Abstract: Computer-implemented systems and methods for generating a personalization score for a constructed response are provided. A constructed response generated by a user is received. The constructed response is processed with a processing system to generate one or more feature values representative of one or more aspects of the constructed response. A personalization score for the constructed response is generated using the processing system by applying a computer scoring model to the one or more feature values. The personalization score provides a measure of a personalization of the constructed response.

Abstract: Computer-implemented systems and methods are provided for scoring content of a spoken response to a prompt. A scoring model is generated for a prompt, where generating the scoring model includes generating a transcript for each of a plurality of training responses to the prompt, dividing the plurality of training responses into clusters based on the transcripts of the training responses, selecting a subset of the training responses in each cluster for scoring, scoring the selected subset of training responses for each cluster, and generating content training vectors using the transcripts from the scored subset. A transcript is generated for a received spoken response to be scored, and a similarity metric is computed between the transcript of the spoken response to be scored and the content training vectors. A score is assigned to the spoken response based on the determined similarity metric.

Abstract: Systems and methods are described for providing a multi-modal evaluation of a presentation. A system includes a motion capture device configured to detect motion an examinee giving a presentation and an audio recording device configured to capture audio of the examinee giving the presentation. One or more data processors are configured to extract a non-verbal feature of the presentation based on data collected by the motion capture device and an audio feature of the presentation based on data collected by the audio recording device. The one or more data processors are further configured to generate a presentation score based on the non-verbal feature and the audio feature.

Abstract: Systems and methods are provided for implementing a parallel Expectation Minimization algorithm for generalized latent variable models. Item response data that is based on responses to items from multiple respondents is accessed. The item response data includes data for multiple response variables. The item response data is analyzed using a generalized latent variable model, and the analysis includes an application of a Parallel-E Parallel-M (PEPM) algorithm. In a parallel Expectation step of the PEPM algorithm, the respondents are subdivided into N groups of respondents, and computations for the N groups are performed in parallel using the N processor cores. In a parallel Maximization step of the PEPM algorithm, the response variables are subdivided into N groups of response variables, and computations for the N groups of response variables are performed in parallel using the N processor cores.

Abstract: Systems and methods are provided for determining whether an examination prompt elicits responses of a type for which the examination prompt is designed. Word types for each of a plurality of words in a plurality of responses to the examination prompt are identified. An evaluation distribution of the identified word types is determined. Predetermined distributions stored on a computer-readable medium associated with a plurality of different response types are accessed, and the evaluation distribution is compared to each of the predetermined distributions to determine which predetermined distribution the evaluation distribution is most similar. A determination is made as to whether the most similar distribution is associated with a same response type as the type for which the examination prompt is designed. An indication is made as to whether the examination prompt is deemed to elicit responses of the type for which the examination prompt is designed.

Abstract: Systems and methods are provided for automatically scoring a constructed response. The constructed response is processed to generate a plurality of numerical vectors that is representative of the constructed response. A model is applied to the plurality of numerical vectors. The model includes an input layer configured to receive the plurality of numerical vectors, the input layer being connected to a following layer of the model via a first plurality of connections. Each of the connections has a first weight. An intermediate layer of nodes is configured to receive inputs from an immediately-preceding layer of the model via a second plurality of connections, each of the connections having a second weight. An output layer is connected to the intermediate layer via a third plurality of connections, each of the connections having a third weight. The output layer is configured to generate a score for the constructed response.

Abstract: Systems and methods described herein utilize supervised machine learning to generate a model for scoring interview responses. The system may access a training response, which in one embodiment is an audiovisual recording of a person responding to an interview question. The training response may have an assigned human-determined score. The system may extract at least one delivery feature and at least one content feature from the audiovisual recording of the training response, and use the extracted features and the human-determined score to train a response scoring model for scoring interview responses. The response scoring model may be configured based on the training to automatically assign scores to audiovisual recordings of interview responses. The scores for interview responses may be used by interviewers to assess candidates.

Abstract: Systems and methods are provided for implementing an educational dialog system. An initial task model is accessed that identifies a plurality of dialog states associated with a task, a language model configured to identify a response meaning associated with a received response, and a language understanding model configured to select a next dialog state based on the identified response meaning. The task is provided to a plurality of persons for training. The task model is updated by revising the language model and the language understanding model based on responses received to prompts of the provided task, and the updated task is provided to a student for development of speaking capabilities.

Abstract: Systems and methods are provided providing a spoken dialog system. Output is provided from a spoken dialog system that determines audio responses to a person based on recognized speech content from the person during a conversation between the person and the spoken dialog system. Video data associated with the person interacting with the spoken dialog system is received. A video engagement metric is derived from the video data, where the video engagement metric indicates a level of the person's engagement with the spoken dialog system.

Abstract: Methods and systems for scoring written text based on use of idiomatic expressions, including reading pre-selected idiomatic expressions in a canonical form into memory, expanding idiomatic expressions from the canonical form, reading a written response into the memory, pre-processing the written response, searching the pre-processed written response for idiomatic expressions, and assigning a score to the written response. The score may be based at least in part on the number of idiomatic expressions in the written response. Corresponding apparatuses, systems, and methods are also disclosed.

Abstract: Provide automatic assessment of oral recitations during computer based language assessments using a trained neural network to automate the scoring and feedback processes without human transcription and scoring input by automatically generating a score of a language assessment. Providing an automatic speech recognition (“ASR”) scoring system. Training multiple scoring reference vectors associated with multiple possible scores of an assessment, and receiving an acoustic language assessment response to an assessment item. Based on the acoustic language assessment automatically generating a transcription, and generating an individual word vector from the transcription. Generating an input vector by concatenating an individual word vector with a transcription feature vector, and supplying an input vector as input to a neural network. Generating an output vector based on weights of a neural network; and generating a score by comparing an output vector with scoring vectors.

Abstract: Systems and methods are provided for a computer-implemented method for identifying pairs of cohesive words within a text. A supervised model is trained to detect cohesive words within a text to be scored. Training the supervised model includes identifying a plurality of pairs of candidate cohesive words in a training essay and an order associated with the pairs of candidate cohesive words based on an order of words in the training essay. The pairs of candidate cohesive words are filtered to form a set of evaluation pairs. The evaluation pairs are provided via a graphical user interface based on the order associated with the pairs of candidate cohesive words. An indication of cohesion or no cohesion is received for the evaluation pairs via the graphical user interface. The supervised model is trained based on the evaluation pairs and the received indications.

Abstract: A computer-implemented method of training an assessment model for assessing constructed texts expressing opinions on subjects includes accessing a plurality of training texts, which are constructed texts. The training texts are analyzed with the processing system to derive values of a plurality of linguistic features of an assessment model. At least one of the plurality of linguistic features relates to sentiment and at least one of the plurality of linguistic feature relates to specificity. The assessment model is trained with the processing system based on the values of the plurality of linguistic features. Based on the training, a weight for each of the plurality of linguistic features is determined. The assessment model is calibrated to include the weights for at least some of the plurality of linguistic features such that the assessment model is configured to generate assessment measures for constructed texts expressing opinions on subjects.

Abstract: Example systems and methods are disclosed for generating facial expressions in an avatar-based user interface for a computing device. An example system may include a digital camera, a display device and a user interface application. The digital camera may be configured to capture image data of a user over time. The user interface application may be stored on a non-transitory computer-readable medium and executable by a processor, and may include: an expression feature extractor configured to process the image data of the user to generate a plurality of facial expression descriptor vectors, a sketch generation module configured to use a first conditional deep convolutional generative adversarial network (DC-GAN) model to generate an expressive facial sketch image of an avatar based on the plurality of facial expression descriptor vectors, and an image generation module configured to use a second conditional DC-GAN model to generate a facial expression image from the expressive facial sketch image.

Abstract: A computer-implemented method of calibrating an assessment model for assessing responses includes accessing a plurality of training responses with a processing system for training an assessment model. The processing system analyzes the plurality of training responses to derive values of multiple features of the training responses. The processing system trains the assessment model based on the values of the multiple features of the training responses and a portfolio score for each individual associated with the plurality of training responses utilized in the training. The portfolio score for each individual corresponds to a measure of proficiency based on multiple writing samples constructed by the individual. The processing system determines, based on said training, a weight for each of the multiple features. The processing system calibrates the assessment model to include the weights for at least some of the features such that the assessment model is configured to generate scores for responses.

Abstract: Systems and methods are provided for generating texts appropriate for a reading level of an individual. An existing exam unit is accessed, wherein the existing exam unit includes a reading passage and a plurality of questions related to the reading passage. The plurality of questions are filtered based on a criterion to form a subset of questions. A first difficulty score is determined based on the reading passage. A second difficulty score is determined based on the subset of questions. A correlation between the first difficulty score and the second difficulty score is determined, and a text is generated that is appropriate for a reading level of an individual based on performance of the individual on the exam unit.

Abstract: A Portal Assessment Design System includes a system for designing assessment models and assessments (e.g., standardized tests). In assessment design, specifications are organized around three basic elements: 1) what knowledge, skill or abilities are important to measure; 2) what is necessary evidence of that knowledge, skill or ability; 3) what performance situations are needed to elicit the required evidence from students and other assessment users. The system includes three modules (i.e., Domain Analysis, Domain Modeling, Conceptual Assessment Framework), each of which targets a specific phase of the design process, for designing an assessment in accordance with the three basic elements. The sequence of modules represents the structure of a design process that helps assessment developers work through successive stages or phases of assessment design, from the gathering of raw information to the creation of a complete set of specifications for an assessment product.

Abstract: Systems and methods are provided for determining whether a user is authorized to take an examination. Facial recognition technology is used to determine whether an image of a user's face corresponds to an image of a set of images of individuals not permitted to take the examination. Based on a determination that the image of the user's face corresponds to an image of the set of images, a confidence score is assigned or modified. Voice recognition technology is used to determine whether a recording of the user's voice corresponds to a recording of a set of recordings of individuals not permitted to take the examination. Based on a determination that the recording of the user's voice corresponds to a recording of the set of recordings, the confidence score is modified. A determination is made as to whether the user is authorized to take the examination based on the confidence score.

Abstract: Computer-based systems and methods are provided for generating a narrative computer scoring model for assessing story narratives. In one embodiment, supervised machine learning is used to generate the narrative computer scoring model. For example, a collection of training story narratives with assigned scores may be used to train the model. In one embodiment, each training story narrative is processed to extract features that signify content relevance, collocation of commonly used words, coherency, detailing, and expressions of sentiment. These features, as well as others, may be selectively used to train a narrative computer scoring model. Once trained, the model can be used to automatically evaluate story narratives and assign appropriate scores.

Abstract: Systems and methods are provided for automatically scoring a constructed response. The constructed response is processed to generate a plurality of numerical vectors that is representative of the constructed response. A model is applied to the plurality of numerical vectors. The model includes an input layer configured to receive the plurality of numerical vectors, the input layer being connected to a following layer of the model via a first plurality of connections. Each of the connections has a first weight. An intermediate layer of nodes is configured to receive inputs from an immediately-preceding layer of the model via a second plurality of connections, each of the connections having a second weight. An output layer is connected to the intermediate layer via a third plurality of connections, each of the connections having a third weight. The output layer is configured to generate a score for the constructed response.