# US 2015/0147740 A1 · Entropy-based sequences of educational modules > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2015/0147740 A1 - Document ID: US20150147740A1 - Status: Published application - Kind: A1 - Application number: 14/499,659 - Filed: 2014-09-29 - Published: 2015-05-28 - Earliest claimed: 2013-11-27 - Assignees: Pearson Education, Inc. - Applicants: Pearson Education, Inc. - Inventors: Randall Barnhart - Web page: https://patentgrep.com/patent/US20150147740A1 ## CPC - G09B 5/02: with visual presentation of the material to be studied, e.g. using film strip - G09B 7/00: Electrically-operated teaching apparatus or devices working with questions and answers (mechanically operated G09B3/00; computing arrangements G06F) - 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) ## Abstract Techniques disclosed herein can determine sequences for educational units by evaluating entropies of multiple potential sequences and biasing selection of next units towards those associated with high sequence entropies. Further, an analysis can determine which units are under-represented (relative to a target proportion) in a past sequence and bias towards inclusion of an under-represented unit in the sequence. The available units that are considered for potential selection can include those matched to a learner's skill (e.g., such that all pre-requisite units have been mastered but the unit itself has not been mastered). Thus, techniques can generate sequences of units that promote unit variation and nonetheless conform to relative unit frequencies and skill level appropriateness. ## Claims ### Claim 1 A method for determining sequences for presentation of educational content objects or educational questions, the method comprising: identifying a set of educational-item types, each education-item type in the set of educational-item type including a type of educational content object or a type of educational question; identifying a target proportion of representation of an educational-item type in the set of educational-item types; accessing a past sequence of educational-item types, the past sequence of educational-item types including a series of identifiers of educational-item types in the set of educational-item types; for each educational-item type in the set of educational-item types: appending an identifier of the educational-item type to the past sequence of educational-item types to produce a potential sequence; determining an entropy of the potential sequence; determining a proportion of the identifiers in the past sequence or potential sequence that identify the educational-item type; and determining a score based on the entropy, the proportion and the target proportion for the educational-item type; selecting an educational-item type from amongst the set of educational-item types based on the determined scores; and appending the past sequence with the selected educational-item type. ### Claim 2 (depends on 1) The method for determining sequences for presentation of educational content objects or educational questions as recited in claim 1, further comprising: determining a past-sequence entropy of the past sequence of educational-item types; and defining an entropy-delta metric for each educational-item type in the set of educational-item types based on a difference between the determined entropy and the past-sequence entropy, wherein the score for each educational-item type in the set of educational-item types is determined based on the entropy-delta metric for the educational-item type. ### Claim 3 (depends on 1) The method for determining sequences for presentation of educational content objects or educational questions as recited in claim 1, further comprising: defining a proportion-delta metric for each educational-item type in the set of educational-item types based on a difference between the determined proportion and the target proportion for the educational-item type, wherein the score for each educational-item type in the set of educational-item types is determined based on the proportion-delta metric for the educational-item type. ### Claim 4 (depends on 1) The method for determining sequences for presentation of educational content objects or educational questions as recited in claim 1, further comprising, for each educational-item type in the set of educational-item types: generating a normalized entropy metric based on of the determined entropy and a normalization factor based on entropies determined for other educational-item types in the set of educational-item types; and generating a normalized proportion metric based on of the determined proportion and a normalization factor based on proportions determined for other educational-item types in the set of educational-item types; wherein the score is determined based on the normalized entropy metric and the normalized proportion metric. ### Claim 5 (depends on 1) The method for determining sequences for presentation of educational content objects or educational questions as recited in claim 1, further comprising: identifying a content object from amongst the set of content objects that corresponds to the selected educational-item type; and causing the identified content object to be presented . ### Claim 6 (depends on 1) The method for determining sequences for presentation of educational content objects or educational questions as recited in claim 1, wherein each educational-item type in the set of educational-item types includes a type of educational content object. ### Claim 7 (depends on 1) The method for determining sequences for presentation of educational content objects or educational questions as recited in claim 1, further comprising: determining that each educational-item type in the set of educational-item types is to be made accessible to a particular learner; monitoring performance of the learner; determining that a new educational-item type is to be added to the set of educational-item types or that an existing educational-item type in the set of educational-item types is to be removed from the set; modifying the set of educational-item types to include the new educational-item type or remove the existing educational-item type; and evaluating the modified set of educational-item types to select a second educational-item type for inclusion in the sequence. ### Claim 8 A system for determining sequences for presentation of educational content objects or educational questions, the system comprising: an educational-item type availer that identifies a set of educational-item types, each education-item type in the set of educational-item type including a type of educational content object or a type of educational question; a target-proportion definer defines a target proportion of representation of an educational-item type in the set of educational-item types within a sequence of educational-item types; an entropy-based sequence engine that: accesses a past sequence of educational-item types, the past sequence of educational-item types including a series of identifiers of educational-item types in the set of educational-item types; for each educational-item type in the set of educational-item types: appends an identifier of the educational-item type to the past sequence of educational-item types to produce a potential sequence; determines an entropy of the potential sequence; determines a proportion of the identifiers in the past sequence or potential sequence that identify the educational-item type; and determines a score based on the entropy, the proportion and the target proportion for the educational-item type; selects an educational-item type from amongst the set of educational-item types based on the determined scores; and appends the past sequence with the selected educational-item type. ### Claim 9 (depends on 8) The system for determining sequences for presentation of educational content objects or educational questions as recited in claim 8, wherein the entropy-based sequence engine further: determines a past-sequence entropy of the past sequence of educational-item types; and defines an entropy-delta metric for each educational-item type in the set of educational-item types based on a difference between the determined entropy and the past-sequence entropy, wherein the score for each educational-item type in the set of educational-item types is determined based on the entropy-delta metric for the educational-item type. ### Claim 10 (depends on 8) The system for determining sequences for presentation of educational content objects or educational questions as recited in claim 8, wherein the entropy-based sequence engine further: defines a proportion-delta metric for each educational-item type in the set of educational-item types based on a difference between the determined proportion and the target proportion for the educational-item type, wherein the score for each educational-item type in the set of educational-item types is determined based on the proportion-delta metric for the educational-item type. ### Claim 11 (depends on 8) The system for determining sequences for presentation of educational content objects or educational questions as recited in claim 8, wherein the entropy-based sequence engine further, for each educational-item type in the set of educational-item types: generates a normalized entropy metric based on of the determined entropy and a normalization factor based on entropies determined for other educational-item types in the set of educational-item types; and generates a normalized proportion metric based on of the determined proportion and a normalization factor based on proportions determined for other educational-item types in the set of educational-item types; wherein the score is determined based on the normalized entropy metric and the normalized proportion metric. ### Claim 12 (depends on 8) The system for determining sequences for presentation of educational content objects or educational questions as recited in claim 8, further comprising a content manager that: identifies a content object from amongst the set of content objects that corresponds to the selected educational-item type; and causes the identified content object to be presented . ### Claim 13 (depends on 8) The system for determining sequences for presentation of educational content objects or educational questions as recited in claim 8, wherein each educational-item type in the set of educational-item types includes a type of educational content object. ### Claim 14 (depends on 8) The system for determining sequences for presentation of educational content objects or educational questions as recited in claim 8, wherein the educational-item type availer further: determines that each educational-item type in the set of educational-item types is to be made accessible to a particular learner; monitors performance of the learner; determines that a new educational-item type is to be added to the set of educational-item types or that an existing educational-item type in the set of educational-item types is to be removed from the set; and modifies the set of educational-item types to include the new educational-item type or remove the existing educational-item type; wherein the entropy-based sequence engine further evaluates the modified set of educational-item types to select a second educational-item type for inclusion in the sequence. ### Claim 15 A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: identifying a set of educational-item types, each education-item type in the set of educational-item type including a type of educational content object or a type of educational question; identifying a target proportion of representation of an educational-item type in the set of educational-item types within a sequence of educational-item types; accessing a past sequence of educational-item types, the past sequence of educational-item types including a series of identifiers of educational-item types in the set of educational-item types; for each educational-item type in the set of educational-item types: appending an identifier of the educational-item type to the past sequence of educational-item types to produce a potential sequence; determining an entropy of the potential sequence; determining a proportion of the identifiers in the past sequence or potential sequence that identify the educational-item type; and determining a score based on the entropy, the proportion and the target proportion for the educational-item type; selecting an educational-item type from amongst the set of educational-item types based on the determined scores; and appending the past sequence with the selected educational-item type. ### Claim 16 (depends on 15) The computer-program product as recited in claim 15, wherein the actions further include: determining a past-sequence entropy of the past sequence of educational-item types; and defining an entropy-delta metric for each educational-item type in the set of educational-item types based on a difference between the determined entropy and the past-sequence entropy, wherein the score for each educational-item type in the set of educational-item types is determined based on the entropy-delta metric for the educational-item type. ### Claim 17 (depends on 15) The computer-program product as recited in claim 15, wherein the actions further include: defining a proportion-delta metric for each educational-item type in the set of educational-item types based on a difference between the determined proportion and the target proportion for the educational-item type, wherein the score for each educational-item type in the set of educational-item types is determined based on the proportion-delta metric for the educational-item type. ### Claim 18 (depends on 15) The computer-program product as recited in claim 15, wherein the actions further include, for each educational-item type in the set of educational-item types: generating a normalized entropy metric based on of the determined entropy and a normalization factor based on entropies determined for other educational-item types in the set of educational-item types; and generating a normalized proportion metric based on of the determined proportion and a normalization factor based on proportions determined for other educational-item types in the set of educational-item types; wherein the score is determined based on the normalized entropy metric and the normalized proportion metric. ### Claim 19 (depends on 15) The computer-program product as recited in claim 15, wherein the actions further include: identifying a content object from amongst the set of content objects that corresponds to the selected educational-item type; and causing the identified content object to be presented. ### Claim 20 (depends on 15) The computer-program product as recited in claim 15, wherein each educational-item type in the set of educational-item types includes a type of educational content object.