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Chess produces unusually detailed records of cognitive performance: move sequences, positions, annotations, engine evaluations, and traces of error. Yet the availability of rich game data does not automatically produce learning. This qualitative interpretive case study examines how ten collegiate chess players in the University of the Philippines Diliman chess context use game records as personal informatics resources for perceived development. Drawing on semi-structured interviews and artifact-elicitation prompts, the study analyzes how players capture, stabilize, annotate, mediate, judge, and act on their own game data. The findings show that collegiate chess self-analysis operates as a form of symbolic performance informatics: a self-tracking process in which symbolic records of cognitive and strategic performance are converted into self-knowledge and training action. Players preserved games through notation sheets, memory-based reconstruction, PGN files, platform histories, and Lichess studies; transformed records through replay, comments, symbolic marks, and critical-position labels; and judged development through both numerical indicators, such as ratings and fewer blunders, and experiential indicators, such as confidence, calmness, preparedness, and pattern recognition. Digital tools including Lichess, Chess.com, ChessBase, Analyze This, CT-ART, and Stockfish expanded access to evaluation, but participants repeatedly emphasized that engine output did not automatically become understanding. Recorded moves and best-move lines required interpretive mediation through prior experience, coaches, peers, databases, books, and self-regulatory judgment. The study contributes to personal informatics by extending the field beyond bodily and sensor-generated tracking, identifies interpretive mediation as the theoretical hinge between reflection and action, and positions personal chess archives as learning infrastructure whose value depends on capture, replayability, annotation, retrieval, and repair under collegiate constraints.

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東京都の不動産(戸建て土地付住宅)取引価格の実証分析

Dokumen R Markdown ini disusun untuk memenuhi tugas mata kuliah Analisis Data Kategori. Studi ini menerapkan model Regresi Logistik Biner, Multinomial, Ordinal, dan Poisson menggunakan data sekunder yang didapatkan dari General Social Survey (GSS).

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