Systems and Methods for Semantic Knowledge Assessment, Instruction, and Acquisition

• US 20110076653A1
• Filed: 04/05/2006
• Published: 03/31/2011
• Est. Priority Date: 04/05/2005
• Status: Abandoned Application

• Current Assignee: AI (UK) Ltd. (Siemens AG)
• Original Assignee: AI (UK) Ltd. (Siemens AG)
• Inventors: Ono, Takashi, Nishijima, Kiyoshi, Cihi, Guy, Browne, Charles, Schaufele, David, Culligan, Brent
• Application Number:
  US11/910,737
• Publication Number:
  US 20110076653A1
• Time in Patent Office: Days
• US Class Current: 434/157
• CPC Class Codes: G09B 19/06 Foreign languages with audible presentation of material to be studied G09B5/04
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F

Abstract

Systems and methods for semantic knowledge assessment, instruction, and acquisition are disclosed. In one embodiment a computer-implemented method for language instruction includes determining a lexical recognition ability level of a user within a lexicon of a particular language. This method further includes, based on item recognizability, creating a target list of unknown lexical items. The target list can be sorted by ranking the importance of the unknown lexical items within the particular lexicon. The method also includes generating a personal language learning sequence for the user based, at least in part, on the target list.

Claims

1. A language training system, comprising:

one or more set and subsets databases for storing multiple vocabulary items;

an item frequency database for storing multiple vocabulary items from the one or more corresponding set and subsets databases, wherein the multiple vocabulary items in the item importance database are ranked in order of frequency of occurrence within the selected sets and/or corresponding subsets;

a user testing component configured to—

(a) present a series of vocabulary items from the one or more set and subsets to a user for response, and

(b) receive and process user input related to the presented vocabulary items;

a calibration component configured to determine, for each vocabulary item—

a vocabulary item recognizability measure,

a vocabulary item depth of knowledge measure, and

a vocabulary item retention measure,

as compared with users within one or more demographic segments or populations; and

a learning sequence compiler configured to generate, for each user, a target list of unknown vocabulary items.

2. The language training system of claim 1 wherein the learning sequence compiler generates the target list for a particular user based, at least in part, on:

the vocabulary item importance data within a particular set or subset thereof;

a vocabulary recognition ability level of the user;

a vocabulary depth of knowledge abilities of the user; and

a vocabulary retention abilities of the user.

3. The language training system of claim 1 wherein the target list is sorted by ranking the frequency of occurrence of the unknown vocabulary items within the particular set or subset.

4. The language training system of claim 1 wherein the user testing component includes a computer-adaptive testing system configured to present Yes/No and multiple choice decision-type questions for each vocabulary item to the user.

5. The language training system of claim 1 wherein the multiple vocabulary items in the item frequency database are further ranked in order of recognizability within one or more demographic segments or populations.

6. The language training system of claim 1, further comprising one or more learning programs or activities configured to present one or more vocabulary items to each user for response, wherein the one or more vocabulary items are selected based, at least in part, on the generated target list of the user.

7. The language training system of claim 6, further comprising:

a feedback component configured to—

process input based on the interaction between the user and the one or more learning programs or activities; and

deliver the input to the learning sequence compiler;

and wherein the learning sequence compiler is configured to generate an updated target list for the user based, at least in part, on the input from the feedback component.

8. The language training system of claim 6 wherein the one or more learning programs include learning programs accessible via a personal computer, mobile communication device, or other electronic device.

9. The language training system of claim 1 wherein the calibration component is further configured to calculate a vocabulary item recognition ogive for one or more demographic segments or populations using item response theory.

10. The language training system of claim 1, further comprising a communication component configured to deliver target lists or portions thereof to corresponding users via electronic messaging at one or more predetermined intervals.

11. One or more computer memories storing a computer-implemented method for language assessment and instruction, the method comprising:

determining a lexical recognition ability level of a user within a lexicon of a particular language or sub-domain thereof;

based on the recognition ability level of the user, creating a target list of unknown lexical items, the target list being sorted by ranking the importance of the unknown lexical items within the particular lexicon; and

generating a personal language learning sequence for the user based, at least in part, on the target list.

12. The method of claim 11 wherein generating a personal language learning sequence for the user comprises—

determining the importance of each particular lexical item within a corpus or sub-domain of the lexicon;

determining a cumulative lexical recognition ogive for one or more demographic segments or populations related to the user;

determining one or more cumulative lexical depth of knowledge ogives for one or more demographic segments or populations related to the user;

determining a cumulative lexical retention ogive for one or more demographic segments or populations related to the user;

determining the lexical recognition ability level of the user for a language or sub-domain thereof;

determining lexical depth of knowledge abilities of the user; and

determining lexical retention abilities of the user.

13. The method of claim 11 wherein determining a lexical recognition ability level of a user comprises:

presenting a series of real lexical items and pseudo-lexical items to the user for identification, wherein the pseudo-lexical items include false lexical items used for conjecturing error correction; and

processing responses from the user to determine (a) the lexical items identified as known by the user, and (b) the lexical items identified as unknown by the user.

14. The method of claim 13, further comprising:

storing in a database one or more of—

demographic information of the user;

each real lexical item and pseudo-lexical item presented for identification; and

each user response to the presented lexical items; and

aggregating the stored user data with data from other users to determine a standard recognizability factor for each lexical item relative to one or more specific demographic segments or populations.

15. The method of claim 11 wherein determining a lexical recognition ability level of a user comprises:

(a) presenting a first lexical item to the user for identification, the first lexical item being selected from a group of lexical items having recognizability at a predetermined level for the demographic segment of the user;

(b) based on the user response, presenting a second lexical item to the user for identification, the second lexical item having recognizability at a set level above or below the predetermined level;

(c) presenting subsequent lexical items to the user for identification, the subsequent lexical items being selected by statistically determining a selection of one or more additional lexical items having more and/or less recognizability compared to the estimated ability of the user, and wherein pseudo-lexical items are randomly dispersed within the presentation of real lexical items to control for the individual conjecturing behavior of a user; and

(d) repeating steps (b) and (c) until the user has identified as being recognized at least one real lexical item, and also has identified at least one real lexical item as being unrecognized.

16. The method of claim 11, further comprising determining a lexical depth of knowledge ability of the user by—

(a) presenting a first lexical depth of knowledge query selected from a series of depth of knowledge queries at an estimated depth of knowledge ability level of the user, wherein the estimated depth of knowledge ability is based on the assessed recognition ability level of the user;

(b) processing a response to the first query from the user to statistically determine a revised estimated depth of knowledge ability of the user;

(c) presenting one or more subsequent depth of knowledge queries to the user, the one or more subsequent queries being selected based on the revised estimated depth of knowledge ability; and

(d) repeating steps (b) and (c) until the lexical depth of knowledge ability of the user is determined within a desired degree of accuracy.

17. The method of claim 11 wherein the lexical items comprise symbols, multi-symbol units, sounds, utterances, words, multi-word units, or idiomatic expressions that have particular meaning within the lexicon.

18. The method of claim 11 wherein the target list includes the next most important set of words to be learned within the particular lexicon.

19. The method of claim 11, further comprising:

repeating determining a lexical recognition ability level of the user and creating a target list of unknown lexical items based on the item recognition ability level at multiple testing periods; and

updating the language learning sequence of the user based, at least in part, on the results from one or more testing periods.

20. The method of claim 11, further comprising generating text materials for the user based, at least in part, on the lexical abilities of the user, wherein the text materials can include reading, listening, and video materials.

21. The method of claim 11, further comprising filtering the text materials before presenting the text materials to the user, wherein a set target percentage of the lexical items in the filtered text materials can be predetermined.

22. The method of claim 11, further comprising delivering the language learning sequence or a portion thereof to the user via electronic messaging at one or more predetermined intervals.

23. The method of claim 11, further comprising generating one or more reports based on the language learning sequence, the one or more reports including any one or more of the following:

graphical and textual descriptions of the lexical items known by the user;

number of known and unknown lexical items to the user within a corpus or sub-domain of the lexicon;

identification of each unknown lexical item;

number of known and unknown lexical items within different importance bands or frequency bands of a corpus or sub-domain of the lexicon;

depth of knowledge ability of the user for the items in the lexicon;

retention ability of the user for the items in the lexicon;

learning rate of the user based on interactions with one or more learning programs; and

comparison between any particular reported user or group attribute and equivalent attributes of one or more desired groups, demographic segments or populations.

24. A language instruction system, comprising:

means for storing multiple lexical items within a corpus or corresponding sub-domains;

means for ranking the multiple lexical items in order of importance within the corpus and/or corresponding sub-domain;

means for receiving and processing user input in response to a presentation of at least a portion of the multiple lexical items to each user for response;

means for calculating, for each user, a lexical recognition ability measure, lexical depth of knowledge measures, and a lexical retention measures as compared with other users within a given demographic segment or population; and

means for generating a target list of unknown lexical items for each user.

25. A system for semantic knowledge assessment and instruction, the system including an item importance database for storing multiple lexical items, wherein the stored multiple lexical items are ranked in order of importance within a selected corpus and/or corresponding sub-domain, the system comprising:

a computer-adaptive testing component configured to—

present a series of lexical items from the selected corpus and/or sub-domain to a user for identification; and

receive and process user input for each presented lexical item;

a calibration component configured to determine, for each user—

a lexical recognition ability level,

multiple lexical item depth of knowledge measures, and

multiple lexical item retention measures,

as compared with users within one or more demographic segments or populations; and

a learning sequence compiler configured to generate, for each user, personal language learning sequence including one or more unknown lexical items, the selected lexical items being organized by priority of item to be learned in sequence within the particular corpus and/or sub-domain.