AU2018236887A1 - CLASS Cognitive Load Adaptive Software System. A complex software system and method for managing the continuous improvement of human learning based on evidence-based strategies arising from Cognitive Load Theory. - Google Patents
CLASS Cognitive Load Adaptive Software System. A complex software system and method for managing the continuous improvement of human learning based on evidence-based strategies arising from Cognitive Load Theory. Download PDFInfo
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- AU2018236887A1 AU2018236887A1 AU2018236887A AU2018236887A AU2018236887A1 AU 2018236887 A1 AU2018236887 A1 AU 2018236887A1 AU 2018236887 A AU2018236887 A AU 2018236887A AU 2018236887 A AU2018236887 A AU 2018236887A AU 2018236887 A1 AU2018236887 A1 AU 2018236887A1
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- 230000001149 cognitive effect Effects 0.000 title claims abstract description 46
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 title claims abstract description 14
- 230000006870 function Effects 0.000 claims abstract description 26
- 238000004458 analytical method Methods 0.000 claims abstract description 18
- 238000013461 design Methods 0.000 claims abstract description 18
- 238000011156 evaluation Methods 0.000 claims abstract description 16
- 238000010586 diagram Methods 0.000 claims abstract description 15
- 230000010354 integration Effects 0.000 claims abstract description 6
- 230000000694 effects Effects 0.000 claims abstract description 5
- 238000012552 review Methods 0.000 claims abstract description 4
- 238000007418 data mining Methods 0.000 claims description 5
- 238000007405 data analysis Methods 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 238000011160 research Methods 0.000 abstract description 3
- 230000007935 neutral effect Effects 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- 230000000052 comparative effect Effects 0.000 description 1
- 238000013481 data capture Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000007787 long-term memory Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008450 motivation Effects 0.000 description 1
- 238000009424 underpinning Methods 0.000 description 1
- 230000003313 weakening effect Effects 0.000 description 1
- 230000003936 working memory Effects 0.000 description 1
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- G—PHYSICS
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- 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
- G06Q50/205—Education administration or guidance
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- 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
- G09B5/00—Electrically-operated educational appliances
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- 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
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Abstract
Patent Claim by David Isaacson, 2018 Title: CLASS Cognitive Load Adaptive Software System CLASS (Cognitive Load Adaptive Software System) is a cloud-based, database driven, mediative-adaptive software system with password-protected access to persona-based interface and dashboards for Administrators, Teachers and Students. The purpose of CLASS is to support educators in the rapid analysis of learning programs and learner needs, then use this information to implement interventions for the continuous improvement of learning design based on strategies arising from cognitive load theory. The learning design in programs or courses may be strengthened based on: a) a standard derived from cognitive load theory without reference to learners, or; b) a standard derived from cognitive load theory with reference to learners' prior knowledge and heutagogical (self-determined learning) capabilities. A central database serves as a repository for evidence-based research information to support teachers in devising learning interventions that are implemented over time in cyclical iterations, thereby facilitating incremental, continuous improvement. The CLASS system is fully integrated across all functionalities (See DIAGRAM 1), contains a dynamically-updateable knowledge database, contains all online forms, templates and interfaces for creating, implementing and monitoring learning interventions, as well as the capability to generate visualised reports at multiple levels (learner, groups, departments, and institutions) via persona-based interfaces. CLASS is not limited to these functions and may be integrated with other systems via LTI (Learning Tools Interoperability) or other software integration protocols. Patent Claim. David Isaacson Title: CLASS Cognitive Load Adaptive Software System CLASS © David Isaacson 2018 All rights reserved Patent Claim by David Isaacson, 2018 Title: CLASS Cognitive Load Adaptive Software System 2. The best method of performing this invention is through the use of cloud-based, database-driven software as described in Description documents Point 1-10 (submitted as part of this application) and illustrated by the functional diagram, (DIAGRAM 1) below (submitted as part of this application): CLASS Cognitive Load Adaptive Software System A mediative-adaptive software system for the continuous improvement of learning design informed by the principles arising from cognitive load theory and heutagogy within a "double-loop" evaluation architecture 1. Administrator global Functions Functionality, A au ~Systems 4. Dynamic Database Integration Knowledge base of evidence-based practices 2. Interface -Teacher -- arising from cognitive load theory - earn Dashboard 4 Information for populating questinnaires/ Dashboard rating forms for evaluating a) program and b) individual learners Recommender system/database search function 6. Learner self-analysis questionnaires/rating s. Questionnaires/ """" •Forms, templates and funcrtions for setting, statements based on cognitive load theory rating statements 4* -= - - implementing and tracking interventions and heutagogical principles; system (course and student) ("nodes of e xpertise") functionality for in-process self-rating of S capability and longitudinal intervention cognitive load tracking/monitoring .0 407. System-generated visualised 11. Evaluation data stored in 40 41,reports on alignment of program database for future reference, o with a) evidence-based principles aayi n aamnnitr and ) idivdualleaer eed and intra- institutional analysis 8. System "recommender" function provides possible 9. Teacher ("mediator") formulates an evidence-based interventions to individual plan (IP) based an 10. Learner is tested on "nodes strengthen the learning program 're commender" to strengthen program of fluency' and result with regard to a) evidence-based for learner. "Node of fluency' activities recorded; Learner completes principles and b) individual and review date is set. IP stored in Step 2 again. learner needs database for montioring and reporting Steps 1-11 represent an analysis/improvement iteration for a) program, b) course, or both a) and b) j 6.VCLASS © David Isaacson 2018 All rights reserved Patent Claim. David Isaacson1 Title: CLASS Cognitive Load Adaptive Software System CLASS © David Isaacson 2018 All rights reserved
Description
invention is through the use of cloud-based, database-driven software as described in Description documents Point 1-10 (submitted as part of this application) and illustrated by the functional diagram, (DIAGRAM 1) below (submitted as part of this application):
CLASS Cognitive Load Adaptive Software System A mediative-adaptive software system for the continuous improvement of learning design informed by the principles arising from cognitive load theory and heutagogy within a double-loop evaluation architecture
1. Administrator-Global Functions
-2. Interface-Teacher Dashboard
S. Questionnaires/ rating statements (course and student)
8. System recommender function provides possible evidence-based interventions to strengthen the learning program with regard to a) evidence-based principles and b) individual learner needs
4. Dynamic Database
Knowledge base of evidence-based practices arising from cognitive load theory Information for populating questinnaires/ rating forms for evaluating a) program and b) individual learners
Recommender system/database search function
Forms, templates and funcrtions for setting, implementing and tracking interventions (nodes of expertise)
Scalability and longitudinal intervention tracking/monitoring
-1*-kA
12. LTI
Functionality,
Systems
Integration ft--’ . Interface-Learner Dashboard
6. Learner self-analysis questionnaires/rating statements based on cognitive load theory and heutagogical principles; system functionality for in-process self-rating of cognitve load
T
| ▼ I 7. System-generated visualised reports on alignment of program with a) evidence-based principles and b) individual learner needs | ' Σι | 11. Evaluation data stored in database for future reference, analysis and data-mining, interart d intra- institutional analysis |
9. Teacher (mediator) formulates an individual plan (IP) based on recommender to strengthen program for learner. Node of fluency activities and review date is set. IP stored in database for montioring and reporting
10. Learner is tested on nodes of fluency and result recorded; Learner completes Step 2 again.
Steps 1-11 represent an analysis/improvement iteration for a) program, b) course, or both a) and b)
CLASS © David Isaacson 2018 All rights reserved
DIAGRAM 1
Patent Claim. David Isaacson
Title: CLASS Cognitive Load Adaptive Software System
CLASS © David Isaacson 2018 All rights reserved
2018236887 29 Sep 2018
Editorial Note
There are four pages of description only
2018236887 29 Sep 2018
Patent Claim by David Isaacson, 2018
Title: CLASS Cognitive Load Adaptive Software System
1. Description of CLASS
A (1) mediative-adaptive (2) software system for the (3) continuous improvement of (4) learning design informed by the principles arising from (5) cognitive load theory and (6) heutagogy within a (7) “double-loop” (8) evaluation architecture including an (9) intervention recommender function and (10) graphical analytics reporting functions.
Each of the numbered terms is described in the table below, including (but not limited to) the following:
| 1 | Mediative-adaptive means that the role of the teacher is central to the mediation of improved learning design, e.g. the teacher interprets feedback from program and/or learner capability analyses through visualised reporting functionalities, as well as devising and implementing learning design improvement strategies that are adapted to the prior knowledge and capability levels of learners. |
| 2 | Software system means a functionally integrated, database-driven architecture with extensive functional capability to administer, record, report, process and manage the continuous improvement of learning design through data-sharing across all functionalities, e.g. graphical analytical reports on the status of any/all items and processes. |
| 3 | Continuous improvement means that learning design intervention cycles may be implemented iteratively without time constraints in order to facilitate longitudinal data capture, reporting and data mining, i.e. the quality of learning design can be monitored and improved over time through frequent analysis and modification programs, courses and learning interventions towards greater inclusion of evidencebased practices. |
| 4 | Learning design means the deliberate choice of specific teaching and learning strategies that strengthen learning; CLASS seeks to align learning strategies with the known functions of human cognitive architecture, informed by research arising from cognitive load theory. |
| 5 | Cognitive Load Theory (CLT) is an instructional design theory that has contributed a range of specific strategies for improving learning based on the known functions of human cognitive architecture including working memory and long-term memory. In addition, cognitive load theory has contributed knowledge of strategies that have a neutral effect on learning, or weakening effect learning. The implications of CLT for CLASS include the capability of educators to strengthen learning by the intentional inclusion of strengthening strategies and elimination of neutral or ineffective learning strategies, as well as monitoring the effect of these learning interventions. |
| 6 | Heutagogy means the factors that comprise the self-determined learning capability of learners including self-efficacy, motivation, agency, self-regulation, reflection and reflexivity. As heutagogical factors operate in all learning instances, they should be accounted for in the design of effective learning environments. |
Patent Claim. David Isaacson
Title: CLASS Cognitive Load Adaptive Software System
CLASS © David Isaacson 2018 All rights reserved
2018236887 29 Sep 2018
Description (cont.)
| 7 | Double-loop evaluation is a unique function of CLASS, which means the facilitation of two levels of evaluation: first, while learners are evaluated on their performance related to set tasks (called nodes of expertise), the underpinning principles on which the learning interventions are designed are evaluated to determine their alignment with evidence-based practices related to the functions of human cognitive architecture. |
| 8 | Evaluation architecture means that the CLASS system has the capabilities and functionalities to include all the functionalities in DIAGRAM 1, e.g. to facilitate the double-loop evaluation process an to accommodate LTI capabilities and integrations. |
| 9 | Intervention recommender function means that that knowledge database has the functionality to be searched or may be programmed to actively propose interventions for improving learning based on analytical feedback from teacher and learner questionnaires/statement rating forms. |
| 10 | Graphical analytics reporting functions means that any data captured within the database can be output in terms of graphical reports, including but not limited to comparative reports between courses, learners, departments, faculties, or institutions. Reports of this nature may be exported to any appropriate devices including computers, laptops, tablets, smartphones and other devices. |
2. The best method of performing this invention is through the use of cloud-based, database-driven software as described above in points 1-10 (above) and illustrated by the functional diagram, DIAGRAM 1 (submitted as part of this application):
Patent Claim. David Isaacson
Title: CLASS Cognitive Load Adaptive Software System
CLASS © David Isaacson 2018 All rights reserved
2018236887 29 Sep 2018
Each step within DIAGRAM 1 (submitted as part of this application) is described, but not limited to, the following:
Step 1: Administrator performs global functions including setting up courses manually or via LTI, logging issues or development tasks and general system management with super-user status.
Step 2: Teachers access the dashboard to devise questionnaires and/or rating statements for evaluation of a) courses or b) individual students. Teachers can access reports, assign teachers to courses, create interventions and intervention plans for improving courses, create nodes of expertise for learners, and run intervention cycles and evaluate feedback via graphical reports. Teachers can search or access the dynamic database containing strategies for improving courses and supporting learners in their progress towards attaining higher levels of expertise, and perform other functions related to managing continuous improvement cycles.
Step 3: Learners access the dashboard to do self-assessments of prior knowledge and heutagogical capabilities, view a record of their assigned intervention plans, view results of previously completed intervention plans, access graphical reports on their progress, provide feedback to teachers related to intervention plans, provide realtime feedback on load levels being experienced during tasks, and other functions. Learner information is captured without limit of academic years and therefore a learner may observe reports of their growth in both domain-specific knowledge (nodes of expertise) and heutagogical (self-determined learning) capability over unlimited time periods as determined by the institution.
Step 4: The dynamic knowledge database is the repository of all data generated within the system through all users and LTI system integration. It is used for reporting, tracking and monitoring interventions and inputting the base body of knowledge and strategies arising from Cognitive Load Theory and heutagogical research factors used to evaluate learner capabilities. The database contains a dynamically-updateable knowledge base of evidence-based practices arising from cognitive load theory; information for populating questionnaires/rating forms for evaluating a) program and b) individual learners; a recommender system/database search function for assisting educators to find appropriate strategies as well as other information for creating nodes of expertise; forms, templates and functions for setting, implementing and tracking the implementation of learning interventions (“nodes of expertise”); in-built scalability and longitudinal intervention tracking/monitoring capability; functionality to export data for data-mining and other purposes.
Step 5: Teachers complete questionnaires/rating forms that have been been precreated (or created by themselves) for evaluating a) courses and b) individual learners. Course are evaluated according to their alignment with evidence-based practices arising form cognitive load theory, as well as their suitability for the prior knowledge needs and heutagogical needs of learners. Questionnaire/rating forms are output as graphical analyses for rapid comparison and formulation of improvement plans.
Patent Claim. David Isaacson
Title: CLASS Cognitive Load Adaptive Software System
CLASS © David Isaacson 2018 All rights reserved
2018236887 29 Sep 2018
Step 6: Learners complete self-analytical questionnaires that have been emailed to them or are accessible within their dashboards. Learner self-analysis questionnaires/rating statements are based on strategies arising from cognitive load theory as well as heutagogical principles. The system includes the functionality for inprocess, rela-time, self-rating of cognitive loads being experienced.
Step 7: System-generated visualised reports on alignment of program with a) evidence-based principles and b) individual learner needs. These reports can be standardised, or new reports can be generated depending on the requirements of the institution.
Step 8: System “recommender” function provides possible evidence-based interventions to strengthen the learning program with regard to a) evidence-based principles and b) individual learner needs. As weaknesses in programs and courses are analysed, the system may be searched, or programmed with the functionality to automatically recommend or suggest strategies for strengthening them.
Step 9: Teacher (’’mediator”) formulates an individual plan (IP) based on “recommender” to strengthen the program for learner. “Node of fluency” activities and review dates may be set. IP data related to program/course improvement stored in the database for monitoring and reporting.
Step 10: Learner is tested on “nodes of fluency” that have been devised based on questionnaire/rating form feedback and results are recorded; Learner completes Step 6 again to ascertain progress levels and to inform teacher regarding the subsequent node of expertise to be devised; Learner progress towards expertise over time is graphically represented.
Step 11: Evaluation data is stored in database for future reference, analysis and data-mining, inter- and intra- institutional analysis, and leaner progress overtime.
Step 12: CLASS has the capability to be integrated with other systems such as Learning Management Systems and Student Management Systems via LTI and other protocols.
Patent Claim. David Isaacson
Title: CLASS Cognitive Load Adaptive Software System
CLASS © David Isaacson 2018 All rights reserved
2018236887 29 Sep 2018
Patent Claim by David Isaacson, 2018
Claims (8)
- ClaimsClaim 1:Object of the invention: A database-driven software system that underpins a method (See DIAGRAM 1) for implementing and managing the continuous improvement of learning design for human learners, comprising:a. a unified, mediative-adaptive (teacher-operated) system for evaluating and continuously improving learning design in programs, courses and other learning interventions, as well as for individual learners; andb. the unique combination and interconnection of functions (including but not limited to supporting Description document points 1-10, as well as functionalities represented in DIAGRAM 1, Steps 1-12; andc. functionalities and processes including but not limited to evaluating learning programs, courses and interventions based on evidence-based standards, arising from (but not limited to) cognitive load theory;d. functionalities and processes including but not limited to evaluating learner prior domain knowledge and learning capability; ande. functionalities and processes including but not limited to creating improvement plans for programs, courses and learning interventions, as well as improvement plans for individual learners; andf. functionalities and processes including but not limited to generating visualised reports and graphic representation of data analyses based on any data within the system; andg. a dynamically-updateable knowledge database including an evaluation standard derived from cognitive load theory; andh. a dynamically-updateable evaluation standard derived from heutagogical (self-determined learning capability) theory; andi. a double-loop evaluation function, wherein learner progress and learning design quality are both evaluated in cyclical evaluation iterations;j. but not limited to, the functionalities and capabilites herein mentioned for improving the quality of learning design in programs, courses and learning interventions, with or without reference to individual learners.Patent Claim. David IsaacsonTitle: CLASS Cognitive Load Adaptive Software SystemCLASS © David Isaacson 2018 All rights reserved2018236887 29 Sep 2018Patent Claim by David Isaacson, 2018Title: CLASS Cognitive Load Adaptive Software System
- 2. The best method of performing this invention is through the use of cloud-based, database-driven software as described in Description documents Point 1-10 (submitted as part of this application) and illustrated by the functional diagram, (DIAGRAM 1) below (submitted as part of this application):CLASS Cognitive Load Adaptive Software SystemA mediative-adaptive software system for the continuous improvement of learning design informed by the principles arising from cognitive load theory and heutagogy within a double-loop evaluation architecture1. Administrator-Global Functions --\4. Dynamic DatabaseKnowledge base of evidence-based practices arising from cognitive load theory Information for populating questinnaires/ rating forms for evaluating a) program and b) individual learnersRecommender system/database search functionForms, templates and funcrtions for setting, implementing and tracking interventions (nodes of expertise)Scalability and longitudinal intervention tracking/monitor! ng !>12. LTIFunctionality,SystemsIntegration
- 3. Interface - Learner Dashboard
- 6. Learner self-analysis questionnaires/rating statements based on cognitive load theory and heutagogical principles; system functionality for in-process self-rating of cognitve loadJLL
- 7. System-generated visualised reports on alignment of program with a) evidence-based principles and b) individual learner needs11. Evaluation data stored in database for future reference, analysis and data-mining, interand intra- institutional analysis
- 8. System recommender function provides possible evidence-based interventions to strengthen the learning program with regard to a) evidence-based principles and b) individual learner needs
- 9. Teacher (mediator) formulates an individual plan (IP) based on recommender to strengthen program for learner. Node of fluency activities and review date is set. IP stored in database for montioring and reporting
- 10. Learner is tested on nodes of fluency and result recorded; Learner completes Step 2 again.Steps 1-11 represent an analysis/improvement iteration for a) program, b) course, or both a) and b)CLASS © David Isaacson 2018 All rights reservedDIAGRAM 1Patent Claim. David IsaacsonTitle: CLASS Cognitive Load Adaptive Software SystemCLASS © David Isaacson 2018 All rights reserved
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2017903963 | 2017-09-29 | ||
| AU2017903963A AU2017903963A0 (en) | 2017-09-29 | CITE Software System Continuous Improvement Tool for Evaluation |
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| Publication Number | Publication Date |
|---|---|
| AU2018236887A1 true AU2018236887A1 (en) | 2019-04-18 |
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| AU2018236887A Abandoned AU2018236887A1 (en) | 2017-09-29 | 2018-09-29 | CLASS Cognitive Load Adaptive Software System. A complex software system and method for managing the continuous improvement of human learning based on evidence-based strategies arising from Cognitive Load Theory. |
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| AU (1) | AU2018236887A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108780309A (en) * | 2016-03-09 | 2018-11-09 | 富士通株式会社 | The visualization system of the visualization procedure of manufacturing process, the method for visualizing of manufacturing process and manufacturing process |
| CN112883723A (en) * | 2021-03-18 | 2021-06-01 | 江西师范大学 | Deep neural network cognition level evaluation model based on Broumm cognition classification theory |
-
2018
- 2018-09-29 AU AU2018236887A patent/AU2018236887A1/en not_active Abandoned
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108780309A (en) * | 2016-03-09 | 2018-11-09 | 富士通株式会社 | The visualization system of the visualization procedure of manufacturing process, the method for visualizing of manufacturing process and manufacturing process |
| CN112883723A (en) * | 2021-03-18 | 2021-06-01 | 江西师范大学 | Deep neural network cognition level evaluation model based on Broumm cognition classification theory |
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| DA3 | Amendments made section 104 |
Free format text: THE NATURE OF THE AMENDMENT IS: AMEND THE INVENTION TITLE TO READ CLASS COGNITIVE LOAD ADAPTIVE SOFTWARE SYSTEM. A COMPLEX SOFTWARE SYSTEM AND METHOD FOR MANAGING THE CONTINUOUS IMPROVEMENT OF HUMAN LEARNING BASED ON EVIDENCE-BASED STRATEGIES ARISING FROM COGNITIVE LOAD THEORY. |
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| MK5 | Application lapsed section 142(2)(e) - patent request and compl. specification not accepted |