Personalized Education

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Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology

Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology

The great advances in Web technologies are promoting the development of new pedagogic models that complement the present education [11]. The new technologies improve the teaching-learning processes, aiding the information broadcasting in an efficient and easy manner, and providing tools for the personal and global communications that allow encouraging the collaborative learning [3, 19]. In this academic scope, personalized education [16] can be very helpful aiding students to reinforce the areas where it is necessary some help as well as maximizing those where they have potential. Education must also have the ability to adapt itself to the necessities of the student dynamically.
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Searching Similar Books Based on Student’s Preference for Personalized Education

Searching Similar Books Based on Student’s Preference for Personalized Education

Abstract: Personalized education aims to give students a personalized learning schedule according to students’ backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student’s preference for personalized education. We build the student-book network based on the students’ ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student’s personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students’ query intentions.
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A Preliminary Study Strategy on Development of Personalized Education Tool Using Joint Requirement Planning Technique

A Preliminary Study Strategy on Development of Personalized Education Tool Using Joint Requirement Planning Technique

The conducted research was supposed to discover user requirements that will be used for building a personalized education tool (PET). PET is a mobile-based application which serves a personal learning environment (PLE) to its users, thus they are able to learn without regard to place and time. According to (Luksha & Peskov, 2014) the development of PET will expand students’ opportunities in organizing their educational purposes. Therefore, PET system must have mechanisms to assist its user’s achievements (Dagger, Wade, & Conlan, 2004).
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News. Personalized Education. Personalized. MyUniversity.com? By Cass R. Sunstein. 32 EDUCAUSE r e v i e w September/October 2002

News. Personalized Education. Personalized. MyUniversity.com? By Cass R. Sunstein. 32 EDUCAUSE r e v i e w September/October 2002

ing can produce narrowness, not breadth. The wider range of choices is likely, in many cases, to mean that people will try to find material that makes them feel comfortable or that is created by and for people like themselves. This is what the “Daily Me” and MyUniversity.com are all about. If diverse groups are seeing and hearing their own points of view or fo- cusing on their own topics of interest, mutual understanding might be difficult, and it might be hard for people to solve problems that they face together as a soci- ety. If millions of people are listening mostly to Rush Limbaugh and Fox News, problems will arise if millions of other people are listening mostly to people and stations with an altogether different point of view. And a key point is that entirely personalized education would present the same risks.
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Research on Active Service Model with Personalized Education Resources

Research on Active Service Model with Personalized Education Resources

the transformation of the economic growth model have also raised demands for education reform. In the era of industrial civilization, it is necessary to inspect a large number of skilled players. Workers pipelining, gave birth to the "class teaching system" and "class industrialization" which adapts the training mode to teach Knowledge, ability and self-development. From the perspective of educational fairness, we have implemented a model of teaching students who are educated and educated. Entering a talented person and diversifying development is the highest level of education equity. The theory of personalized education provides a theoretical basis and guiding ideology for the active promotion of educational resources. The essence of educational resources and active services the qualitative meaning is to provide personalized educational resources and learning based on students' learning style, cognitive foundation, learning situation, and rationality. Requirements include learning objectives, learning content, and learning. Educational resources such as partner, learning path, learning style, learning evaluation, cognitive tools and educational services are all required to learn the needs of the learners are compelling, allowing learners to receive educational resources and learning services that are timely, pleasing, and desirable. Therefore, personality.
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Future for Educati A PERSONALIZED FUTURE EDUCATION. Moving into the 21st Century and Beyond AND STUDENTS

Future for Educati A PERSONALIZED FUTURE EDUCATION. Moving into the 21st Century and Beyond AND STUDENTS

progress. Carpe Diem focuses on measuring and advancing each student’s level of content mastery, rather than course completion and the time they spend sitting in front of a teacher. Carpe Diem’s online school serves grades 7 through 12 and allows students to complete coursework when and where they choose while receiving online academic support from teachers, if needed. Every student enrolled in Carpe Diem’s online school is provided with a personalized education plan designed to meet their specific needs. The school, which is accredited with North Central Association Commission on Accreditation and School Improvement (NCA CASI), has year-round start dates with early graduation options and college credit opportunities for high school students.
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Personalized Pricing as Monopolization

Personalized Pricing as Monopolization

For example, consider the following competitive market, initially without personalized pricing. Suppose that in this market a consumer places a $6 value on a box of a particular brand of cookie, and that the current price is $2. Suppose, further, that the consumer places a $5 value on a competing brand of cookie, which also happens to retail at a price of $2. The maker of the second box of cookies might well know that the consumer will buy the first box, because the surplus the consumer derives from purchasing the first box ($6 less the price of $2) exceeds the surplus the consumer derives from purchasing the second box ($5 less the price of $2), but if the maker of the second box must charge uniform prices, at least to certain groups of consumers, the maker of the second box may be unable to compete for the consumer’s business. Lowering the price of the box to $0.50, which would induce the consumer to buy from the second maker (because now the consumer’s surplus of $4.50 would exceed the $4 of surplus associated with purchase of cookies from the first maker), might force the second maker to reduce output, because although the cost to the second maker of producing a box for this consumer might only be $0.25, the cost of producing boxes of cookies for others might be $1.50, and a new uniform price of $0.50 would make production of those units unprofitable. Overall, the decline in output might reduce profits, even if it permitted sale of one new box of cookies to this particular consumer.
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PERSONALIZED LEARNING IN PROGRESS:

PERSONALIZED LEARNING IN PROGRESS:

New Haven has also initiated additional Race to the Top–District funded projects intended to help students achieve improved academic outcomes. A portion of New Haven’s Race to the Top–Dis- trict funds is being used to create Union City Kids’ Zone—a community partnership focused on providing services to low-income families living in a neighborhood where students have poor academic outcomes. This work is building on ef- forts that New Haven began 10 years ago to work more collaboratively with community agencies that serve these families. Through the Kids’ Zone, students and their families will have access to mental health services, financial coaching, GED preparation classes, and parenting education. Additionally, New Haven has used Race to the Top–District funds to provide more college and career ready experiences. For example, all 10th graders took the PSAT and the district expanded an engineering preparation program to middle school students. These students will have prior- ity to continue the program when they move to high school.
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Personalized Web Search

Personalized Web Search

There are various personalized systems. Based on recommendation technology, they can be assigned as systems based on information filtering and collaborative filtering and rules. Systems based on rules are simple and direct. But it's hard to keep the rules appropriate and it will be harder to manage the system as the rules increase. System based on content filtering can reflect users' individual information well.

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Personalized physiological medicine

Personalized physiological medicine

exists for the quantitative assessment of functional residual capacity (FRC), inhomogeneity of ventilation, and ventilation-perfusion matching. Indeed the import- ance of such measurements have been demonstrated in experimental models of acute lung injury (ALI) where the effects of respiratory movements could be directly observed in exposed mice lungs using dark-field intravi- tal microscopy [29]. Measurement of these parameters has classically required the quantitative measurement of the washout of inert indicator gases requiring the use of complex mass spectrometry [30] at the bedside [31]. More practical measurement of these parameters at the bedside is currently under investigation (e.g., [32]). In addition to these volumetric measures, more compre- hensive physical properties of the lung tissue itself are required beyond conventional dynamic compliance and airway resistance measures. Such information can be obtained, for example, by the forced oscillation technique in which the frequency-dependent impedance of the complete pulmonary system can be obtained, providing detailed information about the mechanical properties of the lung (e.g., [33]). It is clear from these considerations that there is a need to further develop techniques to measure these pulmonary parameters and to integrate them into a single monitoring platform to meet the requirements of this pillar of personalized physiological medicine.
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Learnersourcing Personalized Hints

Learnersourcing Personalized Hints

One-on-one human tutoring is a costly gold standard in edu- cation. Mastery-based instruction with corrective feedback can offer a substantial improvement in learning outcomes over conventional classroom teaching [1]. However, person- alized support does not scale well with the number of stu- dents enrolled. In large classes, it is often not feasible for stu- dents to get personalized hints from a teacher in a timely man- ner. Massive open online courses (MOOCs) have teacher-to- student ratios that are smaller than large residential classes by orders of magnitude. Intelligent tutoring systems have strived to simulate the type of personalized support received in one- on-one tutoring, but they are expensive and time-consuming to build.
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Genomics in Personalized Medicine

Genomics in Personalized Medicine

Results: One of the most important areas that involve the application of genomics is pharmacogenomics, which is the leading technology for personalized medicine. Genomics plays significant roles in personalized medicine by making genomic data and information available, which eventually guides decision-making to choose the most appropriate and most effective treatments, thereby reducing drug adverse reactions. Genomics also plays a vital role in personalized medicine by reducing risk associated with multiple medications (polypharmacy).
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Personalized Online Education A Crowdsourcing Challenge

Personalized Online Education A Crowdsourcing Challenge

Some of these problems have already been partially ad- dressed — for example, by the development of Web-based, open authoring tools (Aleahmad, Aleven, and Kraut 2009), but extending these systems to support thousands (or more) of contributors of varying competence will require consid- erable work. Discussion forums are an obvious first target. Stack Overflow, reddit and similar sites have developed rat- ing systems that draw attention to good questions and an- swers. But we suspect that all these systems will bog down with a morass of disjoint questions and answers over time. How can similar questions get distilled and summarized? How can source material be revised in order to increase clar- ity and obviate the need for individual questions. Perhaps mechanisms such as those introduced by the political dis- course system Considerit (Kriplean et al. 2012), could be adapted to the education context. Another possibility might be to develop a recommender system, like Wikipedia’s Sug- gestBot (Cosley et al. 2007), that could route editing tasks to appropriate volunteers. This approach might work well for transcribing lectures and videos into different languages, adding links to research papers, or rewriting text to make terminology consistent or language easier to understand.
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GLOSSARY OF PERSONALIZED MARKETING

GLOSSARY OF PERSONALIZED MARKETING

Second-party data can be very effective for creating personalized communications for new customer acquisitions. • Third-Party: Information acquired from an outside party. Third-party data can come from one outside source or be aggregated from other sources. It is then used to categorize consumers into different groups, targets and audiences. The collection of third-party data is generally out of your control. It provides a greater breadth and depth of information that you currently possess on customers and prospects. Data degrades over time. For example, about 45 million people change their phone numbers each year; another 16 million people change residences. Your third-party data provider should have strong systems in place to ensure not just accuracy, but real-time updates to data.
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Personalized Health Knowledge Graph

Personalized Health Knowledge Graph

Personalized Healthcare Knowledge Graph (PHKG) 8 is a represen- tation of all relevant medical knowledge and personal data for a patient. PHKG can support development of innovative applications such as digitalized personal- ized coach applications that can keep patients informed and help manage their chronic condition, and empower the physicians to make effective decisions on health-related issues or receive timely alerts as needed through continuous mon- itoring. Typically, PHKG formalizes medical information in terms of relevant relationships between entities. For instance, a knowledge graph (KG) for asthma can describe causes, symptoms and treatments for asthma, and PHKG can be the subgraph containing just those causes, symptoms, and treatments that are applicable to a given patient.
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Personalized and precision orthodontic therapy

Personalized and precision orthodontic therapy

The Consortium for Orthodontic Advances in Science and Technology (COAST) is a collabora- tive interinstitutional working group whose long- term objective is to foster high-caliber, cutting- edge interactions between clinicians, educators, and researchers that will lead to novel develop- ments pertinent to orthodontics. COAST previ- ously held five symposia between 2002 and 2012 on a range of topics, some which have been summarized in previous supplements of Ortho- dontics and Craniofacial Research (1–4). These previous symposia provided the foundation for focusing the next several gatherings on Personal- ized and Precision Orthodontics. Thus, the 2014 initiative, the 6th Biennial COAST Conference, is the first in a series of highly interactive work- shops on the topic of ‘Personalized and Precision Orthodontic Therapy’ and was held in Itasca, Illi- nois, September 11–14, 2014. A follow-up work- shop to build on the 2014 outcomes will be held in 2016. These workshops address the current challenges of how to harness the burgeoning and exciting information and technological developments to provide the best available indi- vidualized orthodontic care to our patients.
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A Metaphor for Personalized Television Programming

A Metaphor for Personalized Television Programming

oriented approach where the human interacts with an application to accomplish a particular goal. The emergence of media-rich computer-mediated leisure ap- plications requires a fresh view of the current paradigms and a careful examina- tion of how this change of perspective affects their relevance. This paper pro- poses a metaphor for accessing personalized television programming and sug- gests an approach for integrating the metaphor into the design of a television user interface. The proposed metaphor is tested in the design of a personalized advertising service. The results of the empirical research are discussed and the suitability of the metaphor for other television programs is examined.
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Personalized Pricing and Quality Differentiation

Personalized Pricing and Quality Differentiation

The market for computer servers, storage devices and workstations in the Asia-Pacific region combines PP and quality differentiation. Major players such as IBM, Hewlett Packard, and Sun Microsystems use personalized discounting for different customers based on ROI, even at the same quality levels. There is also a trend towards increasing the degree of service quality and value-added software differentiation in the industry. For instance, in the UNIX platform, HP and IBM cater to the high-end market, while Sun serves the low-end market. 4 Other examples of value-based personalized pricing are found in the healthcare (Smith and Nagle, 2002) and chemicals industries. Online retailers with their ability to collect data are well-positioned to take advantage of dy- namic pricing. In a well-known example, Amazon offered different prices to different consumers on its popular DVD titles. 5 Although Amazon’s experiment was short-lived due to a consumer backlash, it has since found innovative ways of implementing PP without annoying consumers, through the use of the “Gold Box”. Each consumer is provided access to a prominently displayed Gold Box with their name (e.g. John Doe’s Gold Box) on webpages at Amazon. Opening the Gold Box provides access to a limited number of products with special discounts that are not available outside the Gold Box. The items offered in the Gold Box are different for different consumers. This allows Amazon to charge personalized prices. This is an example of the continuing evolution of PP and an indication of the likely use of such pricing by online retailers. Chen and Iyer (2002) mention several other examples of customized pricing. 6 Wertenbroch and Skiera (2002) provide an empirical study that compares several approaches for determining consumer willingness to pay.
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WELLNESS PERSONALIZED LOCAL QUALITY

WELLNESS PERSONALIZED LOCAL QUALITY

Developing future providers of care who understand the needs of our patients is an important part of Community Hospital Long Beach’s commitment to delivering value and helping the community connect to better health. We joined our sister hospitals, Long Beach Memorial and Miller Children’s & Women’s Hospital Long Beach, for an exciting expansion of a new residency program in the Center for Medical Education, launching a psychiatric residency program for the first time in our history. This new initiative leverages our long-standing expertise in providing comprehensive mental health care for adults through our MemorialCare Center for Mental Health & Wellness. The education program also is planning expansions in urology, pediatric surgery, sports and family medicine, and internal medicine.
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Use of microRNAs in personalized medicine

Use of microRNAs in personalized medicine

Personalized medicine comprises the genetic information together with the phenotypic and environmental factors to yield healthcare tailored to an individual and removes the limitations of the “one-size-fi ts-all” therapy approach. This provides the opportunity to translate therapies from bench to clinic, to diagnose and predict disease, and to improve patient-tailored treatments based on the unique signatures of a patient’s disease and further to identify novel treatment schedules.

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