Ambiguous content display method, device and electronic device for electronic learning equipment

Abstract

This invention relates to a method, apparatus, and electronic device for displaying polysemous content in an electronic learning device, comprising: establishing and storing a mapping relationship between at least two meanings of a target word and corresponding example sentences, wherein each meaning is uniquely associated with at least one corresponding example sentence; displaying a selectable list containing at least two meanings in a first area of ​​the display interface of an electronic paper display, the list having a currently active visual focus indicator; displaying example sentences corresponding to the meaning currently identified by the visual focus indicator in a second area of ​​the display interface of the electronic paper display; switching the visual focus indicator to the target meaning in response to a received selection instruction for the target meaning in the selectable list; and synchronously and uniquely updating the content displayed in the second area in response to the switching of the visual focus indicator. This invention eliminates the cognitive burden on users between different displayed contents, improving focus, comprehension accuracy, and operational efficiency in word learning.

Description

Technical Field

[0001] This invention relates to the field of electronic learning equipment technology, and in particular to a method, apparatus, and electronic device for displaying polysemous content in electronic learning equipment. Background Technology

[0002] In language learning, especially vocabulary learning, mastering words with multiple meanings is an important aspect. Existing electronic vocabulary learning devices, such as flashcards or learning machines based on e-paper screens, typically use a static interface layout to display such polysemous words. A typical display method is to list all the definitions of the word in the upper area of ​​the screen, while one or more example sentences are displayed fixedly in the lower area of ​​the screen.

[0003] However, this static display method has a significant drawback: the word meaning and corresponding example sentences are disconnected at the display level, making it impossible for users to intuitively and efficiently establish a clear correspondence between a specific word meaning and a specific example sentence context. Because multiple definitions are presented simultaneously with fixed example sentences below, users find it difficult to quickly and accurately determine which specific word meaning in the definition list above is being explained by the currently displayed example sentence. This lack of a "word meaning-context" correspondence increases the user's cognitive load, forcing them to make additional memory associations or repeatedly compare information, thus interfering with the continuity and immersion of the learning process, ultimately leading to a decline in learning efficiency and depth of understanding. Summary of the Invention

[0004] Therefore, it is necessary to address the technical problem that the lack of a "meaning-context" correspondence in existing electronic vocabulary learning devices increases the cognitive load on users by providing a method, device, and electronic equipment for displaying polysemous word content in electronic learning devices.

[0005] This invention provides a method for displaying polysemous content in an electronic learning device, the method comprising: Establish and store a proprietary mapping relationship between at least two semantic entries of a target word and corresponding example sentences, wherein each semantic entry is uniquely associated with at least one corresponding example sentence; In the first area of ​​the display interface of the electronic paper display, a selectable list containing the at least two word meanings is displayed, the list having a currently active visual focus indicator, the visual focus indicator being used to identify the currently selected word meaning; In the second area of ​​the display interface of the electronic paper display, example sentences corresponding to the word meaning currently identified by the visual focus indicator are displayed; In response to a received selection instruction for a target word meaning in the selectable list, the visual focus indicator is switched to the target word meaning; and, In response to the switching of the visual focus indicator, the content displayed in the second area is updated synchronously and uniquely to show example sentences corresponding to the target semantic item identified by the visual focus indicator after the switching.

[0006] In one embodiment, the method further includes: The at least two semantic terms are classified and labeled to distinguish them into key semantic terms with a first visual identifier attribute and ordinary semantic terms without the first visual identifier attribute; Based on the classification marker, the first visual identifier is applied to the key word meanings for rendering, and the ordinary word meanings are rendered in a way that is different from the rendering of the key word meanings.

[0007] In one embodiment, the step of synchronously and uniquely updating the content displayed in the second area in response to the switching of the visual focus indication includes: After the visual focus indicator switches, the current display content of the second area is immediately cleared, and a new example sentence that uniquely corresponds to the target semantic item after the switch is loaded and rendered.

[0008] In one embodiment, the response to the received selection instruction for a target semantic item in the selectable list includes: In response to the triggering of the first direction button in the physical direction buttons, the visual focus indicator is moved from the current word meaning to the next adjacent word meaning along the arrangement order of the selectable list; In response to the triggering of the second direction button, which is opposite to the first direction among the physical direction buttons, the visual focus indicator is moved from the current semantic term to the previous adjacent semantic term along the arrangement order.

[0009] In one embodiment, the movement of the visual focus indicator follows a full traversal logic, moving sequentially among all semantic items in the selectable list, including items marked as key semantic items and items marked as ordinary semantic items.

[0010] In one embodiment, the execution of the full traversal logic further includes an intelligent guidance process, specifically comprising: Based on the classification tag attributes of at least two meanings of the target word and the stored historical learning behavior data of the user for the target word, the intensity of visual feedback or the logical access order of at least one meaning is dynamically adjusted during the traversal process; wherein, the historical learning behavior data includes at least one of the following: the historical dwell time of the user on each meaning, and the historical accuracy rate of the tests performed on each meaning.

[0011] In one embodiment, the dynamic adjustment is performed in real time, specifically including: In this learning session, the duration of the user's stay on the current focus word meaning is monitored in real time, or the user's immediate test feedback on the current focus word meaning and its corresponding example sentences is received and processed. Based on whether the dwell time exceeds a preset threshold, or whether the real-time test feedback is correct, the user's mastery of the word meaning is determined in real time. If it is determined that the word meaning is not mastered, at least one of the following operations will be performed automatically: increase the visual feedback intensity of the word meaning in the selectable list, and advance the logical access order of the word meaning in subsequent traversal loops.

[0012] In one embodiment, the method further includes a learning path optimization process, specifically comprising: Record the dynamic adjustment decisions generated by the intelligent guidance steps in each learning session to form a personalized learning path log for the target word and the specific user; Based on the analysis of the personalized learning path logs, the user learning model is constructed or updated; When a user learns new words with similar attributes or belonging to the same category again, the system calls the user learning model to predict and initialize the recommended access order and initial visual feedback intensity of each meaning of the new word in the full traversal logic.

[0013] In one embodiment, the historical learning behavior data also includes the user's historical access status for each word meaning; The method of dynamically adjusting the intensity of visual feedback for at least one word meaning during the traversal process, based on stored historical learning behavior data of users for the target word, specifically includes: In subsequent learning sessions for the target word, based on the historical access status, the visual feedback intensity is increased for word meanings marked as unvisited or with insufficient access in the selectable list, and the visual feedback intensity is reduced for word meanings marked as fully accessed, in order to guide the user to conduct multiple rounds of targeted review.

[0014] The present invention also provides a polysemous content display device for electronic learning devices, the device comprising: A mapping relationship establishment module is used to establish and store proprietary mapping relationships between at least two semantic terms of a target word and corresponding example sentences, wherein each semantic term is uniquely associated with at least one corresponding example sentence; A selectable list display module is used to display a selectable list containing the at least two semantic items in a first area of ​​the display interface of an electronic paper display screen. The list has a currently active visual focus indicator, which is used to identify the currently selected semantic item. The word meaning example sentence display module is used to display example sentences corresponding to the word meaning currently identified by the visual focus indicator in the second area of ​​the display interface of the electronic paper display screen; A target word meaning switching module is configured to, in response to a received selection instruction for a target word meaning in the selectable list, switch the visual focus indicator to the target word meaning; and, The word meaning example sentence switching module is used to synchronously and uniquely update the content displayed in the second area in response to the switching of the visual focus indicator, so as to display example sentences corresponding to the target word meaning identified by the visual focus indicator after the switching.

[0015] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described above.

[0016] The aforementioned method, apparatus, and electronic device for displaying polysemous word content in e-learning devices establish and store a unique mapping relationship between word meanings and corresponding example sentences, laying a technical foundation for the precise correspondence of "one word, one meaning, one example sentence" at the data structure level. Based on this, the mapping relationship is visualized by setting a selectable list of word meanings with visual focus indicators and a dynamically associated example sentence display area in the user interface. Its core interaction mechanism lies in responding to the user's selection command for the target word meaning in the list, controlling the switching of the visual focus indicator, and using this as the sole driving signal to synchronously and uniquely update the content of the example sentence display area, so that it accurately displays the example sentence corresponding to the currently focused word meaning. In turn, it transforms the static and isolated relationship between word meaning and example sentence into a dynamic and visual linkage process driven by the user's direct operation of the focus and the system's instant response based on the preset mapping relationship. This allows users to obtain contextual examples that strictly match the currently focused word meaning in the designated area through clear and intuitive focus switching operations when viewing different meanings of polysemous words. This eliminates the cognitive burden of manual searching and mental matching between different displayed contents, effectively improving the user's focus, comprehension accuracy, and operational efficiency in word learning. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for displaying polysemous content in an electronic learning device according to one embodiment; Figure 2 This is a schematic diagram of the initial state of the learning interface of an electronic learning device according to an embodiment. Figure 3 This is a schematic diagram illustrating the switching states of the learning interface of an electronic learning device according to one embodiment. Figure 4 A schematic diagram of a polysemous content display device for an electronic learning device according to one embodiment; Figure 5 This is an internal structural diagram of an electronic device according to one embodiment. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The following is combined Figures 1-5 This invention describes a method, apparatus, and electronic device for displaying polysemous content in an electronic learning device.

[0021] like Figure 1 As shown, in one embodiment, a method for displaying polysemous content in an e-learning device includes the following steps: Step S110: Establish and store a proprietary mapping relationship between at least two semantic entries of the target word and corresponding example sentences, wherein each semantic entry is uniquely associated with at least one corresponding example sentence.

[0022] Establish and store a proprietary mapping relationship between at least two meanings of the target word and corresponding example sentences, ensuring that each meaning is uniquely associated with at least one corresponding example sentence, thereby laying the foundation for subsequent accurate contextual association at the data structure level. The corresponding e-learning device includes a processor, memory, input unit (such as physical directional buttons), and display unit (such as an e-paper display).

[0023] In step S120, a selectable list containing at least two word meanings is displayed in the first area of ​​the display interface of the electronic paper display screen. The list has a currently active visual focus indicator, which is used to identify the currently selected word meaning.

[0024] In step S130, an example sentence corresponding to the word meaning currently identified by the visual focus indicator is displayed in the second area of ​​the display interface of the electronic paper display screen.

[0025] During the display phase, see Figure 2 , Figure 3 The e-paper display interface is divided into a first area and a second area, for example, arranged vertically. The first area displays a selectable list containing all word meanings. A visual focus indicator is activated in this list using methods such as bold underline, highlighting, boxing, or cursor indication to identify the currently selected word meaning. Simultaneously, the second area of ​​the e-paper display shows example sentences corresponding to the currently focused word meaning, achieving an initial correspondence between a specific word meaning and a specific context. Initially, the visual focus indicator is positioned by default on the first word meaning in the list, and its corresponding example sentence is automatically displayed in the second area, completing the initialization of the learning interface.

[0026] In step S140, in response to the received selection instruction for the target word meaning in the selectable list, the visual focus indicator is switched to the target word meaning.

[0027] In response to a trigger of the first directional button in the physical directional keys, the visual focus indicator moves from the current definition to the next adjacent definition along the order of the selectable list; or in response to a trigger of the second directional button (opposite to the first directional button), the visual focus indicator moves from the current definition to the previous adjacent definition along the order of the selectable list. The movement of the visual focus indicator follows a full traversal logic, that is, it moves sequentially among all definitions in the selectable list, including those marked as priority definitions and those marked as general definitions. When the user uses the physical directional keys (see...) Figure 1 When a selection command is input using the up and down arrow keys, the system responds by switching the visual focus indicator along the list order to the target word meaning (for example, pressing the key moves the focus to the next adjacent item). This process follows a full traversal logic, meaning the focus moves sequentially among all word meanings (including those marked as key word meanings and those marked as ordinary word meanings), ensuring comprehensive learning.

[0028] In step S150, in response to the switching of the visual focus indicator, the content displayed in the second area is updated synchronously and uniquely to display example sentences corresponding to the target word meaning identified by the visual focus indicator after the switching.

[0029] After the visual focus indicator switches, the current display content of the second area is immediately cleared, and a new example sentence that uniquely corresponds to the target word meaning after the switch is loaded and rendered. Once the focus switches, the system immediately and uniquely updates the display content of the second area, that is, clears the current example sentence and loads and renders a new example sentence that uniquely corresponds to the new focus word meaning, thereby achieving dynamic and precise linkage between word meaning and example sentence.

[0030] The aforementioned method for displaying polysemous word content in e-learning devices establishes and stores a unique mapping relationship between word meanings and corresponding example sentences, laying a technical foundation for the precise correspondence of "one word, one meaning, one example sentence" at the data structure level. Based on this, the mapping relationship is visualized by setting up a selectable list of word meanings with visual focus indicators and a dynamically associated example sentence display area in the user interface. Its core interaction mechanism lies in responding to the user's selection command for the target word meaning in the list, controlling the switching of the visual focus indicator, and using this as the sole driving signal to synchronously and uniquely update the content of the example sentence display area, so that it accurately displays the example sentence corresponding to the currently focused word meaning. In turn, it transforms the static and isolated relationship between word meaning and example sentence into a dynamic and visual linkage process driven by the user's direct operation of the focus and the system's instant response based on the preset mapping relationship. This allows users to obtain contextual examples that strictly match the currently focused word meaning in the designated area through clear and intuitive focus switching operations when viewing different meanings of polysemous words. This eliminates the cognitive burden of manual searching and mental matching between different displayed contents, effectively improving the user's focus, comprehension accuracy, and operational efficiency in word learning.

[0031] To optimize learning guidance, at least two word meanings are categorized and marked, distinguishing between key word meanings with primary visual identifiers and ordinary word meanings without such identifiers. Based on the categorization marks, key word meanings are rendered with primary visual identifiers, while ordinary word meanings are rendered differently. Key word meanings with primary visual identifiers (e.g., enclosed in brackets 【】) and ordinary word meanings without such identifiers are differentiated and rendered differently in the list in the first area based on these marks, making the key word meanings visually prominent. The order of word meanings in the selectable list is pre-set based on at least one of the following factors: word meaning attribute, part of speech, or importance.

[0032] In this embodiment, a testing step is also included: after a user completes the learning of a word meaning and its example sentences, a test question for that word meaning can be triggered by a specific button (such as the "test" button), and the user's input feedback result (correct / incorrect) can be recorded as user learning behavior data.

[0033] The execution of the full traversal logic includes an intelligent guidance process: based on the classification tag attributes of at least two meanings of the target word and the stored historical learning behavior data of the user for the target word, the intensity of visual feedback or the logical access order for at least one meaning is dynamically adjusted during the traversal. The historical learning behavior data includes at least one of the following: the historical dwell time of the user on each meaning, and the historical accuracy rate of tests conducted on each meaning. This upgrades the full traversal logic from a fixed, preset navigation mode to an adaptive learning navigation system that can perceive and respond to individual user differences and historical performance. On a limited hardware interface (such as physical buttons and e-paper screens), dynamic learning path planning based on the user's historical learning data (dwell time, test accuracy rate) is implemented. This makes the full traversal process no longer a mechanical sequential loop, but a personalized review with intelligent emphasis, thereby significantly improving memory efficiency within a limited learning time.

[0034] The dynamic adjustment of the visual feedback intensity or logical access order of word meanings is performed in real time. Specifically, this includes: monitoring the user's dwell time on the current focus word meaning in real time during the learning session, or receiving and processing the user's immediate test feedback on the current focus word meaning and its corresponding example sentences; judging the user's mastery of the word meaning in real time based on whether the dwell time exceeds a preset threshold or whether the immediate test feedback is correct; if it is judged that the user has not mastered it well, at least one of the following operations is automatically executed: increasing the visual feedback intensity of the word meaning in the selectable list, and advancing the logical access order of the word meaning in subsequent traversal loops. Through real-time dynamic adjustment, a real-time closed-loop feedback mechanism of "observation-judgment-intervention" is established within a single learning session. The system can immediately make adjustments (such as strengthening visual feedback or adjusting the access order) based on the user's current immediate behavior (such as insufficient dwell time) or test performance (such as incorrect answers). This immediate intervention can most effectively provide reinforcement stimulation when the user is confused or forgets, solving the problems of lag in interaction and untimely feedback in traditional e-learning devices, and greatly enhancing the immersion and consolidation effect of learning.

[0035] This embodiment also includes a learning path optimization process: recording the dynamic adjustment decisions generated by the intelligent guidance process in each learning session to form a personalized learning path log for the target word and the specific user; based on the analysis of the personalized learning path log, constructing or updating the user learning model; when the user learns new words with similar attributes or belonging to the same category again, the system calls the user learning model to predict and initialize the recommended access order and initial visual feedback intensity of each meaning item of the new word in the full traversal logic. This learning path optimization process, based on intelligent guidance and real-time adjustment, further realizes the vertical accumulation and horizontal transfer of learning strategies. By constructing and continuously updating the user learning model, the learning experience for specific words is abstracted into reusable cognitive strategies. When the user encounters new content, the system can make predictions based on the model and provide personalized initial learning settings. This allows the intelligence of the learning device to expand from the review optimization for "known" content to the learning path prediction for "unknown" new content, realizing the continuous improvement of learning efficiency and the consistency of user experience, overcoming the shortcomings of adaptive functions being limited to isolated words or single sessions.

[0036] In this embodiment, the historical learning behavior data also includes the user's historical access status for each word meaning. Based on the stored historical learning behavior data of the user for the target word, the visual feedback intensity for at least one word meaning is dynamically adjusted during the traversal process. Specifically, in subsequent learning sessions for the target word, based on the historical access status, the visual feedback intensity is increased for word meanings marked as unvisited or with insufficient access in the selectable list, and the visual feedback intensity is decreased for word meanings marked as fully accessed, to guide the user to conduct multiple rounds of targeted review. The above-mentioned full traversal logic and intelligent guidance process can also be applied to learning strategies that support users to conduct multiple rounds of distributed review. Specifically, the system records the user's historical access status for each word meaning of the target word in each learning session. The historical access status includes at least a binary identifier of "viewed" or "not viewed," and the judgment criteria may be whether the visual focus indicator has lingered on the corresponding word meaning for more than a minimum time threshold, or whether the user has interacted with the word meaning by triggering a test button or other means. When a user re-enters a learning session (i.e., review) targeting the same word, the system retrieves stored historical access data and implements differentiated visual guidance during the rendering of the selectable list based on this data. For example, for word meanings marked "not viewed" or with significantly fewer accesses, the system can automatically enhance their visual feedback intensity, such as using more prominent fonts, colors, background colors, or adding specific unlearned identifiers (such as "*"). Conversely, for word meanings marked "viewed" or repeatedly studied, the visual feedback intensity can be weakened, such as using grayscale display, reducing transparency, or using regular fonts. In this way, each time a user reviews, the system interface can intuitively prompt them about word meanings they haven't fully learned, guiding them to prioritize and master these areas. Essentially, within the framework of full traversal, this dynamically and automatically plans the focus of each review session based on the user's individual learning progress history. This breaks down a one-time, information-intensive multi-meaning learning task into multiple, more focused learning sessions, achieving a "step-by-step" learning effect. This approach is particularly suitable for language learning scenarios requiring long-term memorization and periodic reinforcement. It allows intelligent guidance to not only focus on real-time mastery within a single session but also extend to long-term progress management and review planning across multiple learning sessions, further optimizing the allocation of learning resources and the efficiency of long-term memory formation.

[0037] The aforementioned method for displaying polysemous word content in e-learning devices begins by loading target word data, then sequentially initializes the interface, receives and processes user navigation operations to switch focus and update example sentences, and selectively enters the testing and intelligent guidance decision-making stages, forming a complete learning interaction loop. By establishing proprietary mappings and dynamically linked refreshes, it effectively solves the problems of information overload and fragmented word meanings and example sentences under limited screen space, strengthening contextual memory. Through a combination of visual marker differentiation and full traversal, it guides users to focus on core test points while ensuring coverage of all word meanings, balancing learning efficiency and systematicity. The introduced testing stage provides immediate feedback on learning outcomes, enhancing interactivity and memory consolidation.

[0038] The polysemous content display device for electronic learning devices provided by the present invention is described below. The polysemous content display device for electronic learning devices described below can be referred to in correspondence with the polysemous content display method for electronic learning devices described above.

[0039] like Figure 4 As shown, in one embodiment, a polysemous content display device for an electronic learning device includes a mapping relationship establishment module 410, a selectable list display module 420, a word meaning example sentence display module 430, a target word meaning switching module 440, and a word meaning example sentence switching module 450.

[0040] The mapping relationship establishment module 410 is used to establish and store a proprietary mapping relationship between at least two semantic terms of a target word and corresponding example sentences, wherein each semantic term is uniquely associated with at least one corresponding example sentence; The selectable list display module 420 is used to display a selectable list containing the at least two semantic items in a first area of ​​the display interface of the electronic paper display screen. The list has a currently active visual focus indicator, which is used to identify the currently selected semantic item. The word meaning example sentence display module 430 is used to display example sentences corresponding to the word meaning currently identified by the visual focus indicator in the second area of ​​the display interface of the electronic paper display screen; The target word meaning switching module 440 is configured to, in response to a received selection instruction for a target word meaning in the selectable list, switch the visual focus indicator to the target word meaning; and, The word meaning example sentence switching module 450 is used to synchronously and uniquely update the content displayed in the second area in response to the switching of the visual focus indicator, so as to display example sentences corresponding to the target word meaning identified by the visual focus indicator after the switching.

[0041] Figure 5This example illustrates a schematic diagram of the physical structure of an electronic device, which can be a smart terminal. Its internal structure diagram can be as follows: Figure 5 As shown. The electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for displaying polysemous content in an electronic learning device, the method including: Establish and store a proprietary mapping relationship between at least two semantic entries of a target word and corresponding example sentences, wherein each semantic entry is uniquely associated with at least one corresponding example sentence; In the first area of ​​the display interface of the electronic paper display, a selectable list containing the at least two word meanings is displayed, the list having a currently active visual focus indicator, the visual focus indicator being used to identify the currently selected word meaning; In the second area of ​​the display interface of the electronic paper display, example sentences corresponding to the word meaning currently identified by the visual focus indicator are displayed; In response to a received selection instruction for a target word meaning in the selectable list, the visual focus indicator is switched to the target word meaning; and, In response to the switching of the visual focus indicator, the content displayed in the second area is updated synchronously and uniquely to show example sentences corresponding to the target semantic item identified by the visual focus indicator after the switching.

[0042] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the electronic device to which the present invention is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0043] On the other hand, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements a method for displaying polysemous content in an electronic learning device, the method comprising: Establish and store a proprietary mapping relationship between at least two semantic entries of a target word and corresponding example sentences, wherein each semantic entry is uniquely associated with at least one corresponding example sentence; In the first area of ​​the display interface of the electronic paper display, a selectable list containing the at least two word meanings is displayed, the list having a currently active visual focus indicator, the visual focus indicator being used to identify the currently selected word meaning; In the second area of ​​the display interface of the electronic paper display, example sentences corresponding to the word meaning currently identified by the visual focus indicator are displayed; In response to a received selection instruction for a target word meaning in the selectable list, the visual focus indicator is switched to the target word meaning; and, In response to the switching of the visual focus indicator, the content displayed in the second area is updated synchronously and uniquely to show example sentences corresponding to the target semantic item identified by the visual focus indicator after the switching.

[0044] In another aspect, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and when the processor executes the computer instructions, it implements a method for displaying polysemous content in an electronic learning device, the method comprising: Establish and store a proprietary mapping relationship between at least two semantic entries of a target word and corresponding example sentences, wherein each semantic entry is uniquely associated with at least one corresponding example sentence; In the first area of ​​the display interface of the electronic paper display, a selectable list containing the at least two word meanings is displayed, the list having a currently active visual focus indicator, the visual focus indicator being used to identify the currently selected word meaning; In the second area of ​​the display interface of the electronic paper display, example sentences corresponding to the word meaning currently identified by the visual focus indicator are displayed; In response to a received selection instruction for a target word meaning in the selectable list, the visual focus indicator is switched to the target word meaning; and, In response to the switching of the visual focus indicator, the content displayed in the second area is updated synchronously and uniquely to show example sentences corresponding to the target semantic item identified by the visual focus indicator after the switching.

[0045] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0046] By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0047] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A polysemy content display method of an electronic learning device, characterized by, The method includes: Establish and store a proprietary mapping relationship between at least two semantic entries of a target word and corresponding example sentences, wherein each semantic entry is uniquely associated with at least one corresponding example sentence; In the first area of ​​the display interface of the electronic paper display, a selectable list containing the at least two word meanings is displayed, the list having a currently active visual focus indicator, the visual focus indicator being used to identify the currently selected word meaning; In the second area of ​​the display interface of the electronic paper display, example sentences corresponding to the word meaning currently identified by the visual focus indicator are displayed; In response to a received selection instruction for a target word meaning in the selectable list, the visual focus indicator is switched to the target word meaning; and, In response to the switching of the visual focus indicator, the content displayed in the second area is updated synchronously and uniquely to show example sentences corresponding to the target semantic item identified by the visual focus indicator after the switching; The method further includes: The at least two semantic terms are classified and labeled to distinguish them into key semantic terms with a first visual identifier attribute and ordinary semantic terms without the first visual identifier attribute; Based on the classification marker, the first visual identifier is applied to the key word meanings for rendering, and the ordinary word meanings are rendered in a way that is different from the rendering of the key word meanings.

2. The polysemy content display method of an electronic learning device according to claim 1, characterized by, The step of synchronously and uniquely updating the content displayed in the second area in response to the switching of the visual focus indication includes: After the visual focus indicator switches, the current display content of the second area is immediately cleared, and a new example sentence that uniquely corresponds to the target semantic item after the switch is loaded and rendered.

3. The method for displaying polysemous content in an electronic learning device according to claim 1, characterized in that, The response to the received instruction to select a target semantic item from the selectable list includes: In response to the triggering of the first direction button in the physical direction buttons, the visual focus indicator is moved from the current word meaning to the next adjacent word meaning along the arrangement order of the selectable list; In response to the triggering of the second direction button, which is opposite to the first direction among the physical direction buttons, the visual focus indicator is moved from the current semantic term to the previous adjacent semantic term along the arrangement order.

4. The method for displaying polysemous content in an electronic learning device according to claim 3, characterized in that, The movement of the visual focus indicator follows a full traversal logic, moving sequentially among all semantic items in the selectable list, including items marked as key semantic items and items marked as ordinary semantic items.

5. The method for displaying polysemous content in an electronic learning device according to claim 4, characterized in that, The execution of the full traversal logic also includes an intelligent guidance process, specifically including: Based on the classification tag attributes of at least two meanings of the target word and the stored historical learning behavior data of the user for the target word, the intensity of visual feedback or the logical access order of at least one meaning is dynamically adjusted during the traversal process; wherein, the historical learning behavior data includes at least one of the following: the historical dwell time of the user on each meaning, and the historical accuracy rate of the tests performed on each meaning.

6. The method for displaying polysemous content in an electronic learning device according to claim 5, characterized in that, The dynamic adjustment is performed in real time and specifically includes: In this learning session, the duration of the user's stay on the current focus word meaning is monitored in real time, or the user's immediate test feedback on the current focus word meaning and its corresponding example sentences is received and processed. Based on whether the dwell time exceeds a preset threshold, or whether the real-time test feedback is correct, the user's mastery of the word meaning is determined in real time. If it is determined that the word meaning is not mastered, at least one of the following operations will be performed automatically: increase the visual feedback intensity of the word meaning in the selectable list, and advance the logical access order of the word meaning in subsequent traversal loops.

7. The method for displaying polysemous content in an electronic learning device according to claim 6, characterized in that, The method also includes a learning path optimization process, specifically including: Record the dynamic adjustment decisions generated by the intelligent guidance steps in each learning session to form a personalized learning path log for the target word and the specific user; Based on the analysis of the personalized learning path logs, the user learning model is constructed or updated; When a user learns new words with similar attributes or belonging to the same category again, the electronic learning device calls the user learning model to predict and initialize the recommended access order and initial visual feedback intensity of each meaning of the new word in the full traversal logic.

8. The method for displaying polysemous content in an electronic learning device according to claim 5, characterized in that, The historical learning behavior data also includes the user's historical access status for each word meaning; The method of dynamically adjusting the intensity of visual feedback for at least one word meaning during the traversal process, based on stored historical learning behavior data of users for the target word, specifically includes: In subsequent learning sessions for the target word, based on the historical access status, the visual feedback intensity is increased for word meanings marked as unvisited or with insufficient access in the selectable list, and the visual feedback intensity is reduced for word meanings marked as fully accessed, in order to guide the user to conduct multiple rounds of targeted review.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method for displaying polysemous content of the electronic learning device according to any one of claims 1 to 8.

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