Abstract: Systems and methods for recording “on-screen” content of a user device via a web browser-based coupling with a content recording system. The content to be recorded may include an audio component and a video component. The audio and video components are merged into a single stream that contains the content displayed via the user device. Segments of the recorded stream are periodically stored to a local storage associated with the web browser. Upon completion of the storage of a segment in the local storage, the segment is uploaded to the content recording system. As the recording session persists, multiple file segments are created in the local storage of the web browser and uploaded, on a segment-by-segment basis, to the server. The server of the content recording system concatenates the multiple uploaded file segments to generate a single file associated with the recorded session.

Abstract: A metric, such as a cosine similarity coefficient, is used to determine the degree of similarity between two responses. The assumption is that similar responses contain similar words with similar frequency counts (i.e., word lists). Word lists of the responses are used as a vector to assess the similarity between the word list for each student response and the word lists of responses having known scores. An iterative modeling process called semantic content similarity scoring, implemented by a content scoring module, augments differences among the various score-point responses by extending word lists to include, for example, synonyms of key words, key phrases (two or more words), key rubric scoring rule components, likelihood that a response would be given a specific score by a human rater. The word lists are also augmented by removing stop words.

Abstract: A method and system for optimizing the interaction and execution of multiple service tasks associated with a logical transaction. The multiple components or “legs” of the transaction consisting of tasks executable by a computing service or software as a service (SAAS) endpoint are identified. The system determines a strategy type or belief level associated with each of the service tasks included in a transaction. The belief level may be categorized as either “optimistic” or “pessimistic” based on one or more performance parameters (e.g., a probability of failure of a service and an expense associated with a failure of the service) derived from historical data associated with a particular transaction or service task. A sequence of execution for the multiple service tasks associated with the transaction is determined based at least in part on the belief level associated with each of the multiple service tasks.