Data communications network and method for improving a user learning experience

Abstract

The present invention relates to a method of operating a data network to improve a user learning experience and, in particular, evaluating users according to psychological, personality, physiological, cognitive and skill assessments combined with one or more predefined evaluation principles to generate a customized educational plan tailored to the user. In particular, an assessment of the user is conducted where one or more evaluation principles are used to determine learning attributes of the user (eg. intrinsic abilities, tendencies, decision making processes, communication preferences, learning or work styles including preferred methods of learning or working, learning strengths, developmental requirements, and professional talents). Knowledge of the user's learning attributes using this multidimensional approach, ensures a more customized educational plan that is more likely to keep the user motivated and engaged since their individual learning styles and interests are recognized and accommodated.

Description

DATA COMMUNICATIONS NETWORK AND METHOD FOR IMPROVING A USER LEARNING EXPERIENCEFIELD OF THE INVENTION

[0001] The present invention relates to a data communications network and method of operating the network to improve a user learning experience and, in particular, evaluating users according to psychological, personality, physiological, cognitive and skill assessments combined with one or more predefined evaluation principles to generate a customized educational plan tailored to the user.BACKGROUND OF THE INVENTION

[0002] Learning and the transfer of knowledge from educators to individuals seeking to learn (eg. students) has always been considered important to society. Educators have searched for methods to improve learning and the communication of knowledge such that it is easier and more efficient. Historically, information has been passed from educator to student through storytelling, traditional classroom teaching methods using published materials, and more recently through multimedia and audio visual presentations.

[0003] When preparing learning courses or experiences for students, educators often face several difficulties. For example, different students learn differently, and it is important to consider as many student attributes (eg. intrinsic tendencies, decisionmaking processes, learning styles, talents, etc) as possible during the creation of a learning experience. However, this is often not possible and educators will generally implement a standard teaching method irrespective of individual user attributes. For this reason, the educational plans prepared for users / students tend to follow a generic path and are not tailored to suit individual students. This problem is exacerbated when educators are not sufficiently resourced or have too many students to teach at the same time.

[0004] Whilst there exist tailored courses for students, these also have inherent problems. For example, such tailored programs tend to require significant time and resources on the part of educators (including significant use and wastage of computer program and network resources) to craft programs and courses that are individuallytailored to students, and such time and resources are potentially not available to educators. Furthermore, the range of data associated with a student that is received and utilized to create an educational plan tends to be insufficiently robust to truly improve the efficacy and personalisation of the learning experience. Since most learning in respect of course material provided by educators is delivered using a data communications network with participants connecting with each other by the use of operably connected data communications devices, any preparation and delivery of learning materials must consider the technical issues involved, and problems presented thereby, when implementing a transfer of knowledge.

[0005] Accordingly, in view of the above-described problems, there is a need to be able to create a more effective and engaging environment for learning that takes into account the technical issues associated with online learning. There is also a need to be able to present improved learning experiences for students in a manner that is more engaging.

[0006] The present invention seeks to mitigate the problems discussed herein, or to at least provide an alternative solution.

[0007] The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any suggestion, that the prior art forms part of the common general knowledge.SUMMARY OF THE INVENTION

[0008] In one aspect, the present invention provides a data communications network operably connected with one or more data communications devices and one or more individual user devices, and a method of operating same to improve a user learning experience, the method including generating, by one or more processors, a user device interface for the user to submit data pertaining to the user, establishing, by one or more processors, a user account according to the data submitted by the user, obtaining, by one or more processors, additional data sufficient to enable analyses to be conducted on the data to establish an assessment of the user according to any one or more of inventories of topics of interest relevant to the user, a psychological evaluation of the user, a personality attributes evaluation of the user, a physiological evaluation of the user, a skills evaluation of the user, and a cognitive profile of the user, evaluating, by one or more processors, the results of the analyses using any one or more evaluation principles to determine learning attributes of the user including any one or more of intrinsic abilities, tendencies, decision making processes, communication preferences, learning or work styles including preferred methods of learning or working, learning strengths, developmental requirements, and professional talents, and generating, by one or more processors, according to the evaluation, one or more customized educational plans tailored to the user and providing the educational plan(s) to the user by the user device interface.

[0009] In an embodiment, the evaluation principles address one or more of, energy type (eg. the type of user including their overall function and intended mode of interaction with others), strategy (eg. a strategy associated with each user type that describes a preferred approach of the user with respect to how they should best approach life’s decisions and situations in relation to the user’s overall function and intended mode of interaction with others, authority (eg. an internal mechanism associated with the user that is designed to make decisions that are correct for them), profile (eg. the user’s personality), energy centres (eg. the user’s physiology), incarnation cross (eg. the user’s life mission), channels (eg. the user’s talents), or variables (eg. the user’s primary health system).

[0010] In an embodiment, using the energy type evaluation principle includes establishing the type of user by classifying the user according to one or more of a, manifestor, generator, manifesting generator, projector, or reflector.

[0011] In an embodiment, the one or more customized educational plans are presented to the user in a report that includes any one or more of, play, lessons, micro-lessons, quizzes, learning activities, educational pathways, analogical explanations, recommendations regarding extracurricular and academic activities, guidance with respect to relationships and careers, or opportunity for ongoing personal development.

[0012] In an embodiment, the analyses conducted to establish any one or more of the inventories of topics of interest relevant to the user, a psychological evaluation of the user, a personality attributes evaluation of the user, a physiological evaluation of the user, a skills evaluation of the user, and a cognitive profile of the user, may implement one or more Al techniques to assist the performance of one or more elements in the evaluation.

[0013] In this way, a data communications network is provided which is operably connected to data communications devices (eg. computer servers), and individual user devices (eg. smartphones), wherein the network has the capability to improve the efficiency and personalization of learning experiences that incorporate various Artificial Intelligence (Al) techniques to perform thorough assessments regarding each user’s talents, abilities, characteristics, learning preferences, cognitive profile, etc, for the purpose of developing a highly customized education strategy.

[0014] In an embodiment, the user interface may include a personalised dashboard with user data entry assisted by the implementation of one or more Al techniques (such as natural language processing (NLP)).

[0015] In an embodiment, the one or more customized educational plans are reported in different styles depending on the age of user.

[0016] In an embodiment, the method further includes adapting the one or more customized educational plans to take into account assessments in respect of the individual user according to continuous evaluations performed.

[0017] In an embodiment, the continuous evaluations performed include one or more of evaluations performed upon receipt of updated user data and / or updated user instructions, or evaluations performed upon receipt of captured user interaction, assessment and / or engagement data.

[0018] In an embodiment, the method further includes modifying educational content difficulty, structure and / or delivery in response to the continuous evaluations.

[0019] In an embodiment, the method further includes conducting an analysis of one or more evaluations and / or modifications over time and devising a preferred adaptation of a customized educational plan for the user.

[0020] In an embodiment, upon receipt of captured user interaction, assessment and / or engagement data, the method further includes causing the user device to undertake learning by locally training partial learning models using the captured user interaction, assessment and / or engagement data.

[0021] In an embodiment, the method further includes aggregating and evaluating, by a server, encrypted interaction, assessment and / or engagement data or updates related thereto from the device whilst avoiding the transfer of underlying personal data of the user.

[0022] In an embodiment, the method further includes redeploying the data, updates and / or evaluation(s) to the device for use by the locally trained models.

[0023] In an embodiment, the method further includes optimising plan delivery aspects in substantially real-time by applying a closed adaptive learning feedback loop.

[0024] In an embodiment, the adaptive learning feedback loop includes monitoring the user interaction, assessment and / or engagement data, assigning reward values corresponding to positive learning outcomes, modifying, according to the reward values, the plan delivery aspects to adapt one or more of sequencing, pacing or content selection of educational materials, and applying the adaptations when generating subsequent plans.

[0025] In an embodiment, the method further includes implementing a tamper-evidence audit trail using a cryptographic hash chain or blockchain-based ledger such that each educational plan and associated update event is hashed and timestamped, providing verifiable authenticity and a compliance-grade data trail.

[0026] In an embodiment, the method further includes conducting analyses using one or more Al techniques to determine groups of users according to compatibility, whereincompatibility is determined according to at least psychological and / or cognitive similarities, and delivering the educational plans to users according to their determined group.

[0027] In an embodiment, the method further includes developing and training one or more machine learning models to identify trends and predict growth trajectories for individual users and / or groups of users to further develop their educational and skill outcomes.

[0028] In an embodiment, each generated educational plan includes explainability metadata explaining or identifying one or more of feature-importance metrics, confidence levels, key feature contributions, or counterfactual comparisons or reasoning.

[0029] In another aspect, the present invention provides a computer-implemented system for improving a user learning experience, the system including one or more processors operable to execute computer instruction code to implement an electronic database including generating a user device interface for the user to submit data pertaining to the user, establishing, according to the data submitted by the user, a user account, obtaining additional data sufficient to enable analyses to be conducted on the data to establish an assessment of the user according to any one or more of inventories of topics of interest relevant to the user, a personality attributes evaluation of the user, a physiological evaluation of the user, a psychological evaluation of the user, a skills evaluation of the user, and a cognitive profile of the user, evaluating, according to results of the analyses using any one or more evaluation principles to determine learning attributes of the user including any one or more of intrinsic abilities, tendencies, decision making processes, communication preferences, learning or work styles including preferred methods of learning or working, learning strengths, developmental requirements, and professional talents, and generating, according to the evaluation, one or more customized educational plans tailored to the user and providing the educational plan(s) to the user by the user device interface.

[0030] In a yet further aspect, the present invention provides a non-transitory computer- readable medium including computer instruction code that, when executed on a computer, causes one or more processors of the computer to perform the steps of generating a user device interface for the user to submit data pertaining to the user,establishing, according to the data submitted by the user, a user account, obtaining additional data sufficient to enable analyses to be conducted on the data to establish an assessment of the user according to any one or more of inventories of topics of interest relevant to the user, a personality attributes evaluation of the user, a physiological evaluation of the user, a psychological evaluation of the user, a skills evaluation of the user, and a cognitive profile of the user, evaluating, the results of the analyses using any one or more evaluation principles to determine learning attributes of the user including one or more of intrinsic abilities, tendencies, decision making processes, communication preferences, learning or work styles including preferred methods of learning or working, learning strengths, developmental requirements, and professional talents, and generating, according to the evaluation, one or more customized educational plans tailored to the user and providing the educational plan(s) to the user by the user device interface.BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Embodiments of the invention will now be described in further detail with reference to the accompanying Figures in which:

[0032] Figure 1 provides an overview of a data communications network according to an embodiment of the present invention showing, in particular, the interaction between various network components;

[0033] Figure 2 illustrates a diagram associated with an exemplary server component within the network illustrated in Figure 1 ;

[0034] Figure 3 illustrates an exemplary flow diagram of a process that enables users to download and install a software application and subsequently access, or register to use, the software application for interaction with the network illustrated in Figure 1 , including to enable a user to submit data with respect to the user for the purpose of establishing a user account;

[0035] Figure 4 illustrates a further exemplary flow diagram of a process that enables additional data that is sufficient to enable analyses to be conducted on the data to be obtained, and for the analyses as well as an evaluation of the results of the analyses based on one or more evaluation principles to be conducted;

[0036] Figure 5 illustrates a diagram associated with various example interfaces of the software application for providing educational plans to users; and

[0037] Figure 6 illustrates a diagram associated with further example interfaces of the software application that enable additional functionality including continuous evaluations and the development of educational and skill outcomes through additional analyses.DETAILED DESCRIPTION OF EMBODIMENT(S) OF THE INVENTION

[0038] For simplicity and illustrative purposes, the present disclosure is described by referring to embodiment(s) thereof. In the following description, numerous specific details are set forth to provide a better understanding of the present disclosure. It will be apparent, however, that the current disclosure may be practiced without limitation to the specific details. In other instances, some features have not been described in detail to avoid obscuring the present disclosure.

[0039] The present invention, according to an embodiment depicted in Figure 1 , relates to a data communication network and method for utilising the network to improve a user learning experience, including by providing users (30) with customized educational plans (70) tailored to the users (30) wherein the education strategy is highly customized due to the collection of a diversified range of data pertaining to the user (30) including learning attributes and evaluations of the user (30) and the incorporation of various artificial intelligence techniques as described herein.

[0040] The network and method provide a platform that hosts a computer implemented software application (40), wherein the application (40) is accessible by the users (30) who are individuals (eg. students) seeking to improve their learning outcomes.

[0041] The platform is provided by a central server (20) which maintains one or more processors and / or databases for performing functions, including generating a user interface (160) that enables the user (30) to submit data pertaining to the user (30) and establish a user account according to the data submitted by the user (30). The server (20) also obtains additional data (60) from the user (30) and / or from additional sources (eg. external sources) sufficient to enable particular analyses to be conducted in order to establish an assessment of the user (30) according to at least one of inventories of topics of interest relevant to the user (30), a psychological evaluation of the user (30), a personality attribution evaluation of the user (30), a physiological evaluation of the user (30), a skill evaluation of the user (30) and / or a cognitive profile of the user (30). It will be appreciated that this initial analysis establishes an overall evaluation of the user (30).

[0042] The server (20) is further configured to evaluate the results of the analyses using one or more evaluation principles (eg. energy type, strategy, authority, profile, energy centers, incarnation cross, channels, variables, etc, as described in further detail below)to determine the user’s learning attributes including one or more of the user’s intrinsic abilities, tendencies, decision making processes, learning styles (including preferred method of learnings), learning strengths, developmental requirements, and / or professional talents. The evaluation principle(s) used in the evaluation may be automatically selected based on the initial analyses or may be manually selected by the user (30) or another user (eg. an educator). According to the user evaluation, the server (20) may then generate a customized educational plan (70) which will be tailored to the user (30) and provided to the user (30) via the user interface (180).

[0043] The person skilled in the relevant field of technology will appreciate that the platform provides a solution to existing problems associated with the different learning needs and preferences of different students, including the highly generic teaching methods which currently exist that do not address individual user attributes and needs. There is a need to improve the efficacy and personalisation of learning experiences and this is provided by one or more implementations of the present invention described herein, including through the utilisation of Artificial Intelligence (Al) techniques.

[0044] The person skilled in the relevant field of technology will also appreciate that the efficiencies resulting from the use of such a platform will give rise to conservation of computer processing, memory and networking resources since there is a reduced need for educators and users to seek such information on an individual basis, ie. using each of their devices to access multiple platforms, resources and websites in an effort to address the above problems, noting that increased use of such devices give rise to substantive utilization of resources.

[0045] In view of these observable effects arising from implementation of the present invention, the use of data communications devices (50) on which the software application (40) described herein is operated, in combination with the functionality of server (20), presents a more efficient use of computing resources as compared with known computing technology utilized for assisting the development of educational programs.

[0046] Figure 1 is divided into segments which are further expanded in subsequent Figures 2-6. In particular, segment 200 of Figure 1 illustrates the server component (20) with which a software application (40) operating on data communications devices (50) of individual users (30) are configured to communicate. It will be apparent to the personskilled in the relevant field of technology that the software application (40) may be a mobile application or web-based application. Similarly, the device (50) utilised by each user (30) may be a portable device such as a mobile (cell) phone, laptop, or tablet, or alternatively, a fixed location device such as a workstation or personal computer (not shown). The server component (20) is further detailed in Figure 2.

[0047] The person skilled in the relevant field of technology will also appreciate that the steps described herein may be executed by the devices (50), wherein such operations are facilitated by the software application (40) operating on each device (50). According to another implementation of the present invention, the server (20) may be programmed to provide all of the functions described herein, particularly where they cannot be provided locally on the user device (50) or where it may be incompatible or commercially infeasible to operate such a configuration. In other words, the steps described herein as performed by the devices (50), or components thereof, may be associated with hardware that is located externally of the devices, such as the remote central server (20) (ie. in a distributed architecture).

[0048] Different arrangements are possible in this regard and alternate variations will be available to the person skilled in the relevant field of technology. For example, in another implementation, one or more trained models may operate in a federated architecture using a federated model update module which performs on-device learning at the user device (50) (eg. where the user devices (50) locally train partial learning models using captured user interaction, assessment and / or engagement data), with secure aggregation to the central server (20) using a model aggregator (eg. where the server (20) aggregates encrypted data and / or updates without transferring underlying personal data, and where the same are subsequently redeployed to the devices (50) for continuous improvement). This results in technical benefits including reduced network load, enhanced privacy, and distributed model evolution.

[0049] Segment 300 of Figure 1 illustrates that the server (20) may be configured for communication (140) with devices (50) associated with the user (30). As detailed in Figure 3, according to an example, the server (20) may receive data from the user device (50) for the purpose of creating a user profile (eg. based upon entry of details using interface (160) from users (30)). The server (20) may also verify such details prior to the establishment of an account and account profile. Segment 300 of Figure 1 furtherillustrates a user (30) downloading and installing the application (40) and subsequently accessing the application (40) to establish their account and user profile. Segment 400 of Figure 1 illustrates an example interface (170) accessible by users (30) to provide, or to instruct the platform to obtain additional data (60), which may include data from one or more additional sources including external sources. The additional data (60) will be sufficient to enable analyses to be conducted on the data to establish the abovedescribed outcomes of the analyses (eg. a psychological evaluation of the user (30), personality attributes evaluation of the user (30), etc), and for such analyses to be combined with one or more evaluation principles to enable an additional evaluation regarding the user (eg. evaluation(s) to determine the users’ intrinsic abilities, tendencies, decision making processes, etc), as further detailed in Figure 4.

[0050] Segment 500 of Figure 1 illustrates example interfaces (180) that enable various different customized educational plans which are tailored to the user (30) to be delivered to the user (30) via the interface (180) which is accessible by the user device (50), as further detailed in Figure 5. Finally, segment 600 of Figure 1 illustrates additional example data interfaces including interface (190) that enables continuous evaluations to be conducted and, if applicable, the delivery of updated educational plans to the user (30), and interface (210) which enables further analyses and / or assessment to be performed in order to further develop educational and skill outcomes of the user (30), as further detailed in Figure 6.

[0051] As mentioned above, Figure 2 illustrates in greater detail segment 200 of Figure 1 and, in particular, Figure 2 illustrates the server component (20) which includes infrastructure upon which the platform of the present invention operates. The infrastructure may be local or cloud-based. The central server (20) may operate one or more processors and maintain one or more databases to enable the following functionality and / or storage:• User account register (100) storing details relating to registered users (30) (eg. name, address, contact details and any additional detail may be relevant to the purpose of identifying each user). Additional details may be stored in the register (100) including the user’s account profile, and details relating to additional data received including from one or more external sources as described herein;• Evaluation principles database (105) which may be used to store evaluation principles that are available for selection, which include but are not limited to Energy Type (eg. the type of user including the overall function and intended mode of interaction with others), Strategy (eg. a strategy associated with each user that describes a referred approach of the user with respect to how best the approach life decision and situation in relation to the user’s overall functions and intended mode of interaction with others), Authority (eg. an internal mechanism associated with the user that is designed to make decision that are correct for them), Profile (eg. the user’s personality), Energy Centres (eg. the user’s physiology), Incarnation Cross (eg. the user’s life mission), Channel(s) (eg. the user’s talents), and Variables (eg. the user’s primary health system);• Analysis results database (110) for storing the results of various analyses conducted on received data relating to the user (30) as well as additional data (60), including the results of analyses conducted on the data to establish one or more of inventories of topics of interests relevant to the user (30), a physiological evaluation of the user (30), a personality attributes evaluation of the user (30), a physiological evaluation of the user (30), a skills evaluation of the user (30), and / or a cognitive profile of the user (30);• Evaluation results database (115) for storing the outcome of evaluations performed on the results of the above-mentioned analyses based on the evaluation principle(s) in order to determine one or more of the user’s intrinsic abilities, tendencies, decision making processes, learning styles including preferred methods of learning, learning strengths, developmental requirements, and / or professional talents;• Educational Plan database (120) storing customized educational plans that are generated according to the user evaluations and thereby tailored to the users (30). The customized educational plans (70) stored in this database may also undergo updating as described in greater detail below, and may incorporate explainability metadata and provenance markers;• Data processing functionality (125) for processing functions including processing user input commands and any additional data received for the purpose of generating output(s) in response. For example, functionality (125) may be responsible for conducting the above-mentioned analysesand evaluations, and may apply one or more Al techniques to assist performance in respect of functions, as well as in the generation of customized educational plans (70) for users (30) and updating the plans when required. The data processing functionality (125) may also assist data entry via interface (160) and / or (170) through the use of Al techniques including Natural Language Processing (NLP). Additional functionality which may be provided via server (20) using functionality (125) includes, but is not limited to, a model aggregator, an explainability engine, a provenance logger, and a federated model update module, each of which is described in greater detail below;• Payment gateway functionality (130) to manage all financial transactions that may be required through the platform including handling payments in respect of subscription fees as well as payments in respect of access to educational programs (70) that are generated on behalf of user (30). The payment functionality may also include an associated shopping cart in accordance with standard online shopping interfaces; and• Alerts / notifications functionality (135) to manage in a timely manner the generation and dispatch of alerts and / or notifications to users (30) of the platform. For example, alerts and / or notifications may be generated based on the receipt of communications from other users or from an administrator, as well as when the outcome of particular analyses and / or evaluations and of course the customized educational plan (70) itself are available for viewing by the user (30).

[0052] Figure 2 also depicts that server (20) is configured to enable communications (140) with the devices (50) and, in particular, the software application (40) operating on each device. Such communications may occur via the internet or similar network.

[0053] Figure 3 illustrates in greater detail segment 300 of Figure 1 and, in particular, the steps associated with a user (30) (eg. a student) installing (150) the software application (40) in a device (50) and subsequently accessing a user login and registration interface (160) associated with the application (40). Such access may be granted after the user (30) has installed the application (40) which may be achieved by downloading the application (40) from an application store. Each user (30) may create an account(which may include a user profile) and sign in to the application (40) based upon verification of the users details, and the account / profile information may be stored in the user account register (100).

[0054] Once each user (30) has access to the application (40), the user (30) may be presented with one or more interfaces, such as interface (160), that the user (30) may utilize to create and maintain their profile and subsequently enable other users to view such information. Whilst the user (30) described herein relates to a learner, it will be appreciated that other types of users can sign up and register to use the software application (40) including educators and administrators. Multi-role access levels may also be implemented with versioned consent tracking for parental or institutional oversight.

[0055] Figure 4 illustrates in greater detail segment 400 of Figure 1 and, in particular, an interface (170) representing an interface in which additional data (60) sufficient to enable analyses to be conducted on the data to establish inventories of topics of interest relevant to the user (30) and various evaluations of the user (30) to be obtained and established. In this regard, interface (170) may allow the user (30) to enter additional information beyond that which was previously entered during the initial registration process via interface (160). However, additional data may also be retrieved from one or more external sources such as one or more external databases, social media accounts, etc, associated with the user (30). The user (30) may be provided a personalized dashboard which assists the user (30) to enter and edit the data, and such data entry may be assisted by the implementation of one or more Al techniques such as NLP.

[0056] Where there is insufficient data retrievable to make a comprehensive assessment in relation to the user (30), the user (30) may be requested to upload particular data or respond to one or more questionnaires or surveys.

[0057] By ensuring that multi-modal data is received, ie. user input across psychological, physiological and cognitive modalities, each data source can be encoded into latent representations, and those representations may be fused to derive user-specific learning vectors used to generate personalized educational content.

[0058] Figure 4 also illustrates additional steps of evaluating the results of the one or more analyses based on the selected one or more evaluation principles to determine learning attributes of the user (30), including the user’s intrinsic abilities, tendencies,decision making processes, etc, wherein such evaluation may also be assisted with the use of Al (80) in order to produce an evaluation outcome (90) which is ultimately used to generate a customized educational plan (70) tailored to the user (30).

[0059] Accordingly, significant and robust data is obtained in respect of the user (30) and thorough analyses and assessments, and the incorporation of various Al techniques, a highly customized education plan (70) for the user (30) is established. By first analysing data to establish an assessment of the user (30) (eg. psychological, personality attributes, physiological and cognitive profile evaluations, etc), and subsequently conducting further evaluation which takes into account one or more selected evaluation principles (eg. energy type, strategy, authority, profile, etc), one or more learning attributes of the user (eg. intrinsic abilities, decision making processes, learning styles, etc) is determined. In this way, a very comprehensive and robust assessment is made with respect to the user (30) and any educational plan (70) that is generated based on such data will be far more customized and tailored to the user (30) as compared with report(s) generated based on conventional methods for generating educational plans and strategies.

[0060] Figure 5 illustrates in greater detail segment 500 of Figure 1 and, in particular, various examples of customized educational plans (70) that may be presented to users (30) depending on the outcome of the evaluation, in the form of digital reports (70A-70D). In the embodiments shown, the reports (70A-70D) are directed to users (30) of varying ages, starting with report (70A) directed to early learning, activities and environment, report (70B) directed to education pathways for kids as they get older, report (70C) addressing social and emotional development for young adults, and report (70D) addressing adulthood and in particular career and personal life and ongoing self discovery.

[0061] For example, the customized educational plan that is provided in report (70A) is directed to early learning and may be presented with an emphasis on assisting children in identifying their inclination and preferences. With this knowledge, educators and parents may be provided with play and learning activities that support a child’s natural ability in ways that enable connection with the outside world, and educators and partners may also be provided with recommendations to edit the generated plan via the customized educational plan in report (70A). Further, early education can be tailored to a child’s learning style based on the particular evaluation principle(s) selected to determinethe learning attributes of the user (30) (eg. energy type, strategies, etc). In one example, if the evaluation principle used to evaluate learning attributes of the user (30) relates to “energy type”, then the early educational plan may be tailored to the user’s learning style and may take into account whether or not the child is a manifestor, generator, manifesting generator, projector or reflector. In other words, the type of user (30) and their overall function and intended mode of interaction with others may be the focus of the customized educational plan, and this may occur regardless of whether the user (30) prefers group activities, more alone time for exploration, etc.

[0062] As kids get older, the customized educational plan provided in report (70B) may be generated to help such users (30) make decisions about extra curricula and academic activities that are aligned with their energy type and strategy and authority, which guarantees a more interesting and rewarding learning environment.

[0063] The customized education plan in report (70C) relating to social and emotional development for growing kids and young adults may be generated in circumstances where the assessment of the user (30) requires that the user (30) develop emotional intelligence and a sense of self (eg. when the evaluation principle selected to evaluate the user’s learning attributes relates to “profile”, “energy centres” or “authority”). Such a plan will also assist the user (30) to navigate social connections and build interpersonal skills.

[0064] The customized education plan in report (70D) directed to career and personal life and ongoing self discovery is directed to users (30) who have reached maturity, wherein the full complexity of the plan becomes pertinent, offering guidance on relationships, careers and other life decisions that are in harmony with one’s purpose and inherent energies (eg. wherein the evaluation principle utilised to evaluate the user and generate the plan includes “Incarnation Cross”). Adults can utilize such a plan as a tool for ongoing personal development, learning how to deal with life obstacles and changes in accordance with their design.

[0065] The generated educational plans (70) may also include explainability metadata, such as data explaining or identifying feature-importance metrics, confidence levels, key feature contributions, and counterfactual comparisons / reasoning. Such outputs may be generated by an explainability engine and stored in the plan database (120) and viewablethrough interface (180). This will provide additional technical benefits including traceability, and interpretable decision pathways satisfying Al governance obligations.

[0066] In a preferred embodiment, the plans (70) are also stored and that they are secure and tamper-proof. For example, the platform may implement a tamper-evidence audit trail using a cryptographic hash chain or blockchain-based ledger such that each educational plan (70) and associated update event is hashed and timestamped, providing verifiable authenticity and a compliance-grade data trail. A provenance logger within server (20) may be utilised to log and store provenance markers in database (120). In this way, the platform may provide blockchain-anchored provenance for adaptive educational outputs.

[0067] Figure 6 illustrates in greater detail segment 600 of Figure 1 and, in particular, example interface (190) that enables continuous evaluations to be performed and previously generated customized educational plans (70) to be updated if required, and a further interface (210) that enables analyses of assessment data, additional functionality including the identification of trends, and the prediction of growth trajectories to further develop educational and skills outcomes.

[0068] Regarding interface (190), customized educational plans (70) may take into account assessment in respect of the individual user (30) according to continuous evaluations performed upon receipt of updated user data and / or updated user instructions, or receipt of captured user interaction, assessment and / or engagement data (eg. real-time performance feedback in relation accuracy, completion rate, etc), hence it will be appreciated that the customized educational plans (70) may evolve over time based on additional information and feedback received via the platform. This may further include modifying educational content difficulty, structure and / or delivery methods in response to the continuous evaluations. Further, an analysis may be conducted which devises a preferred adaptation of a customized education plan for a user (30).

[0069] Delivery aspects such as lesson sequence, pacing and assessment order may be optimized in substantially real-time by applying a Reinforcement-Learning (RL) feedback loop. For example, the platform may incorporate a closed adaptive learning feedback system involving interface (190), data processing (125), and plan database (120). In a particular example, a reinforcement-learning controller may be used to monitoruser interaction, assessment and / or engagement data, assign reward values corresponding to positive learning outcomes, modify, according to the reward values, the plan delivery aspects to adapt one or more of sequencing, pacing, or content selection of educational materials, and apply the updated parameters when generating subsequent educational plans (70).

[0070] The additional functionality provided by interface (210) includes the ability to conduct analyses including with the use of one or more Al techniques to determine trends and compatibility between groups of users (30). For example, one or more machine learning models may be implemented such that the models are developed and trained to identify trends and predicted growth trajectories for individual users (30) and / or teams of users (30) to further develop their educational and skill outcomes. In this regard, a clustering engine could be used to implement Al-based cohort clustering where users (30) are mapped into groups according to compatibility based on psychological and / or cognitive similarities (eg. based on learned embeddings of psychological and cognitive similarity), enabling Al-optimised peer learning and group-based plan variations. Educational plans may be delivered to users (30) according to their allocated groupings.

[0071] In one example, a science project is divided into groups of students by a high school teacher. Once sufficient data has been collected to enable an assessment of each student, the platform may then suggest group configurations that strike a balance between different student energy types such as generators (students with energy to carry out ideas), projectors (students who are best placed to direct the work), and reflectors (students who provide insightful criticism).

[0072] Accordingly, it will be appreciated that when the present invention is implemented (eg. in the education, corporate training and compliance management, coaching, mentoring and / or HR talent assessment and employment sectors), a foundation for improving the efficacy and personalisation of learning experiences is provided. The described network and method will assist to create a more effective learning environment by focusing the unique skills, preferences and learning styles of each user (30). This is further enhanced with the use of Al techniques to perform a thorough assessment of each user’s (30) distinct talents, learning preferences, cognitive profile, etc, in order to collect as much diversified information as possible with respect to the user (30) and their learning abilities and preferences to develop highly customized outputs including education plans(70). Any Al outputs may be monitored for bias across demographic or psychometric factors and may have their weighting adjusted to equalize outcomes, with audit logs maintained in plan database (120).

[0073] The methods and systems described herein may be deployed in part or in whole with a machine that executes computer software, program codes, and / or instructions on a processor. The processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like. The processor may be or include a signal processor, digital processor, embedded processor, microprocessor or any variant such as a co-processor (math co-processor, graphic coprocessor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned priorities associated with them, the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores methods, codes, instructions and programs as described herein and elsewhere. The processor may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

[0074] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In some embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).

[0075] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software on a server, cloud server, client, firewall, gateway, hub, router, or other such computer and / or networking hardware. The software program may be associated with a server that may include a file server, print server, domain server, internet server, intranet server and other variants such as secondary server, host server, distributed server and the like. The server may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

[0076] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers and the like. Additionally, this coupling and / or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and / or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

[0077] The software program may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client and the like. The client may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, programs or codes as described herein and elsewhere may be executed by the client. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

[0078] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers and the like. Additionally, this coupling and / or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code and / or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

[0079] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules and / or components as known in the art. The computing and / or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM and the like. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructural elements.

[0080] The methods, program codes, and instructions described herein and elsewhere may be implemented in different devices which may operate in wired or wireless networks. Examples of wireless networks include 4th Generation (4G) networks (e.g., Long-Term Evolution (LTE)) or 5th Generation (5G) networks, as well as non-cellular networks such as Wireless Local Area Networks (WLANs). However, the principles described therein may equally apply to other types of networks.

[0081] The operations, methods, programs codes, and instructions described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players and the like. These devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled to execute program codes, methods, and instructions stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute program codes. The mobile devices may communicate on a peer-to-peer network, mesh network, or other communications network. The program code may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store program codes and instructions executed by the computing devices associated with the base station.

[0082] The computer software, program codes, and / or instructions may be stored and / or accessed on machine readable media that may include computer components, devices, and recording media that retain digital data used for computing for some interval of time, semiconductor storage known as random access memory (RAM), mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory, optical storage such as CD, DVD, removable media such as flash memory (eg. USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like, other computer memory such as dynamic memory, static memory, read / write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

[0083] The methods and systems described herein may transform physical and / or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another, such as from usage data to a normalized usage dataset.

[0084] The elements described and depicted herein, including in flow charts and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable media having a processor capable of executing program instructions storedthereon as a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure. Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements depicted in the flow chart and block diagrams or any other logical component may be implemented on a machine capable of executing program instructions. Thus, while the foregoing drawings and descriptions set forth functional aspects of the disclosed systems, no particular arrangement of software for implementing these functional aspects should be inferred from these descriptions unless explicitly stated or otherwise clear from the context. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and / or description of an order for various steps should not be understood to require a particular order of execution for those steps, unless required by a particular application, or explicitly stated or otherwise clear from the context.

[0085] The methods and / or processes described above, and steps thereof, may be realized in hardware, software or any combination of hardware and software suitable for a particular application. The hardware may include a general-purpose computer and / or dedicated computing device or specific computing device or particular aspect or component of a specific computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, along with internal and / or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine-readable medium.

[0086] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.

[0087] It will be appreciated by persons skilled in the relevant field of technology that numerous variations and / or modifications may be made to the invention as detailed in the embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all aspects as illustrative and not restrictive.

[0088] Throughout this specification and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated feature or step, or group of features or steps, but not the exclusion of any other feature or step or group of features or steps.

Claims

The claims defining the invention are as follows:

1. A data communications network operably connected with one or more data communications devices and one or more individual user devices, and a method of operating same to improve a user learning experience, the method including: generating, by one or more processors, a user device interface for the user to submit data pertaining to the user; establishing, by one or more processors, a user account according to the data submitted by the user; obtaining, by one or more processors, additional data sufficient to enable analyses to be conducted on the data to establish an assessment of the user according to any one or more of: inventories of topics of interest relevant to the user; a psychological evaluation of the user; a personality attributes evaluation of the user, a physiological evaluation of the user, a skills evaluation of the user; and a cognitive profile of the user; evaluating, by one or more processors, the results of the analyses using any one or more evaluation principles to determine learning attributes of the user including any one or more of: intrinsic abilities, tendencies; decision making processes; communication preferences;learning or work styles including preferred methods of learning or working; learning strengths; developmental requirements; and professional talents; and generating, by one or more processors, according to the evaluation, one or more customized educational plans tailored to the user and providing the educational plan(s) to the user by the user device interface.

2. A data communications network according to claim 1 , wherein the evaluation principles address one or more of: energy type, strategy, authority, profile, energy centres, incarnation cross, channels, or variables.

3. A data communications network according to claim 2, wherein using the energy type evaluation principle includes establishing the type of user by classifying the user according to one or more of a: manifestor, generator,manifesting generator, projector, or reflector.

4. A data communications network according to any one of the preceding claims, wherein the one or more customized educational plans are presented to the user in a report that includes one or more of: play, lessons, micro-lessons, quizzes, learning activities, educational pathways, analogical explanations, recommendations regarding extracurricular and academic activities, guidance with respect to relationships and careers, or opportunity for ongoing personal development.

5. A data communications network according to any one of the preceding claims, wherein the one or more customized educational plans are reported in different styles depending on the age of user.

6. A data communications network according to any one of the preceding claims, wherein the method further includes: adapting the one or more customized educational plans to take into account assessments in respect of the individual user according to continuous evaluations performed.

7. A data communications network according to claim 6, wherein the continuous evaluations performed include one or more of: evaluations performed upon receipt of updated user data and / or updated user instructions, or evaluations performed upon receipt of captured user interaction, assessment and / or engagement data.

8. A data communications network according to claim 7, wherein the method further includes: modifying educational content difficulty, structure and / or delivery in response to the continuous evaluations.

9. A data communications network according to either claim 7 or claim 8, further including: conducting an analysis of one or more evaluations and / or modifications over time and devising a preferred adaptation of a customized educational plan for the user.

10. A data communications network according to any one of claims 7 to 9, wherein upon receipt of captured user interaction, assessment and / or engagement data, the method further includes: causing the user device to undertake learning by locally training partial learning models using the captured user interaction, assessment and / or engagement data.

11. A data communications network according to claim 10, further including: aggregating and evaluating, by a server, encrypted interaction, assessment and / or engagement data or updates related thereto from the device whilst avoiding the transfer of underlying personal data of the user.

12. A data communications network according to claim 1 1 , further including: redeploying the data, updates and / or evaluation(s) to the device for use by the locally trained models.

13. A data communications network according to any one of claims 7 to 12, further including: optimising plan delivery aspects in substantially real-time by applying a closed adaptive learning feedback loop.

14. A data communications network according to claim 13, wherein the adaptive learning feedback loop includes: monitoring the user interaction, assessment and / or engagement data; assigning reward values corresponding to positive learning outcomes; modifying, according to the reward values, the plan delivery aspects to adapt one or more of sequencing, pacing or content selection of educational materials; and applying the adaptations when generating subsequent plans.

15. A data communications network according to any one of the preceding claims, further including: implementing a tamper-evidence audit trail using a cryptographic hash chain or blockchain-based ledger such that each educational plan and associated update event is hashed and timestamped.

16. A data communications network according to any one of the preceding claims, wherein the method further includes: conducting analyses using one or more Al techniques to determine groups of users according to compatibility, wherein compatibility is determined according to at least psychological and / or cognitive similarities; and delivering the educational plans to users according to their determined group.

17. A data communications network according to any one of the preceding claims, wherein the method further includes:developing and training one or more machine learning models to identify trends and predict growth trajectories for individual users and / or groups of users to further develop their educational and skill outcomes.

18. A data communications network according to any one of the preceding claims, wherein each generated educational plan includes explainability metadata explaining or identifying any one or more of: feature-importance metrics, confidence levels, key feature contributions, or counterfactual comparisons or reasoning.

19. A computer-implemented system for improving a user learning experience, the system including one or more processors operable to execute computer instruction code to implement an electronic database including: generating a user device interface for the user to submit data pertaining to the user; establishing, according to the data submitted by the user, a user account; obtaining additional data sufficient to enable analyses to be conducted on the data to establish an assessment of the user according to any one or more of: inventories of topics of interest relevant to the user; a personality attributes evaluation of the user, a physiological evaluation of the user, a psychological evaluation of the user; a skills evaluation of the user; and a cognitive profile of the user;evaluating, according to results of the analyses using any one or more evaluation principles to determine learning attributes of the user including any one or more of: intrinsic abilities, tendencies; decision making processes; communication preferences; learning or work styles including preferred methods of learning or working; learning strengths; developmental requirements; and professional talents; and generating, according to the evaluation, one or more customized educational plans tailored to the user and providing the educational plan(s) to the user by the user device interface.

20. A non-transitory computer-readable medium including computer instruction code that, when executed on a computer, causes one or more processors of the computer to perform the steps of: generating a user device interface for the user to submit data pertaining to the user; establishing, according to the data submitted by the user, a user account; obtaining additional data sufficient to enable analyses to be conducted on the data to establish an assessment of the user according to any one or more of: inventories of topics of interest relevant to the user; a personality attributes evaluation of the user,a physiological evaluation of the user, a psychological evaluation of the user; a skills evaluation of the user; and a cognitive profile of the user; evaluating, the results of the analyses using any one or more evaluation principles to determine learning attributes of the user including one or more of: intrinsic abilities, tendencies; decision making processes; communication preferences; learning or work styles including preferred methods of learning or working; learning strengths; developmental requirements; and professional talents; and generating, according to the evaluation, one or more customized educational plans tailored to the user and providing the educational plan(s) to the user by the user device interface.

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