Britannica has been continuously published for nearly 250 years [20] (since 1768). Although Encyclopedia Britannica content has existed digitally since 1981, it was not until the advent of Wikipedia in 2001 that open access to encyclopedic content became available to users worldwide. Access to Encyclopedia Britannica remains restricted to a limited number of paid subscribers [21], but access to Wikipedia is open, and the website receives over 2.7 billion US monthly page views [81]. Thus, although the technology and digital content was available to enable free access to encyclopedic content, ideological roadblocks prevented this from happening. It was not until these ideologies had been overcome that humanity was empowered to create what has become the world’s largest, most up-to-date encyclopedia [81]. In a similar way, we are beginning to see the combined effects of these two movements on higher education. In the technological arena, research has made significant advances. Studies show that video lectures (slightly) outperform in-person lectures [9], with interactive online videos doing even better (Effect size=0.5) [83,51]. Online homework is just as effective as paper-and-pencil homework [8,27], and carefully developed intelligent tutoring systems have been shown to be just as effective as human tutors [77]. Despite these advancements, adoption has been slow, as the development of good educational systems can be prohibitively expensive. However, the corresponding ideological movement is breaking down these financial barriers. Ideologically, MIT took a significant step forward when it announced its OpenCourseWare (OCW) initiative in 2001 [53]. This opened access to information that had previously only been available to students who paid university tuition, which is over $40,000/yr at MIT [54]. Continuing this trend, MIT alum Salman Khan founded the Khan Academy in 2006, which has released a library of over 3200 videos and 350 practice exercises 2012. The stated mission of the Khan Academy is to provide “a free world-class education to anyone anywhere2012.” In the past year, this movement has rapidly gained momentum. Inspired by Khan’s efforts, Stanford professors Sebastian Thrun and Andrew Ng opened access to their online courses in Fall 2011. Thrun taught artificial intelligence with Peter Norvig, attracting over 160,000 students to their free online course. Subsequently, Thrun left the university and founded Udacity, which is now hosting 11 free courses [76]. With support from Stanford, Ng also started his own open online educational initiative, Coursera. Princeton, the University of Pennsylvania, and the University of Michigan have joined the Coursera partnership, which has expanded its offerings to 42 courses [10]. MIT has also upgraded its open educational initiative, and joined with Harvard in a $60 million dollar venture, edX [19]. EdX will, “offer Harvard and MIT classes online for free.” While online education is improving, expanding, and becoming openly available for free, university tuition at brick-and-mortar schools is rapidly rising [56]. Tuition in the University of California system has nearly tripled since 2000 [32]. Naturally, this is not being received well by university students in California [2]. Likewise, students in Quebec are actively protesting planned tuition hikes [13]. In resistance to planned tuition hikes, student protestors at Rutgers interrupted (on June 20, 2012) a board meeting to make their voices heard [36]. Adding fuel to the fire, results from a recent study by Gillen et al. [31] indicate that undergraduate student tuition is used to subsidize research. As a result, the natural question being asked by both students and educational institutions is exactly what students are getting for their money. This is applying a certain pressure on physical academic institutions to improve and enhance the in-person educational experience of their

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student’s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar’s tutoring helpful and achieved an average learning gain of 13%.

Neuroscience has experienced rapid growth in recent years, spurred in part by the U.S. government’s designation of the 1990s as “The Decade of the Brain” (Jones & Mendell, 1999). The rapid development of functional neuroimaging techniques has given researchers unprecedented access to the behaving brains of healthy children and adults. The result has been a wave of new insights into thinking, emotion, motivation, learning, and development. As these insights suffuse the social sciences, they sometimes inspire reconsideration of existing explanations. This is most true in psychology, as marked by the births of cognitive neuroscience (Gazzaniga, Ivry, & Mangun, 2002), developmental neuroscience (Johnson, Munakata, & Gilmore, 2001), and social neuroscience (Cacioppo, Visser, & Pickett, 2005). It is increasingly true in economics, where the rapid rise of neuroeconomics (Camerer, Loewenstein, & Prelec, 2005) has caught the attention of the popular press (Cassidy, 2006). Other social sciences, including communication (Anderson et al., 2006), political science (McDermott, 2004), and sociology (Wexler, 2006), are just beginning to confront the question of whether their research can be informed by neuroscience. Education is somewhere between the two poles of early adopters and tentative newcomers. A decade ago, in this journal, Bruer (1997) forcefully considered the relevance of neuroscience to education. His conclusion—that neuroscience is “a bridge too far”—was noteworthy because Bruer was then director of the McDonnell Foundation, which was actively funding research in both disciplines. Although it was in his best interests to find connections between the disciplines, he found instead poorly drawn extrapolations that inflated neuroscience findings into educational neuromyths. Since Bruer’s cautionary evaluation, a number of commentators have considered the prospects for educational neuroscience. Many sound a more optimistic note (Ansari & Coch, 2006; Byrnes & Fox, 1998; Geake & Cooper, 2003; Goswami, 2006; Petitto & Dunbar, in press), and a textbook has even appeared (Blakemore & Frith, 2005). In this article, we negotiate the middle ground between the pessimism of Bruer and the optimism of those who followed. Table 1 summarizes eight concerns about connecting education and neuroscience. Some are drawn from Bruer (1997) and the ensuing commentaries. Others come from conversations with colleagues in both disciplines, and still others from our own experiences. These concerns do not seem to represent a blanket dismissal but rather a genuine curiosity (tempered by a healthy skepticism) about the implications of neuroscience for education. We begin by articulating the concerns along with some facts about neuroscience that make the concerns more concrete. We voice them in the strong tone in which we have heard them espoused. We then revisit the concerns, reinterpreting them as potential opportunities (also in Table 1). This approach permits us to review a selection of neuroscience studies relevant to content learning. We focus on recent functional magnetic resonance imaging (fMRI), or neuroimaging, studies for reasons of space and because these are the findings that have captured the most attention, both in the academy and in the popular press. Ideally, our review illustrates some elements of neuroscience so that education researchers can think more specifically about the prospects of educational neuroscience. We conclude with two reflections on moving from armchair arguments of a philosophical nature to scientific action on the ground. First, we argue that education and neuroscience can be bridged if (and only if) researchers collaborate across disciplinary lines on tractable problems of common interest. It is the success

This paper gives a detailed description of how adaptivity is realized in e-learning systems, with a focus on adaptivity of courseware. The review aims to answer three basic questions on adaptive courseware: what has been adapted, how and why. Researchers have tried to adapt according to these different elements: student knowledge, behaviour, interests, preferences, learning style, cognitive style, goals, courseware presentation, navigation, annotation, sequencing and knowledge testing. An historical background of adaptivity is described: from word definition, through adaptive instructions and adaptive systems, all the way to adaptive courseware. The biggest challenge was to find the most prominent representatives of adaptive systems in courseware generation that were selected after an exhaustive search through relevant scientific paper databases. Each prevailing system is briefly described and the student features, to which the system adapts, are highlighted, together with the level of adaptation. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from elearning systems in general to intelligent tutoring systems in particular. Such an analysis has not been done so far, especially in a way that adaptive systems are described uniformly. Finally, a comparative analysis of those systems and conclusions and demands about the ultimate adaptive system is given. This paper can be used as a guide for making decisions about what to adapt, how and why, while designing an adaptive e-learning system.


Question: The Smart Web Surfer: Probabilistic Integration of Link and Content Data in the PageRank Algorithm

This paper delves into the potential of hyperlinked environments to provide valuable insights about their content, contingent upon the development of effective methodologies for interpreting their network structures. We present a suite of algorithmic tools designed to extract meaningful information from the link architecture of these environments, substantiating their efficacy through experimental evaluations in diverse contexts on the World Wide Web. At the heart of our investigation is the challenge of isolating broad search themes by identifying "authoritative" sources of information pertinent to these themes. We introduce and evaluate an algorithmic interpretation of authority, centered on the interplay between a cluster of authoritative pages and a set of "hub pages" that function as connectors within the link network. This interpretation is rooted in the eigenvectors of matrices associated with the hyperlink graph, inspiring supplementary heuristics for in-depth link-based analysis.

The Intelligent Web Crawler: A Probabilistic Integration of Link and Content Analysis in PageRank
Navigating the complex landscape of the World Wide Web, where content and link structures are intrinsically intertwined, calls for sophisticated algorithms that can effectively interpret and leverage this dual nature. We propose an innovative approach that integrates link analysis and content information within the PageRank framework, designed to enhance the discovery of high-quality web pages. Our method, grounded in probabilistic models, combines the strengths of both link-based and content-based analysis to yield a more comprehensive understanding of web page importance. Specifically, we extend the PageRank algorithm to incorporate content relevance, thereby enriching its capacity to rank pages based on their authority and topical significance. We present empirical evidence from our experiments, demonstrating the superiority of our integrated approach in identifying authoritative sources compared to conventional link analysis techniques. This enhancement to PageRank not only refines the search for authoritative content but also enriches the user's web browsing experience by prioritizing pages that are both relevant and highly regarded within their respective domains.

Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to hyperlinked tweets, we argue that the functions of hashtags such as providing the right context to interpret the tweets, tweet categorization, and tweet promotion, can be extended to the linked documents. The proposed solution for hashtag recommendation consists of two phases. In the first phase, we select candidate hashtags through five schemes by considering the similar tweets, the similar documents, the named entities contained in the document, and the domain of the link. In the second phase, we formulate the hashtag recommendation problem as a learning to rank problem and adopt RankSVM to aggregate and rank the candidate hashtags. Our experiments on a collection of 24 million tweets show that the proposed solution achieves promising results.

In this paper, we present the findings of a qualitative analysis of 15,750 comments left by 2,041 participants in a Reconcile web credibility evaluation study. While assessing the credibility of the presented pages, respondents of the Reconcile studies were also asked to justify their ratings in writing. This work attempts to give an insight into the factors that affected the credibility assessment. To the best of our knowledge, the presented study is the most-recent large-scale study of its kind carried out since 2003, when the Fogg et al. śHow do users evaluate the credibility of Web sites? A study with over 2,500 participants’ paper was published. The performed analysis shows that the findings made a decade ago are still mostly valid today despite the passage of time and the advancement of Internet technologies. However we report a weaker impact of webpage appearance. A much bigger dataset (as compared to Fogg’s studies) allowed respondents to reveal additional features, which influenced the credibility evaluations.

This paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search. As this task is very similar to work in meta-search and data fusion, we adapt several hypotheses from those research areas and investigate them in this context. To investigate these hypotheses, we present a mixture-based language model and also examine many of the current meta-search algorithms. We find that compatible output from systems is important for successful combination of document representations. We also demonstrate that combining low performing document representations can improve performance, but not consistently. We find that the techniques best suited for this task are robust to the inclusion of poorly performing document representations. We also explore the role of variance of results across systems and its impact on the performance of fusion, with the surprising result that the correct documents have higher variance across document representations than highly ranking incorrect documents.

Users of World Wide Web utilize search engines for information retrieval in web as search engines play a vital role in finding information on the web. However, the performance of a web search is greatly affected by flooding of search results with information that is redundant in nature i.e., existence of nearduplicates. Such near-duplicates holdup the other promising results to the users. Many of these near-duplicates are from distrusted websites and/or authors who host information on web. Such nearduplicates may be eliminated by means of Provenance. Thus, this paper proposes a novel approach to identify such near-duplicates based on provenance. In this approach a provenance model has been built using web pages which are the search results returned by existing search engine. The proposed model combines both content based and trust based factors for classifying the results as original or near-duplicates. Keywords— Web search, Near-duplicates, Provenance, Semantics,

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.

The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.

Seed URLs selection for focused Web crawler intends to guide related and valuable information that meets a user’s personal information requirement and provide more effective information retrieval. In this paper, we propose a seed URLs selection approach based on user-interest ontology. In order to enrich semantic query, we first intend to apply Formal Concept Analysis to construct user-interest concept lattice with user log profile. By using concept lattice merger, we construct the user-interest ontology which can describe the implicit concepts and relationships between them more appropriately for semantic representation eed URLs ormal concept analysis ser-interest ontology ipartite graph eb crawler and query match. On the other hand, we make full use of the user-interest ontology for extracting the user interest topic area and expanding user queries to receive the most related pages as seed URLs, which is an entrance of the focused crawler. In particular, we focus on how to refine the user topic area using the bipartite directed graph. The experiment proves that the user-interest ontology can be achieved effectively by merging concept lattices and that our proposed approach can select high quality seed URLs collection and improve the average precision of focused Web crawler.

Web sites invest significant resources in trying to influence their visibility among online search results. In addition to paying for sponsored links, they invest in methods known as search engine optimization (SEO) that improve the ranking of a site among the search results without improving its quality. We study the economic incentives of Web sites to invest in SEO and its implications on search engine and advertiser payoffs. We find that the process is equivalent to an all-pay auction with noise and headstarts. Our results show that, under certain conditions, a positive level of search engine optimization improves the search engine’s ranking quality and thus the satisfaction of its visitors. In particular, if the quality of sites coincides with their valuation for visitors then search engine optimization serves as a mechanism that improves the ranking by correcting measurement errors. While this benefits consumers and increases traffic to the search engine, sites participating in search engine optimization could be worse off due to wasteful spending unless their valuation for traffic is very high. We also investigate how search engine optimization affects the revenues from sponsored links. Surprisingly, we find that in many cases search engine revenues are increased by SEO.

Techniques for both exploratory and known item search tend to direct only to more specific subtopics or individual documents, as opposed to allowing directing the exploration of the information space. We present an interactive information retrieval system that combines Reinforcement Learning techniques along with a novel user interface design to allow active engagement of users in directing the search. Users can directly manipulate document features (keywords) to indicate their interests and Reinforcement Learning is used to model the user by allowing the system to trade off between exploration and exploitation. This gives users the opportunity to more effectively direct their search nearer, further and following a direction. A task-based user study conducted with 20 participants comparing our system to a traditional query-based baseline indicates that our system significantly improves the effectiveness of information retrieval by providing access to more relevant and novel information without having to spend more time acquiring the information.

Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation.
 In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.
 The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.


Question: The Smart Web Surfer: Probabilistic Integration of Link and Content Data in the PageRank Algorithm

This article explores advanced methods for the computation of PageRank, a pivotal ranking metric for web documents. We demonstrate the feasibility of computing PageRank for extensive web subgraphs, encompassing hundreds of millions of nodes, even on systems with constrained memory. Performance benchmarks for PageRank calculation on the Stanford WebBase archive, comprising 24 million pages, are provided across diverse memory configurations. We delve into various strategies for assessing the convergence of PageRank based on the hierarchical ordering of web pages. Additionally, we offer insights into the number of iterations required for achieving a meaningful PageRank allocation, both in the general case and when incorporating search queries, to ensure optimal ranking outcomes.

The evolution of the Web of documents into a Web of services and data has resulted in an increased availability of data from almost any domain. For example, general domain knowledge bases such as DBpedia or Wikidata, or domain specific Web sources like the Oxford Art archive, allow for accessing knowledge about a wide variety of entities including people, organizations, or art paintings. However, these data sources publish data in different ways, and they may be equipped with different search capabilities, e.g., SPARQL endpoints or REST services, thus requiring data integration techniques that provide a unified view of the published data. We devise a semantic data integration approach named FuhSen that exploits keyword and structured search capabilities of Web data sources and generates on-demand knowledge graphs merging data collected from available Web sources. Resulting knowledge graphs model semantics or meaning of merged data in terms of entities that satisfy keyword queries, and relationships among those entities. FuhSen relies on both RDF to semantically describe the collected entities, and on semantic similarity measures to decide on relatedness among entities that should be merged. We empirically evaluate the results of FuhSen data integration techniques on data from the DBpedia knowledge base. The experimental results suggest that FuhSen data integration techniques accurately integrate similar entities semantically into knowledge graphs.

The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs. We present in this paper a personalized document ranking model based on an extended graph-based distance measure that exploits a semantic user profile derived from a predefined web ontology (ODP). The measure is based on combining Minimum Common Supergraph (MCS) and Maximum Common Subgraph (mcs) between graphs representing respectively the document and the user profile. We extend this measure in order to take into account a semantic recovery between the document and the user profile through common concepts and cross links connecting the two graphs. Results show the effectiveness of our personalized graph-based ranking model compared to Yahoo search results.

Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insights in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.

Web Search Engines employ multiple so-called crawlers to maintain local copies of web pages. But these web pages are frequently updated by their owners, and therefore the crawlers must regularly revisit the web pages to maintain the freshness of their local copies. In this paper, we propose a two-part scheme to optimize this crawling process. One goal might be the minimization of the average level of staleness over all web pages, and the scheme we propose can solve this problem. Alternatively, the same basic scheme could be used to minimize a possibly more important search engine embarrassment level metric: The frequency with which a client makes a search engine query and then clicks on a returned url only to find that the result is incorrect. The first part our scheme determines the (nearly) optimal crawling frequencies, as well as the theoretically optimal times to crawl each web page. It does so within an extremely general stochastic framework, one which supports a wide range of complex update patterns found in practice. It uses techniques from probability theory and the theory of resource allocation problems which are highly computationally efficient -- crucial for practicality because the size of the problem in the web environment is immense. The second part employs these crawling frequencies and ideal crawl times as input, and creates an optimal achievable schedule for the crawlers. Our solution, based on network flow theory, is exact as well as highly efficient. An analysis of the update patterns from a highly accessed and highly dynamic web site is used to gain some insights into the properties of page updates in practice. Then, based on this analysis, we perform a set of detailed simulation experiments to demonstrate the quality and speed of our approach.

User generated content has been growing tremendously in recent years. This content reflects the interests and the diversity of online users. In turn, the diversity among internet users is also reflected in the quality of the content being published online. This increases the need to develop means to gauge the support available for content posted online. In this work, we aim to make use of the web-content to calculate a statistical support score for textual documents. In the proposed algorithm, phrases representing key facts are extracted to construct basic elements of the document. Search is used thereon to validate the support available for these elements online, leading to assigning an overall score for each document. Experimental results have shown a difference between the score distribution of factual news data and false facts data. This indicates that the approach seems to be a promising seed for distinguishing different articles based on the content.

Information retrieval (IR) approaches have proven useful in recovering traceability links between free-text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trust race, inspired by Web trust models to improve the precision and recall of traceability links: Trust race uses intractability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, His trace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with His trace to build Trust race in which we use Histraceas one expert adding knowledge to the traceability links extracttedfrom CVS/SVN  change logs. We apply Trustrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that Trustrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.

Surveying a suite of algorithms that offer a solution to managing large document archives.

In web search, information about times between user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed between a pair of user actions does not depend on the context, in which the first action takes place. We validate this assumption using logs of a commercial web search engine and discover that it does not always hold. For between 37% to 80% of query-result pairs, depending on the number of observations, the distributions of click dwell times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect, we propose a context-aware time model (CATM). The CATM allows us (i) to predict times between user actions in contexts, in which these actions were not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times between user actions.

Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, an item's general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this dataset show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks, and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most

This paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search. As this task is very similar to work in meta-search and data fusion, we adapt several hypotheses from those research areas and investigate them in this context. To investigate these hypotheses, we present a mixture-based language model and also examine many of the current meta-search algorithms. We find that compatible output from systems is important for successful combination of document representations. We also demonstrate that combining low performing document representations can improve performance, but not consistently. We find that the techniques best suited for this task are robust to the inclusion of poorly performing document representations. We also explore the role of variance of results across systems and its impact on the performance of fusion, with the surprising result that the correct documents have higher variance across document representations than highly ranking incorrect documents.


Question: The Smart Web Surfer: Probabilistic Integration of Link and Content Data in PageRank Algorithm.

A Unified Framework: Probabilistic Integration of Content and Link Structures in Document Analysis

This paper introduces a comprehensive probabilistic model designed to analyze the content and interlinking structure of document collections, such as web pages or scholarly databases. The model employs a probabilistic factorization technique, enabling the identification of key themes within the document set and pinpointing documents that are authoritative in each theme. Moreover, it maps the relationships between different themes to create a predictive model of the content likely to be found in hyperlinks. The proposed approach has broad applicability, spanning information retrieval and search, theme extraction, query clarification, targeted web crawling, web content creation, and bibliographic analysis. By seamlessly integrating both content and link information, this framework offers a more nuanced understanding of document collections, enhancing various analytical tasks.

Semantic and context knowledge have been envisioned as an appropriate solution for addressing the content heterogeneity and information overload in mobile Web information access, but few have explored their full potential in mobile scenarios, where information objects refer to their physical counterparts, and retrieval is context-aware and personalized for users. We present SMARTMUSEUM, a mobile ubiquitous recommender system for the Web of Data, and its application to information needs of tourists in context-aware on-site access to cultural heritage. The SMARTMUSEUM system utilizes Semantic Web languages as the form of data representation. Ontologies are used to bridge the semantic gap between heterogeneous content descriptions, sensor inputs, and user profiles. The system makes use of an information retrieval framework wherein context data and search result clustering are used in recommendation of suitable content for mobile users. Results from laboratory experiments demonstrate that ontology-based reasoning, query expansion, search result clustering, and context knowledge lead to significant improvement in recommendation performance. The results from field trials show that the usability of the system meets users’ expectations in real-world use. The results indicate that semantic content representation and retrieval can significantly improve the performance of mobile recommender systems in knowledge-rich domains.

Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session.
 Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model~(TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.

We present and evaluate methods for diversifying search results to improve personalized web search. A common personalization approach involves reranking the top N search results such that documents likely to be preferred by the user are presented higher. The usefulness of reranking is limited in part by the number and diversity of results considered. We propose three methods to increase the diversity of the top results and evaluate the effectiveness of these methods.

Users of World Wide Web utilize search engines for information retrieval in web as search engines play a vital role in finding information on the web. However, the performance of a web search is greatly affected by flooding of search results with information that is redundant in nature i.e., existence of nearduplicates. Such near-duplicates holdup the other promising results to the users. Many of these near-duplicates are from distrusted websites and/or authors who host information on web. Such nearduplicates may be eliminated by means of Provenance. Thus, this paper proposes a novel approach to identify such near-duplicates based on provenance. In this approach a provenance model has been built using web pages which are the search results returned by existing search engine. The proposed model combines both content based and trust based factors for classifying the results as original or near-duplicates. Keywords— Web search, Near-duplicates, Provenance, Semantics,

Web Search Engines employ multiple so-called crawlers to maintain local copies of web pages. But these web pages are frequently updated by their owners, and therefore the crawlers must regularly revisit the web pages to maintain the freshness of their local copies. In this paper, we propose a two-part scheme to optimize this crawling process. One goal might be the minimization of the average level of staleness over all web pages, and the scheme we propose can solve this problem. Alternatively, the same basic scheme could be used to minimize a possibly more important search engine embarrassment level metric: The frequency with which a client makes a search engine query and then clicks on a returned url only to find that the result is incorrect. The first part our scheme determines the (nearly) optimal crawling frequencies, as well as the theoretically optimal times to crawl each web page. It does so within an extremely general stochastic framework, one which supports a wide range of complex update patterns found in practice. It uses techniques from probability theory and the theory of resource allocation problems which are highly computationally efficient -- crucial for practicality because the size of the problem in the web environment is immense. The second part employs these crawling frequencies and ideal crawl times as input, and creates an optimal achievable schedule for the crawlers. Our solution, based on network flow theory, is exact as well as highly efficient. An analysis of the update patterns from a highly accessed and highly dynamic web site is used to gain some insights into the properties of page updates in practice. Then, based on this analysis, we perform a set of detailed simulation experiments to demonstrate the quality and speed of our approach.

We have developed an efficient way to determine the syntactic similarity of files and have applied it to every document on the World Wide Web. Using this mechanism, we built a clustering of all the documents that are syntactically similar. Possible applications include a “Lost and Found” service, filtering the results of Web searches, updating widely distributed web-pages, and identifying violations of intellectual property rights.

Information retrieval (IR) approaches have proven useful in recovering traceability links between free-text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trust race, inspired by Web trust models to improve the precision and recall of traceability links: Trust race uses intractability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, His trace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with His trace to build Trust race in which we use Histraceas one expert adding knowledge to the traceability links extracttedfrom CVS/SVN  change logs. We apply Trustrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that Trustrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.

This paper explores the effectiveness of different semantic web page segmentation algorithms on modern websites. We compare three known algorithms each serving as an example of a particular approach to the problem, and one self-developed algorithm, WebTerrain, that combines two of the approaches. With our testing framework we have compared the performance of four algorithms for a large benchmark we have constructed. We have examined each algorithm for a total of eight different configurations (varying datasets, evaluation metric and the type of the input HTML documents). We found that all algorithms performed better on random pages on average than on popular pages, and results are better when running the algorithms on the HTML obtained from the DOM rather than on the plain HTML. Overall there is much room for improvement as we find the best average F-score to be 0.49, indicating that for modern websites currently available algorithms are not yet of practical use.

The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used in the Rocchio algorithm, particularly the word weighting scheme and the similarity metric. It also suggests improvements which lead to a probabilistic variant of the Rocchio classi er. The Rocchio classi er, its probabilistic variant, and a naive Bayes classi er are compared on six text categorization tasks. The results show that the probabilistic algorithms are preferable to the heuristic Rocchio classi er not only because they are more well-founded, but also because they achieve better performance.

Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.


Question: The Smart Web Surfer: Probabilistic Integration of Link and Content Data in PageRank Algorithm.

This paper delves into the PageRank Citation Ranking, a pioneering technique designed to introduce a systematic and ordered approach to navigating the vast expanse of the World Wide Web. Recognizing that the significance of a web page is inherently subjective and influenced by the user's interests, knowledge, and perspective, the paper explores objective measures to evaluate the relative importance of web pages. It introduces PageRank, a method that quantitatively and automatically assesses page importance, mirroring the human interest and attention paid to them. Drawing parallels between PageRank and the behavior of an idealized random web surfer, the paper elucidates the efficiency in computing PageRank for a large scale of web pages. Additionally, it outlines the practical applications of PageRank in enhancing search functionality and facilitating user navigation across the web, thereby illustrating its potential to revolutionize web browsing experiences.

Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worst-case performance even if users' interests change.

The problem of recommending items from some fixed database has been studied extensively, and two main paradigms have emerged. In content-based recommendation one tries to recommend items similar to those a given user has liked in the past, whereas in collaborative recommendation one identifies users whose tastes are similar to those of the given user and recommends items they have liked. Our approach in Fab has been to combine these two methods. Here, we explain how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither. In addition to what one might call the “generic advantages” inherent in any hybrid system, the particular design of the Fab architecture brings two additional benefits. First, two scaling problems common to all Web services are addressed—an increasing number of users and an increasing number of documents. Second, the system automatically identifies emergent communities of interest in the user population, enabling enhanced group awareness and communications. Here we describe the two approaches for contentbased and collaborative recommendation, explain how a hybrid system can be created, and then describe Fab, an implementation of such a system. For more details on both the implemented architecture and the experimental design the reader is referred to [1]. The content-based approach to recommendation has its roots in the information retrieval (IR) community, and employs many of the same techniques. Text documents are recommended based on a comparison between their content and a user profile. Data

Mobile devices enable people to look for information at the moment when their information needs are triggered. While experiencing complex information needs that require multiple search sessions, users may utilize desktop computers to fulfill information needs started on mobile devices. Under the context of mobile-to-desktop web search, this article analyzes users’ behavioral patterns and compares them to the patterns in desktop-to-desktop web search. Then, we examine several approaches of using Mobile Touch Interactions (MTIs) to infer relevant content so that such content can be used for supporting subsequent search queries on desktop computers. The experimental data used in this article was collected through a user study involving 24 participants and six properly designed cross-device web search tasks. Our experimental results show that (1) users’ mobile-to-desktop search behaviors do significantly differ from desktop-to-desktop search behaviors in terms of information exploration, sense-making and repeated behaviors. (2) MTIs can be employed to predict the relevance of click-through documents, but applying document-level relevant content based on the predicted relevance does not improve search performance. (3) MTIs can also be used to identify the relevant text chunks at a fine-grained subdocument level. Such relevant information can achieve better search performance than the document-level relevant content. In addition, such subdocument relevant information can be combined with document-level relevance to further improve the search performance. However, the effectiveness of these methods relies on the sufficiency of click-through documents. (4) MTIs can also be obtained from the Search Engine Results Pages (SERPs). The subdocument feedbacks inferred from this set of MTIs even outperform the MTI-based subdocument feedback from the click-through documents.

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. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion 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 adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models.

Users tend to articulate their complex information needs in only a few keywords, making underspecified statements of request the main bottleneck for retrieval effectiveness. Taking advantage of feedback information is one of the best ways to enrich the query representation, but can also lead to loss of query focus and harm performance in particular when the initial query retrieves only little relevant information when overfitting to accidental features of the particular observed feedback documents. Inspired by the early work of Luhn [23], we propose significant words language models of feedback documents that capture all, and only, the significant shared terms from feedback documents. We adjust the weights of common terms that are already well explained by the document collection as well as the weight of rare terms that are only explained by specific feedback documents, which eventually results in having only the significant terms left in the feedback model.
 Our main contributions are the following. First, we present significant words language models as the effective models capturing the essential terms and their probabilities. Second, we apply the resulting models to the relevance feedback task, and see a better performance over the state-of-the-art methods. Third, we see that the estimation method is remarkably robust making the models in- sensitive to noisy non-relevant terms in feedback documents. Our general observation is that the significant words language models more accurately capture relevance by excluding general terms and feedback document specific terms.

Users of World Wide Web utilize search engines for information retrieval in web as search engines play a vital role in finding information on the web. However, the performance of a web search is greatly affected by flooding of search results with information that is redundant in nature i.e., existence of nearduplicates. Such near-duplicates holdup the other promising results to the users. Many of these near-duplicates are from distrusted websites and/or authors who host information on web. Such nearduplicates may be eliminated by means of Provenance. Thus, this paper proposes a novel approach to identify such near-duplicates based on provenance. In this approach a provenance model has been built using web pages which are the search results returned by existing search engine. The proposed model combines both content based and trust based factors for classifying the results as original or near-duplicates. Keywords— Web search, Near-duplicates, Provenance, Semantics,

We give an improved algorithm for computing personalized PageRank vectors with tight error bounds which can be as small as Ω(n−p) for any fixed positive integer p. The improved PageRank algorithm is crucial for computing a quantitative ranking of edges in a given graph. We will use the edge ranking to examine two interrelated problems – graph sparsification and graph partitioning. We can combine the graph sparsification and the partitioning algorithms using PageRank vectors to derive an improved partitioning algorithm.

In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially, Lucene is used to retrieve top fifty documents that are relevant to the query and then our proposed approach is applied on these retrieved documents to rerank them. Experimental results show that our approach did not perform well as the documents retrieved by Lucene are not among the top 50 documents in the Gold Standard.

CiteSeer is a digital library for scientific publications written by Computer Science researchers. Users are able to retrieve relevant documents from the database by searching by author name and/or keyword queries. Users may also receive recommendations of papers they might want to read provided by an existing conceptual recommender system. This system recommends documents based on an automaticallyconstructed user profile. Unlike traditional content-based recommender systems, the documents and the user profile are represented as concepts vectors rather than keyword vectors and papers are recommended based on conceptual matches rather than keyword matches between the profile and the documents. Although the current system provides recommendations that are on-topic, they are not necessarily high quality papers. In this work, we introduce the Conceptual Impact-Based Recommender (CIBR), a hybrid recommender system that extends the existing conceptual recommender system in CiteSeer by including an explicit quality factor as part of the recommendation criteria. To measure quality, our system considers the impact factor of each paper’s authors as measured by the authors’ h-index. Experiments to evaluate the effectiveness of our hybrid system show that the CIBR system recommends more relevant papers as compared to the conceptual recommender system.

In this paper, we present the findings of a qualitative analysis of 15,750 comments left by 2,041 participants in a Reconcile web credibility evaluation study. While assessing the credibility of the presented pages, respondents of the Reconcile studies were also asked to justify their ratings in writing. This work attempts to give an insight into the factors that affected the credibility assessment. To the best of our knowledge, the presented study is the most-recent large-scale study of its kind carried out since 2003, when the Fogg et al. śHow do users evaluate the credibility of Web sites? A study with over 2,500 participants’ paper was published. The performed analysis shows that the findings made a decade ago are still mostly valid today despite the passage of time and the advancement of Internet technologies. However we report a weaker impact of webpage appearance. A much bigger dataset (as compared to Fogg’s studies) allowed respondents to reveal additional features, which influenced the credibility evaluations.