In certain implementations, creation of an educational test may be facilitated based on prior performance with individual test questions. A user input indicating a subject matter for a test, one or more question categories related to the subject matter, and a questionee group for which the test is intended may be received. A plurality of questions may be obtained based on the user input. For each question of the plurality of questions, a question performance metric value associated with the question (that is calculated based on prior performance of one or more questionees with the question) may be obtained. A test performance metric value associated with the test may be calculated based on the question performance metric values associated with at least some questions of the plurality of questions. The at least some questions and the test performance metric value may be provided for presentation on a user interface.

In certain implementations, creation of an educational test may be facilitated based on prior performance with individual test questions. A user input indicating a subject matter for a test, one or more question categories related to the subject matter, and a questionee group for which the test is intended may be received. A plurality of questions may be obtained based on the user input. For each question of the plurality of questions, a question performance metric value associated with the question (that is calculated based on prior performance of one or more questionees with the question) may be obtained. A test performance metric value associated with the test may be calculated based on the question performance metric values associated with at least some questions of the plurality of questions. The at least some questions and the test performance metric value may be provided for presentation on a user interface.

Systems and methods for measuring user engagement with subject matter content are disclosed. One computer-implemented method may include: receiving, at a computer system, an indication to calculate a user engagement score with respect to a subject matter unit; determining, via an algorithm, a value for one or more behavioral variables associated with the user for the subject matter unit; generating, by aggregating the value for each of the one or more behavioral variables, the user engagement score for the subject matter unit; and performing, based on the generated user engagement score, an action.

Systems and methods for measuring user engagement with subject matter content are disclosed. One computer-implemented method may include: receiving, at a computer system, an indication to calculate a user engagement score with respect to a subject matter unit; determining, via an algorithm, a value for one or more behavioral variables associated with the user for the subject matter unit; generating, by aggregating the value for each of the one or more behavioral variables, the user engagement score for the subject matter unit; and performing, based on the generated user engagement score, an action.

Data processing systems according to various embodiments are adapted to process data regarding the self-awareness of a user for use in the development of the user's executive function. In various embodiments, the data processing system: (1) executes computer-readable instructions that, when executed by the at least one computer processor, facilitate the playing of a game by the user; (2) gathers performance data indicating the user's performance while the user plays the game; and (3) automatically evaluates the performance data to assess, for the user, the ability to remember to perform various tasks, to stay on task, and to coach others by taking perspective, giving feedback, and mentoring them to do well. The data processing system may gather and process related data on various levels and then use this information to assist the user in improving the user's executive function skills.

A system and process for dynamic security monitoring of test taking. The system combines historical information about the test taker or relevant conditions, and includes sensors which objectively monitor test taker's actions, inactions, and involuntary response to the test given before and during the test, and at a specific test location. The information collected from the sensors is compared to a plurality of predetermined individual risk factors, which indicate a possibility of test fraud by the individual test taker to create a test event risk profile. The individual risk profile is combined with non-individual specific risk factors to create a holistic test event security profile. Security resources are then dynamically assigned to or removed from the test event based on the unique test event security profile.

A system and process for dynamic security monitoring of test taking. The system combines historical information about the test taker or relevant conditions, and includes sensors which objectively monitor test taker's actions, inactions, and involuntary response to the test given before and during the test, and at a specific test location. The information collected from the sensors is compared to a plurality of predetermined individual risk factors, which indicate a possibility of test fraud by the individual test taker to create a test event risk profile. The individual risk profile is combined with non-individual specific risk factors to create a holistic test event security profile. Security resources are then dynamically assigned to or removed from the test event based on the unique test event security profile.

There is provided an information processing apparatus including a processor that obtains, from a first P2P database, evaluation information for evaluating learning of a user, which is obtained by an acquisition device, and performs evaluation on learning performed by the user on the basis of the evaluation information.

The learning device 80 includes a first learning means 81 and a second learning means 82. The first learning means 81 generates a skill state sequence representing time-series changes in a learner's skill state by machine learning using learner's learning results. The second learning means 82 learns a model in which problem characteristics that represent characteristics of problems used by a learner for learning, user characteristics that represent characteristics of the learner, and time information that represents time the learner solved the problem are explanatory variables, and the learner's skill state represented by the skill state sequence is an objective variable.

The learning device 80 includes a first learning means 81 and a second learning means 82. The first learning means 81 generates a skill state sequence representing time-series changes in a learner's skill state by machine learning using learner's learning results. The second learning means 82 learns a model in which problem characteristics that represent characteristics of problems used by a learner for learning, user characteristics that represent characteristics of the learner, and time information that represents time the learner solved the problem are explanatory variables, and the learner's skill state represented by the skill state sequence is an objective variable.