Method for Building Highly Adaptive Instruction Based on the Structure as Opposed to the Semantics of Knowledge Representations

• US 20040230951A1
• Filed: 04/02/2003
• Published: 11/18/2004
• Est. Priority Date: 04/02/2003
• Status: Abandoned Application

• Current Assignee: Joseph M. Scandura
• Original Assignee: Joseph M. Scandura
• Inventors: Scandura, Joseph M.
• Application Number:
  US10/249,355
• Publication Number:
  US 20040230951A1
• Time in Patent Office: Days
• US Class Current: 717/120
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

Instructional systems are assumed to include one or more learners, human and/or automated, content, means of presenting information to and receiving responses from learners, and an automated tutor capable of deciding what and when information is to be presented to the learner and how to react to feedback from the learner based on configurable options and the current status of the learner model. This invention discloses a method for authoring and delivering highly adaptive instructional systems based on abstract syntax tree representations of the problems to be solved by learners and, of the requisite knowledge structures to be acquired. Authoring includes: a) receiving and/or constructing abstract syntax tree representations of essentially any kind of to-be-acquired knowledge (KR), b) methods for representing problem schemas in an observable medium enabling communication between tutors and learners and c) configuring the learning and tutorial environment to achieve desired learning. Delivery includes general-purpose methods for: d) generating specific problems, updating the learner model and sequencing diagnosis and instruction.

Claims

1. a computer implemented method for creating and delivering a plurality of learning and tutoring environments, wherein each said system is comprised of at least one learner, a general purpose learning and/or tutoring system, and an optional authoring environment, said method comprising the steps of:

a) one of receiving and constructing at least one abstract syntax tree (AST) representing problems to be mastered by said learners, wherein each said AST includes at one said node representing a goal variable;

b) one of receiving and constructing at least one abstract syntax tree (AST) representing the knowledge to be acquired by said learners, wherein each node in said AST represents at least one of an operation, wherein said operation represents to be acquired procedural knowledge specifying input-output behavior, and data, wherein parent-child mappings between said data nodes represent to be acquired declarative knowledge, wherein at least one of said input-output operation and said parent-child mapping is executable;

c) assigning semantic attributes to nodes in at least one of said problem ASTs of step a) and said knowledge ASTs of step b), wherein said semantic attributes include at least one of tutoring options and context, questions, instructions, answers, hints and feedback and are used by said learning and tutoring system to determine what and how learning and instruction is to be delivered;

wherein at least one said semantic attribute represents each said learner'"'"'s status with respect to said nodes in said AST, wherein said learner status nodes for each said learner collectively represents that said learner'"'"'s learner model, and wherein said tutoring options include at least one of learner control, tutoring strategy, adaptive instruction, diagnosis, progressive instruction, simulation, practice, initial learner model, and custom variations thereof;

d) assigning “

display”

properties to at least one of said nodes in step a) and said nodes in step b) and said semantic attributes in said step c), wherein said properties designate what observable object is to represent at least one of said node and said semantic attribute and at least one of the timing, position, duration, the kind of response to be received from said learner and other attributes of said observable object;

e) when said learning and tutoring system is to interact with said learner,

i. at least one of selecting and constructing at least one said problem AST of step a);

ii. selecting a node in at least one said knowledge AST of step b);

iii. generating executing said knowledge ASTs of step b) on said problem AST of step i) up to said selected node in step ii), wherein the resulting problem state represents the subproblem defined by said selected node;

wherein values of the input parameters to said selected node constitute the givens in said subproblem and the output parameters of said selected node constitutes the goal in said subproblem;

iv. generating a step-by-step solution to said subproblem of step iii) by executing nodes in the subtree defined by said selected node in said knowledge AST of step ii), wherein execution of the last node in said subtree is the solution to said subproblem;

v. using said “

display”

properties to at least one of make said nodes and said semantic attributes of step d) observable to said learner and to request responses from said learner;

vi. at least one of said learner responding to said response requests and making decisions as to what to present next and said tutoring system using the result of executing said nodes of step iv) and said learner responses of step v) to update said learner model of step c) and to decide what to present next; and

vii. repeating steps i) through vi) continuing said process until at least one of said learner and said general purpose learning and/or tutoring environment decide to stop.

3. A method in accordance with claim 1, wherein step a) of receiving and creating each said problem AST, further comprises the steps of:

a) at least one of receiving and constructing a problem AST containing at least one data node, wherein said AST consists of one or more nodes;

b) when a node structure in said problem AST is a copy of a prototype data structure in said AST of step b) of claim 1, creating at least one copy of said prototype structure;

c) when a node resulting from steps a) and b) is to serve as a given in said problem AST, assigning an initial value to said node;

d) assigning “

display”

properties to at least one node in said problem AST resulting from steps a), b) and c), wherein said properties designate what observable object is to represent said node and at least one of the timing, position, duration, and other attributes of said observable object; and

e) when a node is to serve as a goal in said problem AST, specifying the kind of response to be received from said learner.

2. A method in accordance with claim 7, wherein step b) of creating each said executable knowledge AST is in accordance with the methods revealed in U.S. Pat. No. 6,976,275 and Scandura (2003), and further comprises the steps of:

a) for each node in said knowledge AST, specifying at least one of input and output parameters for an operation defined by said node;

b) in response to a request, making said operation of step a) executable, wherein given values of said input parameters of step a), said operation generates corresponding values of said output parameters of step a);

c) refining each said node of step a) into child nodes, wherein said refinement may but need not be in accordance methods revealed in U.S. Pat. No. 6,976,275 and Scandura (2003), wherein each refinement is one of a sequence, parallel, selection, loop, interaction, abstract operation, navigation sequence and terminal;

d) refining each said parameter node of step a) into child nodes, wherein said refinement may but need not be in accordance with the methods revealed in U.S. Pat. No. 6,976,275 and Scandura (2003), wherein each refinement is one of a component, category, prototype, dynamic and terminal;

e) in response to a request, defining an executable parent-child mapping for said parameter refinement of step c), and

f) repeating steps a), b), c), d) and e) on said child nodes until all nodes in said knowledge AST are terminal, wherein each said terminal node defines at least one of an executable terminal operation and an executable parent-child mapping.