[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85626-en":3,"doc-seo-85626-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85626,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Grade Encoding and the Structural Representation of Student Academic Trajectories","Exam score recoding from percentage values to letter grades does more than change measurement precision; it systematically reshapes the structural representation of students’ longitudinal achievement data. Using 68 mathematics scores from 75 primary students and Encoding Transformation simulation, the study compares two encoding schemes across information loss, distance-structure change, and clustering stability. Letter-grade coding compresses mean pairwise distances and flattens distance-distribution density, while increasing sensitivity to small perturbations and revealing a Compression Paradox between silhouette scores and clustering reproducibility.","Grade Encoding and the Structural Representation of Student Academic Trajectories  \n——An Empirical Study Based on Encoding Transformation Simulation  \nLi Li1,Yu Cao2  \n1Hefei No.62 Middle School, Hefei 230031,Anhui, China  \n2Anhui NARI ZT Electric Co., Ltd., Hefei 230031, Anhui,China  \nAbstract  \nDoes the conversion of academic assessment from percentage scores to letter grades merely represent an adjustment in information precision, or does it systematically alter the underlying structure of student academic data? Drawing on 68 mathematics exam scores from 75 primary school students, this study employs Encoding Transformation simulation to compare structural differences in the same dataset under two encoding schemes across three dimensions: information loss, distance structure change, and clustering stability. Results indicate that lettergrade encoding compresses the mean pairwise distance in the trajectory feature space from 20.50 to 1.06 (a compression ratio of approximately 19:1); after standardization, the density gradient of the distance distribution is systematically flattened, with kurtosis decreasing by 0.54 and the coefficient of variation decreasing by 0.16;; and the clustering structure becomes highly sensitive to minor sample perturbations—removing a single extreme observation causes the optimal K to jump from 4 to 8 and clustering reproducibility to plummet from 95% to 62% . Concurrently, a Compression Paradox is observed: letter-grade encoding increases the Silhouette coefficient while simultaneously reducing clustering stability. These findings demonstrate that letter-grade encoding systematically alters the structural representation of longitudinal academic data and exerts measurable effects on clustering stability.  \nKeywords: grade encoding; distance structure; clustering stability; Compression Paradox; Data Silence  \n1.Introduction  \n1.1 Problem Statement  \nSince the implementation of the \"Double Reduction\" policy, academic assessment in China's compulsory education has undergone a structural shift from percentage scores to letter grades[1] . Existing research has largely focused on its socially protective functions, such as alleviating test anxiety[2–4], while rarely interrogating a more fundamental question: Does the change in assessment format merely alter data precision, or does it alter the underlying structure of the data itself?  \nThe significance of this question lies in the following: if encoding schemes can alter the distance structure of data, then all analytical methods that rely on distance computations—cluster profiling, risk early warning, academic portrait construction—will have the stability of their results contingent on the encoding scheme itself. This is not a normative question about \"whether the assessment system is good or bad,\" but an empirical question about \"how data structure is shaped by encoding schemes.\"  \n1.2 Research Questions  \nThe central question of this paper is: After recoding continuous percentage scores into discrete letter grades, does the data structure of student academic trajectories undergo systematic change? If so, what is the mechanism underlying this change?  \nThis is decomposed into three sub-questions:  \n􀁸 RQ1 (Information Layer): How much information loss does letter-grade encoding incur? How does the distance structure of the trajectory feature space change?  \n􀁸 RQ2 (Structure Layer): Is the clustering structure stable after letter-grade encoding? Are clustering results sensitive to sample perturbations?  \n􀁸 RQ3 (Mechanism Layer): If the structure is unstable, what is the generative mechanism?  \n1.3 Research Approach  \nThis study does not rely on a control group in a real policy experiment—under conditions where letter-grade assessment is already widely implemented, such a comparison is institutionally unavailable. Instead, this study employs Encoding Transformation simulation: retaining the original percentage scores of the same cohort of students, convert","cbCainQ46mitlc47","https://ap.wps.com/l/cbCainQ46mitlc47","pdf",1507956,1,16,"English","en",105,"# Introduction\n## Problem Statement\n## Research Questions\n## Research Approach\n## Preview of Core Findings\n# Literature Review\n## Assessment Reform Research: Attention to Function, Neglect of Structure","[{\"question\":\"What is the main question about changing assessment formats in this study?\",\"answer\":\"Whether recoding scores from percentage to letter grades merely changes precision or also alters the underlying structure of student academic trajectory data.\"},{\"question\":\"How does letter-grade encoding affect the distance structure of student trajectories?\",\"answer\":\"It compresses mean pairwise distances dramatically and, after standardization, flattens the density gradient of the distance distribution.\"},{\"question\":\"What is the Compression Paradox reported by the study?\",\"answer\":\"Letter-grade encoding increases the Silhouette coefficient while simultaneously reducing clustering stability, indicating divergent effects at different stages of the analysis pipeline.\"}]",1784205035,40,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"grade-encoding-and-the-structural-representation-of-student-academic-trajectories","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/grade-encoding-and-the-structural-representation-of-student-academic-trajectories/85626/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What 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