Content-based instruction

Content-based instruction (CBI) is a significant approach in language education (Brinton, Snow, & Wesche, 1989), designed to provide second-language learners instruction in content and language (hence it is also called content-based language teaching; CBLT). CBI is considered an empowering approach which encourages learners to learn a language by using it as a real means of communication from the very first day in class. The idea is to make them become independent learners so they can continue the learning process even outside the class.

Content-based image retrieval - Content comparison using image distance measures

The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others. For example, a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images. Search results then can be sorted based on their distance to the queried image. Many measures of image distance (Similarity Models) have been developed.

Content-based image retrieval - History

The term "content-based image retrieval" seems to have originated in 1992 when it was used by Japanese Electrotechnical Laboratory engineer Toshikazu Kato to describe experiments into automatic retrieval of images from a database, based on the colors and shapes present. Since then, the term has been used to describe the process of retrieving desired images from a large collection on the basis of syntactical image features. The techniques, tools, and algorithms that are used originate from fields such as statistics, pattern recognition, signal processing, and computer vision.

Content-based image retrieval

"Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness.

Content-based image retrieval

Content-based image retrieval, also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey for a recent scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing).

Content-based instruction - Motivating students

Keeping students motivated and interested are two important factors underlying content-based instruction. Motivation and interest are crucial in supporting student success with challenging, informative activities that support success and which help the student learn complex skills (Grabe & Stoller, 1997). When students are motivated and interested in the material they are learning, they make greater connections between topics, elaborations with learning material and can recall information better (Alexander, Kulikowich, & Jetton, 1994: Krapp, Hidi, & Renninger, 1992). In short, when a student is intrinsically motivated the student achieves more. This in turn leads to a perception of success, of gaining positive attributes which will continue a circular learning pattern of success and interest. Krapp, Hidi and Renninger (1992) state that, "situational interest, triggered by environmental factors, may evoke or contribute to the development of long-lasting individual interests" (p. 18). Because CBI is student centered, one of its goals is to keep students interested and motivation high by generating stimulating content instruction and materials.

Content-based image retrieval - History

Content-based video browsing was introduced by Iranian engineer Farshid Arman, Taiwanese computer scientist Arding Hsu, and computer scientist Ming-Yee Chiu, while working at Siemens, and it was presented at the ACM International Conference in August 1993. They described a shot detection algorithm for compressed video that was originally encoded with discrete cosine transform (DCT) video coding standards such as JPEG, MPEG and H.26x. The basic idea was that, since the DCT coefficients are mathematically related to the spatial domain and represent the content of each frame, they can be used to detect the differences between video frames. In the algorithm, a subset of blocks in a frame and a subset of DCT coefficients for each block are used as motion vector representation for the frame. By operating on compressed DCT representations, the algorithm significantly reduces the computational requirements for decompression and enables effective video browsing. The algorithm represents separate shots of a video sequence by an r-frame, a thumbnail of the shot framed by a motion tracking region. A variation of this concept was later adopted for QBIC video content mosaics, where each r-frame is a salient still from the shot it represents.

Recommender system - Content-based filtering

A key issue with content-based filtering is whether the system is able to learn user preferences from users' actions regarding one content source and use them across other content types. When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended. For example, recommending news articles based on browsing of news is useful, but would be much more useful when music, videos, products, discussions etc. from different services can be recommended based on news browsing. To overcome this, most content-based recommender systems now use some form of hybrid system.

Freedom of speech in the United States - Content-based restrictions

The Court pointed out in Snyder v. Phelps (2011) that one way to ascertain whether a restriction is content-based versus content-neutral is to consider if the speaker had delivered a different message under exactly the same circumstances: "A group of parishioners standing at the very spot where Westboro stood, holding signs that said 'God Bless America' and 'God Loves You,' would not have been subjected to liability. It was what Westboro said that exposed it to tort damages."

Freedom of speech in the United States - Content-based restrictions

Content-based restrictions can either discriminate based on viewpoint or subject matter. An example of a law regulating the subject matter of speech would be a city ordinance that forbids all picketing in front of a school except for labor picketing. This law would amount to subject matter discrimination because it favors one subject over another in deciding who it will allow to speak. An example of a law that regulates a speaker's viewpoint would be a policy of a government official who permitted ‘‘pro-life’’ proponents to speak on government property but banned ‘‘pro-choice’’ proponents because of their views would be engaged in ‘‘viewpoint discrimination.’’ Restrictions that apply to certain viewpoints but not others face the highest level of scrutiny, and are usually overturned, unless they fall into one of the court's special exceptions. An example of this is found in the United States Supreme Court's decision in Legal Services Corp. v. Velazquez in 2001. In this case, the Court held that government subsidies cannot be used to discriminate against a specific instance of viewpoint advocacy.

Recommender system - Content-based filtering

Basically, these methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf–idf representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques. Simple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques such as Bayesian Classifiers, cluster analysis, decision trees, and artificial neural networks in order to estimate the probability that the user is going to like the item.

Matal v. Tam - Viewpoint-based and content-based discrimination

In the alternative, the Federal Circuit determined that the disparaging provision is content-based discriminatory, in addition to viewpoint-discriminatory. Additionally, the Federal Circuit came to the determination that even though trademarks inherently deal more with commercial speech versus expressive speech, that when the government cancels a mark under the disparaging provision, it is affecting more of the expressive aspects of speech and not the commerciality of it. Therefore, the Federal Circuit found that if the disparaging provision is not found to be viewpoint discriminatory (in light of a higher court potentially overturning parts of this case) that it must be content-based discriminatory and must survive strict scrutiny unless an exception applied.

Document classification - "Content-based" versus "request-based" classification

Content-based classification is classification in which the weight given to particular subjects in a document determines the class to which the document is assigned. It is, for example, a common rule for classification in libraries, that at least 20% of the content of a book should be about the class to which the book is assigned. In automatic classification it could be the number of times given words appears in a document.

Recommender system - Content-based filtering

Another common approach when designing recommender systems is content-based filtering. Content-based filtering methods are based on a description of the item and a profile of the user's preferences. These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on product features.

Freedom of speech in the United States - Content-based restrictions

Content-based restrictions "are presumptively unconstitutional regardless of the government’s benign motive, content-neutral justification, or lack of animus toward the ideas contained in the regulated speech." Restrictions that require examining the content of speech to be applied must pass strict scrutiny.

Google AdSense - Content

The content-based advertisements can be targeted for users with certain interest or contexts. The targeting can be CPC (cost per click) or CPM (cost per thousand impressions) based, the only significant difference in CPC and CPM is that with CPC targeting, earnings are based on clicks while CPM earnings recently are actually based not just per views/impression but on a larger scale, per thousand impression, therefore driving it from the market, which makes CPC ads more common.

Language for specific purposes - Relationship to content-based instruction

Content-based language instruction (CBI) is also sometimes confused with ESP. At the post-secondary level it is frequently used to motivate groups of learners who may be interested in the same professional field, providing meaningful communication opportunities. However, as in their regular studies they are usually not studying through a foreign/second language (except for sheltered courses), they do not need English as a tool in their immediate studies. "Content-based instruction (CBI) is the integration of selected content with language teaching aims". Thus, when trying to identify which approach being taken, the question is: "Is it English for Specific Purposes or English through specific content themes or content areas?"

File format - File content based format identification

Another but less popular way to identify the file format is to examine the file contents for distinguishable patterns among file types. The contents of a file are a sequence of bytes and a byte has 256 unique permutations (0–255). Thus, counting the occurrence of byte patterns that is often referred as byte frequency distribution gives distinguishable patterns to identify file types. There are many content-based file type identification schemes that use byte frequency distribution to build the representative models for file type and use any statistical and data mining techniques to identify file types

Project-based learning - Roles

Instructor role in Project Based Learning is that of a facilitator. They do not relinquish control of the classroom or student learning but rather develop an atmosphere of shared responsibility. The Instructor must structure the proposed question/issue so as to direct the student's learning toward content-based materials. The instructor must regulate student success with intermittent, transitional goals to ensure student projects remain focused and students have a deep understanding of the concepts being investigated. The students are held accountable to these goals through ongoing feedback and assessments. The ongoing assessment and feedback are essential to ensure the student stays within the scope of the driving question and the core standards the project is trying to unpack. According to Andrew Miller of the Buck Institute of Education, formative assessments are used "in order to be transparent to parents and students, you need to be able to track and monitor ongoing formative assessments, that show work toward that standard." The instructor uses these assessments to guide the inquiry process and ensure the students have learned the required content. Once the project is finished, the instructor evaluates the finished product and learning that it demonstrates

Technological pedagogical content knowledge - Criticism

Other authors have questioned the central construct, the TPCK, asking if it is actually a knowledge or rather an action. Philips, Koehler and Rosenberg (2016) provided an updated diagram which has the central overlap described as 'TPACK enactment'. Harris and Hofer's (2011) study group used the term 'Fit' to describe the conceptualisation and operationalisation of TPACK. These views of the central component, led other authors such as Byrne (2017) to describe the TPCK of TPACK as an action rather than a knowledge. Byrne altered Harris and Hoffer's description of TPCK from "How to teach specific content based material, using technologies that best embody and support it, in ways that are appropriately matched to students' needs and preferences" to "The actions we employ to teach specific content-based material, using technologies that best embody and support it, in ways that are appropriately matched to students' needs and preferences".