# US 2018/0322798 A1 · Systems and methods for real time assessment of levels of learning and adaptive instruction delivery > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0322798 A1 - Document ID: US20180322798A1 - Status: Published application - Kind: A1 - Application number: 15/968,052 - Filed: 2018-05-01 - Published: 2018-11-08 - Earliest claimed: 2017-05-03 - Assignees: Florida Atlantic University Board of Trustees - Applicants: Florida Atlantic University Board of Trustees - Inventors: Hari Kalva; Saurin Parikh - Web page: https://patentgrep.com/patent/US20180322798A1 ## CPC - G09B 5/12: different stations being capable of presenting different information simultaneously (G09B5/14 takes precedence) - A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet} - A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens} - A61B 5/163: {by tracking eye movement, gaze, or pupil change} - A61B 5/7267: {involving training the classification device} - A61B 5/7405: {using sound} - G06N 20/00: Machine learning - G09B 5/065: {Combinations of audio and video presentations, e.g. videotapes, videodiscs, television systems} - G09B 7/02: of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student - G09B 7/07: providing for individual presentation of questions to a plurality of student stations - G09B 17/003: {electrically operated apparatus or devices} - A61B 3/112: {for measuring diameter of pupils} - A61B 3/113: for determining or recording eye movement - G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B) - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and methods for predicting a user's learning level or an Area Of Concern (“AOC”). The methods comprise: presenting multimedia content to a user of a computing device; collecting, by at least one learning level indicator device, observed sense data specifying the user's behavior while the user views the multimedia content; analyzing the observed sense data to determine a plurality of metric values for each of a plurality of word categories, a plurality of graphical element categories and/or a plurality of concept categories; and using the metric values for predicting the learning level or AOC based on results of the comparing. ## Claims ### Claim 1 A method for predicting at least one of a user's learning level and Area Of Concern (“AOC”), comprising: presenting multimedia content to a user of a computing device; collecting, by at least one learning level indicator device, observed sense data specifying the user's behavior while the user views the multimedia content; analyzing the observed sense data to determine a plurality of metric values for each of a plurality of word categories; and using the metric values for predicting at least one of the learning level and the AOC. ### Claim 2 (depends on 1) The method according to claim 1, wherein the metric values are used in a previously trained machine learning model for predicting at least one of the learning level and the AOC. ### Claim 3 (depends on 1) The method according to claim 1, wherein a machine learning model is trained with observed sense data collected while a user is presented with training multimedia content. ### Claim 4 (depends on 1) The method according to claim 1, wherein a machine learning model is trained with observed sense data collected from a plurality of users while each user is presented with training multimedia content. ### Claim 5 (depends on 1) The method according to claim 1, wherein the at least one learning level indicator device comprises at least one of an eye tracker, an Electroencephalogram, a biometric sensor, a camera, and a speaker. ### Claim 6 (depends on 1) The method according to claim 1, wherein the plurality of metric values comprises at least one of a single fixation duration value, a first fixation duration value, a gaze duration value, a mean fixation duration value, a fixation count value, a spillover value, a mean saccade length value, a preview benefit value, a perceptual span value, a mean pupil diameter of a left eye value, a mean pupil diameter of a right eye value, a regression count value, a second pass time value, a determinism observed value, a lookback fine detail observed value, a lookback re-glance observed value, a mean reanalysis pupil diameter of the left eye value, and a mean reanalysis pupil diameter of the right eye value. ### Claim 7 (depends on 1) The method according to claim 1, wherein the plurality of word categories comprises a big-size/high-frequency word category, a big-size/low-frequency word category, a big-size/common-word category, a big-size/novel-word category, a mid-size/high-frequency word category, a mid-size/low-frequency word category, a mid-size/common-word category, a mid-size/novel-word category, a small-size/high-frequency word category, a small-size/low-frequency word category, a small-size/common-word category, and/or a small-size/novel-word category. ### Claim 8 (depends on 1) The method according to claim 1, wherein the metric values are also determined for a plurality of concept categories comprising a high familiar category, a novel category, and a low familiar category. ### Claim 9 (depends on 1) The method according to claim 1, further comprising dynamically selecting supplementary learning content for the user based on at least one of the predicted learning level and the predicted AOC. ### Claim 10 (depends on 9) The method according to claim 9, further comprising presenting the supplementary learning content to the user via the computing device. ### Claim 11 (depends on 1) The method according to claim 1, further comprising generating a report of at least one of the user's learning state and the user's progress based on at least one of the predicted learning level and the predicted AOC. ### Claim 12 (depends on 3) The method according to claim 3, wherein the training multimedia content comprises content of different difficulty levels ranging from (i) text content having only common and high frequency words, (ii) text content having combination of high and low frequency words, (iii) text content having high, low frequency and novel words, and (iv) multi-media content along with textual content. ### Claim 13 A method for predicting at least one of a user's learning level and Area Of Concern (“AOC”), comprising: presenting multimedia content to a user of a computing device; collecting, by at least one learning level indicator device, observed sense data specifying the user's behavior while the user views the multimedia content; analyzing the observed sense data to determine a plurality of metric values for each of a plurality of word categories; and comparing the metric values obtained for the same word at different times for predicting at least one of the learning level and the AOC. ### Claim 14 (depends on 13) The method according to claim 13, wherein the metric values are used in a previously trained machine learning model for predicting at least one of the learning level and the AOC. ### Claim 15 (depends on 13) The method according to claim 13, wherein a machine learning model is trained with observed sense data collected while a user is presented with training multimedia content. ### Claim 16 (depends on 13) The method according to claim 13, wherein a machine learning model is trained with observed sense data collected from a plurality of users while each user is presented with training multimedia content. ### Claim 17 A system, comprising: a processor; and a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for predicting at least one of a user's learning level and Area Of Concern (“AOC”), wherein the programming instructions comprise instructions to: present multimedia content to a user of a computing device; obtain observed sense data specifying the user's behavior which was collected by at least one learning level indicator device while the user views the multimedia content; analyze the observed sense data to determine a plurality of metric values for each of a plurality of word categories and a plurality of concept categories; compare the metric values respectively to metric threshold values of a machine learning model previously trained with training sense data specifying the user's behavior while taking an electronic test survey; and predict at least one of the learning level and the AOC based on results of the comparing. ### Claim 18 (depends on 17) The system according to claim 17, wherein the at least one learning level indicator device comprises at least one of an eye tracker, an Electroencephalogram, a biometric sensor, a camera, and a speaker. ### Claim 19 (depends on 17) The system according to claim 17, wherein the plurality of metric values comprises at least one of a single fixation duration value, a first fixation duration value, a gaze duration value, a mean fixation duration value, a fixation count value, a spillover value, a mean saccade length value, a preview benefit value, a perceptual span value, a mean pupil diameter of a left eye value, a mean pupil diameter of a right eye value, a regression count value, a second pass time value, a determinism observed value, a lookback fine detail observed value, a lookback re-glance observed value, a mean reanalysis pupil diameter of the left eye value, and a mean reanalysis pupil diameter of the right eye value. ### Claim 20 (depends on 17) The system according to claim 17, wherein the plurality of word categories comprises a big-size/high-frequency word category, a big-size/low-frequency word category, a big-size/common-word category, a big-size/novel-word category, a mid-size/high-frequency word category, a mid-size/low-frequency word category, a mid-size/common-word category, a mid-size/novel-word category, a small-size/high-frequency word category, a small-size/low-frequency word category, a small-size/common-word category, and/or a small-size/novel-word category. ### Claim 21 (depends on 17) The system according to claim 17, wherein the plurality of concept categories comprises a high familiar category, a novel category, and a low familiar category. ### Claim 22 (depends on 17) The system according to claim 17, wherein the programming instructions further comprise instructions to dynamically select supplementary learning content for the user based on at least one of the predicted learning level and the predicted AOC. ### Claim 23 (depends on 22) The system according to claim 22, wherein the programming instructions further comprise instructions to present the supplementary learning content to the user. ### Claim 24 (depends on 17) The system according to claim 17, wherein the programming instructions further comprise instructions to update the machine learning model based on the observed sense data. ### Claim 25 (depends on 17) The system according to claim 17, wherein the programming instructions further comprise instructions to generate a report of at least one of the user's learning state and the user's progress based on at least one of the predicted learning level and the predicted AOC. ### Claim 26 (depends on 17) The system according to claim 17, wherein the electronic test survey comprises content of different difficulty levels ranging from (i) text content having only common and high frequency words, (ii) text content having combination of high and low frequency words, (iii) text content having high, low frequency and novel words, and (iv) multi-media content along with textual content. ### Claim 27 A method for predicting at least one of a user's learning level and Area Of Concern (“AOC”), comprising: presenting multimedia content to a user of a computing device; collecting, by at least one learning level indicator device, observed sense data specifying the user's behavior while the user views the multimedia content; analyzing the observed sense data to determine a plurality of metric values for each of a plurality of graphical element categories; and using the metric values for predicting at least one of the learning level and the AOC. ### Claim 28 A method for adapting content, comprising: presenting multimedia content to a user of a computing device; predicting, determining and calculating at least one of a level of learning and an area of concern; and modifying the presented multimedia content based on at least one of the level of learning and the area of concern. ### Claim 29 (depends on 28) The method according to claim 28, wherein the multimedia content is modified by providing a supplementary content that clarifies the multimedia content. ### Claim 30 (depends on 29) The method according to claim 29, wherein the multimedia content is modified by providing definitions of one or more terms in the multimedia content. ### Claim 31 A method for grouping learners, comprising: presenting multimedia content to a user of a computing device; predicting, determining and calculating at least one of a level of learning and an area of concern; and creating a group of learners with at least one of a similar level of learning and a similar area of concern. ### Claim 32 (depends on 31) The method according to claim 31, wherein learners are grouped and placed in a common chat room. ### Claim 33 (depends on 31) The method according to claim 31, wherein learners are grouped and placed in a common online study space.