Pearson Education, Inc.

Quinn N Lathrop

Malgorzata Schmidt

Mateusz Otmianowski

Kacper Lodzikowski

Krzysztof Jedrzejewski

Tomasz Matysiak

Mikolaj Olszewski

David Strong

Johann A Larusson

Luis Manuel Oros

Timothy J Stewart

David King

Scott Hellman

Peter Foltz

Mark Mercury

Jarin Schmidt

Alex Nickel

Kristina Evans

Daniel Tonks

Jake Noble

Brett Van De Sande

Krzysztof Jȩdrzejewski

James D Corbin, Ii

Johann Larusson

David W Strong

Jake Nobel

Peter W Folz

Systems and methods of the present invention may be used to determine metrics and health scores for content that may correspond to an educational course or textbook, which may be in a digital format. The metrics and health scores may be determined at various hierarchical content levels, and may be used to quantitatively assess how well the corresponding content is performing based on responses submitted to assessment item parts of the content by responders. The metrics may include difficulty and discrimination metrics, which may be determined using maximum likelihood estimation methods based on a modified two parameter item response model. A content analytics interface corresponding to a given content element may be generated and displayed via a user device, and may include content health scores of subcontent within that content element. The subcontent may be ordered according to content health score.

Scoring system for digital assessment quality with harmonic averaging

Systems and methods of the present invention provide for estimating latent ability of responders to a digital assessment in the form of ability scores and estimating item parameters an assessment item of the digital assessment including difficulty scores and discrimination scores. Maximum likelihood estimation may be performed based on an item response theory model to estimate the item parameters. Supervisory, extraction, and worker modules of a workflow manager module may initiate general purpose graphics processing unit instances and cause these instances to perform the maximum likelihood estimation calculations. The item response theory model may be a two parameter model that is modified to account for changes in difficulty caused by the use of hints.

Systems and methods for item response modelling of digital assessments

Systems and methods of the present invention provide for one or more machine learning models configured to generate one or more student outcome predictions, such as whether a student will correctly respond to items of an assessment or will pass the assessment, based on monitored student activity and associated parameters. Inputs to the machine learning models may include outputs of other predictive models, such as an item response theory model and a short term prediction model. The machine learning models may output a student outcome prediction vector containing student outcome predictions for each uncompleted item of an assessment being delivered to a student in real-time. Remediation activities may be recommended to or automatically initiated for the student based on the student outcome predictions. A delivery order of the assessment may be modified based on the student outcome predictions.

Systems and methods for prediction of student outcomes and proactive intervention

Systems and methods of the present invention may be used to determine metrics and health scores for content that may correspond to an educational course or textbook, which may be in a digital format. The metrics and health scores may be determined at the assessment-item-part-level, assessment-item-level, section-level, chapter-level, and title-level, and may be used to quantitatively assess how well the corresponding content is performing based on responses submitted to assessment item parts of the content by one or more responders. The assessment-item-part-level metrics may include difficulty and discrimination values, scores, weights, and reliability values, which may be determined in whole or in part using maximum likelihood estimation methods based on a modified two parameter item response model.

Scoring system for digital assessment quality

Systems and methods for automated and direct network positioning are disclosed herein. The system can include memory that can include a content library database and a structure sub-database. The system can include at least one server. The at least one server can: receive a data packet previously unassociated with the hierarchy in the structure sub-database; identify one of the plurality of positions within the hierarchy for the data packet; receive user information; present a series of assessment data packets to the user; receive a response from the user subsequent to presentation of each of the assessment data packets; evaluate the received responses; adjust a location of the user within the hierarchy based on the evaluating of the received responses; and present a content data packet to the user.

Systems and methods for automated and direct network positioning

Systems and methods for virtual reality interaction evaluation are disclosed herein. The system can include a memory including: an interaction sub-database containing information relating to user interactions with at least one virtual asset in a virtual environment, and a content library database containing a plurality of virtual assets and information relating to those virtual assets. The system can include at least one server that can determine user engagement with at least one of the plurality of virtual assets, receive data indicative of an interaction with at least one of the plurality of virtual assets, and determine an interaction type of the interaction associated with the received data. The server can perform a speech capture and analysis process, perform a manipulation process, generate an evaluation of the user interactions with the at least one of the plurality of virtual assets, and deliver the generated evaluation.

Systems and methods for virtual reality-based grouping evaluation

Systems and methods for automated objective identification are disclosed herein. The system can include a user device and a memory. The memory can include a filed database including field data associated with previously identified objectives. The memory can include a scoring database containing a machine-learning scoring algorithm. The system can include at least one server that can receive an identifier of a content portion, extract field information identifying at least one attribute of the identified content portion from the content portion, input extracted field information into the machine-learning scoring algorithm; receive identification of objectives forming a set of objectives from the machine-learning scoring algorithm; sort the objectives forming the set of objectives according to scores generated for each of the objectives in the set of objectives; and output the sorted objectives to the user device.

System and methods for automatic machine-learning based objective recommendation

Techniques described herein relate to mapping of digital credential objects to various field data objects. For example, requests may be received by a digital credential platform server from digital credential template owner devices, issuer devices, and/or receiver devices. In response, the digital credential platform server may determine and transmit back mappings between the digital credentials and the selected field data objects. To generate mappings, digital credential objects may be tokenize and transformed into vectors within a multi-dimensional vector space. Individual field data objects stored within a high-performance text search engine also may be transformed into vectors within the same multi-dimensional vector space, and the distances between the vectors may be calculated to select a number of field data objects corresponding to the digital credential objects.

Digital credential field mapping

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Portland, OR, United States of America

Quinn N Lathrop