Abstract: A computer-implemented technique for characterizing lexical concreteness in narrative includes receiving data encapsulating narrative text having a plurality of words. Thereafter, the function words can be removed from the narrative text to result in only content words. A concreteness score can then be assigned to each content word by polling a database to identify matching words and to use concreteness scores associated with such matching words as specified by the database. Data can then be provided which characterizes the assigned concreteness scores. Related apparatus, systems, techniques and articles are also described.

Abstract: Human-machine dialog is characterized by receiving data comprising a recording of an individual interacting with a dialog application simulating a conversation. Thereafter, the received data is parsed using automated speech recognition to result in text comprising a plurality of words. Features are extracted from the parsed data and then input an ensemble of different machine learning models each trained to generate a score characterizing a plurality of different dialog constructs. Thereafter, scores generated by the machine learning models for each of the dialog constructs are fused. A performance score is then generated based on the fused scores which characterizes a conversational proficiency of the individual interacting with the dialog application. Data can then be provided which includes or otherwise characterizes the generated score. Related apparatus, systems, techniques and articles are also described.

Abstract: An electronic audio file is received that comprises spontaneous speech responsive to a prompt in a non-native language of a speaker. Thereafter, the electronic audio file is parsed into a plurality of spoken words. The spoken words are then normalized to remove stop words and disfluencies. At least one trained content scoring model is then used to determine an absence of pre-defined key points associated with the prompt in the normalized spoken words. A list of the determined absent key points can be generated. This list can then be displayed/caused to be displayed in a graphical user interface along with feedback to improve content completeness. Related apparatus, systems, techniques and articles are also described.

Abstract: Systems and methods are provided for processing a group of essays to develop a classifier that detects nonsensical computer-generated essays. A data structure associated with a group of essays is accessed, wherein the group of essays includes nonsensical computer-generated essays and good-faith essays. Both the nonsensical computer-generated essays and the good-faith essays are assigned feature values. The distribution of feature values between the nonsensical computer-generated essays and the good-faith essays is measured. A classifier that detects whether an essay is a nonsensical computer-generated essay is developed, wherein the classifier is developed using the distribution of feature values.

Abstract: Systems and methods are disclosed for automatically scoring a constructed response using a neural network. In embodiments, a constructed response received by a processing system may be processed to divide the constructed response into multiple series of word tokens, wherein each word token includes a sequence of characters. The constructed response may be further processed to correct one or more spelling errors. The word tokens may be encoded to generate representation vectors for the constructed response. A set of nonlinear operations may be applied to the plurality of representation vectors in a neural network to generate a single vector output. A set of predetermined network weights may be applied to the vector output of the neural network to generate a scalar output for scoring the constructed response.

Abstract: A method comprising accessing a first data structure that is associated with a first product prepared by a student and that includes first process data associated with a process performed by the student in generating the first product, analyzing the first data structure to generate a first characterization score based on the first product and the first process data, accessing a second data structure that is associated with a second product prepared by the student and that includes second process data associated with a process performed by the student in generating the second product, analyzing the second data structure to generate a second characterization score based on the second product and the second process data, and calculating a skill level change metric based on the first characterization score and the second characterization score indicating a change in ability level of the student over a course of the scenario-based assessment.

Abstract: Data is received that encapsulates a document of text. The text is then segmented into a plurality of semantically coherent units using a coherence-aware text segmentation (CATS) machine learning model. Data is then provided that characterizes the segmenting. Related apparatus, systems, techniques and articles are also described.

Abstract: Reading comprehension of a user can be assessed by presenting, in a graphical user interface, sequential reading text comprising a plurality of passages. The graphical user interface can alternate between (i) automatically advancing through passages of the reading text and (ii) manually advancing through passages of the reading text within the graphical user interface which is in response to user-generated input received via the graphical user interface. An audio narration is provided during the automatic advancing of the reading text. An audio file is recorded during the manual advancing of the reading text which is used to automatically determine an estimated level of reading comprehension of the user. Data characterizing the determined level of reading comprehension of the user can then be provided (e.g., displayed, loaded into memory, stored on a hard drive, transmitted to a remote computing system, etc.). Related apparatus, systems, techniques and articles are also described.

Abstract: Biometric keystroke measure data derived from a computer-implemented long form examination taken by an examinee is received. Features are the extracted from the biometric keystroke measure data for the examinee. A similarity value is then determined, using one or more of a direct distance approach or a machine learning approach, for the extracted features relative to features extracted from biometric keystroke measure data derived from each of a plurality of other examinees while taking the long form examination. At least one of the determined similarity values is then identified having a value above a pre-defined threshold. The pre-defined threshold indicates a likelihood of the examinee being the same as one of the other examinees. Data can then be provided that characterizes the identification. Related apparatus, systems, techniques and articles are also described.

Abstract: Systems and methods are provided for the design and implementation of experiments that facilitate the investigation of process data. The experiments involve recording the completion of a task by participants and then playing back the video of task completion to automatically probe participants about their affective, behavioral, and cognitive experiences. As a result of this system, information about affective, behavioral, and cognitive processes can be more easily investigated by researchers without computer programming knowledge. Corresponding apparatuses, systems, and methods are also discussed.

Abstract: Systems and methods for computer-implemented evaluation of a performance are provided. In a first aspect, a computer-implemented method of evaluating an interaction generates a first temporal record of first behavior features exhibited by a first entity during an interaction between a first entity and a second entity. A second temporal record is generated including second behavior features exhibited by a second entity during an interaction with a first entity. A determination is made that a first feature of a first temporal record is associated with a second feature of a second temporal record. The length of time that passes between the first feature and second feature is evaluated, and a determination is made that the length of time satisfies a temporal condition. A co-occurrence record associated with a first feature and a second feature is generated and included in a co-occurrence record data-structure.

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 are provided for processing a drawing in a modeling prototype. A data structure associated with a visual model is accessed. The visual model is analyzed to extract construct-relevant features, where the construct-relevant features are extracted using a drawing object by identifying visual attributes of the visual model and populating a data structure for each object drawn. The visual model is analyzed to generate a statistical model, where the statistical model is generated using a multidimensional scoring rubric by targeting different constructs which compositely estimate learning progression levels, wherein the statistical model is based on features that are principally aligned with one or more of the constructs. An automated scoring is determined based on the construct-relevant features and the statistical model, where the automated scoring is stored in a computer readable medium. and is outputted for display, transmitted across a computer network, or printed.

Abstract: Data is received that encapsulates a spoken response to a prompt text comprising a string of words. Thereafter, the received data is transcribed into a string of words. The string of words is then compared with a prompt so that a similarity grid representation of the comparison can be generated that characterizes a level of similarity between the string of words in the spoken response and the string of words in the prompt text. The grid representation is then scored using at least one machine learning model. The score indicates a likelihood of the spoken response having been off-topic. Data providing the encapsulated score can then be provided. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that encapsulates a spoken response to a test question. Thereafter, the received data is transcribed into a string of words. The string of words is then compared with at least one source string so that a similarity grid representation of the comparison can be generated that characterizes a level of similarity between the string of words and the at least one source string. The grid representation is then scored using at least one machine learning model. The score indicates a likelihood of the spoken response having been plagiarized. Data providing the encapsulated score can then be provided. Related apparatus, systems, techniques and articles are also described.

Abstract: Systems and methods are provided for the detection of sentiment in writing. A plurality of texts is received from a larger collection of writing samples with a computer system. A set of seed words from the plurality of texts are labeled as being of positive sentiment or of negative sentiment with the computer system. The set of seed words is expanded in size with the computer system to provide an expanded set of seed words. Intensity values are assigned to words of the expanded set of seed words. Each of the words of the expanded set of seed words is assigned three intensity values: a value corresponding to the strength of the word's association with a positive polarity class, a value corresponding to the strength of the word's association with a negative polarity class, and a value corresponding to the strength of the word's association with a neutral polarity class.

Abstract: Computer-implemented systems and methods are described herein for detecting unusually frequent exactly matching and nearly matching test responses. A plurality of test responses for each of a plurality of test takers is received. Evidence of a circulated key from the plurality of test responses is identified by detecting two or more test takers with exactly matching test responses for a plurality of questions. The strength of the evidence of the circulated key is analyzed by determining the probability that the two or more test takers would produce exactly matching test responses, wherein the strength of the evidence is inversely related to the probability. Test takers with nearly matching test responses to the circulated key are then identified. All test takers with exactly matching and nearly matching test responses are tagged for further investigation.

Abstract: Systems and methods for identifying a person's native language and/or non-native language based on code-switched text and/or speech, are presented. The systems may be trained using various methods. For example, a language identification system may be trained using one or more code-switched corpora. Text and/or speech features may be extracted from the corpora and used, in combination with a per-word language identify of the text and/or speech, to train at least one machine learner. Code-switched text and/or speech may be received and processed by extracting text and/or speech features. These features may be fed into the at least one machine learner to identify the person's native language.

Abstract: Systems and methods are provided for conducting a simulated conversation with a language learner include determining a first dialog state of the simulated conversation. First audio data corresponding to simulated speech based on the dialog state is transmitted. Second audio data corresponding to a variable length utterance spoken in response to the simulated speech is received. A fixed dimension vector is generated based on the variable length utterance. A semantic label is predicted for the variable-length utterance based on the fixed dimension vector. A second dialog state of the simulated conversation is determined based on the semantic label, and third audio data corresponding to simulated speech is transmitted based on the second dialog state.

Abstract: Systems and methods for defining and using an optimal burst boundary threshold to assess the reliability of a manual/automatic writing score are presented. Keystroke data, including inter-key interval data, such as inter-word interval data, may be gathered from writings. Clustering analyses may be performed on the inter-key interval data to determine an optimal number of bursts for the writings. An optimal burst boundary may be determined from the optimal number of bursts. Other burst-related measures and statistics, including the average and maximum burst lengths, may be determined from the writings based on the optimal burst boundary threshold. A score may be received for each of the writings. A validation indication metric may be generated for each of the writings based on the received score and the optimal burst boundary threshold. The resulting measures and statistics may be used or applied in different ways and provide personalized feedback as learning analytics.