Patent Application
20250225891
The present disclosure relates generally to automated systems and methods for student second-language instruction.
This section introduces aspects that may help facilitate a better understanding of the disclosure. Accordingly, the statements of this section are to be read in this light and are not to be understood as admissions about what is prior art or what is not prior art.
A number of automated, highly popular, second-language instructional systems abound. Duolingo is typical of the approach taken by these systems. Systems in this category follow what is essentially a linear learning path or map. At each step along the path, a new set of vocabulary and/or new linguistic skills is/are introduced for students to begin to master.
A student's facility or prior experience with a language may allow them to move quickly through this sequence. They may skip steps along the path if they have related prior experience or can intuit larger linguistic patterns without having to be explicitly taught. They may also be able to skip steps if they have a facility for vocabulary acquisition such that large amounts of review are unnecessary. Regardless, the path in such apps remains essentially linear.
Along this linear path, the user is frequently offered a choice of “side” exercises, but these exercises are predetermined. New offerings become available based on where a person is on the language-acquisition pathway or, based on their performance level, on various elements along the path prior to that point.
One example of such a side offering is a canned iconic or epic story. Once a user reaches a certain point on the learning path, iconic story number-one becomes a side offering. Whether a user avails himself of the offering is purely up to him.
As users progress, additional stories are added to the menu of choices. For example, one iconic story about family, one about breakfast, one about shopping, and so on. These stories vary little; they are the same or similar every time. This is purposeful because the comprehensible-input theory of second-language acquisition hypothesizes that these iconic, unchanging narratives build confidence by allowing repeated reference to familiar, predictable stories.
A second example of such a side offering is presenting possible exercises composed of the highest probability errors a student has made to that point along the path.
There are numerous problems with this traditional, linear approach. But the most-important problem is that familiarity and predictability have their limits. Although the traditional approach is perfect for easy implementation in a computerized, automated-application environment, it has shortcomings in terms of generating the intense interest that can produce the highest learning gains, especially those required to reach the fluency levels necessary to obtain a visa or work permit.
Fahrurrozi “shows there is a strong connection between interest and reading comprehension, and that self-selected reading resulted in substantial vocabulary gains and higher TOEIC (Test of English for International Communication) scores.” See Fahrurrozi, “Relationship Between Students” Reading Interest and Vocabulary Mastery with Reading Comprehension Ability (2017) Atlantis Press, 118 (59), 357-363, the teachings of which are incorporated herein by reference. Krashen, who many consider the father of the theory that underpins most modern, second-language teaching methods, has conducted research that shows the same for self-selected spoken language and vocabulary. See Beniko Mason and Stephen Krashen, “Self-Selected Reading and TOEIC Performance: Evidence from Case Histories” (March 2017), the teachings of which are incorporated herein by reference.
Other research shows that, when information is learned in highly interrelated chunks, the brain stores those parts more efficiently, as if they are a single whole. So, learning the words “dog,” “bark,” “pet,” and “run” together is more advantageous to word acquisition, retention, and learning rate than learning the word “dog” in isolation.
Various embodiments disclosed herein are directed at creating an interactive, computerized learning environment that overcomes the shortcoming of existing tools and methods currently in or proposed for use to increase the rate of student second-language acquisition. All methods for meeting this need proposed herein are based on the present disclosure of user choice-mediated exercises.
The present disclosure relates to a method of increasing the rate of second-language acquisition by providing a system that can target and train students in second-language elements that intersect individually important daily routines, areas of interest, expertise, or concern.
Because of these efficiency-enhancing effects, examples abound of the person who needs to gain high proficiency in a narrow section of a language and who is able to do so in a short span of time because the need and interest are high-the math student who barely speaks the language but soon has little trouble with written or spoken math problems in his new language; the athlete who converses fluently with same-sport coaches, referees, and athletes, but who has considerably more trouble with her new language outside that narrow context; the piano teacher who quickly masters a small segment of a second language so she can converse with a new foreign student, and so on.
These examples highlight ways an automated learning system that harnesses the power of high-interest user choices would enhance learning of the target language.
But app producers are concerned with more than just ensuring students learn the target language. They are also concerned with profit and app-usage rates.
The field of habit formation shows that a new habit is more easily formed if it is attached to an existing habit. See Maddy Osman, “Use Habit Stacking To Change Your Behavior and Create New Routines” (May 2, 2023). Attaching the study of language to a recurrent daily need will increase both the regularity and frequency of app usage.
Each time the user enters a recurrent real-life situation of linguistic concern, using the app to aid language acquisition specific to that recurrent setting will be a natural secondary occurrence. The secondary habit of using the app in that setting will, thus, attach itself to the primary habit of regularly needing to navigate that real-life situation.
This will aid language acquisition, of course. But as a matter of pure economics and profit, harnessing this pattern of habit formation by incorporating this disclosure into old and new language-acquisition applications can increase profits. It does so by providing a method of learning that is powerful enough to drive profits via the desirability of a service that fits naturally into the habit-formation flow of a user's established daily routine.
For all these reasons, a computerized system is needed that can harness the power of practicing high-interest vocabulary and narratives that falls outside of the standard linear progression of a computerized application but within the sphere of immediate user needs and interests. This disclosure describes such a system.
In one such preferred embodiment of a user-choice-mediated system, a user can enter a desired word (aka choice-word) from their native language. The system will assess where along the linear learning path the user is. It will then supply an exercise set composed of words, phrases, sentences, and/or images that a) employs the choice-word in a realistic, real-world context and b) uses linguistic structures and vocabulary that are appropriate for where the user is along his learning path—neither too simple nor too complex for the user's current skill level.
In one embodiment of this disclosure, no new learning-path words beyond the single choice-word a user has entered will be used to construct the user choice-mediated exercises. All other words will be drawn from the learning bank as it exists at that point on the learning path. In the early stages of learning, template lessons in such an embodiment would be simplistic.
In more-advanced stages along the linear learning path, the choice-word could be employed in more creative ways because more vocabulary is available to compose creative phrases and sentences. In one version of this embodiment, the word could be used in sentences that are simple but true-to-life statements. For example, if the user entered the word “dog”, the user could be asked to work with target-language phrases like, “Is this a dog?”, “This dog is large,” “Is this your dog?”, “I like dogs,” “I like large dogs,” and so on.
In preferred embodiments of this disclosure, a user can enter a choice-word from their native language. The system will then assess where along the learning path the user is. Then the system will supply an exercise set composed of words, phrases, sentences, and images that a) employs a translation of the choice-word in the target language in realistic, real-world contexts and b) uses linguistic patterns/templates and vocabulary that are appropriate for where a user is along his linear learning path, and c), as an extension of the last embodiment, also introduces new vocabulary that has a high-probability, real-world association to the user's choice-word.
For instance, reconsider the last example where “dog” was the native-language choice-word. Even though a user might be fairly early in their learning path, new vocabulary associated with the choice-word could be introduced in versions of this preferred embodiment. Instead of working with target-language phrases like “The dog” or “I like dogs,” the application could now employ phrases like “The furry dog,” “The barking dog,” “I like to walk dogs,” or “The dog is running.” In this case, “furry,” “barking,” and “running” are words with a high-frequency association to dogs. But low-frequency words, like “furry” and “barking”, will likely not have been previously encountered along the learning path.
In some versions of this preferred embodiment, new target-language words that have a high-probability association with the choice-word (but that have not yet been introduced along the linear learning path) can be individually introduced at the exercise skill level via a) picture-mediated exercises that suggest the new word's meaning by pictorial context, b) written-language or spoken-language exercises that suggest the new word's meaning by verbal, written, and/or pictorial contextual clues.
In some versions of this embodiment, using linguistic patterns already introduced along the learning path would also not be the only option. Idiomatic expressions associated with the choice-word in the target language might also be introduced.
Within the context of a broader learning system, one preferred embodiment would add new user choice-words to the standard vocabulary word-banks associated with each point along the learning path. These new choice-words could then be incorporated at future points along the learning path by being drawn from the newly modified word bank to construct future exercises.
Along the same lines, another embodiment would add both the user choice-words as well as the words with a high-frequency association to the user choice-words. Using the dog example one more time, this would mean that, not only would “dog” be added to the word bank and used in future learning-path exercises, but words having a high-frequency association with “dog”—like “bark,” “run,” and “fur(ry)”—would also be added to the word bank for use in future exercises.
And some preferred embodiments would save the exercise sets as iconic exercises that could then be reselected and reworked by selecting from an iconic story bank or by re-entering the choice-word to recall a repeat of the same earlier exercise.
A preferred embodiment would allow the regeneration of an exercise set in the same statistical way that an earlier version of the set was generated from the same choice-word. This might not result in the exact same exercise as the prior one. But it would save considerably on memory.
In at least one embodiment of the present disclosure, an app for teaching a foreign language to a user proficient in a native language (a) tracks a current level of proficiency of the user in the foreign language, where the current level of proficiency increases with the proficiency of the user in the foreign language, (b) receives from the user a current word or phrase in the native language, and (c) generates a number of current exercises for the user to perform based on (i) a translation of the current word or phrase in the foreign language and (ii) the current level of proficiency of the user in the foreign language.
Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment disclosed and claimed herein. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments necessarily mutually exclusive of other embodiments. The same applies to the term “implementation.” Embodiments of the disclosure will become more fully apparent from the following detailed description, the appended claims, and the accompanying drawings in which like reference numerals identify similar or identical elements.
Detailed illustrative embodiments of the present disclosure are disclosed herein. However, specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments of the present disclosure. The present disclosure may be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein. Further, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the disclosure.
As used herein, the singular forms “a,” “an,” and “the,” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It further will be understood that the terms “comprises,” “comprising,” “contains,” “containing,” “includes,” and/or “including,” specify the presence of stated features, steps, or components, but do not preclude the presence or addition of one or more other features, steps, or components. It also should be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functions/acts involved.
This is only one embodiment among other possible embodiments. By way of example, stand-alone PC embodiments, as shown in
Referring back to
A preferred embodiment of a home screen should also contain a button 37 that allows access to the choice-world exercise screen, which is the gateway to the choice-word exercises, the main subject of this disclosure (see
As previously described, some embodiments can operate on a web-based platform, a standalone PC platform, or an intranet platform. An example of a standalone computer platform is shown in
In accordance with an aspect of another embodiment, a local area network as shown in
In step 21, that choice-word will be checked against a full dictionary of words stored in memory, whether instantiated in code 19, residing in the cloud on a server 3 or 4 of
If the user's choice-word is not found in the full native-language-to-target-language dictionary in step 21, then, in step 22, a message will be displayed stating as much and asking, in step 23, if the user would like to enter a new word in step 20.
If the user choice-word is found in the full native-language-to-target-language dictionary, then, in steps 24-30, the app will use code to construct a set of native-language exercises that will allow the user to practice and acquire fluency with the associated choice-word in the target language. For a more granular description of these steps, see the detailed descriptions for steps 21-24 beginning in paragraph [0088], for step 25 beginning in paragraph [0096], for step 26 beginning in paragraph [00101], for step 27-29 beginning in paragraph [00107], and for step 30 beginning in paragraphs and [00145].
Once the system has executed these steps to construct a set of level-appropriate exercises centered around the user's target word, in step 31, the app will then present the exercises to the user so that they can practice and acquire fluency with the choice-word.
In one preferred embodiment, once the exercise is fully worked, the app will exit the choice-word exercise set and return to the home screen 32A of
That is an overview of the workflow in one preferred embodiment of this disclosure. Other workflows are possible in other embodiments because the order of certain elements of this workflow can be changed. In still other embodiments, elements of the workflow can be left out and compensated for by adding additional informational columns to the language banks or linguistic patterns used in those banks. Or elements can be added to the workflow that require less detail to be included in the language banks and their linguistic patterns. See
The remainder of this discussion will focus on the details of implementing each step of this workflow and the figures given to help illustrate the particulars of those details.
In step 20 of the workflow of
A multi-word entry system that uses parentheses or some other indicator to specify a context-word is one preferred embodiment of this disclosure.
Allowing multi-word entries with no context indicators introduces programming challenges related to deciding which word the user sees as key. One embodiment of the code could be configured to spot infinitives and reduce them to their primary verbal element.
In one preferred embodiment of this disclosure, a dedicated virtualized keyboard 39 is included because it can easily be configured to include special characters that are unique to the user's native language. On touch screens, the user can use this virtualized keyboard 39 by tapping desired keys. On non-touch screens, the virtualized keyboard 39 can be used by via a mouse by moving a pointer to the desired keys. A mouse-click can then be used to select the desired character-key. In the same preferred embodiment, input may also be entered via a voice prompt initiated by a voice input button 38. Hitting the enter key on a keyboard 9 (
Column 44 is not an actual part of most preferred embodiments of a learning language dictionary. It is provided mainly to show how a full target-language dictionary could be employed to populate level-appropriate syntactical structures, either before or after translation into the target language. Importantly, in various embodiments of such dictionaries, column 42 might or might not be included because appropriately trained large language models would generally already contain access to parts-of-speech information. Though such language models would likely also be able to infer learning levels, preferred embodiments of such dictionaries should contain this column so as to allow programmers/instructors to retain tighter control over the structure of a user's language learning journey.
In a preferred embodiment of this disclosure, once a user has entered a single word-choice in their native language, a coded method or set of methods will then be used to determine if the user choice-word exists in the full target-language dictionary stored in memory for the native and target languages.
These latter embodiments could rely on AI to also translate the choice word into the target language and then work from leveled dictionaries composed solely or primarily of target-language words, phrases, and syntactical structures. Or, the dictionary might be composed only of native language entries, relying on AI to bridge the gap between native-language and target-language words, phrases, and syntactical structures.
Regardless of the approach, this contrasts with most language apps that generally teach a far smaller subset of the total words available in the target language. For instance, mastering eight hundred words in a target language is generally enough to allow a speaker to function reasonably well in everyday settings. Therefore, an embodiment of a language-teaching app might include only those eight hundred words, barely one percent of the words a person fluent in the target language will have mastered.
Other embodiments of a language-teaching app might have much larger dictionaries, but still nothing close to one hundred percent of the words required for fluency.
In a preferred embodiment of this disclosure, the target language dictionary will include as close as possible to one hundred percent of the highest-frequency words required for full fluency.
As mentioned earlier, in preferred embodiments of this disclosure, skill levels are attached to individual words based on various pedagogical concerns. Increasing skill levels are also associated with increasingly complex syntactical structures.
To understand the structure of these full-language dictionaries and how they are employed in this disclosure to automate the generation of level-appropriate exercises that fall outside the scope of a standard, linear-language-learning pathway, a person should consider what an embodiment of such a pathway and its associated syntactical structures might look like if it was written out in table form.
In the preferred embodiments of most language apps, new language and grammatical structures are introduced in successive skill levels. As a loose generalization, the highest-frequency words and easier grammatical structures are introduced at lower skill levels. Lower-frequency words and more-complicated grammatical structures are introduced at successively higher skill levels. This continues until the user has traversed every skill level of the linear learning path represented by a language-learning app.
In this example of the language banks and grammar patterns that characterizes one embodiment of a learning path of
For instance, the words “sword” and “crawl” are not high-frequency words in the everyday conversation of most languages. So, a traditional version of a language-learning-app pathway might not include them or some of the various words or phrases associated with them.
This embodiment functions by structuring a method by which low-frequency words that are more typically employed by high-fluency users (or even standard-learning-path words) that are highly useful or interesting to the user can be acquired outside the standard linear sequence of a learning path. This embodiment, aided by harnessing the level-organized elements of an application's learning path, maximizes user interest and language acquisition by ensuring the resulting exercises are level-appropriate for each individual user.
For instance, in the learning path embodied by
The basic idea here is that words can now be acquired outside the standard linear sequence when they are of greatest concern to the user and interest is highest. Level-2 students can learn high-interest words from level 8 or the fluency skill level. And level-10 students can decide if they want to review a specific word from level 4. This embodiment makes such out-of-sequence but grammatically (syntactically) level-appropriate learning easily possible.
It is important to note that, in some embodiments, an actual, coded learning-pathway data structure might not explicitly exist anywhere in the coded embodiment of some language-learning apps. In some apps, such a learning pathway exists simply as an artifact of the fact that words and grammatical structures are introduced sequentially and, if the sequence of introductions is noted and recorded, it would present as a chart similar to the one of
In some embodiments, the degree to which an actual, coded, language-learning pathway's data structure will be visible in memory depends on the degree to which that structure is taken for granted by virtue of being invisibly embedded in the statistical links that make up the AI large-language models that are becoming increasingly central to many language-learning apps.
In an embodiment where the instructional levels reside mostly within an AI's statistical links, it would be useful to ask the AI to list its own description of how it organizes its learning-path levels. Then the coder could use that instance of a learning path to configure the application to construct level-appropriate, user-choice exercises.
Description of
An observer can see that preferred embodiments of a full-language dictionary derive the skill-level data associated with each of their individual word entries from the skill level at which it is introduced along its associated language-learning pathway. The level associated with any given word is the level at which it first appears in the linear, language-learning pathway just explained as in
With that learning-pathway structure in mind, it makes sense that, for full-language dictionaries like those embodied in
Understanding where learning-levels come from and how they are associated with increasing degrees of grammatical (syntactical) complexity but decreasing degrees of word frequency, hierarchical dictionaries like in
To begin an example of this aspect of one embodiment of this disclosure, assume a user entered the choice-word “plane.” Once an enter button or key is then pressed, a coded method would take the user's choice-word and compare it to successive native-language entries in column element 41 of
If no match was found in column element 41 in step 21 of
If a match is found in column element 41 in step 21 of
In this example, column 42 of
In some embodiments, the target language would not require a gender determination, and this information would not be present in the full-language dictionary. In other embodiments, a noun is “gendered” based on whether it is animate or inanimate. In still other embodiments, there can be up to twenty different “gender” classes. Regardless of the embodiment, the way in which gender information is quantified would be dependent on the number of genders required by the target language.
In AI-mediated versions of this disclosure, the gender might or might not be indicated in the full dictionary because that information could reside within the AI model.
Also, other embodiments of this disclosure might include information on the transitive, intransitive, reflexive, or other verb characteristics. In still other embodiments, other characteristics of other parts of speech might be helpful to indicate in column 42 of
Having determined the choice-word's part of speech and gender, or other pertinent attributes, if necessary, this would then bring us to step 25 in the workflow of
In the embodiment of this disclosure represented by the
A user at skill-level 4 would have all templates available in column 47 up to and including those at level 4. In this case, that would include all the available templates from levels 1, 2, 3 (none in this case), and 4. As seen in column 47 of
These two examples provide a clear illustration of why a student operating at a lower skill level could be easily lost if a higher-level exercise with a more-complex grammatical structure was supplied during a choice-word exercise. The language input supplied by such a lesson would cease being comprehensible to most students and the learning value of the lesson would be lost.
Having 1) a choice-word that has been determined to reside in the dictionary, 2) the part of speech that choice-word belongs to, and 3) a set of available grammatical templates that are level-appropriate for the user at that point along his or her learning path, a preferred embodiment of this disclosure (as illustrated in the
In one embodiment based on this example, a method would be employed that was coded to search for all the templates that were determined as level-appropriate in workflow step 25 that contain the part of speech to which the user choice-word belongs. This subset of grammatical templates would then be stored in memory and used for constructing the exercises to come.
In another embodiment of this disclosure, it has to be acknowledged that any lesson set that is restricted to templates containing only the part of speech to which the choice-word belongs might have limited flexibility. For instance, suppose the user choice-word was “plane”. In such an embodiment, it might be useful to include grammatical templates and their associated exercises that match parts of speech for words related to the user choice-word. For example, the word “plane” indicates that the verbs “land”, “taxi”, “takeoff”, and “crash” are related to “plane”. In many embodiments, it might be useful to make sure to include grammatical templates that deal with verbs in the absence of the target noun.
Therefore, some preferred embodiments of this template would allow the subset of templates selected for to include the parts of speech characterizing both the choice-word and related words belonging to one or more other parts of speech.
In some of these preferred embodiments, at least one determining factor as to what parts of speech would be used to extend the choice of templates would be whether or not the related word has already been introduced in the learning sequence. In other embodiments, the determining factor might be whether a related verb was irregular or not. In still another embodiment, the determining factor might depend on noting if the plural form of a related noun was irregular. And so on.
Having selected a subset of available grammatical templates in a preferred embodiment represented by the workflow in
For example, a level-1 user might choose to practice the word “throw”, which is a verb. Unfortunately, looking at the available level-1 templates in our example learning-path language bank, only [noun] and [article, noun] templates are currently available. See column 47 of
Other embodiments of this disclosure might make other workflow choices at this juncture. This is simply one preferred example of a possible embodiment for illustration purposes.
Having selected a subset of available grammatical templates in a preferred embodiment represented by the workflow in
Many different ways of populating those templates are possible resulting in many possible embodiments of this disclosure. This portion of the detailed description will include a preferred embodiment along with a partial example of the structure of such an embodiment of a full-language dictionary necessary to support those choices. As mentioned earlier, the choice of embodiments at this juncture will largely depend on how AI is employed in an embodiment to populate the chosen templates.
Some preferred embodiments would use a large-language AI model configured to populate these templates. In this case, the model would access 1) the available templates, 2) the choice-word and its part of speech, 3) the set of all words at or below the skill-level of the user as read from the language-learning path in column 46 of
In a code-intensive preferred embodiment, if an English-to-German user at level 2 entered the choice-word “plane,” the available templates might be:
The choice-word would then be used to populate those templates by coding a method to do a direct substitution into these templates where appropriate:
Closely associated words such as “land”, “takeoff”, “taxi”, and “crash” could then be substituted using a second coded method, resulting in a set that looked something like:
Finally, words from skill levels lower than or equal to the user's current skill level from column 46 of the language-learning path of
Once the templates have been populated by the various coded methods mentioned above, in this embodiment, the templates would then be fed into a large-language model configured to translate each filled template into a legitimate, grammatically correct phrase of sentence in the target language.
As an example of the effectiveness of this preferred embodiment, the following ten sentences were constructed using ChatGPT in just this manner. After feeding ChatGPT the desired templates and target words, the model returned the following output.
Building such a system could be quite labor intensive for several reasons.
First, the full-language dictionaries necessary to support the full languages would have to be built with care. An AI large-language model could be used to construct such a dictionary in short order. The result could then be stored in memory and used in the fashion just described.
Even then, another shortcoming remains if a programmer decides to employ traditionally coded methods for substituting into the templates while selecting from the appropriate pools of words. Unfortunately, it is possible to generate sentences that, although grammatically correct and correctly leveled, do not make full sense.
In the last example, the app would easily have ended up with a template filled like this: [The, plane, crash, quietly] from the template [article, noun, verb, adverb]. The resulting sentence fails to make real-world sense in either the native or target language.
In some embodiments of this disclosure, the app could use a large-language AI model to eliminate any resulting sentences that fail to make sufficient sense.
Given the current power of these models, it makes sense to employ AI on the front end in making reasonable substitutions rather than eliminating unreasonable results on the back end. So, in one preferred embodiment of this disclosure, the app could supply the AI the templates. Then, the app could ask the AI to use those templates to construct a set of real-world sentences from these pools of words that make the most real-world sense.
In still another preferred embodiment, one making ambitious use of AI's capabilities, the app could supply the level-appropriate group of templates, the available level-appropriate vocabulary words from the learning-path's column 46 of
Within properly trained, large-language models, the probability that verbs like “taxi”, “fly”, and “land” will be statistically associated with “plane” is extremely high. Because that statistical knowledge is native to a trained AI model, large, cumbersome full-language dictionaries that are hard to construct could be mostly or completely dispensed with in some versions of a preferred embodiment.
There is an important caveat to this entire discussion of grammatical templates and substitutions. At least in the early stages of a native English speaker learning German, the grammatical templates in the native language parallel most translations in the target language. This is not true for all native-target language pairs. In some of those pairs, the distance between a native-language grammatical structure and the target-language equivalent can be quite substantial even in the earliest learning stages. For example, in German, once verb tenses other than the present tense are introduced, grammatical structures can differ radically between languages.
To compensate for this potential distance, in some embodiments, the coder might make the decision to map the target-language templates onto the native-language equivalents before substituting into those templates. In some embodiments, like the one described by this example, the coder could choose to include a keyword (like “negative”) to signal that the English versus German structure will differ. In still other embodiments, the coder might choose to translate the native-language words into their target equivalent before substituting them into a now native-language syntax template.
The one thing to be said in all of this is that, prior to the advent of AI large-language models, smoothing out the differences between what has been substituted in and the grammatically correct, target-language equivalent finally delivered on the other end would have been considerably more code-intensive, to the point of likely having to be accounted for by a) programming a new set of translation methods for each language pair or b) picking a central language through which all translations would be piped, and then creating methods to facilitate piping to and from that language.
With the advent of AI large-language options, models can now be trained to circumvent this need for an army of language-specific methods to ensure grammatically correct translations between the native-language or target-language approximations that result after substitution and the resulting grammatically correct equivalent sentences in the target language.
Having a set of target-language words, phrases, and sentences now available in the target language, it is time to use them to construct an actual exercise set in step 30 of
Several considerations arise at this point in all preferred embodiments, considerations like how many exercises to include in a generated exercise set, which of the available grammatical templates to use when building a set, and how best to map each template onto the most-appropriate possible exercises for learning the target vocabulary or mastering the grammar that characterizes each sentence.
These are pedagogical decisions the answers to which will vary from embodiment to embodiment. In one preferred embodiment, the number of problems in a user-choice-generated exercise set should match the number of problems in a language application's standard problem set. If a standard set has ten problems in it, then so should the user-choice-generated set.
Regarding which exercises to use, some embodiments may choose to randomly select from among the available grammatical templates until the selections equal the required number of problems in a set. Some embodiments may select from the least-complex available templates to the most-complex, returning to the beginning to repeat that procedure until the selections equal the required number of problems in a set. The procedure would prioritize emphasizing the basics of a language over the complexities. Or that process could be reversed, selecting from the most-complex templates and moving to the least-complex ones, to prioritize quick mastery of newer grammatical structures.
In this example of one preferred embodiment, the selection process takes place from simpler templates to more-complex ones until the number of selected templates equals the maximum number of problems in an exercise set. This choice is based on the belief that new vocabulary is best mastered using less-complex grammatical structures so as to allow more cognitive resources to be allocated to remembering the vocabulary rather than to handling a challenging grammar structure that is not already a part of an application's standard learning-path sequence.
As for how to think about mapping grammatical templates onto available exercises such as those illustrated by
Suppose the template [article, noun] has been used to generate the phrase “die brücke.” If brücke, or bridge, is the choice-word, and this is one of the first problems in the set, it makes sense to map this phrase to the very basic exercise template of
The preceding exercises are primarily reading and writing exercises. The exercise of
Whether or not each generating template is saved in memory with the resulting sentence (a preferred embodiment), or an AI is used to infer the template used to generate the phrase (another possible embodiment), the preferred method of mapping a phrase or sentence to an exercise template is via its associated grammar template.
If this same simple template and its next associated phrase appeared again later in the exercise set, then it might make more sense to map this phrase to one of the more-complex exercise templates, like in
In this example of a preferred embodiment, exercises like those in
In preferred embodiments of this disclosure, the exercises should be presented in the order they were generated. Whether or not to allow a user to revisit an incorrectly answered problem will depend on what choice was made in an application's standard problem sets. Some preferred embodiments of this disclosure will match the preferences of the applications in which they are embedded. But other embodiments might embrace other approaches.
In preferred embodiments of this disclosure, upon exercise completion, the generated exercise can then be stored in memory as an iconic exercise that can later be accessed via the standard exercise button 35 of
Although not explicitly shown in the figures, each node in the figures has at least one processor (e.g., a CPU) for processing incoming and/or outgoing data, memory (e.g., RAM, ROM) for storing data and (in some implementations) program code to be executed by the processor, and communication hardware (e.g., transceivers) for communicating with one or more other nodes.
In certain embodiments, the present disclosure is an apparatus for teaching a foreign language to a user proficient in a native language, the apparatus comprising a memory and at least one processor connected to the memory and operative to cause the apparatus to (a) track a current level of proficiency of the user in the foreign language, wherein the current level of proficiency increases with the proficiency of the user in the foreign language; (b) receive from the user a current word or phrase in the native language; and (c) generate a number of current exercises for the user to perform based on (i) a translation of the current word or phrase in the foreign language and (ii) the current level of proficiency of the user in the foreign language.
In at least some of the above embodiments, higher levels of proficiency in the foreign language are associated with syntaxes of greater complexity.
In at least some of the above embodiments, one or more of the current exercises comprise sentences in the foreign language containing the translation of the current word or phrase.
In at least some of the above embodiments, the apparatus is configured to maintain, for the user, a user database of words and/or phrases contained in previous exercises; and generate one or more of the current exercises based on at least some of the words and/or phrases in the user database.
In at least some of the above embodiments, the apparatus is configured to add the current word or phrase to the user database.
In at least some of the above embodiments, the apparatus is configured to independently include at least one new word or phrase in at least one of the current exercises, wherein the at least one new word or phrase is related to the current word or phrase received from the user.
In at least some of the above embodiments, the apparatus is configured to add the at least one new word or phrase to a user database for the user of words and/or phrases contained in previous exercises for the user.
Unless explicitly stated otherwise, each numerical value and range should be interpreted as being approximate as if the word “about” or “approximately” preceded the value or range.
Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments necessarily mutually exclusive of other embodiments. The same applies to the term “implementation.”
Unless otherwise specified herein, the use of the ordinal adjectives “first,” “second,” “third,” etc., to refer to an object of a plurality of like objects merely indicates that different instances of such like objects are being referred to, and is not intended to imply that the like objects so referred-to have to be in a corresponding order or sequence, either temporally, spatially, in ranking, or in any other manner.
Also for purposes of this description, the terms “couple,” “coupling,” “coupled,” “connect,” “connecting,” or “connected” refer to any manner known in the art or later developed in which energy is allowed to be transferred between two or more elements, and the interposition of one or more additional elements is contemplated, although not required. Conversely, the terms “directly coupled,” “directly connected,” etc., imply the absence of such additional elements. The same type of distinction applies to the use of terms “attached” and “directly attached,” as applied to a description of a physical structure. For example, a relatively thin layer of adhesive or other suitable binder can be used to implement such “direct attachment” of the two corresponding components in such physical structure.
The described embodiments are to be considered in all respects as only illustrative and not restrictive. In particular, the scope of the disclosure is indicated by the appended claims rather than by the description and figures herein. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.
The functions of the various elements shown in the figures, including any functional blocks labeled as “processors” and/or “controllers,” may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. Upon being provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and/or custom, may also be included. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.
It should be appreciated by those of ordinary skill in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
As will be appreciated by one of ordinary skill in the art, the present disclosure may be embodied as an apparatus (including, for example, a system, a network, a machine, a device, a computer program product, and/or the like), as a method (including, for example, a business process, a computer-implemented process, and/or the like), or as any combination of the foregoing. Accordingly, embodiments of the present disclosure may take the form of an entirely software-based embodiment (including firmware, resident software, micro-code, and the like), an entirely hardware embodiment, or an embodiment combining software and hardware aspects that may generally be referred to herein as a “system” or “network”.
Embodiments of the disclosure can be manifest in the form of methods and apparatuses for practicing those methods. Embodiments of the disclosure can also be manifest in the form of program code embodied in tangible media, such as magnetic recording media, optical recording media, solid state memory, floppy diskettes, CD-ROMs, hard drives, or any other non-transitory machine-readable storage medium, wherein, upon the program code being loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the disclosure. Embodiments of the disclosure can also be manifest in the form of program code, for example, stored in a non-transitory machine-readable storage medium including being loaded into and/or executed by a machine, wherein, upon the program code being loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the disclosure. Upon being implemented on a general-purpose processor, the program code segments combine with the processor to provide a unique device that operates analogously to specific logic circuits. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).
In this specification including any claims, the term “each” may be used to refer to one or more specified characteristics of a plurality of previously recited elements or steps. When used with the open-ended term “comprising,” the recitation of the term “each” does not exclude additional, unrecited elements or steps. Thus, it will be understood that an apparatus may have additional, unrecited elements and a method may have additional, unrecited steps, where the additional, unrecited elements or steps do not have the one or more specified characteristics.
As used herein, “at least one of the following: <a list of two or more elements>” and “at least one of <a list of two or more elements>” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements. For example, the phrases “at least one of A and B” and “at least one of A or B” are both to be interpreted to have the same meaning, encompassing the following three possibilities: 1—only A; 2—only B; 3—both A and B.
All documents mentioned herein are hereby incorporated by reference in their entirety or alternatively to provide the disclosure for which they were specifically relied upon.
While preferred embodiments of the disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the technology of the disclosure.
This application claims the benefit of the filing date of U.S. provisional application No. 63/619,093, filed on Jan. 9, 2024, the teachings of which are incorporated herein by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63619093 | Jan 2024 | US |
