Education and demonstration – Question or problem eliciting response
Reexamination Certificate
2000-09-06
2004-02-10
Rovnak, John Edmund (Department: 3714)
Education and demonstration
Question or problem eliciting response
C434S323000, C434S332000, C434S353000, C434S362000
Reexamination Certificate
active
06688888
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to learning and more particularly to using a computer to enhance learning.
The foundation of a vibrant society depends on skilled workers. To strengthen this foundation, every year the U.S. Government with the private industry have poured billions and billions of dollars to improve on learning systems and methods. Money has been spent in areas such as laboratory facilities, educational materials, teacher recruitment and retention, and others. However, for decades, the way to test a student has remained the same; learning has been treated typically as a reward in itself; a fixed syllabus usually controls the educational process of a subject without taking into account students' individual progress; what students have learnt are rarely selectively reviewed; and typically, the students can access non-educational materials when they should be using computers to learn.
It should be obvious that we need methods and systems that are based on computers to remedy the above deficiencies.
SUMMARY OF THE INVENTION
In one embodiment, the present invention provides a learning method and system that assess and enhance a student's or a user's understanding in a subject. Based on the user's understanding, individually-tailored tests are generated, whose difficulties can be geared towards the user's level of understanding in the subject. The user not only can use the tests to prepare for an examination, but can also use the tests to learn the subject.
In another embodiment, the invented method and system are based on the latest test results from the latest test taken by the user on the subject, which can be divided into line-items. Each line-item covers one area in the subject. In yet another embodiment, at least one line-item is more difficult than another line-item. The latest test includes questions from different line-items.
In one embodiment, the invented system includes a score generator coupled to a recommendation generator. In one embodiment, the recommendation generator includes an inference engine; and in another embodiment, the recommendation generator includes a pre-requisite analyzer. The recommendation generator can be coupled to a report generator and a question generator.
In one embodiment, the score generator accesses the user's latest test result and his prior-to-the-latest test results from a storage medium to generate an overall score for each set of questions related to the same line-item. In one embodiment, the prior-to-the-latest test results are test results from the test immediately before the latest test. In another embodiment, each overall score reflects the user's degree of forgetfulness as a function of time for that group of questions. Based on the calculated overall scores, the score generator updates information in the storage medium to include the latest test results.
Both the pre-requisite analyzer and the inference engine in the recommendation generator can generate recommendations based on the user's test results. The prerequisite analyzer accesses pre-requisite rules, which, based on the complexity levels of the line items, determines a complexity-hierarchy among the line-items. Then, applying the complexity-hierarchy to the test results, the pre-requisite analyzer determines the user's level of understanding in the subject to provide recommendations for the user which, for example, can be providing suggestions to the user as to the line-item to work on.
The inference engine accesses a set of relationship rules that define relationship among the line items and the subject. Then applying the set of relationship rules to the user's test results, the inference engine determines the user's level of understanding in the subject to provide recommendations for the user.
If there is any conflict among one or more relationship rules with the contents in the test results, or if there is any conflict among two or more relationship rules, the inference engine can resolve it. Resolving such conflicts helps to ensure a consistent assessment of the user's understanding in the subject.
In one embodiment, the report generator accesses a report format. Based on the recommendations and the report format, the report generator generates a report, which can provide assessment of the user's understanding in line-items of the latest test and the prior-to-the-latest tests, and which can provide action items to improve on the user's understanding in the subject.
The question generator, based on the recommendations, generates a number of questions, which, in another embodiment, can be categorized into at least two line items—one being the one suggested by the recommendations, and the other being different from the one suggested by the recommendations. The user can take this new set of questions to further enhance his understanding in the subject.
In one embodiment, the invented system and method enhance a user's understanding in a subject through associating the subject's different areas that the user has studied.
The subject can be divided into line-items and relationship-items. Each relationship-item covers areas that relate two or more items. The items include learnt and un-learnt items, with a learnt item being an item that the user has achieved a preset level of learning, and with an un-learnt item being an item that the user has not yet achieved a preset level of learning.
In one embodiment, the recommendation generator also selects and classifies the items. That embodiment includes a learning-material generator for generating learning materials for the user.
In one embodiment of the invented method, first, the recommendation generator selects one un-learnt item. After the selection, the learning-material generator generates learning materials for the user to learn the selected item, and the system assesses the user's learning progress in the selected item. If the assessment on the selected un-learnt item is satisfactory, then the recommendation generator classifies one or more relationship-items to be learnt as un-learnt items, with each classified relationship-item relating the selected item with one or more learnt items. The recommendation generator can also re-classify the selected item as a learnt item. Then, another un-learnt item can be selected, which can be a line-item, or a relationship-item. The process can continue on until all of the items have been learnt. At that point, the user has mastered the subject.
There are different ways for the recommendation generator to select an un-learnt item. In one embodiment, the recommendation generator selects an un-learnt line-item or an un-learnt relationship-item, depending on a value set by an instructor. If the un-learnt item is a line-item, the process to select a line-item can be based on the difficulty level of the line-item; if the un-learnt item is a relationship-item, the selection process can be based on the difficulty level of the relationship-item, the time the relationship-item was classified as an un-learnt item, and/or whether the user has previously failed to learn the relationship-item.
For the learning materials, in one embodiment, the learning material is includes questions. In another embodiment, the learning material does not include questions.
One embodiment of the invented system and method provide users dynamic reviews. After a user has learnt certain areas in a subject, summarized learning materials on those areas can be selectively generated for the user so as to reinforce the user's learning in the subject through reviews. Unlike prior systems, the reviews in the present invention can be dynamic; they can be specifically tailored to the needs of individual users, or the characteristics of the subject.
In one embodiment, the present invention selects an un-learnt item, and generates detailed learning materials for it. Then a learnt item is selected, for example, based on one or more learnt-item-selection rules, depending on factors such as the ti
Ho Chi Fai
Tong Peter P.
Rovnak John Edmund
Tong Peter
LandOfFree
Computer-aided learning system and method does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Computer-aided learning system and method, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Computer-aided learning system and method will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3332390