US20130095461A1 - Course skeleton for adaptive learning - Google Patents

Course skeleton for adaptive learning Download PDF

Info

Publication number
US20130095461A1
US20130095461A1 US13/271,328 US201113271328A US2013095461A1 US 20130095461 A1 US20130095461 A1 US 20130095461A1 US 201113271328 A US201113271328 A US 201113271328A US 2013095461 A1 US2013095461 A1 US 2013095461A1
Authority
US
United States
Prior art keywords
learning
student
data
goal
items
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US13/271,328
Other languages
English (en)
Inventor
Satish Menon
Jayakumar Muthukumarasamy
Partha Saha
Kurtis S. Taylor
James R. Utter
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Phoenix Inc, University of
Original Assignee
Apollo Group Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Apollo Group Inc filed Critical Apollo Group Inc
Priority to US13/271,328 priority Critical patent/US20130095461A1/en
Assigned to APOLLO GROUP, INC. reassignment APOLLO GROUP, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: MENON, SATISH, MUTHUKUMARASAMY, JAYAKUMAR, SAHA, PARTHA, UTTER, James R., TAYLOR, Kurtis S.
Priority to US13/409,006 priority patent/US20130095465A1/en
Priority to PCT/US2012/059822 priority patent/WO2013055966A1/en
Priority to BR112014008823A priority patent/BR112014008823A2/pt
Priority to MX2014004409A priority patent/MX341699B/es
Priority to CN201280060239.7A priority patent/CN103975362A/zh
Priority to CA2851797A priority patent/CA2851797A1/en
Priority to EP12840658.4A priority patent/EP2766868A4/en
Publication of US20130095461A1 publication Critical patent/US20130095461A1/en
Priority to US13/913,965 priority patent/US10360809B2/en
Assigned to THE UNIVERSITY OF PHOENIX, INC. reassignment THE UNIVERSITY OF PHOENIX, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: APOLLO EDUCATION GROUP, INC.
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
    • G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers 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

Definitions

  • the present invention relates to learning management systems.
  • the present invention relates to dynamic course skeletons for adaptive learning.
  • Intelligent learning systems are systems that attempt to assist students in achieving specific learning goals. To date, these systems have mainly used a computerized teaching approach that mirrors the approach taken in brick-and-mortar classrooms. Each student is presented with the same lecture, content, and assessment, regardless of learning style, intelligence, or cognitive characteristics.
  • Online courses are examples of “containers” that may employ adaptive learning technology to achieve a specific goal.
  • the container for each course is designed to include all of the information required to achieve success within the course. For example, the content required for achieving the goals of a particular Mathematics course would be directly associated with the container of the Mathematics course.
  • the container for each course also includes the logic that determines which content (in that finite set of content that is directly associated with the container) should be delivered to the student. By performing in a particular way, the student merely traverses down a pre-existing path through the course's logical hierarchy that is hard-coded into the course's container.
  • an adaptive learning tool may be designed to teach a student a course on the fundamentals of calculus. The designer of the tool will assume that the student possesses the foundational knowledge of mathematics required to begin the course, but since the tool is unaware of any other attribute of the student, the tool may provide a certain amount of “review” information as a means of calibration. All students receive the same information, and performance on assessments will dictate which learning item is presented to the student next.
  • FIG. 1 is a block diagram illustrating a learning management platform on which an embodiment may be implemented
  • FIG. 2 is a block diagram illustrating a course skeleton structure that may be used in an embodiment
  • FIG. 3 illustrates a computer system upon which an embodiment may be implemented.
  • a “learning item” may be any type of item that can be used to assist in the achievement of a learning goal.
  • a learning item may be content, such as text, video, or an audio podcast.
  • a learning item may be a learning activity or application, such as a flashcard app, a cognitive tutor app, etc. The techniques described herein are not limited to any particular types of learning items.
  • a course “skeleton” is associated with a course of instruction.
  • the course skeleton is made up of a hierarchy of goals, with each goal being associated with goal metadata that describes the goal and provides information about the type of content that may be used to achieve the goal.
  • the course skeleton is used to provide a more dynamic adaptive learning experience.
  • the nodes of the course skeleton are associated with metadata about the corresponding goal.
  • a learning item repository stores learning items and learning item metadata describing the learning items. Instead of directly mapping these learning items to the nodes or goals, the goal metadata is used to dynamically generate a query that can be executed against the learning item repository in order to retrieve learning items for the student.
  • learner-specific information is also used to generate the query that determines which content will be presented to the student.
  • student profile information that describes the student's interests, academic history, location information, or type of device the student is using may be used to generate the query.
  • FIG. 1 is a block diagram illustrating an adaptive learning engine, according to an embodiment of the invention.
  • Adaptive learning engine 100 generally represents a set of one or more computer programs, and associated resources, configured to manage educational data and information about students, provide learning items to students, and use information gathered from analyzing student interaction with the system to increase the effectiveness of future learning items.
  • Adaptive learning engine 100 facilitates the delivery of learning items based on student attributes.
  • Adaptive learning engine 100 includes logic that facilitates communication between its various components.
  • adaptive learning engine 100 includes a processor 190 , learning analysis logic 130 and presentation logic 140 .
  • Adaptive learning engine 100 may also include or be coupled with other components such as a learning space platform, a learning items manager, a learning tools manager, a data analysis engine, a knowledge base, a personal cognitive DNA manager, and a skills hierarchy manager (not shown). Each of these components shall be described in greater detail hereafter.
  • an input 112 is received by the adaptive learning engine 100 at an input/output (IO) interface 110 .
  • IO interface 110 may be a network interface such as an Ethernet-based interface. Input 112 may include requests from students such as learning item requests.
  • IO logic 120 is coupled to IO interface 110 .
  • IO logic is configured to parse and distribute incoming data and prepare output 114 for sending via IO interface 110 , according to an embodiment.
  • IO logic 120 may implement one or more communications protocols.
  • IO logic 120 is coupled to presentation logic 140 , in an embodiment.
  • Presentation logic 140 determines the method of delivery of output 114 .
  • output 114 may include a multimedia file, or it may include only a link to the multimedia file in an embodiment.
  • Learning analysis logic 130 generally represents a decision-making unit that interacts with all other components of adaptive learning engine 100 and uses information gathered from these components to provide content that is relevant to each individual student based on information gathered from other elements of the adaptive learning engine 100 , such as a knowledge base.
  • learning analysis logic 130 does not merely on traverse a predetermined path that is based only on the student's degree program or class. Rather, learning analysis logic 130 takes into consideration attributes of each student, and dynamically generates queries to be executed against a database or learning item repository. Queries generated for the purpose of learning item retrieval may be based on many factors, which are described hereinafter. The queries may be in any form that takes these attributes into consideration. In one embodiment, learning item retrieval queries are formed using a query language such as Structured Query Language (SQL), and include the desired attributes or arguments as predicates in the query. For example, an example query may be as follows:
  • SQL Structured Query Language
  • learning item retrieval queries may be semantic queries, such as those that use SPARQL (Simple Protocol and RDF Query Language) and RDF for representing the data elements and their relationships.
  • SPARQL Simple Protocol and RDF Query Language
  • education-related attributes refers to any attributes that relate to a student's learning history, goals or abilities.
  • Education-related attributes may include non-transient attributes, such as a student's prior classes and grades, and transient attributes such as a student's current mood. Any education-related data may be used for generating queries for content.
  • learning analysis logic 130 can generate queries to retrieve individualized learning items with a high degree of confidence in the expected success of each student. For example, it may become clear that a particular student performs poorly when he tries to learn skills using only audio content, even though that student has expressed a preference for audio content. In this case, learning analysis logic 130 may subsequently require content other than audio content to be delivered to the user, instead of or in addition to audio content.
  • the learning items manager stores and manages learning items.
  • Learning items includes any items that may be used to achieve a goal. Examples of learning items include text, audio files such as mp3 files, video files such as QuickTime files, interactive flash movies, or any other type of multimedia content.
  • learning items manager includes a learning item repository and a content categorization system for storing and organizing learning items.
  • the learning item repository stores content in non-volatile memory, such as a shared hard disk system or a database system such as database 150 .
  • Learning items may be stored in a database table, such as the learning items table 170 .
  • the learning item categorization system provides content indexing services, along with an interface for creating and associating metadata with learning items stored in the learning item repository.
  • Learning items may be associated with metadata that describes the learning items. This metadata can be compared against the selection criteria specified in queries generated by learning analysis logic 130 to return learning items that are appropriate for helping students to achieve particular goals.
  • metadata associated with a video may include a title attribute that includes the text “how to factor polynomials.”
  • Other attributes may include a general category, such as “math” and a content type, such as “QuickTime video.
  • Metadata may be embedded within the learning items being described by the metadata, may be in a separate Meta file such as an XML file that is associated with the learning items being described, or may be stored in a database with an association to the learning items being described.
  • learning items are “tagged” with metadata describing the learning items, such as keywords, skills, associated learning objects, the characteristics of learners (e.g. limited prior knowledge) that may benefit from the instructional strategies employed by the learning items (e.g. worked examples), the type of learning items (e.g. video or text), and statistical information regarding the learning item usage.
  • the query e.g., the query
  • the learning space platform and learning analysis logic 130 may be authorized to add, remove, or alter tags associated with learning items via the learning items manager.
  • Learning items manager also includes learning item delivery logic configured to manage requests for learning items that are stored in the learning item repository. For example, some learning items may be streamed in order to preserve bandwidth.
  • learning items manager may be directed by learning analysis logic 130 to deliver learning items in a particular way, depending on attributes of the student.
  • certain content formats may not be supported by certain devices.
  • learning item delivery logic may choose or even change the format of the learning items being delivered if the device requesting the learning items does not support a particular format, such as the FLASH format.
  • the knowledge base may be implemented using database 150 .
  • the knowledge base manages persistent data and persistently stores snapshots of certain transient data. For example, student categorization information, student study group information, cognitive DNA relationship information, and persistent student profile information may all be stored in the knowledge base. Although this data is persistently stored, the data may change as required by other elements of the adaptive learning engine 100 .
  • data analysis engine may provide a report to learning analysis logic 130 that causes learning analysis logic 130 to indicate to the knowledge base, based on the report, that student categorization information for a particular student should be changed. The knowledge base will then alter the persistent data to reflect the indicated change.
  • Various mechanisms may be used to implement the knowledge base. For example, data for the knowledge base may be stored as triples in a triplestore.
  • a relational database management system may be used to facilitate the storage and retrieval of data.
  • the knowledge base is communicatively coupled to learning analysis logic 130 , and provides learning analysis logic 130 with student information to assist in creating an individualized learning recommendation. All data stored in the knowledge base, such as user data 180 , can be used to form queries that are used to retrieve individualized learning items for students.
  • the personal cognitive DNA manager manages data associated with students, and may be implemented using database 150 .
  • a collection of data associated with a student is known as personal cognitive DNA (PDNA), and may be stored in user data 180 .
  • the portions of a PDNA that are stored at the personal cognitive DNA manager include transient data, while persistent portions of the PDNA are be stored in the knowledge base in an embodiment.
  • PDNA data stored in the personal cognitive DNA manager may also include references to persistent data stored in the knowledge base.
  • the personal cognitive DNA manager may include a database management system, and may manage PDNA for all students. In an embodiment, instances of the personal cognitive DNA manager may reside on the client computing devices of students, and may be part of a learning space platform.
  • PDNA for users of the client computing device or the associated learning space platform may be stored in volatile or non-volatile memory.
  • a combination of these embodiments may also be used, where a portion of the personal cognitive DNA manager resides on a client while another portion resides on one or more servers.
  • the personal cognitive DNA manager is communicatively coupled to learning analysis logic 130 , and provides learning analysis logic 130 with transitory student information to assist in creating an individualized query for retrieving relevant learning items from the learning items manager. PDNA and all other user-related information can be used to generate individualized queries for retrieving learning items.
  • a user's location, local time, client device type, or client operating system may be provided to learning analysis logic 130 to assist in determining what type of learning items are appropriate for the environment and device.
  • the personal cognitive DNA manager and the knowledge base may be combined.
  • Learning goal metadata can be associated with a goal, and is used to describe the corresponding goal and provide information related to the corresponding goal.
  • a goal generally represents an ability, skill, or knowledge that a student is meant to acquire.
  • a skill may represent the ability to perform addition of single-digit numbers, form a complete sentence using a particular language, or type a certain number of words-per-minute.
  • goal metadata may include, by way of example and not by way of limitation, assessments, remediation data, skills hierarchy data, and other data describing the goal.
  • Content may also be included in goal metadata.
  • content that is known to teach or facilitate the achievement of a particular goal is included in goal metadata.
  • this content may simply be semantically analyzed to determine keywords to use in queries that are executed against a learning items manager or repository.
  • a well-known essay describing a particular type of poem may be associated with the goal of teach about that type of poem.
  • keywords can be extracted from the essay and used as selection criteria to find other learning items to present to the student to teach about that type of poem.
  • FIG. 2 is a block diagram illustrating a course skeleton structure 200 that may be used in an embodiment.
  • Course skeleton 200 includes goals 201 - 210 .
  • data defining a course skeleton is stored in the course skeleton data table 160 in database 150 .
  • Course skeleton data may be stored in any format that preserves the hierarchical nature of the course skeleton.
  • a course skeleton may be stored in a relational database system and/or in XML format.
  • XML format see the code listing submitted concurrently herewith.
  • a course skeleton can be created in a variety of ways, and the techniques described herein are not limited to any particular way of creating a course skeleton.
  • course skeletons can be created manually, where Subject Matter Experts (SME) and Instructional designers (ID) interact using a collaborative online application. Starting from a set of high level objectives and required material in a course, SMEs and IDs can engage in Socratic dialogue, or use a question template to break down the objectives into finer grained objectives.
  • course skeletons can be created using an automated process where Natural Language Processing (NLP) is used to process the required reading material to discover finer grained objectives.
  • NLP Natural Language Processing
  • Course skeletons can also be created using a mixed process, where the SME and ID can use some parts of the automated process to validate and quality control their work, or even use it to get suggestions.
  • a course skeleton may represent a group of goals for a portion of a course, an entire course, a field of study, a certificate program, a degree program, or an individual competency map that represents the skills acquired by a student, or any other education related structure.
  • Course skeletons may include goal metadata that describes a wide variety of goals and various learning theories, content types, and keywords used to find learning items to facilitate the achievement of those goals.
  • Links between goals in the hierarchy represent the relationship between those goals. For example, a link between two learning goals may mean that the subject matter covered in one of the learning goals requires the achievement of the other goal. A different link may indicate that one learning goal is a prerequisite of the other.
  • course skeleton 200 for example, the goal associated with node 205 must be acquired before attempting to achieve the goal associated with node 203 .
  • the course skeleton is hierarchical.
  • a non-hierarchical approach may be used in an embodiment.
  • a non-hierarchical directed-graph approach may be used in an embodiment that is based on a different learning model.
  • the course skeleton manager manages goal hierarchy information that describes the relationships between goals. For example, a student may be required to learn how to add and subtract before he learns how to multiply and divide.
  • a complete course skeleton may be made up of interconnected goals that represent all of the goals required to complete a traditional educational course.
  • the nodes in the hierarchy correspond to goals and goal metadata. Since a single node may be considered a prerequisite for many other nodes, and many nodes may be prerequisites for a single node, the hierarchy may be multidimensional.
  • the course skeleton manager manages the relationships between nodes that represent learning goals. For example, a relational database may be used to keep track of the node dependency information.
  • the course skeleton manager stores goal metadata data that describes skills, remediation information, assessment information, hierarchy association information, and other metadata associated with learning goals.
  • the course skeleton manager interfaces with learning analysis logic 130 and provides learning analysis logic 130 with course skeleton data to assist learning analysis logic 130 in dynamically generating learning item request queries that are specific to each goal.
  • Behaviorism is a view that is based on the assumption that people learn based on stimulation. Positive or negative reinforcement shapes the behavior of the student.
  • Constructivism is a view that assumes that the student learns as an experience, and that the learner actually constructs their own representations of reality.
  • Cognitivism is a view that assumes that people learn as they mentally process information in a manner that coincides with their cognitive architecture, and do not merely react to stimulation.
  • Learning models are created based on learning theories, and are meant to operationalize one or more learning theories. Different instructional strategies may be used, depending on the model to be implemented. Instructional strategies might vary content sequencing, instructional technique (direct, indirect), type of content presentation (lecture, case study, group discussion, etc.), level of collaboration (individual, pairs, small group), amount of practice, type of feedback (peer, instructor) and so forth. Learning items and tools that facilitate the delivery of learning items can be used to implement instructional strategies. Examples of learning items include text, video, audio, and games. Examples of tools include video players, browser plug-ins, e-book readers, shared whiteboard systems, and chat systems. Many other examples of learning items and tools exist.
  • learning models and theories may be operationalized using each student's PDNA to generate learning item request queries for students, which are executed against a learning items manager or repository. As the information associated with a particular student changes, the queries generated on behalf of that student will change to reflect the latest understanding of that student's learning requirements, preferences, and goals.
  • PDNA is referred to herein as “personal cognitive DNA,” this label does not indicate that PDNA data collection and usage is limited to embodiments that are based upon cognitive models.
  • PDNA data includes information about a student's cognitive strengths and weaknesses (as well as preferences) that are provided explicitly by the student or inferred by the system as the student interacts with the system and the outcomes are measured.
  • PDNA may be used in any embodiment, independent of any particular learning model.
  • PDNA data is a collection of data associated with a student.
  • Transient profile data may be stored in the personal cognitive DNA manager, while persistent profile data may be stored in the knowledge base.
  • PDNA data stored in the personal cognitive DNA manager may include references to persistent data directly or indirectly associated with the student that is stored in the knowledge base.
  • a rich data layer generally refers to information that is gathered and linked to create intelligence that may be used to inform learning analysis logic 130 , which uses this information to generate learning item request queries.
  • the rich data layer is dynamically updating in that the data being collected changes over time, and data that does not conform to the changes becomes incorrect. For example, as a student achieves a high degree of proficiency with a particular skill, data that suggests that the student needs to become proficient with the skill becomes outdated and incorrect. Thus, the dynamic data layer must keep up with the current information available for each student.
  • Each student using adaptive learning engine 100 is associated with PDNA for that student.
  • the PDNA for that student may contain minimal information, such as demographic information, a student's declared major, self-proclaimed learning preferences, and imported transcript data such as grades and coursework done at other institutions.
  • the data analysis engine vast amounts of data may be collected and analyzed by the data analysis engine, resulting in new PDNA information that describes how the student learns, what level the student has achieved in a particular course, whether the student understands a particular concept or possesses a particular skill, the pace at which the student learns, or even the time of day the student is most likely to correctly answer a question.
  • the PDNA may include data that identifies the student's current location, what client computing device they are using (e.g. iPhone, laptop, or netbook), what operating system they are using, whether or not their web browser supports the Flash plug-in, or whether the student sets his status as “tired.”
  • a particular student performs differently depending on environmental factors, while another student may be capable of learning regardless of the environment. For example, one student may be able to study on a commuter train while another may not. One student may be capable of learning via an audio program while another requires text information or video.
  • certain tasks may be reserved for particular times or places in order to calibrate the system.
  • the system may be configured to only offer assessments when a student's transient PDNA data shows that the time in the student's current time zone is between certain daylight hours.
  • Metrics may be assigned to particular attributes in each student's PDNA. For example, metrics may describe expected or historical success with different learning characteristics. These metrics may help learning analysis logic 130 determine whether the student is successful when participating in collaborative learning exercises, or whether the student would benefit more from self-study. A student may have a metric of “7” for the attribute “performs well with individual work” and a metric of “2” for the “performs well with collaborative work” attribute. Higher scores are not necessarily the only factor used in determining the learning strategy for the student, however. For example, the learning track that the student is on may actually require that the student develop collaboration skills Therefore, the learning analysis logic 130 may generate queries that request learning items for the student that will bolster his ability to learn collaboratively. In other words, the system will provide learning items that teach the student the underlying skills required to allow the student to become successful at collaborative work.
  • PDNA for a particular student may be analyzed and compared to PDNA of other students in order to generate more accurate learning item request queries. This feature is especially useful when limited information is available about a student, but enough is known about the student to associate the student with a particular group of students for which more information is available.
  • learning analysis logic 130 may analyze the PDNA information for all students in the system, over time, to predict various things, including: what method of learning is best for each student, which track will yield the highest chance of success for a given student in a particular program, which programs the student would be successful in, which courses the student may be expected to struggle with, and even which career would best suit the student.
  • a DNA fingerprint is based on aggregate PDNA data. Generally, a DNA fingerprint is made by selecting a set of PDNA data having one or more PDNA attributes in common and generating a single profile that is representative of the entire set.
  • a DNA fingerprint may be generated for students that have recently completed a learning object that teaches the calculus skill of taking the derivative of a second degree polynomial.
  • the recency of the completion of the learning object is determined based on the time that the data was stored, so snapshots that were taken at checkpoints occurring immediately after students completed the learning object will qualify for inclusion in the set of PDNA data considered for use in the creation of the DNA fingerprint.
  • Each attribute in the PDNA data considered in the creation of the DNA fingerprint may be aggregated, averaged, or otherwise considered, resulting in a fingerprint of that attribute. For example, if the average value of the attribute “abstract learning ability” is “80” in the PDNA data in the set, then the DNA fingerprint may inherit this value for the same attribute. Any method of considering or combining PDNA data to generate DNA fingerprint data may be used. For example, the lowest value, the median value, or a sum of the values may be used as the fingerprint value for a particular attribute. Some attributes, especially those with very little correlation to the common PDNA attribute, may not be assigned a DNA fingerprint value, or may be assigned a NULL value, indicating that conclusions about that attribute are statistically invalid for that set of PDNA. Once each attribute has been considered for the set, then the resulting values for each attribute are stored in one or more records as a DNA fingerprint for that set of PDNA data.
  • assessments designed to determine whether a student has achieved a goal are associated with the corresponding learning metadata for that goal.
  • a student uses assessments to demonstrate the skills and knowledge associated with the learning goal. More than one assessment may be included in the learning metadata for a particular goal. Learners may be required to successfully complete all, or a subset, of the assessments in order to receive an advancement recommendation from learning analysis logic 130 .
  • An advancement recommendation is essentially a determination that a student is ready to move on to a different goal because a perquisite goal has been achieved by the student.
  • Performance on specific assessments may be analyzed to determine the level, amount or type of learning items a student needs next. For example, a student that performs poorly on an assessment may require remedial learning items that cover a general overview of the subject.
  • the learning item repository includes learning items, such as multimedia, audio, text, and video files.
  • the learning item repository includes learning item metadata that describes the learning items.
  • the learning item metadata may be stored in a database with a relational link to the learning items described by the metadata.
  • a logical hierarchy such as course skeleton 200 is stored in the course skeleton data table 160 in database 150 .
  • the course skeleton is representative of a course of instruction, and each node in the hierarchy represents a learning goal.
  • Corresponding goal metadata that describes each learning goal is associated with each node in the course skeleton.
  • a first user that is working on a goal associated with a particular node in the course skeleton makes a request for learning items to adaptive learning engine 100 .
  • Learning analysis logic 130 generates a query to be executed against the learning item repository using goal metadata associated with the learning goal that the student is attempting to achieve.
  • a learning item or a reference to a learning item from learning items table 170 is returned, and presentation logic 140 presents the learning item to the student.
  • a second user requests learning items from adaptive learning engine in an embodiment.
  • the second user is attempting to achieve the same goal as the first user, and therefore an identical query is generated for the second user.
  • a learning item is returned that is different than the learning item that was returned for the first user.
  • additional learning items is added to the learning item repository between the execution of the query for the first user and the execution of the query for the second user. This additional learning items is determined to be more relevant to the query, and therefore the updated learning items is delivered to the second user.
  • profiles such as PDNA profiles are maintained for users. Queries are generated using arguments (such as predicates) that are based on education-related attributes that are associated with the users. For example, a query is generated to request video learning items that last no longer than 20 minutes for a user that has a low attention span attribute stored in his PDNA profile.
  • learning analysis logic determines that a first student has a profile that is similar to the profile of a second student, and therefore generates the same query for both students. However, other factors may cause the students to receive different learning items, as discussed above.
  • goal metadata is semantically analyzed to determine keywords that should be used in a query to request learning items.
  • a semantic analysis may be performed on user generated data such as forum postings in order to determine topics of interest to the user.
  • One or more keywords associated with the topics of interest are then used to generate a learning item request query.
  • one or more arguments used in a query are identified by the learning items manager as failing to match learning items in the learning item repository.
  • Learning items manager generates a report that identifies the failing arguments.
  • These arguments correspond to attributes that would possibly further enrich the learning experience of one or more users. Therefore, learning items having these attributes may be desirable.
  • the report ranks these attributes from the most to least desirable in an embodiment. For example, the attributes requested most frequently or most recently may be listed at the top of the report, while unpopular attributes are listed at the bottom.
  • the techniques described herein are implemented by one or more special-purpose computing devices.
  • the special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination.
  • ASICs application-specific integrated circuits
  • FPGAs field programmable gate arrays
  • Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques.
  • the special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.
  • FIG. 3 is a block diagram that illustrates a computer system 300 upon which an embodiment of the invention may be implemented.
  • Computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 coupled with bus 302 for processing information.
  • Hardware processor 304 may be, for example, a general purpose microprocessor.
  • Computer system 300 also includes a main memory 306 , such as a random access memory (RAM) or other dynamic storage device, coupled to bus 302 for storing information and instructions to be executed by processor 304 .
  • Main memory 306 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 304 .
  • Such instructions when stored in non-transitory storage media accessible to processor 304 , render computer system 300 into a special-purpose machine that is customized to perform the operations specified in the instructions.
  • Computer system 300 further includes a read only memory (ROM) 308 or other static storage device coupled to bus 302 for storing static information and instructions for processor 304 .
  • ROM read only memory
  • a storage device 310 such as a magnetic disk or optical disk, is provided and coupled to bus 302 for storing information and instructions.
  • Computer system 300 may be coupled via bus 302 to a display 312 , such as a cathode ray tube (CRT), for displaying information to a computer user.
  • a display 312 such as a cathode ray tube (CRT)
  • An input device 314 is coupled to bus 302 for communicating information and command selections to processor 304 .
  • cursor control 316 is Another type of user input device
  • cursor control 316 such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 304 and for controlling cursor movement on display 312 .
  • This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.
  • Computer system 300 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 300 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306 . Such instructions may be read into main memory 306 from another storage medium, such as storage device 310 . Execution of the sequences of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
  • Non-volatile media includes, for example, optical or magnetic disks, such as storage device 310 .
  • Volatile media includes dynamic memory, such as main memory 306 .
  • Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
  • Storage media is distinct from but may be used in conjunction with transmission media.
  • Transmission media participates in transferring information between storage media.
  • transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 302 .
  • transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.
  • Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 304 for execution.
  • the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer.
  • the remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem.
  • a modem local to computer system 300 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal.
  • An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 302 .
  • Bus 302 carries the data to main memory 306 , from which processor 304 retrieves and executes the instructions.
  • the instructions received by main memory 306 may optionally be stored on storage device 310 either before or after execution by processor 304 .
  • Computer system 300 also includes a communication interface 318 coupled to bus 302 .
  • Communication interface 318 provides a two-way data communication coupling to a network link 320 that is connected to a local network 322 .
  • communication interface 318 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line.
  • ISDN integrated services digital network
  • communication interface 318 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN.
  • LAN local area network
  • Wireless links may also be implemented.
  • communication interface 318 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
  • Network link 320 typically provides data communication through one or more networks to other data devices.
  • network link 320 may provide a connection through local network 322 to a host computer 324 or to data equipment operated by an Internet Service Provider (ISP) 326 .
  • ISP 326 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 328 .
  • Internet 328 uses electrical, electromagnetic or optical signals that carry digital data streams.
  • the signals through the various networks and the signals on network link 320 and through communication interface 318 which carry the digital data to and from computer system 300 , are example forms of transmission media.
  • Computer system 300 can send messages and receive data, including program code, through the network(s), network link 320 and communication interface 318 .
  • a server 330 might transmit a requested code for an application program through Internet 328 , ISP 326 , local network 322 and communication interface 318 .
  • the received code may be executed by processor 304 as it is received, and/or stored in storage device 310 , or other non-volatile storage for later execution.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Educational Administration (AREA)
  • Educational Technology (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Health & Medical Sciences (AREA)
  • Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Electrically Operated Instructional Devices (AREA)
US13/271,328 2011-10-12 2011-10-12 Course skeleton for adaptive learning Abandoned US20130095461A1 (en)

Priority Applications (9)

Application Number Priority Date Filing Date Title
US13/271,328 US20130095461A1 (en) 2011-10-12 2011-10-12 Course skeleton for adaptive learning
US13/409,006 US20130095465A1 (en) 2011-10-12 2012-02-29 Course skeleton for adaptive learning
EP12840658.4A EP2766868A4 (en) 2011-10-12 2012-10-11 KURSSKELETT FOR ADAPTIVE LEARNING
MX2014004409A MX341699B (es) 2011-10-12 2012-10-11 Esqueleto de curso para el aprendizaje adaptativo.
BR112014008823A BR112014008823A2 (pt) 2011-10-12 2012-10-11 método de apredizado adaptativo, um ou mais menos de armazenamento não-transitórios e sistema de comoputador
PCT/US2012/059822 WO2013055966A1 (en) 2011-10-12 2012-10-11 Course skeleton for adaptive learning
CN201280060239.7A CN103975362A (zh) 2011-10-12 2012-10-11 用于自适应学习的课程骨架
CA2851797A CA2851797A1 (en) 2011-10-12 2012-10-11 Course skeleton for adaptive learning
US13/913,965 US10360809B2 (en) 2011-10-12 2013-06-10 Course skeleton for adaptive learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US13/271,328 US20130095461A1 (en) 2011-10-12 2011-10-12 Course skeleton for adaptive learning

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US13/409,006 Continuation US20130095465A1 (en) 2011-10-12 2012-02-29 Course skeleton for adaptive learning
US13/913,965 Continuation US10360809B2 (en) 2011-10-12 2013-06-10 Course skeleton for adaptive learning

Publications (1)

Publication Number Publication Date
US20130095461A1 true US20130095461A1 (en) 2013-04-18

Family

ID=48082455

Family Applications (3)

Application Number Title Priority Date Filing Date
US13/271,328 Abandoned US20130095461A1 (en) 2011-10-12 2011-10-12 Course skeleton for adaptive learning
US13/409,006 Abandoned US20130095465A1 (en) 2011-10-12 2012-02-29 Course skeleton for adaptive learning
US13/913,965 Active 2034-09-14 US10360809B2 (en) 2011-10-12 2013-06-10 Course skeleton for adaptive learning

Family Applications After (2)

Application Number Title Priority Date Filing Date
US13/409,006 Abandoned US20130095465A1 (en) 2011-10-12 2012-02-29 Course skeleton for adaptive learning
US13/913,965 Active 2034-09-14 US10360809B2 (en) 2011-10-12 2013-06-10 Course skeleton for adaptive learning

Country Status (7)

Country Link
US (3) US20130095461A1 (pt)
EP (1) EP2766868A4 (pt)
CN (1) CN103975362A (pt)
BR (1) BR112014008823A2 (pt)
CA (1) CA2851797A1 (pt)
MX (1) MX341699B (pt)
WO (1) WO2013055966A1 (pt)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110101806A1 (en) * 2008-07-11 2011-05-05 Loevgren Torbjoern Safety Device for a Linear Actuator and a Linear Actuator
US20110177483A1 (en) * 2010-01-15 2011-07-21 Catherine Needham Recommending competitive learning objects
US20130262449A1 (en) * 2012-04-02 2013-10-03 Playence GmBH System and method for search refinement using knowledge model
US20140214385A1 (en) * 2013-01-30 2014-07-31 Mark Gierl Automatic item generation (aig) manufacturing process and system
US20150243177A1 (en) * 2014-02-24 2015-08-27 Eopin Oy Providing an and audio and/or video component for computer-based learning
US20160358494A1 (en) * 2015-06-03 2016-12-08 D2L Corporation Methods and systems for providing a learning path for an electronic learning system
US20170011642A1 (en) * 2015-07-10 2017-01-12 Fujitsu Limited Extraction of knowledge points and relations from learning materials
US9576494B2 (en) 2014-01-29 2017-02-21 Apollo Education Group, Inc. Resource resolver
US20170193847A1 (en) * 2015-12-31 2017-07-06 Callidus Software, Inc. Dynamically defined content for a gamification network system
US9997083B2 (en) 2014-05-29 2018-06-12 Samsung Electronics Co., Ltd. Context-aware recommendation system for adaptive learning
CN109920288A (zh) * 2019-03-29 2019-06-21 上海乂学教育科技有限公司 自适应学习任务智能生成装置及计算机学习系统
US10460245B2 (en) * 2015-09-04 2019-10-29 Civitas Learning, Inc. Flexible, personalized student success modeling for institutions with complex term structures and competency-based education
US20210342418A1 (en) * 2013-04-05 2021-11-04 Eab Global, Inc. Systems and methods for processing data to identify relational clusters

Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130095461A1 (en) 2011-10-12 2013-04-18 Satish Menon Course skeleton for adaptive learning
US20160035238A1 (en) * 2013-03-14 2016-02-04 Educloud Co. Ltd. Neural adaptive learning device using questions types and relevant concepts and neural adaptive learning method
US9779084B2 (en) 2013-10-04 2017-10-03 Mattersight Corporation Online classroom analytics system and methods
US20150154878A1 (en) * 2013-12-04 2015-06-04 The Trustees Of The Stevens Institute Of Technology Intelligent Adaptive Integrated Learning Environment
CA2896037A1 (en) * 2014-07-03 2016-01-03 Mentorum Solutions Inc. Adaptive e-learning system and method
US10546507B2 (en) 2014-09-18 2020-01-28 International Business Machines Corporation Recommending a set of learning activities based on dynamic learning goal adaptation
US20160155345A1 (en) * 2014-12-02 2016-06-02 Yanlin Wang Adaptive learning platform
JP6396813B2 (ja) * 2015-01-27 2018-09-26 Kddi株式会社 学習映像からその学習に費やされた学習項目を推定するプログラム、装置及び方法
EP3304438A4 (en) * 2015-06-07 2019-01-16 Sarafzade, Ali DEVELOPMENT OF THE TRAINING LEVEL AND EVALUATION SYSTEM
US10614368B2 (en) 2015-08-28 2020-04-07 Pearson Education, Inc. System and method for content provisioning with dual recommendation engines
US10325215B2 (en) * 2016-04-08 2019-06-18 Pearson Education, Inc. System and method for automatic content aggregation generation
CN106205244A (zh) * 2016-07-04 2016-12-07 杭州医学院 基于信息融合与机器学习的智能计算机辅助教学系统
US10373511B2 (en) 2016-09-06 2019-08-06 International Business Machines Corporation Automatic learning curriculum generation
US11010849B2 (en) 2017-08-31 2021-05-18 East Carolina University Apparatus for improving applicant selection based on performance indices
US10896620B2 (en) * 2018-06-07 2021-01-19 Microsoft Technology Licensing, Llc Programmable interface for automated learning refreshers
WO2024006154A1 (en) * 2022-06-28 2024-01-04 HonorEd Technologies, Inc. Comprehension modeling and ai-sourced student content recommendations

Family Cites Families (64)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH04357549A (ja) * 1991-03-07 1992-12-10 Hitachi Ltd 教育システム
US5493688A (en) * 1991-07-05 1996-02-20 Booz, Allen & Hamilton, Inc. Pattern categoritzation system having self-organizing analog fields
US6334779B1 (en) * 1994-03-24 2002-01-01 Ncr Corporation Computer-assisted curriculum
US5779486A (en) 1996-03-19 1998-07-14 Ho; Chi Fai Methods and apparatus to assess and enhance a student's understanding in a subject
IL128979A (en) * 1996-09-25 2004-06-20 Sylvan Learning Systems Inc System for automated experiments, electronic instructional delivery and student management
US6039575A (en) * 1996-10-24 2000-03-21 National Education Corporation Interactive learning system with pretest
US6091930A (en) * 1997-03-04 2000-07-18 Case Western Reserve University Customizable interactive textbook
US5957699A (en) * 1997-12-22 1999-09-28 Scientific Learning Corporation Remote computer-assisted professionally supervised teaching system
US6322366B1 (en) * 1998-06-30 2001-11-27 Assessment Technology Inc. Instructional management system
US6149441A (en) * 1998-11-06 2000-11-21 Technology For Connecticut, Inc. Computer-based educational system
US6301462B1 (en) * 1999-01-15 2001-10-09 Unext. Com Online collaborative apprenticeship
US6353447B1 (en) * 1999-01-26 2002-03-05 Microsoft Corporation Study planner system and method
US20010031456A1 (en) 1999-12-30 2001-10-18 Greg Cynaumon Education system and method for providing educational exercises and establishing an educational fund
US20010049087A1 (en) * 2000-01-03 2001-12-06 Hale Janet B. System and method of distance education
US6505031B1 (en) * 2000-02-25 2003-01-07 Robert Slider System and method for providing a virtual school environment
WO2001086611A1 (en) * 2000-05-11 2001-11-15 Reynolds Thomas J Interactive method and system for teaching decision making
US6507726B1 (en) * 2000-06-30 2003-01-14 Educational Standards And Certifications, Inc. Computer implemented education system
US20020049689A1 (en) * 2000-10-20 2002-04-25 Srinivas Venkatram Systems and methods for visual optimal ordered knowledge learning structures
US6606480B1 (en) * 2000-11-02 2003-08-12 National Education Training Group, Inc. Automated system and method for creating an individualized learning program
US6626679B2 (en) * 2000-11-08 2003-09-30 Acesync, Inc. Reflective analysis system
WO2002056279A1 (en) * 2001-01-09 2002-07-18 Prep4 Ltd. Training system and method for improving user knowledge and skills
JP3731868B2 (ja) * 2001-03-19 2006-01-05 本田技研工業株式会社 学習システム
US6978115B2 (en) * 2001-03-29 2005-12-20 Pointecast Corporation Method and system for training in an adaptive manner
US20020142273A1 (en) 2001-03-30 2002-10-03 Dollins James T. Interactive process learning aid
US20020188583A1 (en) * 2001-05-25 2002-12-12 Mark Rukavina E-learning tool for dynamically rendering course content
US6953344B2 (en) * 2001-05-30 2005-10-11 Uri Shafrir Meaning equivalence instructional methodology (MEIM)
US6782396B2 (en) * 2001-05-31 2004-08-24 International Business Machines Corporation Aligning learning capabilities with teaching capabilities
US20030003433A1 (en) 2001-06-29 2003-01-02 Ignite, Inc. Method and system for constructive, modality focused learning
US20030039948A1 (en) * 2001-08-09 2003-02-27 Donahue Steven J. Voice enabled tutorial system and method
US7165054B2 (en) 2001-09-14 2007-01-16 Knowledgextensions, Inc. Custom electronic learning system and method
US20040018479A1 (en) * 2001-12-21 2004-01-29 Pritchard David E. Computer implemented tutoring system
US7677896B1 (en) 2002-02-19 2010-03-16 Nishikant Sonwalkar System, method, and computer-readable medium for course structure design to support adaptive learning
US20040024569A1 (en) * 2002-08-02 2004-02-05 Camillo Philip Lee Performance proficiency evaluation method and system
US20040161728A1 (en) * 2003-02-14 2004-08-19 Benevento Francis A. Distance learning system
WO2004075015A2 (en) * 2003-02-14 2004-09-02 Ctb/Mcgraw-Hill System and method for creating, assessing, modifying, and using a learning map
JP4086298B2 (ja) 2003-06-17 2008-05-14 アルパイン株式会社 物体検出方法及び装置
US8182270B2 (en) * 2003-07-31 2012-05-22 Intellectual Reserve, Inc. Systems and methods for providing a dynamic continual improvement educational environment
US20050058978A1 (en) * 2003-09-12 2005-03-17 Benevento Francis A. Individualized learning system
JP2005099914A (ja) * 2003-09-22 2005-04-14 Fuji Xerox Co Ltd コンテンツ推薦システム及びコンテンツ推薦方法、並びにコンピュータ・プログラム
US20050186550A1 (en) * 2004-02-23 2005-08-25 Mubina Gillani System and method for dynamic electronic learning based on continuing student assessments and responses
US20070203589A1 (en) 2005-04-08 2007-08-30 Manyworlds, Inc. Adaptive Recombinant Process Methods
US20060068367A1 (en) * 2004-08-20 2006-03-30 Parke Helen M System and method for content management in a distributed learning system
US20060188860A1 (en) * 2005-02-24 2006-08-24 Altis Avante, Inc. On-task learning system and method
US8326659B2 (en) * 2005-04-12 2012-12-04 Blackboard Inc. Method and system for assessment within a multi-level organization
US20070009871A1 (en) * 2005-05-28 2007-01-11 Ctb/Mcgraw-Hill System and method for improved cumulative assessment
US20070099161A1 (en) * 2005-10-31 2007-05-03 Krebs Andreas S Dynamic learning courses
US20070111188A1 (en) 2005-11-17 2007-05-17 Shell Timothy A Reference card creation system and method
WO2007062450A1 (en) * 2005-12-02 2007-06-07 University Of New England Assessment of educational services
US20070202484A1 (en) 2006-02-28 2007-08-30 Michael Toombs Method and System for Educating Individuals
US20080014569A1 (en) 2006-04-07 2008-01-17 Eleutian Technology, Llc Teacher Assisted Internet Learning
US8364514B2 (en) 2006-06-27 2013-01-29 Microsoft Corporation Monitoring group activities
US20100035225A1 (en) * 2006-07-11 2010-02-11 President And Fellows Of Harvard College Adaptive spaced teaching method and system
US20080134045A1 (en) 2006-07-13 2008-06-05 Neustar, Inc. System and method for adaptively and dynamically configuring a graphical user interface of a mobile communication device
US20080038705A1 (en) * 2006-07-14 2008-02-14 Kerns Daniel R System and method for assessing student progress and delivering appropriate content
US20080254438A1 (en) 2007-04-12 2008-10-16 Microsoft Corporation Administrator guide to student activity for use in a computerized learning environment
US20080254434A1 (en) * 2007-04-13 2008-10-16 Nathan Calvert Learning management system
US20090035733A1 (en) * 2007-08-01 2009-02-05 Shmuel Meitar Device, system, and method of adaptive teaching and learning
US8506304B2 (en) 2008-01-23 2013-08-13 Carol Conner Method for recommending a teaching plan in literacy education
US8366449B2 (en) * 2008-08-13 2013-02-05 Chi Wang Method and system for knowledge diagnosis and tutoring
US8417660B2 (en) 2009-07-10 2013-04-09 Medimpact Healthcare Systems, Inc. Modifying a patient adherence score
US20110039249A1 (en) * 2009-08-14 2011-02-17 Ronald Jay Packard Systems and methods for producing, delivering and managing educational material
CA2787133A1 (en) * 2010-01-15 2011-07-21 Apollo Group, Inc. Dynamically recommending learning content
KR20110090387A (ko) * 2010-02-03 2011-08-10 테크빌닷컴 주식회사 사용자 학습 진행 상태를 고려한 온라인 교육 시스템 및 방법
US20130095461A1 (en) 2011-10-12 2013-04-18 Satish Menon Course skeleton for adaptive learning

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110101806A1 (en) * 2008-07-11 2011-05-05 Loevgren Torbjoern Safety Device for a Linear Actuator and a Linear Actuator
US20110177483A1 (en) * 2010-01-15 2011-07-21 Catherine Needham Recommending competitive learning objects
US20110177482A1 (en) * 2010-01-15 2011-07-21 Nitzan Katz Facilitating targeted interaction in a networked learning environment
US20130262449A1 (en) * 2012-04-02 2013-10-03 Playence GmBH System and method for search refinement using knowledge model
US20140214385A1 (en) * 2013-01-30 2014-07-31 Mark Gierl Automatic item generation (aig) manufacturing process and system
US20210342418A1 (en) * 2013-04-05 2021-11-04 Eab Global, Inc. Systems and methods for processing data to identify relational clusters
US9576494B2 (en) 2014-01-29 2017-02-21 Apollo Education Group, Inc. Resource resolver
US20150243177A1 (en) * 2014-02-24 2015-08-27 Eopin Oy Providing an and audio and/or video component for computer-based learning
US9997083B2 (en) 2014-05-29 2018-06-12 Samsung Electronics Co., Ltd. Context-aware recommendation system for adaptive learning
US20160358494A1 (en) * 2015-06-03 2016-12-08 D2L Corporation Methods and systems for providing a learning path for an electronic learning system
US9852648B2 (en) * 2015-07-10 2017-12-26 Fujitsu Limited Extraction of knowledge points and relations from learning materials
US20170011642A1 (en) * 2015-07-10 2017-01-12 Fujitsu Limited Extraction of knowledge points and relations from learning materials
US10460245B2 (en) * 2015-09-04 2019-10-29 Civitas Learning, Inc. Flexible, personalized student success modeling for institutions with complex term structures and competency-based education
US20170193847A1 (en) * 2015-12-31 2017-07-06 Callidus Software, Inc. Dynamically defined content for a gamification network system
CN109920288A (zh) * 2019-03-29 2019-06-21 上海乂学教育科技有限公司 自适应学习任务智能生成装置及计算机学习系统

Also Published As

Publication number Publication date
BR112014008823A2 (pt) 2017-04-18
EP2766868A4 (en) 2015-04-01
CA2851797A1 (en) 2013-04-18
MX341699B (es) 2016-08-31
MX2014004409A (es) 2014-08-27
WO2013055966A1 (en) 2013-04-18
US20130280690A1 (en) 2013-10-24
US20130095465A1 (en) 2013-04-18
EP2766868A1 (en) 2014-08-20
CN103975362A (zh) 2014-08-06
WO2013055966A4 (en) 2013-07-18
US10360809B2 (en) 2019-07-23

Similar Documents

Publication Publication Date Title
US10360809B2 (en) Course skeleton for adaptive learning
US9583016B2 (en) Facilitating targeted interaction in a networked learning environment
Klašnja-Milićević et al. E-learning systems: Intelligent techniques for personalization
Hardin et al. Data science in statistics curricula: Preparing students to “think with data”
Wang et al. Using a style-based ant colony system for adaptive learning
US11715385B2 (en) Systems and methods for autonomous creation of personalized job or career training curricula
US11776080B2 (en) Automatically generating a personalized course profile
US20070224586A1 (en) Method and system for evaluating and matching educational content to a user
US20190189020A1 (en) Arrangements for delivery of a tailored educational experience
Tummons Theoretical trajectories within communities of practice in higher education research
Han Student modelling and adaptivity in web-based learning systems
Conceição et al. Using developmental research to design, develop, and evaluate an urban education portal
Holden From local challenges to a global community: learning repositories and the global learning repositories summit
Brooks et al. Flexible learning object metadata
Ismaili et al. D-learning and COVID-19 crisis: Appraisal of reactions and means of perpetuity
GanChanyawudhiwan Use of Smart Learning Resource Management Systems for Sustainable Learning
Zavadskas et al. The use of the intelligent library and tutoring system at all stages of a building life cycle
Aboraya Exploring the Need for Using Digital Repositories to Enhance Teaching and Learning in Omani Schools: Teachers’ Perceptions
Blank et al. A web-based ITS for OO design
Baghaei et al. Problem-Solving Support in a Constraint-based Tutor for UML Class Diagrams
Song An authoring and presentation environment for interactive worked examples
Woolf et al. Customizing the instructional grid
Sosnovsky Ontology-based open-Corpus Personalization for e-Learning
CN112258359A (zh) 一种自适应推荐方法及系统
Shi Template-driven teacher modelling approach: a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Information Science at Massey University, Palmerston North

Legal Events

Date Code Title Description
AS Assignment

Owner name: APOLLO GROUP, INC., ARIZONA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MENON, SATISH;MUTHUKUMARASAMY, JAYAKUMAR;SAHA, PARTHA;AND OTHERS;SIGNING DATES FROM 20111006 TO 20111011;REEL/FRAME:027048/0942

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION

AS Assignment

Owner name: THE UNIVERSITY OF PHOENIX, INC., ARIZONA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:APOLLO EDUCATION GROUP, INC.;REEL/FRAME:053308/0512

Effective date: 20200626