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In computational complexity theory, a branch of computer science, bounded-error probabilistic polynomial time (BPP) is the class of decision problems solvable by a probabilistic Turing machine in polynomial time with an error probability bounded by 1/3 for all instances. BPP is one of the largest practical classes of problems, meaning most problems of interest in BPP have efficient probabilistic algorithms that can be run quickly on real modern machines. BPP also contains P, the class of problems solvable in polynomial time with a deterministic machine, since a deterministic machine is a special case of a probabilistic machine.
Learning theory describes how students receive, process, and retain knowledge during learning. Cognitive, emotional, and environmental influences, as well as prior experience, all play a part in how understanding, or a worldview, is acquired or changed and knowledge and skills retained.
The Graduate Management Admission Test is a computer adaptive test (CAT) intended to assess certain analytical, quantitative, verbal, and data literacy skills for use in admission to a graduate management program, such as a Master of Business Administration (MBA) program. Answering the test questions requires reading comprehension, and mathematical skills such as arithmetic, and algebra. The Graduate Management Admission Council (GMAC) owns and operates the test, and states that the GMAT assesses critical thinking and problem-solving abilities while also addressing data analysis skills that it believes to be vital to real-world business and management success. It can be taken up to five times a year but no more than eight times total. Attempts must be at least 16 days apart.
Problem-based learning (PBL) is a teaching method in which students learn about a subject through the experience of solving an open-ended problem found in trigger material. The PBL process does not focus on problem solving with a defined solution, but it allows for the development of other desirable skills and attributes. This includes knowledge acquisition, enhanced group collaboration and communication.
A screen reader is a form of assistive technology (AT) that renders text and image content as speech or braille output. Screen readers are essential to people who are blind, and are useful to people who are visually impaired, illiterate, or have a learning disability. Screen readers are software applications that attempt to convey what people with normal eyesight see on a display to their users via non-visual means, like text-to-speech, sound icons, or a braille device. They do this by applying a wide variety of techniques that include, for example, interacting with dedicated accessibility APIs, using various operating system features, and employing hooking techniques.
Active learning is "a method of learning in which students are actively or experientially involved in the learning process and where there are different levels of active learning, depending on student involvement." Bonwell & Eison (1991) states that "students participate [in active learning] when they are doing something besides passively listening." According to Hanson and Moser (2003) using active teaching techniques in the classroom can create better academic outcomes for students. Scheyvens, Griffin, Jocoy, Liu, & Bradford (2008) further noted that "by utilizing learning strategies that can include small-group work, role-play and simulations, data collection and analysis, active learning is purported to increase student interest and motivation and to build students ‘critical thinking, problem-solving and social skills". In a report from the Association for the Study of Higher Education, authors discuss a variety of methodologies for promoting active learning. They cite literature that indicates students must do more than just listen in order to learn. They must read, write, discuss, and be engaged in solving problems. This process relates to the three learning domains referred to as knowledge, skills and attitudes (KSA). This taxonomy of learning behaviors can be thought of as "the goals of the learning process." In particular, students must engage in such higher-order thinking tasks as analysis, synthesis, and evaluation.
The interference theory is a theory regarding human memory. Interference occurs in learning. The notion is that memories encoded in long-term memory (LTM) are forgotten and cannot be retrieved into short-term memory (STM) because either memory could interfere with the other. There is an immense number of encoded memories within the storage of LTM. The challenge for memory retrieval is recalling the specific memory and working in the temporary workspace provided in STM. Retaining information regarding the relevant time of encoding memories into LTM influences interference strength. There are two types of interference effects: proactive and retroactive interference.
In accounting, fair value is a rational and unbiased estimate of the potential market price of a good, service, or asset. The derivation takes into account such objective factors as the costs associated with production or replacement, market conditions and matters of supply and demand. Subjective factors may also be considered such as the risk characteristics, the cost of and return on capital, and individually perceived utility.
A complex question, trick question, multiple question, fallacy of presupposition, or plurium interrogationum is a question that has a complex presupposition. The presupposition is a proposition that is presumed to be acceptable to the respondent when the question is asked. The respondent becomes committed to this proposition when they give any direct answer. When a presupposition includes an admission of wrongdoing, it is called a "loaded question" and is a form of entrapment in legal trials or debates. The presupposition is called "complex" if it is a conjunctive proposition, a disjunctive proposition, or a conditional proposition. It could also be another type of proposition that contains some logical connective in a way that makes it have several parts that are component propositions.
A mental block is an uncontrollable suppression or repression of painful or unwanted thoughts/memories. It can also be an inability to continue or complete a train of thought, as in the case of writer's block. In the case of writer's block, many find it helpful to take a break and revisit their topic. Another tactic that is used when people with mental blocks are learning new information is repetition. A similar phenomenon occurs when one cannot solve a problem in mathematics which one would normally consider as simple. Mental blocks can be caused by physical disabilities or simply a lack of focus. Mental blocks are also often used to describe a temporary inability to recall a name or other information. A sudden cessation of speech or a thought process without an immediate observable cause sometimes can be considered a consequence of repression.
In probability theory and machine learning, the multi-armed bandit problem is a problem in which a decision maker iteratively selects one of multiple fixed choices when the properties of each choice are only partially known at the time of allocation, and may become better understood as time passes. A fundamental aspect of bandit problems is that choosing an arm does not affect the properties of the arm or other arms.
Branch and cut is a method of combinatorial optimization for solving integer linear programs (ILPs), that is, linear programming (LP) problems where some or all the unknowns are restricted to integer values. Branch and cut involves running a branch and bound algorithm and using cutting planes to tighten the linear programming relaxations. Note that if cuts are only used to tighten the initial LP relaxation, the algorithm is called cut and branch.
Proactivity or proactive behavior refers to self-initiated behavior that endeavours to solve a problem before it has occurred. Proactive behavior involves acting in advance of a future situation, rather than reacting. It refers to taking control of a situation and making early changes, rather than adjusting to a situation or waiting for something to happen.
The eureka effect refers to the common human experience of suddenly understanding a previously incomprehensible problem or concept. Some research describes the Aha! effect as a memory advantage, but conflicting results exist as to where exactly it occurs in the brain, and it is difficult to predict under what circumstances one can predict an Aha! moment.
Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences. The technology encompasses aspects derived from various fields of study including computer science, AI, psychometrics, education, psychology, and brain science.
In the field of artificial intelligence (AI) design, AI capability control proposals, also referred to as AI confinement, aim to increase our ability to monitor and control the behavior of AI systems, including proposed artificial general intelligences (AGIs), in order to reduce the danger they might pose if misaligned. However, capability control becomes less effective as agents become more intelligent and their ability to exploit flaws in human control systems increases, potentially resulting in an existential risk from AGI. Therefore, the Oxford philosopher Nick Bostrom and others recommend capability control methods only as a supplement to alignment methods.
Vowpal Wabbit (VW) is an open-source fast online interactive machine learning system library and program developed originally at Yahoo! Research, and currently at Microsoft Research. It was started and is led by John Langford. Vowpal Wabbit's interactive learning support is particularly notable including Contextual Bandits, Active Learning, and forms of guided Reinforcement Learning. Vowpal Wabbit provides an efficient scalable out-of-core implementation with support for a number of machine learning reductions, importance weighting, and a selection of different loss functions and optimization algorithms.
In the fields of Information Technology (IT) and Systems Management, IT operations analytics (ITOA) is an approach or method to retrieve, analyze, and report data for IT operations. ITOA may apply big data analytics to large datasets to produce business insights. In 2014, Gartner predicted its use might increase revenue or reduce costs. By 2017, it predicted that 15% of enterprises will use IT operations analytics technologies.
In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classification, a bag may be labeled negative if all the instances in it are negative. On the other hand, a bag is labeled positive if there is at least one instance in it which is positive. From a collection of labeled bags, the learner tries to either (i) induce a concept that will label individual instances correctly or (ii) learn how to label bags without inducing the concept.
Anchored Instruction is a technology centered learning approach, which falls under the social constructionism paradigm. It is a form of situated learning that emphasizes problem-solving within an integrated learning context, which can be examined from multiple perspectives. "In other words, the learning is contextualized to provide students with realistic roles that serve to enhance the learning process",. During teaching, activities are designed or tied around an "anchor", such as an adventure or story, with a problem at the end, that needs to be resolved. The connection made between the content and the authentic context is referred to as "anchoring". These models typically embed all the information needed for the problem to be solved, such data and hints. Anchored instruction is akin to problem-based learning (P.B.L.) with the exception of its open-endedness.
References
- 1 2 Donmez, P., Carbonell, J.G.: Proactive Learning: Cost-Sensitive Active Learning with Multiple Imperfect Oracles, in Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM '08), Napa Valley 2008. https://www.cs.cmu.edu/~pinard/Papers/cikm0613-donmez.pdf