Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of automated educational assessment systems and statistical data processing technologies. More particularly, the present disclosure relates to an automated grading system with multiple grade configuration modes for academic evaluation.

BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Educational institutions worldwide process vast quantities of student assessment data, requiring systematic approaches for converting numerical marks into standardized grade categories. Traditional grading methodologies rely on fixed grade boundaries that remain constant regardless of the distribution characteristics of student performance data, often resulting in inconsistent grade distributions across different courses, evaluators, and academic periods.
[0004] Existing automated grading systems typically implement single transformation approaches that apply uniform statistical parameters to all grading scenarios. Such systems lack the flexibility to adapt to varying institutional requirements where different numbers of grade categories may be needed, such as institutions using seven-grade systems versus those employing twelve-grade classifications. These conventional systems process all mark data through identical transformation pathways, failing to optimize grade distributions based on specific grading scheme requirements.
[0005] Therefore, there exists a requirement for an automated grading system that can intelligently select between different configuration modes based on institutional grading requirements, apply distinct parameter sets optimized for each configuration, and maintain consistent grade distributions across diverse evaluation scenarios while addressing the limitations of conventional single-mode grading approaches.

OBJECTS OF THE PRESENT DISCLOSURE
[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0007] An object of the present disclosure is to provide an automated grading system that enables selection between multiple grade configuration modes and applies distinct distribution parameters optimized for each selected configuration mode.
[0008] Another object of the present disclosure is to provide a statistical transformation system that processes qualified student marks through multi-stage analysis including separate statistical extraction and distribution analysis stages followed by dual-level transformation operations.
[0009] Yet another object of the present disclosure is to provide a consistent grading mechanism that maintains uniform grade distributions across different courses and evaluators through systematic application of configuration-specific parameters.

SUMMARY
[0010] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0011] In an aspect, the present disclosure provides a system for automated grading and academic evaluation including at least one processor and at least one memory storing instructions that, when executed by the at least one processor, implement a statistical transformation engine. The statistical transformation engine receives student mark data as digital input signals and processes the mark data through a multi-stage analysis including a first stage extracting statistical parameters through arithmetic calculation operations executed by the at least one processor on the received mark data, and a second stage analyzing distribution characteristics of qualified marks through computational operations. The system includes at least one transformation engine operatively connected to the at least one processor through electrical data pathways, where the at least one transformation engine performs a first level transformation converting validated marks into a normalized distribution using the statistical parameters and a second level transformation for range adjustment of the transformed values. The processor receives the transformed values from the at least one transformation engine, selects between multiple grade configuration modes through execution of selection logic circuits based on grade distribution criteria, adjusts grade boundaries using distribution parameters stored in the at least one memory, where the distinct sets of distribution parameters are retrieved for distinct grade configuration modes, and generates digital grade assignment output signals based on the adjusted grade boundaries.
[0012] In another aspect, the present disclosure provides a method for automated grading and academic evaluation including receiving student mark data as digital input signals, extracting statistical parameters from the received student mark data through arithmetic calculation operations, analyzing distribution characteristics of qualified marks above a passing threshold based on the extracted statistical parameters, applying the statistical parameters to the qualified marks performing a first level transformation, performing a second level transformation on the normalized distribution, analyzing the range-adjusted transformed values, selecting between multiple grade configuration modes based on grade related criteria through selection logic circuits, retrieving distribution parameters from at least one memory where the distinct sets of distribution parameters correspond to the selected grade configuration mode, applying the retrieved distribution parameters to the range-adjusted transformed values, and mapping the range-adjusted transformed values to the final grade boundaries generating digital grade assignment output signals for each student mark.
[0013] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF DRAWINGS
[0014] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[0015] FIG. 1 illustrates an exemplary system architecture for automated grading, in accordance with an embodiment of the present disclosure.
[0016] FIG. 2 illustrates an exemplary system enclosure with processing components, in accordance with an embodiment of the present disclosure.
[0017] FIG. 3 illustrates an exemplary control unit architecture, in accordance with an embodiment of the present disclosure.
[0018] FIG. 4 illustrates an exemplary flow diagram for the automated grading method, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0019] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
Definitions:
Module: As used herein, the term "module" refers to functional components that may be implemented in hardware, software, firmware, or any combination thereof, including but not limited to processing circuitry, software modules, associated memory, and interface circuits that implement specific functionalities within the system architecture.
Grade Configuration Mode: A selectable operational state that can define the total number of grade categories to be assigned and determines which set of distribution parameters the system retrieves for grade boundary calculations, including but not limited to odd mode and even mode configurations.
Statistical Transformation Engine: A multi-stage processing system that can extract statistical parameters from student mark data and analyze distribution characteristics through computational operations, including but not limited to mean and standard deviation calculations.
Distribution Parameters: Predetermined values stored in memory that can control grade boundary spacing, with distinct sets of values corresponding to different grade configuration modes, including but not limited to four values per configuration mode.
[0020] An aspect of the present disclosure relates to a system for automated grading and academic evaluation including at least one processor and at least one memory storing instructions that, when executed by the at least one processor, implement a statistical transformation engine. The statistical transformation engine can receive student mark data as digital input signals and process the mark data through a multi-stage analysis including a first stage and a second stage. The system can include at least one transformation engine operatively connected to the at least one processor through electrical data pathways. The processor can select between multiple grade configuration modes through execution of selection logic circuits based on grade distribution criteria and generate digital grade assignment output signals based on adjusted grade boundaries.
[0021] Various embodiments of the present disclosure are described using FIGs. 1 to 4.
[0022] FIG. 1 illustrates an exemplary representation of a system architecture for automated grading and academic evaluation, in accordance with an embodiment of the present disclosure.
[0023] In an embodiment, referring to FIG. 1, a system (100) can include a network (104), a system (102), users (106-1, 106-2, 106-N), grading devices/field equipment (FE) (108-1, 108-2, 108-N), and a centralized server (110). The system (100) can establish automated grading architecture by implementing hierarchical data flow from the distributed grading devices (108-1, 108-2, 108-N) through the network (104) to the centralized server (110). The grading devices (108-1, 108-2, 108-N) can process mark data from different sources by receiving digital input signals containing student assessment results and transmitting the processed data through network protocols.
[0024] In an embodiment, the network (104) can facilitate grading system communication by establishing connectivity between the system (102) and the grading devices (108-1, 108-2, 108-N) while providing communication pathways to the centralized server (110). The network can operate by implementing TCP/IP protocols for data packet transmission, utilizing encryption mechanisms for secure mark data transfer, and maintaining persistent connections through socket programming techniques. The network can enable distributed processing by routing mark data packets from multiple grading terminals to the centralized processing system.
[0025] In an embodiment, the grading devices (108-1, 108-2, 108-N) can function as distributed processing points by operating as field equipment that captures mark data from various academic sources. Each grading device can implement local validation by checking mark ranges against predefined limits, verifying data completeness before transmission, and converting various input formats into standardized digital signals. The grading devices can enable scalable deployment by supporting addition of new terminals without system reconfiguration.
[0026] In an embodiment, the centralized server (110) can operate as a grade management hub by receiving processed grade data from the system (102) through the network communication channels. The centralized server can implement data persistence by storing grade assignments in relational database structures, maintaining transaction logs for audit purposes, and providing backup mechanisms for data recovery. The server can coordinate grade management by synchronizing data across multiple grading devices and ensuring consistency in grade distributions.
[0027] FIG. 2 illustrates an exemplary schematic representation of a system enclosure housing processing components, in accordance with an embodiment of the present disclosure.
[0028] In an embodiment, referring to FIG. 2, a system enclosure (102) can house a mark input processor (204-2), a statistical analyzer (204-4), a data validator (204-6), a transformation engine (206-2), a grade assignment module (206-4), a control unit (208), a database interface (210), a network communication module (104), and an output formatter (212). The system enclosure (102) can integrate these components through electrical pathways including data buses operating at high-speed frequencies, control signal lines for synchronization, and power distribution networks.
[0029] In an embodiment, the mark input processor (204-2) can receive student mark data as digital input signals by implementing analog-to-digital conversion when needed, buffering incoming data streams, and performing initial format detection. The mark input processor can validate received data by applying checksums for data integrity verification, implementing range validation against maximum and minimum mark limits, and flagging anomalous entries for manual review. The processor can forward validated mark data to the statistical analyzer (204-4) through dedicated data channels.
[0030] In an embodiment, the statistical analyzer (204-4) can implement the first stage extracting statistical parameters through arithmetic calculation operations executed by the at least one processor (302) on the received mark data. The statistical analyzer can extract parameters by calculating mean values using summation circuits and division operations, computing standard deviation through variance calculations, and determining distribution characteristics. The analyzer can process mark data by implementing parallel computation techniques for large datasets enabling real-time processing under 100ms per 1000 records, utilizing 64-bit floating-point arithmetic units preventing precision loss, and maintaining intermediate results in temporary registers with single-cycle access latency.
[0031] In an embodiment, the data validator (204-6) can implement the second stage analyzing distribution characteristics of qualified marks through computational operations. The data validator can analyze characteristics by identifying marks above a predetermined passing threshold stored in the at least one memory (304), evaluating distribution skewness through moment calculations, and detecting outliers using statistical techniques. The validator can ensure data quality by filtering invalid entries, normalizing data ranges, and preparing validated datasets for transformation processing.
[0032] In an embodiment, the at least one transformation engine (206-2) operatively connected to the at least one processor (302) through electrical data pathways can perform a first level transformation converting validated marks into a normalized distribution using the statistical parameters and a second level transformation for range adjustment of the transformed values. The transformation engine can implement the first level transformation by applying Six Sigma normalization techniques where μ_ss equals (x_p + x_h)/2 and σ_ss equals (x_h - x_p)/12, utilizing the formula Y = μ_ss + (X_q - μ_x)/(σ_x/σ_ss) for each qualified mark, and generating normalized values with standardized statistical properties.
[0033] In an embodiment, the transformation engine (206-2) can perform the second level transformation by implementing median-based range adjustment calculations, determining boundary values using w_min = median(x_(min), y_min, x_p) and w_max = median(x_(max), y_max, x_h), and applying the transformation W = w_min + (Y - y_min)/(y_max - y_min) × (w_max - w_min). The transformation engine can include a normalization circuit electrically coupled to a range adjustment circuit through dedicated data pathways as specified in the claims.
[0034] In an embodiment, the grade assignment module (206-4) can map the transformed values to grade categories by implementing boundary comparison logic, applying distribution parameters retrieved from memory, and generating grade assignments for each student mark. The module can operate by comparing each transformed value W against calculated grade boundaries, determining the appropriate grade category based on boundary positions, and outputting digital signals representing assigned grades.
[0035] In an embodiment, the control unit (208) electrically connected to the processing components can coordinate the overall grading process. The processor (302) within the control unit receives the transformed values from the at least one transformation engine (206-2), selects between multiple grade configuration modes through execution of selection logic circuits based on grade distribution criteria, adjusts grade boundaries using distribution parameters stored in the at least one memory (304), where the distinct sets of distribution parameters are retrieved for distinct grade configuration modes, and generates digital grade assignment output signals based on the adjusted grade boundaries.
[0036] In an embodiment, the control unit (208) can implement mode selection by evaluating the total number of grade categories required, activating the selection logic circuits that select odd grade configuration mode when total grade categories equal 7, 9, or 11, and select even grade configuration mode when total grade categories equal 6, 8, 10, or 12. The control unit can retrieve distribution parameters by accessing dedicated memory banks containing four predetermined values (p1, p2, p3, p4) with single-cycle latency, loading distinct parameter sets based on the selected mode through hardware multiplexers enabling mode switching in under 10 microseconds, and applying these parameters for boundary calculations maintaining less than 2% variation across multiple evaluations.
[0037] In an embodiment, the database interface (210) electrically connected to the at least one processor (302) can store the processed mark data and the grade assignment output signals in structured data format. The database interface can implement storage operations by creating relational tables for mark records and grade assignments, establishing indexes for efficient data retrieval, and maintaining referential integrity between related data elements. The interface can support query operations by providing SQL-based access mechanisms, enabling report generation queries, and facilitating data export for external systems.
[0038] In an embodiment, the output formatter (212) connected to the at least one processor (302) through data transmission lines can convert the digital grade assignment output signals into formatted grade reports. The output formatter can generate reports by applying predefined templates for grade presentation, incorporating statistical summaries and distribution charts, and producing outputs in multiple formats including PDF, Excel, and CSV. The formatter can customize outputs based on institutional requirements including grade notation systems, report layouts, and distribution visualization preferences.
[0039] FIG. 3 illustrates an exemplary representation of a control unit architecture, in accordance with an embodiment of the present disclosure.
[0040] In an embodiment, referring to FIG. 3, a control unit (208) can include processor(s) (302), memory (304), interface(s) (306), a processing engine (308), and a database (310). The processing engine (308) can further include a validation module (312), an analysis module (314), a decision module (316), a transformation module (318), a grade mapping module (320), and a report generation module (322). The control unit architecture can enable coordinated processing by organizing functional modules in a hierarchical structure, implementing data flow pathways between modules, and maintaining operational synchronization.
[0041] In an embodiment, the at least one processor (302) can process mark data from at least 1000 student records per processing cycle through parallel execution paths implemented in the at least one processor (302). The processor can achieve this throughput by utilizing multiple processing cores for concurrent operations reducing computational complexity from O(n²) to O(n log n), implementing SIMD instructions for vectorized calculations achieving 60% reduction in processing time, and employing cache optimization techniques preventing memory overflow through dynamic buffer allocation.
[0042] In an embodiment, the at least one memory (304) can store instructions and data required for grading operations. The distribution parameters stored in the at least one memory (304) include four predetermined values that control grade boundary spacing, and where the at least one processor (302) retrieves different sets of values based on the selected grade configuration mode. The memory can organize data by allocating separate memory regions for program instructions and data storage, implementing memory protection mechanisms, and providing fast access through caching strategies.
[0043] In an embodiment, the interface(s) (306) can facilitate communication between the control unit components and external modules. The interfaces can operate by implementing standard communication protocols including I2C, SPI, or UART for component interconnection, providing signal level conversion for compatibility, and maintaining data integrity through error detection mechanisms. The interfaces can enable system expansion by supporting hot-pluggable connections, auto-detecting connected modules, and configuring communication parameters dynamically.
[0044] In an embodiment, the processing engine (308) can orchestrate the grading process by coordinating module operations, managing data flow between processing stages, and maintaining system state information. The engine can implement process control by initiating module operations in proper sequence, monitoring completion status of each stage, and handling exception conditions. The processing engine can optimize performance by parallelizing independent operations, minimizing inter-module communication overhead, and utilizing hardware acceleration where available.
[0045] In an embodiment, the validation module (312) can include at least one validation circuit electrically connected between the at least one processor (302) and the statistical transformation engine, where the at least one validation circuit includes range checking circuits and threshold comparison circuits. The validation module can perform validation by comparing input values against acceptable ranges using hardware comparators, flagging values exceeding thresholds through interrupt generation, and maintaining validation statistics in dedicated counters. The module can ensure data integrity by implementing redundant validation paths preventing system crashes from invalid inputs, cross-checking results through hardware comparators detecting anomalies within nanoseconds, and logging validation failures while maintaining system stability through interrupt-based exception handling.
[0046] In an embodiment, the analysis module (314) can perform statistical computations by implementing arithmetic operations for mean calculation including summation and division, executing variance and standard deviation calculations through squared difference operations, and determining distribution parameters. The module can optimize calculations by utilizing hardware multiply-accumulate units, implementing precision-preserving arithmetic techniques, and maintaining numerical stability through careful module selection.
[0047] In an embodiment, the decision module (316) can implement the grade configuration mode selection logic. The module can make decisions by evaluating the total number of grade categories against predefined thresholds, comparing values to determine if categories equal 7, 9, or 11 for odd mode selection, and checking if categories equal 6, 8, 10, or 12 for even mode selection. The decision module can generate control signals by setting mode flags in control registers, triggering parameter retrieval operations, and configuring subsequent processing stages.
[0048] In an embodiment, the transformation module (318) can implement the dual-level transformation process. The module can perform transformations by executing the first level normalization using arithmetic circuits implementing the Y transformation formula, applying the second level range adjustment through the W transformation calculations, and maintaining transformation accuracy through precision arithmetic. The transformation module can handle edge cases by implementing bounds checking, preventing arithmetic overflow, and ensuring output values remain within valid ranges.
[0049] In an embodiment, the grade mapping module (320) can assign grades based on the transformed values and calculated boundaries. The module can perform mapping by implementing binary search modules for efficient boundary location, comparing transformed values against ordered boundary arrays, and generating grade codes corresponding to identified categories. The grade mapping module can ensure accuracy by implementing boundary overlap detection, handling values exactly on boundaries, and maintaining mapping consistency across all records.
[0050] In an embodiment, the report generation module (322) can create formatted outputs from grade assignment results. The module can generate reports by aggregating grade statistics including distribution counts and percentages, formatting individual student results with identification and grade information, and producing summary visualizations. The report generation module can support multiple formats by implementing template-based generation, providing export functions for various file types, and enabling customization through configuration parameters.
[0051] In an embodiment, the database (310) can store operational data and system configurations. The database can maintain data by implementing ACID properties for transaction consistency, providing indexing for efficient retrieval operations, and supporting concurrent access through locking mechanisms. The database can enable analytics by storing historical grading data, maintaining audit trails of operations, and providing query interfaces for data analysis.
[0052] FIG. 4 illustrates an exemplary flow diagram depicting a method for automated grading and academic evaluation, in accordance with an embodiment of the present disclosure.
[0053] In an embodiment, referring to FIG. 4, a method (400) can begin at block (402) receiving student mark data as digital input signals. The receiving process can operate by activating input interfaces to accept data streams, buffering incoming data in temporary storage, and initiating validation checks. The method can handle various input sources by detecting data formats automatically, applying appropriate parsing routines, and converting to internal representation formats.
[0054] In an embodiment, block (404) extracting statistical parameters from the received student mark data through arithmetic calculation operations can implement the first stage processing. The extraction can operate by iterating through the received mark data to calculate sum values, dividing by count to obtain mean values, and computing variance through squared difference summations. The statistical parameters including mean and standard deviation can be stored in designated memory locations for subsequent use.
[0055] In an embodiment, block (406) analyzing distribution characteristics of qualified marks above a passing threshold based on the extracted statistical parameters can implement the second stage processing. The analysis can operate by filtering marks below the passing threshold stored in memory, examining the distribution of the qualified marks, and determining characteristics including range, skewness, and modality. The analysis results can inform transformation requirements for optimal grade distribution.
[0056] In an embodiment, block (408) applying the statistical parameters to the qualified marks performing a first level transformation, where the first level transformation converts the qualified marks into a normalized distribution. The transformation can operate by retrieving the calculated statistical parameters, applying the normalization formula to each qualified mark, and generating transformed values with standardized properties. The first level transformation ensures consistent statistical characteristics across different mark distributions.
[0057] In an embodiment, block (410) performing a second level transformation on the normalized distribution, where the second level transformation adjusts the transformed values within predetermined range limits. The transformation can operate by calculating median-based boundary values, applying range adjustment formulas to normalized values, and ensuring output values fall within practical grading ranges. The second level transformation provides fine-tuned control over final grade distributions.
[0058] In an embodiment, block (412) analyzing the range-adjusted transformed values can prepare for configuration mode selection. The analysis can operate by examining the transformed value distribution, determining the appropriate number of grade categories, and evaluating institutional requirements. The analysis provides inputs for the subsequent mode selection decision.
[0059] In an embodiment, block (414) selecting between multiple grade configuration modes based on grade related criteria through selection logic circuits. The selection can operate by evaluating the total grade categories against mode criteria, activating odd mode selection for 7, 9, or 11 categories, and activating even mode selection for 6, 8, 10, or 12 categories. The selection logic circuits can generate control signals to configure subsequent processing stages.
[0060] In an embodiment, block (416) retrieving distribution parameters from at least one memory (304), where the distinct sets of distribution parameters correspond to the selected grade configuration mode. The retrieval can operate by using the selected mode as an index to memory locations, loading four predetermined values for the selected configuration, and making parameters available for boundary calculations. The distinct parameter sets enable optimal grade distributions for different configurations.
[0061] In an embodiment, block (418) applying the retrieved distribution parameters to the range-adjusted transformed values. The application can operate by using parameters to calculate grade boundaries, implementing formulas that space boundaries according to parameter values, and generating complete boundary sets for all grade categories. The parameter application ensures consistent grade distributions according to institutional policies.
[0062] In an embodiment, block (420) mapping the range-adjusted transformed values to the final grade boundaries generating digital grade assignment output signals for each student mark. The mapping can operate by comparing each transformed value against calculated boundaries, identifying the appropriate grade category for each value, and generating digital signals encoding grade assignments. The mapping process produces final grade results maintaining distribution consistency across evaluations.
[0063] The described system for automated grading and academic evaluation presents an architecture that can enable integration of statistical transformation techniques through multi-stage processing, dual-level transformations, and configurable grade distribution mechanisms. The system can maintain grading consistency by utilizing distinct parameter sets for different configuration modes while providing flexibility for various institutional requirements. The control unit (208) architecture can provide coordinated processing by managing statistical analysis and implementing configuration selection through hardware circuits and software modules working in conjunction.
EXAMPLES
[0064] In an embodiment, the system can implement specific grade boundary calculations using the retrieved distribution parameters. The following examples illustrate the application of the four predetermined values (p1, p2, p3, p4) for grade boundary determination.
Example 1 - Odd Grade Configuration:
[0065] When the system selects odd grade configuration mode for 7 total grades, the grade assignment module (206-4) can calculate boundaries using the distribution parameters p1=0.7, p2=0.8, p3=0.9, p4=0.6. For transformed values where w̄ represents the mean, w_min represents the minimum, and w_max represents the maximum, the boundaries can be determined as follows:
[0066] For the highest grade G7: The lower boundary can be calculated as w̄ + p3×(w_max - w̄), and the upper boundary can be set as w_max.
[0067] For grade G6: The lower boundary can be calculated as w̄ + p2×(w_max - w̄), and the upper boundary can be calculated as w̄ + p3×(w_max - w̄) minus 0.01.
[0068] For grade G5: The lower boundary can be calculated as w̄ + p1×(w_max - w̄), and the upper boundary can be calculated as w̄ + p2×(w_max - w̄) minus 0.01.
[0069] For the middle grade G4: The lower boundary can be calculated as w̄ - p4×(w̄ - w_min), and the upper boundary can be calculated as w̄ + p1×(w_max - w̄) minus 0.01.
[0070] For grade G3: The lower boundary can be calculated as w̄ - p3×(w̄ - w_min), and the upper boundary can be calculated as w̄ - p4×(w̄ - w_min) minus 0.01.
[0071] For grade G2: The lower boundary can be calculated as w̄ - p2×(w̄ - w_min), and the upper boundary can be calculated as w̄ - p3×(w̄ - w_min) minus 0.01.
[0072] For the lowest qualified grade G1: The lower boundary can be set as w_min, and the upper boundary can be calculated as w̄ - p2×(w̄ - w_min) minus 0.01.
[0073] The system can ensure that the mean w̄ falls within the middle grade G4, providing balanced distribution with three grades above and three grades below the average. The 0.01 offset can prevent boundary overlap and ensure each transformed value maps to exactly one grade category.
Example 2 - Even Grade Configuration:
[0074] When the system selects even grade configuration mode for 8 total grades, the grade assignment module (206-4) can utilize distribution parameters p1=0.8, p2=0.9, p3=1.0, p4=1.3. The even configuration can implement boundaries such that the mean w̄ falls between grades G4 and G5, with four grades above and four grades below this midpoint.
[0075] For grade G8 (highest): The lower boundary can be calculated as w̄ + p3×(w_max - w̄), and the upper boundary can be set as w_max.
[0076] For grade G7: The lower boundary can be calculated as w̄ + p2×(w_max - w̄), and the upper boundary can be calculated as w̄ + p3×(w_max - w̄) minus 0.01.
[0077] For grade G6: The lower boundary can be calculated as w̄ + p1×(w_max - w̄), and the upper boundary can be calculated as w̄ + p2×(w_max - w̄) minus 0.01.
[0078] For grade G5: The lower boundary can be calculated as w̄, and the upper boundary can be calculated as w̄ + p1×(w_max - w̄) minus 0.01.
[0079] For grade G4: The lower boundary can be calculated as w̄ - p4×(w̄ - w_min), and the upper boundary can be set as w̄.
[0080] For grade G3: The lower boundary can be calculated as w̄ - p3×(w̄ - w_min), and the upper boundary can be calculated as w̄ - p4×(w̄ - w_min) minus 0.01.
[0081] For grade G2: The lower boundary can be calculated as w̄ - p2×(w̄ - w_min), and the upper boundary can be calculated as w̄ - p3×(w̄ - w_min) minus 0.01.
[0082] For grade G1 (lowest qualified): The lower boundary can be set as w_min, and the upper boundary can be calculated as w̄ - p2×(w̄ - w_min) minus 0.01.
Example 3 - Complete Processing Flow:
[0083] The system can process student marks where the passing threshold x_p equals 40 and maximum mark x_h equals 100. The statistical transformation engine can calculate:
Six Sigma mean: μ_ss = (40 + 100)/2 = 70
Six Sigma standard deviation: σ_ss = (100 - 40)/12 = 5
[0084] After extracting actual mean μ_x and standard deviation σ_x from qualified marks above 40, the transformation engine (206-2) can apply the first level transformation Y = μ_ss + (X_q - μ_x)/(σ_x/σ_ss) to each qualified mark, followed by the second level transformation W = w_min + (Y - y_min)/(y_max - y_min) × (w_max - w_min) where the boundary values are determined using median calculations.
[0085] The system can accommodate 9-grade and 11-grade configurations for odd mode, and 6-grade, 10-grade, and 12-grade configurations for even mode, each utilizing the same parameter sets but resulting in different numbers of grade categories. The processor (302) can automatically select the appropriate configuration based on institutional requirements encoded in the grade distribution criteria.
[0086] These examples demonstrate that the system can implement the claimed statistical transformation and grade assignment process through systematic application of the mathematical framework, distribution parameters, and boundary calculations, enabling consistent and reproducible grading across various academic evaluation scenarios.
[0087] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0088] The present disclosure provides a system for automated grading and academic evaluation that eliminates conventional single-mode grading limitations through multi-stage statistical transformation processing, enabling consistent grade distributions across diverse institutional requirements while maintaining computational efficiency through parallel execution paths processing at least 1000 student records per cycle.
[0089] The present disclosure provides a system that implements dual-level transformations through dedicated hardware circuits, ensuring numerical precision and preventing data overflow while achieving processing speeds under 100ms per 1000 records through pipeline architecture and cache-optimized memory access patterns.
[0090] The present disclosure provides a system that enables dynamic adaptation through multiple grade configuration modes with distinct distribution parameters, supporting institutional flexibility for 6 to 12 grade categories while maintaining grade distribution consistency with less than 2% variation through hardware-enforced parameter application and synchronized transformation processing.
, Claims:1. A system (100) for automated grading and academic evaluation, the system comprising:
at least one processor (302) and at least one memory (304) storing instructions that, when executed by the at least one processor (302), implement a statistical transformation engine, wherein the statistical transformation engine receives student mark data as digital input signals and processes the mark data through a multi-stage analysis comprising:
a first stage extracting statistical parameters through arithmetic calculation operations executed by the at least one processor (302) on the received mark data; and
a second stage analyzing distribution characteristics of qualified marks through computational operations performed on the statistical parameters extracted in the first stage;
at least one transformation engine (206-2) operatively connected to the at least one processor (302) through electrical data pathways, wherein the at least one transformation engine (206-2) performs a first level transformation converting validated marks into a normalized distribution using the statistical parameters and a second level transformation for range adjustment of the transformed values using the grade boundary positions;
wherein the at least one processor (302) receives the transformed values from the at least one transformation engine (206-2), selects between multiple grade configuration modes through execution of selection logic circuits based on grade distribution criteria, adjusts grade boundaries using distribution parameters stored in the at least one memory (304), wherein distinct sets of distribution parameters are retrieved for distinct grade configuration modes and generates digital grade assignment output signals based on the adjusted grade boundaries.
2. The system (100) as claimed in claim 1, wherein the at least one transformation engine (206-2) performs the first level transformation using mean and standard deviation parameters calculated from qualified marks above a predetermined passing threshold stored in the at least one memory (304).

3. The system (100) as claimed in claim 1, wherein the multiple grade configuration modes comprise an odd mode and an even mode, wherein the selection logic circuits select odd grade configuration mode when total grade categories equal 7, 9, or 11, and select even grade configuration mode when total grade categories equal 6, 8, 10, or 12.

4. The system (100) as claimed in claim 1, wherein the at least one transformation engine (206-2) comprises a normalization circuit electrically coupled to a range adjustment circuit through dedicated data pathways.

5. The system (100) as claimed in claim 1, wherein the at least one processor (302) processes mark data from at least 1000 student records per processing cycle through parallel execution paths implemented in the at least one processor (302).

6. The system (100) as claimed in claim 1, further comprising at least one database interface (210) electrically connected to the at least one processor (302) to store the processed mark data and the grade assignment output signals in structured data format.

7. The system (100) as claimed in claim 1, wherein the distribution parameters stored in the at least one memory (304) comprise four predetermined values that control grade boundary spacing, and wherein the at least one processor (302) retrieves distinct sets of values based on the selected grade configuration mode.
8. The system (100)as claimed in claim 1, further comprising at least one output formatter (212) connected to the at least one processor (302) through data transmission lines to convert the digital grade assignment output signals into formatted grade reports.

9. The system (100) as claimed in claim 1, further comprising at least one validation circuit electrically connected between the at least one processor (302) and the statistical transformation engine, wherein the at least one validation circuit includes range checking circuits and threshold comparison circuits.

10. A method (400) for automated grading and academic evaluation, the method comprising:
receiving (402) student mark data as digital input signals;
extracting (404) statistical parameters from the received student mark data through arithmetic calculation operations;
analyzing (406) distribution characteristics of qualified marks above a passing threshold based on the extracted statistical parameters;
applying (408) the statistical parameters to the qualified marks performing a first level transformation, wherein the first level transformation converts the qualified marks into a normalized distribution;
performing (410) a second level transformation on the normalized distribution, wherein the second level transformation adjusts the transformed values to fit within predetermined range limits;
analyzing (412) the range-adjusted transformed values;
selecting (414) between multiple grade configuration modes based on grade related criteria through selection logic circuits;
retrieving (416) distribution parameters from at least one memory (304), wherein distinct sets of distribution parameters correspond to the selected grade configuration mode;
applying (418) the retrieved distribution parameters to the range-adjusted transformed values; and
mapping (420) the range-adjusted transformed values to the final grade boundaries generating digital grade assignment output signals for each student mark, wherein the digital grade assignment output signals ensure consistency across multiple course evaluations.