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MCQ Dataset 6 different real multiple choice-based exams (735 answer sheets and 33,540 answer boxes) to evaluate computer vision techniques and systems developed for multiple choice test assessment systems. None 735 answer sheets and 33,540 answer boxes Images and .mat file labels Development of multiple choice test assessment systems 2017
Multiple choice questions lend themselves to the development of objective assessment items, but without author training, questions can be subjective in nature. Because this style of test does not require a teacher to interpret answers, test-takers are graded purely on their selections, creating a lower likelihood of teacher bias in the results. [8]
3.4 TB English text, 1.4 TB Chinese text, 1.1 TB Russian text, 595 MB German text, 431 MB French text, and data for 150+ languages (figures for version 23.01) JSON Lines [459] Natural Language Processing, Text Prediction 2021 [460] [461] Ortiz Suarez, Abadji, Sagot et al. OpenWebText An open-source recreation of the WebText corpus.
The examination for all the papers is carried out in an online Computer Based Test (CBT) mode where the candidates are shown the questions in a random sequence on a computer screen. The questions consist of some Multiple Choice Questions or MCQs (four answer options out of which only ONE is correct, which has to be chosen).
The model provides a core set of activities including systems engineering and program management tasks that are tailored to the technology development and management goals. This approach is comprehensive, yet it consolidates the complex activities that are relevant to the development and transition of a specific technology program into one ...
English adjectives, as with other word classes, cannot in general be identified as such by their form, [24] although many of them are formed from nouns or other words by the addition of a suffix, such as -al (habitual), -ful (blissful), -ic (atomic), -ish (impish, youngish), -ous (hazardous), etc.; or from other adjectives using a prefix ...
Course Hero was founded by Andrew Grauer at Cornell University in 2006 for college students to share lectures, class notes, exams and assignments. [4] In November 2014, the company raised $15 million in Series A Funding, with investors that included GSV Capital and IDG Capital. Seed investors SV Angel and Maveron also participated. [5]
Used to teach, explain and/or simply concepts. For example, organisation charts and decision trees. idea generation (conceptual & exploratory). [64] Used to discover, innovate and solve problems. For example, a whiteboard after a brainstorming session. visual discovery (data-driven & exploratory). [64] Used to spot trends and make sense of data.