WO2010020086A1

WO2010020086A1 - Computer-aided language learning

Info

Publication number: WO2010020086A1
Application number: PCT/CN2008/072019
Authority: WO
Inventors: Honglin Wu; Anhui Wang
Original assignee: Xingke Medium And Small Enterprises Service Center Of Northeastern University
Priority date: 2008-08-18
Filing date: 2008-08-18
Publication date: 2010-02-25

Abstract

The present disclosure describes a language teaching tool and methods that are able to provide computer-aided language learning courses. A computer device configured as the teaching tool provides training sessions. During each training session, the teaching tool may receive an input of time availability for the current training session. The teaching tool is then operable to automatically adapt the content of a training program applied during the training session according to the time availability, wherein the training program may comprise a number of selected text passages to read, and training exercises for assisting the assimilation of unknown vocabulary words detected from the read text passages.

Description

COMPUTER-AIDED LANGUAGE LEARNING

BACKGROUND

Description of the Related Art

Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.

Learning a new language may be a difficult task and a heavy burden for a student. Indeed, progress can be obtained only after the student has invested a sufficient amount of time and effort to attend the language courses and practice training exercises, more particularly reading exercises. Unfortunately, the student's time availability may not be always compatible with the amount of effort required. As a result, the student may be left behind as the language course advances.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate embodiments of this disclosure and are therefore not to be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments.

FIG. 1 is a schematic diagram of a language teaching system 100 according to one embodiment of the present disclosure;

FIG. 2 is a general flowchart of method steps performed by the teaching system during one training session, according to one embodiment of the present disclosure;

FIG. 3 is a flowchart of method steps for adjusting the content of a training program for one training session according to a time availability, according to one embodiment of the present disclosure; FIG. 4 is a schematic diagram illustrating how new vocabulary words are identified by the teaching system, according to an embodiment of the present disclosure;

FIG. 5 is a flowchart of method steps for updating an evaluation table after the reading of a text passage is completed, according to one embodiment of the present disclosure;

FIG. 6 is a diagram illustrating analysis results obtained for an unknown vocabulary word, according to one embodiment of the present disclosure;

FIG. 7 is a flowchart of method steps for assisting the learning of unknown new vocabulary words, according to one embodiment of the present disclosure;

FIG. 8 is a flowchart of method steps for computing a training progress parameter after a training session ends, according to one embodiment of the present disclosure; and

FIG. 9 is a schematic diagram illustrating one embodiment of a software architecture for the language training program shown in FIG. 1.

DETAILED DESCRIPTION

The present application describes a language teaching tool and method that are able to provide computer-aided language learning courses. A computer device configured to dispense language learning courses provides training sessions. During each training session, the computer device may receive an input of time availability for the current training session. The computer device is then operable to automatically adapt the content of a training program applied during the training session according to the time availability, wherein the training program may comprise a number of selected text passages to read, and training exercises for assisting the assimilation of unknown vocabulary words. In some embodiments, the language teaching tool described herein may be implemented as a computer station provided on the site of a language learning institute. In other embodiments, the language teaching tool may also be implemented as a software program executable on any suitable computer device, including a desktop computer, a laptop computer, a computer terminal, a workstation, or the like. FIG. 1 is a schematic diagram of a language teaching system 100 according to one embodiment of the present disclosure. The language teaching system 100 is implemented as a computer device that comprises a central processing unit (CPU) 102, a system memory 104, a display device 106, an input device 108, and a system interface 110 through which the CPU 102 is able to exchange data with the display device 106 and the input device 108. The CPU 102 is adapted to access the system memory 104 to execute certain program instructions, receive commands through the input device 108, and issue instructions for displaying images on the display device 106. The system memory 104 stores a language training program 120 configured to conduct language training sessions to exercise reading skills.

FIG. 2 is a general flowchart of method steps performed by the teaching system 100, according to one embodiment of the present disclosure. In step 202, the system 100 organizes a training program of reading exercises for the current training session in accordance with a level of knowledge and a current training progress. In one embodiment, the content of the program may include a number of text passages to read, and training exercises to be practiced in connection with unknown vocabulary words detected from the read text passages. In step 204, the system 100 queries whether a limited time availability has been provided for the training session. When there is no input of limited time availability, step 206 is performed, by which a text passage is presented on the display device 106, and the system 100 receives input of the actual time spent for completing the reading of the text passage. On the other hand, if a limited time availability has been provided for the training session that is less than a time reference for the training session, step 208 is performed to adjust the content of the training program in accordance with the inputted time availability. In one embodiment, the time reference may be a duration of the training session predetermined according to a level of knowledge. The level of knowledge may be evaluated after multiple training sessions have been provided. The content of the training program may be adjusted through various ways such as, for example, reducing the number of text passages to read, selecting text passages of shorter content, or a combination thereof. One example of computation method for adjusting the content of the training program will be described below with reference to FIG. 3. In step 210, once the system 100 receives input that the reading of the text passage has been completed, the system 100 generates a list of unknown vocabulary words detected from the text passage. The unknown vocabulary words may include directly inputted vocabulary words, or vocabulary words automatically detected as unknown by applying a computation method. One example of computation method for detecting unknown vocabulary words will be described below with reference to FIG. 4.

In step 212, the identified unknown vocabulary words are analyzed by the system 100, and training exercises are then conducted to assist in the assimilation of the unknown vocabulary words. Steps 206-212 may be repeated until the training session ends when all the text passages and training exercises to accomplish have been completed, or when the training session has been prematurely ended before the program has even been completed. Once the training program ends, steps 216 and 218 are applied to verify whether the training course has been followed in a diligent manner. More particularly, in step 216, the system 100 determines whether the current training program has been completed. If it is not the case, step 218 is performed to determine whether an amount of incomplete programs accumulated over a past period of time exceeds a predetermined threshold value. In one embodiment, the accumulative amount of incomplete programs may be evaluated for three and five past consecutive training sessions. When the accumulative amount of incomplete programs exceeds the threshold value, the system 100 in step 220 evaluates a training progress parameter indicative of a lag in the learning progress. The training progress parameter may be used to adapt the content of the training program for a next training session. On the other hand, if the system 100 has determined from step 216 that the training program has been completed, or from step 218 that the accumulated amount of incomplete programs does not exceed the predetermined threshold value in step 218, the training session may then be completed in step 222.

FIG. 3 is a flowchart of method steps for adjusting the content of a training program for one training session according to a time availability, according to one embodiment of the present disclosure. In step 302, based on the content of the training program, the system 100 evaluates a time reference for completing the current training session corresponding to a level of knowledge. In step 304, the system 100 then evaluates a ratio of the time availability relative to the time reference. In step 306, the system then modifies the content of the training program based on the result of the ratio. For example, when the ratio is less than 1 , the system may reduce the content of the program by selecting either a smaller number of text passages, or text passages of shorter content. The ability to adjust the content of the training program at each session as a function of the time availability enables the system 100 to adapt each training session in accordance with the learning progress. The dispensed language course can thus be adapted to the learning pace.

FIG. 4 is a schematic diagram illustrating how new vocabulary words are identified by the teaching system 100, according to an embodiment of the present disclosure. Each text passage stored in the system 100, such as text passages 402 and 404, comprises many text words. According to one embodiment, the system 100 stores an evaluation table 412 that associates each text word with an evaluation parameter Ev used to measure the likelihood that the associated text word is unknown. Once the reading of a text passage is completed, the system 100 updates the evaluation parameters Ev associated to all text words occurring in the read text passage, based on the inputted actual time spent for reading the text passage. After a sufficient number of text passages are read, the text words of highest evaluation parameters Ev in the evaluation table 412 may be identified as new unknown vocabulary words.

To illustrate, FIG. 5 is a flowchart of method steps for updating the evaluation table 412 after the reading of a text passage is completed, according to one embodiment of the present disclosure. In initial step 502, after the reading of a text passage has been completed, the system 100 receives the input of the actual time for reading the text passage. For the purpose of illustration, suppose the read text passage is the text passage 402 shown in FIG. 4 having 100 words, the inputted actual time is 15 minutes, and a time reference for reading the text passage 402 is 10 minutes. In step 504, the system 100 then computes a time difference between the actual time spent on the text passage 402 and the time reference associated with the text passage 402, which is equal to 15-10 = 5 minutes. It can be noted that the time difference may be negative when the inputted actual time is less than the time reference. In step 506, the system 100 then computes an average coefficient C that is the ratio of the time difference to the number of words in the read text passage, i.e., C= 5/100 = 0.05. In step 508, for each occurrence of a text word in the read text passage, the associated evaluation parameter Ev is added once by the average coefficient C, i.e. Ev = Ev + C. With respect to the example of the read text passage 402, the average coefficient C is thus added at least once to at least the evaluation parameters Ev1 , Ev2, Ev3, Ev4 and Ev5, because the associated text words "the", "text", "comprises", "many" and "words" occur at least once in the text passage 402. In step 510, the system 100 then evaluates whether the accumulative number of read text passages is greater than a preset threshold value. The purpose of this evaluation is to ensure that the number of read text passages is sufficient for making an accurate determination of unknown vocabulary words based on the values of the evaluation parameters Ev. If the accumulative number of read text passages is not greater than the preset threshold value, the system 100 in step 512 requires a manually input of all new unknown vocabulary words in the read text passage.

When the accumulative number of read text passages is greater than the preset threshold value, the system in step 514 disregards all word entries in the evaluation table 412 that map into a stop list containing vocabulary words that are so common that they are presumably not unknown. For example, the stop list may comprise the words "a", "the", or like common words. In step 516, the system then identifies in the evaluation table 412 the word entries with the highest evaluation parameters as new unknown vocabulary words. It is worth noting that in alternate embodiments, the elimination of presumably known vocabulary words may also be made by removing these word entries from the evaluation table 412. In this case, step 514 may no longer be necessary. By repeating steps 502-516 for multiple text passages, the evaluation table 412 thus can provide an accurate determination of the unknown vocabulary words.

After the unknown vocabulary words have been identified, e.g., either by manual input or based on the evaluation table 412, the system 100 is configured to analyze the unknown vocabulary words, and determine a priority for treating the unknown vocabulary words. FIG. 6 is a diagram illustrating analysis results obtained for an unknown vocabulary word, according to one embodiment of the present disclosure. In one embodiment, the analysis results may be gathered in a result table 602 that comprises a field containing the unknown vocabulary word, a field indicative of the degree of seriousness of the unknown vocabulary word, a field indicative of the degree of importance of the unknown vocabulary word in the learned language, and a field that resumes the phrase context in which the unknown vocabulary word appeared. In one embodiment, the degrees of seriousness and importance of the unknown vocabulary word may respectively have ten levels. The degree of seriousness of the unknown vocabulary word measures the frequency of occurrence of the unknown vocabulary word, whereas the degree of importance of the unknown vocabulary word provides an indication of the importance in language use of the unknown vocabulary word. For example, verbs may have a degree of importance higher than nouns that, in turn, have a degree of importance higher than adjectives, whereas adverbs are more important than any other types of words. The degrees of seriousness and importance of the unknown vocabulary word may also be modified according to the results of prior training exercises. For example, if the correct answer to a test exercise in connection to one unknown vocabulary word is not provided, the associated degrees of seriousness and importance may be incremented by different levels. In contrast, if the correct answer is provided, the associated degrees of seriousness and importance may be decremented. Based on the degrees of seriousness and importance of each unknown vocabulary word, the training exercises can therefore be suitably adapted according to an order of priority of the reading problems.

FIG. 7 is a flowchart of method steps for assisting the learning of unknown new vocabulary words, according to one embodiment of the present disclosure. In step 702, the system 100 may display explanations of the meanings and uses of a new vocabulary word. In step 704, the system 100 may also show one or more phrase contexts in which the new vocabulary word may appear, so as to facilitate the memorization of the new vocabulary word. In step 706, the system 100 may then conduct one or more training exercises to ensure the assimilation of the new vocabulary word. In this manner, reading problems due to unknown vocabulary words may be solved in an effective manner.

As described previously in connection to FIG. 2, a training session may end prematurely before the training program has even been completed. When the accumulative amount of incomplete programs exceeds a threshold value, the system 100 evaluates a training progress parameter that measures a lag in the learning progress. FIG. 8 is a flowchart of method steps for computing a training progress parameter after a training session ends, according to one embodiment of the present disclosure. In step 802, the system 100 evaluates a time reference for the current training session corresponding to a level of knowledge. In step 804, the system 100 then determines whether a limited time availability is provided. When a limited time availability is provided, the system 100 in step 806 then computes a training progress parameter defined as the ratio of the inputted time availability to the time reference. When a limited time availability has not been provided, the system 100 computes in step 808 a training progress parameter defined as the ratio of the number of incomplete programs relative to a standard requirement value. Based on the training progress parameter, the content of the training program for the next training session may be adequately adjusted in accordance with the actual progress to reduce the lag in the learning progress.

FIG. 9 is a schematic diagram illustrating one embodiment of a software architecture 900 for the language training program 120 shown in FIG. 1. In one example of implementation, the software architecture 900 may comprise a text database 922, a detector module 924, an analysis module 934, a database of analysis results 936, a training module 938, and a training progress evaluating module 940. The text database 922 may store all the text passages used for each training session. The detector module 924 may be configured to detect new unknown vocabulary words by, for example, implementing the method steps described in conjunction with FIG. 5. The analysis module 934 is configured to analyze unknown new vocabulary words identified from each read text passage, and generate analysis results classifying the degrees of seriousness and importance of each unknown vocabulary word. The analysis results may be gathered in the form of tables, such as shown in FIG. 6, which are stored in the database of analysis results 936. The training module 938 may be configured to apply training exercises to assist the assimilation of new vocabulary words. The training progress evaluating module 940 is configured to compute the training progress parameter by implementing, for example, the method steps described in conjunction with FIG. 8.

While the foregoing is directed to embodiments of the disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof. For example, aspects of the disclosure may be implemented in hardware or software or in a combination of hardware and software. One embodiment of the disclosure may be implemented as a program product for use with a computer system. The program(s) of the program product define functions of the embodiments (including the methods described herein) and can be contained on a variety of computer-readable storage media. Illustrative computer-readable storage media include, but are not limited to: (i) non-writable storage media (e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive, DVD disks readable by a DVD driver, ROM chips or any type of solid-state non-volatile semiconductor memory) on which information is permanently stored; and (ii) writable storage media (e.g., floppy disks within a diskette drive, hard-disk drive, CD-RW, DVD- RW, solid-state drive, flash memory, or any type of random-access memory) on which alterable information is stored. Such computer-readable storage media, when carrying computer-readable instructions that direct the functions of the disclosure, are embodiments of the disclosure. Therefore, the above examples, embodiments, and drawings should not be deemed to be the only embodiments, and are presented to illustrate the flexibility and advantages of the disclosure as defined by the following claims.

Claims

1. A computer-aided language training method comprising: setting a training program to accomplish for a training session, wherein the training program comprise a plurality of text passages to read and training exercises to practice with respect to one or more unknown content portions of the text passages; querying a time availability for the training session; receiving an input of the time availability; and modifying the training program when the inputted time availability is less than a time reference for the training session.

2. The method according to claim 1 , wherein the step of modifying the training program comprises reducing the number of texts to read during the training session, selecting texts of shorter content, or a combination thereof.

3. The method according to claim 2, wherein the step of modifying the training program is performed according to a level of knowledge.

4. The method according to claim 1 , further comprising determining the occurrence of one or more unknown vocabulary words in the read text passages.

5. The method according to claim 4, wherein the step of determining the occurrence of one or more unknown vocabulary words comprises: tracking an actual time spent for reading one text passage; for each word in the read text passage, updating an evaluation parameter associated with the word on each occurrence of the word in the read text passage, based on a difference between the actual time and a time reference for reading the text passage; and identifying a plurality of word occurrences having the highest evaluation parameters as the unknown vocabulary words.

6. The method according to claim 5, wherein the step of updating an evaluation parameter associated to each word occurrence in the read text passage comprises: computing a difference value between the actual time and the time reference; computing an average coefficient obtained by dividing the difference value by a total number of words contained in the read text passage; and adding the average coefficient to the evaluation parameter associated to each word occurrence.

7. The method according to claim 6, wherein the average coefficient is positive when the actual time is greater than the time reference value, and negative when the actual time is less than the time reference value.

8. The method according to claim 5, wherein the step of identifying a plurality of word occurrences having the highest evaluation parameters as the unknown vocabulary words is performed after the step of updating an evaluation parameter has been applied for a requisite amount of text passages.

9. The method according to claim 8, wherein the step of determining the occurrence of one or more unknown vocabulary words further comprises: skipping the step of identifying a plurality of word occurrences having the highest evaluation parameters as the unknown vocabulary words when a number of read text passages is lower than the requisite amount of text passages; and requesting an input of the unknown vocabulary words.

10. The method according to claim 9, further comprising classifying each identified unknown vocabulary words according to a degree of seriousness and a degree of importance of the unknown vocabulary word.

11. The method according to claim 10, wherein the degree of seriousness indicates a frequency of occurrence of the identified unknown vocabulary word.

12. The method according to claim 10, wherein the degree of importance indicates an importance in language use of the identified unknown vocabulary word.

13. The method according to any of claim 10 to 12, further comprising conducting training exercises where priority is given to unknown vocabulary words that have higher degrees of seriousness or higher degrees of importance.

14. The method according to claim 1 , wherein the step of setting the training program comprises evaluating a training progress parameter computed during a previous training session.

15. The method according to claim 12, wherein the training progress parameter is defined either by the ratio of a number of incomplete training programs relative to a predetermined requirement value, or the ratio of the inputted time availability time relative to the time reference.

16. The method according to claim 15, wherein the number of incomplete training programs comprises an accumulative amount of incomplete training programs over a past period of time.

17. A computer device comprising: a display device; an input device; a memory; and a processing unit configured to set a training program to accomplish for a training session, wherein the training program comprise a plurality of text passages to read and training exercises to practice with respect to one or more unknown content portions of the text passages; query a time availability for the training session; receive an input of the time availability; and modify the training program when the inputted time availability is less than a time reference for the training session.

18. The computer device according to claim 17, wherein the processing unit is configured to modify the training program by reducing the number of texts to read during the training session, selecting texts of shorter content, or a combination thereof.

19. The computer device according to 17, wherein the processing unit is further configurable to determine the occurrence of one or more unknown vocabulary words in the read text passages.

20. The computer device according to claim 19, wherein the processing unit is configured to determine the occurrence of one or more unknown vocabulary words by performing the steps of: tracking an actual time spent for reading one text passage; for each word in the read text passage, updating an evaluation parameter associated with the word on each occurrence of the word in the read text passage, based on a difference between the actual time and a time reference for reading the text passage; and identifying a plurality of word occurrences having the highest evaluation parameters as the unknown vocabulary words.

21. The computer device according to claim 20, wherein the processing unit is configured to update an evaluation parameter associated to each word occurrence in the read text passage by performing the steps of: computing a difference value between the actual time and the time reference; computing an average coefficient obtained by dividing the difference value by a total number of words contained in the read text passage; and adding the average coefficient to the evaluation parameter associated to each word occurrence.

22. The computer device according to claim 21 , wherein the average coefficient is positive when the actual time is greater than the time reference value, and negative when the actual time is less than the time reference value.

23. The computer device according to claim 20, the step of identifying a plurality of word occurrences having the highest evaluation parameters as the unknown vocabulary words is performed after the step of updating an evaluation parameter has been applied for a requisite amount of text passages.

24. The computer device according to claim 20, wherein the processing unit is configured to determine the occurrence of one or more unknown vocabulary words by performing the steps of: skipping the step of identifying a plurality of word occurrences having the highest evaluation parameters as the unknown vocabulary words when a number of read text passages is lower than the requisite amount of text passages; and requesting an input of the unknown vocabulary words.

25. The computer device according to claim 24, wherein the processing unit is further configured to classify each identified unknown vocabulary words according to a degree of seriousness and a degree of importance of the unknown vocabulary word.

26. The computer device according to claim 25, wherein the degree of seriousness indicates a frequency of occurrence of the identified unknown vocabulary word.

27. The computer device according to claim 25, wherein the degree of importance indicates an importance in language use of the identified unknown vocabulary word.

28. The computer device according to any of claim 25 to 27, wherein the processing unit is further configured to conduct training exercises where priority is given to unknown vocabulary words that have higher degrees of seriousness or higher degrees of importance.

29. The computer device according to claim 17, wherein the processing unit is further configured to evaluate a training progress parameter computed during a previous training session for setting the training program.

30. The computer device according to claim 29, wherein the training progress parameter is defined either by the ratio of a number of incomplete training programs relative to a predetermined requirement value, or the ratio of the inputted time availability time relative to the time reference.

31. The computer device according to claim 30, wherein the number of incomplete training programs comprises an accumulative amount of incomplete training programs over a past period of time.