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H. R. Paz

Publications and source records attributed to H. R. Paz.

18 recordsLinked to original sources

The Causal Effect of First-Time Academic Failure on University Dropout: Evidence from a Regression Discontinuity Design

University dropout remains a persistent challenge in higher education systems, yet causal evidence on the mechanisms triggering early disengagement is limited. This study estimates the causal effect of first-time academic failure on subsequent university attrition. Exploiting a sharp institutional grading threshold on a 0-10 scale, we implement a regression discontinuity design (RDD) comparing students who narrowly fail to those who narrowly pass their first attempt. Using longitudinal administrative data spanning multiple cohorts and degree programmes, we estimate local average treatment effects (LATE) for students at the margin of success and examine dropout outcomes within 12 and 24 months following the initial evaluation. Contrary to conventional assumptions, the results indicate that marginal first-time failure is associated with a lower probability of subsequent dropout relative to marginal passing at both horizons. A comprehensive battery of robustness checks - including donut RDD specifications, placebo cutoffs, and formal density tests - supports the validity of the identification strategy. These findings suggest that early academic failure may function as a salient signal that prompts behavioural adjustment or reorientation, while marginal passing may sustain a state of "fragile persistence". The study provides causal evidence on the non-linear effects of early academic performance and highlights the importance of carefully designed institutional responses at critical evaluation thresholds.

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Homeostasis Under Technological Transition: How High-Friction Universities Adapt Through Early Filtering Rather Than Reconfiguration

Universities are widely expected to respond to technological transitions through rapid reconfiguration of programme demand and curricular supply. Using four decades of longitudinal administrative cohorts (1980-2019) from a large public university, we examine whether technological change is translated into observable shifts in programme hierarchy, or instead absorbed by institutional mechanisms that preserve structural stability. We show that programme rankings by entrant volume remain remarkably stable over time, while the translation of technological transitions into enrolment composition occurs with substantial delay. Short-run adjustment appears primarily in early persistence dynamics: attrition reacts sooner than choice, and "growth" in entrants can coexist with declining early survival - producing false winners in which expansion is decoupled from persistence. Macroeconomic volatility amplifies attrition and compresses between-programme differences, masking technological signals that would otherwise be interpreted as preference shifts. To explain why stability dominates responsiveness, we situate these patterns within nationally regulated constraints governing engineering education - minimum total hours and mandated practice intensity - which materially limit the speed of curricular adaptation (Ministerio de Educacion, 2021; Ley de Educacion Superior, 1995). National system metrics further support the plausibility of a high-friction equilibrium in which large inflows coexist with standardised outputs (Secretaria de Politicas Universitarias [SPU], 2022). These findings suggest that apparent rigidity is not an anomaly but the predictable outcome of a system optimised for stability over responsiveness.

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Technological Transitions and the Limits of Inference in Adaptive Educational Systems

In contemporary educational systems, academic performance indicators play a central role in institutional evaluation and in the interpretation of student trajectories. However, under conditions of rapid technological change, the inferential validity of such indicators becomes increasingly fragile. This article examines how, in adaptive educational systems, statistically correct inferences may nevertheless become systematically misleading when structural conditions change. Adopting a theory-informed interpretive approach, the paper conceptualises technological transitions as exogenous structural perturbations that reconfigure incentives, constraints, and participation strategies, without necessarily implying a deterioration of underlying student capabilities. Drawing on prior empirical evidence for illustrative purposes, the analysis identifies recurring patterns of inferential instability, including level shifts, trend reconfigurations, and increased heterogeneity across cohorts. The argument integrates insights from complex adaptive systems theory, the sociology of quantification, and measurement theory to show how strategic behavioural adaptation can decouple the meaning of performance metrics from the constructs they are intended to represent. The paper concludes by emphasising the need for inferential caution when interpreting educational metrics in contexts of structural and technological transformation.

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Longitudinal Trends in Pre University Preparation. A Cohort Evaluation Using Introductory Mathematics and Physics Courses (1980-2019)

The transition from secondary to higher education represents a critical point in academic trajectories, particularly in programmes with a strong emphasis on basic sciences. Across different higher education systems, introductory Mathematics and Physics courses consistently concentrate high rates of early failure and attrition, yet most available evidence relies on cross-sectional analyses or limited time spans. This study presents a longitudinal evaluation of pre-university preparation based on early academic outcomes in Mathematics and Physics, conceptualised as "sensor" courses of initial academic demands. Using complete administrative records from a large public university in Argentina, the analysis covers entry cohorts from 1980 to 2019 with census-level coverage and a population-based approach. Pre-university preparation is operationally defined as cohort-level compatibility between students' prior educational background and the functional demands of introductory university coursework, observed through first-attempt outcomes. For each cohort and by type of secondary school (public or private), we estimate the probability of course approval, the probability of non-attempt (enrolment without evaluative participation), and the public-private success gap. The results reveal consistent long-term patterns: a gradual decline in early approval probabilities, a sustained increase in non-attempt behaviour, and the persistence of moderate but stable public-private gaps. These findings point to structural changes in the articulation between secondary education and higher education rather than short-term fluctuations or individual-level effects. The study contributes to the international literature on educational evaluation by providing rare long-horizon longitudinal evidence from an Ibero-American context.

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The Topology of Hardship: Empirical Curriculum Graphs and Structural Bottlenecks in Engineering Degrees

Engineering degrees are often perceived as "hard", yet this hardness is usually discussed in terms of content difficulty or student weaknesses rather than as a structural property of the curriculum itself. Recent work on course-prerequisite networks and curriculum graphs has shown that study plans can be modelled as complex networks with identifiable hubs and bottlenecks, but most studies rely on official syllabi rather than on how students actually progress through the system (Simon de Blas et al., 2021; Stavrinides & Zuev, 2023; Yang et al., 2024; Wang et al., 2025). This paper introduces the notion of topology of hardship: a quantitative description of curriculum complexity derived from empirical student trajectories in long-cycle engineering programmes. Building on the CAPIRE framework for multilevel trajectory modelling (Paz, 2025a, 2025b), we reconstruct degree-curriculum graphs from enrolment and completion data for 29 engineering curricula across several cohorts. For each graph we compute structural metrics (e.g., density, longest path, bottleneck centrality) and empirical hardship measures capturing blocking probability and time-to-progress. These are combined into a composite hardship index, which is then related to observed dropout rates and time to degree. Our findings show that curriculum hardness is not a vague perception but a measurable topological property: a small number of structurally dense, bottleneck-heavy curricula account for a disproportionate share of dropout and temporal desynchronisation. We discuss implications for curriculum reform, accreditation, and data-informed policy design.

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Quantised Academic Mobility: Network and Cluster Analysis of Degree Switching, Plan Changes, and Re-entries in an Engineering Faculty (1980-2019)

This study challenges the traditional binary view of student progression (retention versus dropout) by conceptualising academic trajectories as complex, quantised pathways. Utilising a 40-year longitudinal dataset from an Argentine engineering faculty (N = 24,016), we introduce CAPIRE, an analytical framework that differentiates between degree major switches, curriculum plan changes, and same-plan re-entries. While 73.3 per cent of students follow linear trajectories (Estables), a significant 26.7 per cent exhibit complex mobility patterns. By applying Principal Component Analysis (PCA) and DBSCAN clustering, we reveal that these trajectories are not continuous but structurally quantised, occupying discrete bands of complexity. The analysis identifies six distinct student archetypes, including 'Switchers' (10.7 per cent) who reorient vocationally, and 'Stable Re-entrants' (6.9 per cent) who exhibit stop-out behaviours without changing discipline. Furthermore, network analysis highlights specific 'hub majors' - such as electronics and computing - that act as systemic attractors. These findings suggest that student flux is an organised ecosystemic feature rather than random noise, offering institutions a new lens for curriculum analytics and predictive modelling.

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The Stagnant Persistence Paradox: Survival Analysis and Temporal Efficiency in Exact Sciences and Engineering Education

Research on student progression in higher education has traditionally focused on vertical outcomes such as persistence and dropout, often reducing complex academic histories to binary indicators. While the structural component of horizontal mobility (major switching, plan changes, re-entries) has recently been recognised as a core feature of contemporary university systems, the temporal cost and efficiency of these pathways remain largely unquantified. Using forty years of administrative records from a large faculty of engineering and exact sciences in Argentina (N = 24,016), this study applies a dual-outcome survival analysis framework to two key outcomes: definitive dropout and first major switch. We reconstruct academic trajectories as sequences of enrolment spells and typed transitions under the CAPIRE protocol, and then deploy non-parametric Kaplan-Meier estimators to model time-to-event under right-censoring. Results uncover a critical systemic inefficiency: a global median survival time of 4.33 years prior to definitive dropout, with a pronounced long tail of extended enrolment. This pattern reveals a phenomenon of stagnant persistence, where students remain formally enrolled for long periods without commensurate curricular progression. In contrast, major switching follows an early-event regime, with a median time of 1.0 year among switchers and most switches concentrated within the first academic year. We argue that academic failure in rigid engineering curricula is not a sudden outcome but a long-tail process that generates high opportunity costs, and that institutional indicators should shift from static retention metrics towards measures of curricular velocity based on time-to-event analysis.

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Differential Filtering in a Common Basic Cycle: Multi-Major Trajectories and Structural Bottlenecks in Exact Sciences and Engineering Degrees

Universities often present the Common Basic Cycle (CBC) as a neutral levelling stage shared by several degree programmes. Using twenty years of longitudinal administrative records from a Faculty of Engineering and Exact Sciences, this study tests whether the CBC actually operates as a uniform gateway or as a differential filter across majors. We reconstruct student trajectories for 24,017 entrants, identifying CBC subjects (year level <= 1), destination major, time to exit from the CBC, and final outcome (progression to upper cycle, drop-out, or right-censoring). The analysis combines transition matrices, Kaplan-Meier survival curves, stratified Cox models and subject-level logistic models of drop-out after failure, extended with multi-major enrolment data and a pre/post 2006 curriculum reform comparison. Results show that the CBC functions as a strongly differential filter. Post-reform, the probability of progressing to the upper cycle in the same major ranges from about 0.20 to 0.70 across programmes, while overall drop-out in the CBC exceeds 60%. Early Mathematics modules (introductory calculus and algebra) emerge as structural bottlenecks, combining low pass rates with a two- to three-fold increase in the hazard of leaving the system after failure, with markedly different severity by destination major. Multi-major enrolment, often treated administratively as indecision, is instead associated with lower drop-out, suggesting an adaptive exploration of feasible trajectories. The findings portray the CBC not as a neutral academic foyer, but as a structured sorting device whose impact depends sharply on the targeted degree and on the opportunity to explore alternative majors.

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From Administrative Chaos to Analytical Cohorts: A Three-Stage Normalisation Pipeline for Longitudinal University Administrative Records

The growing use of longitudinal university administrative records in data-driven decision-making often overlooks a critical layer: how raw, inconsistent data are normalised before modelling. This article presents a three-stage normalisation pipeline for a dataset of 24,133 engineering students at a Latin American public university, spanning four decades (1980-2019). The pipeline comprises: (i) N1 CENSAL, harmonising demographics into a single person-level layer; (ii) N1b IDENTITY RESOLUTION, consolidating duplicate identifiers into a canonical ID while preserving an audit trail; and (iii) N1c GEO and SECONDARY-SCHOOL NORMALISATION, which builds reference tables, classifies school types (state national, state provincial, private secular, private religious), and flags irrecoverable cases as DATA_MISSING. The pipeline preserves 100% of students, achieves full geocoding, and yields valid school types for 56.6% of the population. The remaining 43.4% are identified as structurally missing due to legacy enrolment practices rather than stochastic non-response. Forensic analysis (chi-square, logistic regression) shows missingness is highly predictable from entry decade and geography, confirming a structural, historically induced mechanism. The article contributes: (a) a transparent, reproducible normalisation pipeline tailored to higher education; (b) a framework for treating structurally missing information without speculative imputation; and (c) guidance on defining analytically coherent cohorts (full population vs. secondary-school-informed subcohorts) for downstream learning analytics and policy evaluation.

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Free Tuition, Stratified Pipelines: Four Decades of Administrative Cohorts and Equity in Access to Engineering and Science in an Argentine Public University

Latin American higher education is often portrayed as equitable when tuition is free and access to public universities is formally unrestricted. Yet, growing research shows that massification under tuition-free policies often coexists with strong social and territorial stratification. This article uses four decades of administrative records from a faculty of engineering in north-western Argentina to examine how cohort composition has changed over time. Drawing on 24,133 first-time entrants (1980-2019), we construct a leakage-aware "background census" layer (N1c) harmonising school type, province, and age across legacy systems. We combine descriptive analyses, UMAP+DBSCAN clustering, and a reconstructed macroeconomic panel (inflation, unemployment, poverty, GDP) anchored at entry. All analyses explicitly report structural missingness patterns. Results show that missingness in background variables is historically patterned, declining sharply after the 1990s. Among students with observed data, the share coming from private-especially religious-secondary schools in high-income areas increased from less than half in the 1980s to roughly two-thirds in the 2010s. The catchment area became more local, with the home province gaining weight while distant origins lost ground. Median age at entry remained stable at 19, with persistent right tails of older entrants. Macro-linkage analyses reveal moderate associations between unemployment and older entry age, and between inflation and higher shares of students from interior provinces. We argue that free tuition and open entry have operated within, rather than against, stratified school and residential pipelines. The article illustrates how administrative data can support equity monitoring and discusses implications for upstream school policies and institutional accountability in tuition-free systems.

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Dual Stressors in Engineering Education: Lagged Causal Effects of Academic Staff Strikes and Inflation on Dropout within the CAPIRE Framework

This study provides a causal validation of the dual-stressor hypothesis in a long-cycle engineering programme in Argentina, testing whether academic staff strikes (proximal shocks) and inflation (distal shocks) jointly shape student dropout. Using a leak-aware longitudinal panel of 1,343 students and a manually implemented LinearDML estimator, we estimate lagged causal effects of strike exposure and its interaction with inflation at entry. The temporal profile is clear: only strikes occurring two semesters earlier have a significant impact on next-semester dropout in simple lagged logit models (ATE = 0.0323, p = 0.0173), while other lags are negligible. When we move to double machine learning and control flexibly for academic progression, curriculum friction and calendar effects, the main effect of strikes at lag 2 becomes small and statistically non-significant, but the interaction between strikes and inflation at entry remains positive and robust (estimate = 0.0625, p = 0.0033). A placebo model with a synthetic strike variable yields null effects, and a robustness audit (seed sensitivity, model comparisons, SHAP inspection) confirms the stability of the interaction across specifications. SHAP analysis also reveals that Strikes_Lag2 and Inflation_at_Entry jointly contribute strongly to predicted dropout risk. These findings align with the CAPIRE-MACRO agent-based simulations and support the view that macro shocks act as coupled stressors mediated by curriculum friction and financial resilience rather than isolated events.

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Regularity as Structural Amplifier, Not Trap: A Causal and Archetype-Based Analysis of Dropout in a Constrained Engineering Curriculum

Engineering programmes, particularly in Latin America, are often governed by rigid curricula and strict regularity rules that are claimed to create a Regularity Trap for capable students. This study tests that causal hypothesis using the CAPIRE framework, a leakage-aware pipeline that integrates curriculum topology and causal estimation. Using longitudinal data from 1,343 civil engineering students in Argentina, we formalize academic lag (accumulated friction) as a treatment and academic velocity as an ability proxy. A manual LinearDML estimator is employed to assess the average (ATE) and conditional (CATE) causal effects of lag on subsequent dropout, controlling for macro shocks (strikes, inflation). Results confirm that academic lag significantly increases dropout risk overall (ATE = 0.0167, p < 0.0001). However, the effect decreases sharply for high-velocity (high-ability) students, contradicting the universal Trap hypothesis. Archetype analysis (UMAP/DBSCAN) shows that friction disproportionately harms trajectories already characterized by high initial friction and unstable progression. 8 We conclude that regularity rules function as a Structural Amplifier of pre-existing vulnerability rather than a universal trap. This has direct implications for engineering curriculum design, demanding targeted slack allocation and intervention policies to reduce friction at core basic-cycle courses

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CAPIRE Intervention Lab: An Agent-Based Policy Simulation Environment for Curriculum-Constrained Engineering Programmes

Engineering programmes in Latin America combine high structural rigidity, intense assessment cultures and persistent socio-economic inequality, producing dropout rates that remain stubbornly high despite increasingly accurate early-warning models. Predictive learning analytics can identify students at risk, but they offer limited guidance on which concrete combinations of policies should be implemented, when, and for whom. This paper presents the CAPIRE Intervention Lab, an agent-based simulation environment designed to complement predictive models with in silico experimentation on curriculum and teaching policies in a Civil Engineering programme. The model is calibrated on 1,343 students from 15 cohorts in a six-year programme with 34 courses and 12 simulated semesters. Agents are initialised from empirically derived trajectory archetypes and embedded in a curriculum graph with structural friction indicators, including backbone completion, blocked credits and distance to graduation. Each agent evolves under combinations of three policy dimensions: (A) curriculum and assessment structure, (B) teaching and academic support, and (C) psychosocial and financial support. A 2x2x2 factorial design with 100 replications per scenario yields over 80,000 simulated trajectories. Results show that policy bundles targeting early backbone courses and blocked credits can reduce long-term dropout by approximately three percentage points and substantially increase the number of courses passed by structurally vulnerable archetypes, while leaving highly regular students almost unaffected. The Intervention Lab thus shifts learning analytics from static prediction towards dynamic policy design, offering institutions a transparent, extensible sandbox to test curriculum and teaching reforms before large-scale implementation.

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The Promotion Wall: Efficiency-Equity Trade-offs of Direct Promotion Regimes in Engineering Education

Progression and assessment rules are often treated as administrative details, yet they fundamentally shape who is allowed to remain in higher education, and on what terms. This article uses a calibrated agent-based model to examine how alternative progression regimes reconfigure dropout, time-to-degree, equity and students' psychological experience in a long, tightly sequenced engineering programme. Building on a leakage-aware longitudinal dataset of 1,343 students and a Kaplan-Meier survival analysis of time-to-dropout, we simulate three policy scenarios: (A) a historical "regularity + finals" regime, where students accumulate exam debt; (B) a direct-promotion regime that removes regularity and finals but requires full course completion each term; and (C) a direct-promotion regime complemented by a capacity-limited remedial "safety net" for marginal failures in bottleneck courses. The model is empirically calibrated to reproduce the observed dropout curve under Scenario A and then used to explore counterfactuals. Results show that direct promotion creates a "promotion wall": attrition becomes sharply front-loaded in the first two years, overall dropout rises, and equity gaps between low- and high-resilience students widen, even as exam debt disappears. The safety-net scenario partially dismantles this wall: it reduces dropout and equity gaps relative to pure direct promotion and yields the lowest final stress levels, at the cost of additional, targeted teaching capacity. These findings position progression rules as central objects of assessment policy rather than neutral background. The article argues that claims of improved efficiency are incomplete unless they are evaluated jointly with inclusion, equity and students' psychological wellbeing, and it illustrates how simulation-based decision support can help institutions rehearse assessment reforms before implementing them.

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When Administrative Networks Fail: Curriculum Structure, Early Performance, and the Limits of Co-enrolment Social Synchrony for Dropout Prediction in Engineering Education

Social integration theories suggest that students embedded in supportive peer networks are less likely to drop out. In learning analytics, this has motivated the use of social network analysis (SNA) from institutional co-enrolment data to predict attrition. This study tests whether such administrative network features add predictive value beyond a leakage-aware, curriculum-graph-informed model in a long-cycle Civil Engineering programme at a public university in Argentina. Using a three-semester observation window and a 16-fold leave-cohort-out design on 1,343 students across 15 cohorts, we compare four configurations: a baseline model (M0), baseline plus network features (M1), baseline plus curriculum-graph features (M2), and a full model (M3). After a leakage audit removed two post-outcome variables that had produced implausibly perfect performance, retrained models show that M0 and M2 achieve F1 = 0.9411 and ROC-AUC = 0.9776, while adding network features systematically degrades performance (M1 and M3: F1 = 0.9367; ROC-AUC = 0.9768). We conclude that in curriculum-constrained programmes, administrative co-enrolment SNA does not provide additional risk information beyond curriculum topology and early academic performance.

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An Agent-Based Simulation of Regularity-Driven Student Attrition: How Institutional Time-to-Live Constraints Create a Dropout Trap in Higher Education

High dropout rates in engineering programmes are conventionally attributed to student deficits: lack of academic preparation or motivation. However, this view neglects the causal role of "normative friction": the complex system of administrative rules, exam validity windows, and prerequisite chains that constrain student progression. This paper introduces "The Regularity Trap," a phenomenon where rigid assessment timelines decouple learning from accreditation. We operationalize the CAPIRE framework into a calibrated Agent-Based Model (ABM) simulating 1,343 student trajectories across a 42-course Civil Engineering curriculum. The model integrates empirical course parameters and thirteen psycho-academic archetypes derived from a 15-year longitudinal dataset. By formalizing the "Regularity Regime" as a decaying validity function, we isolate the effect of administrative time limits on attrition. Results reveal that 86.4% of observed dropouts are driven by normative mechanisms (expiry cascades) rather than purely academic failure (5.3%). While the overall dropout rate stabilized at 32.4%, vulnerability was highly heterogeneous: archetypes with myopic planning horizons faced attrition rates up to 49.0%, compared to 13.2% for strategic agents, despite comparable academic ability. These findings challenge the neutrality of administrative structures, suggesting that rigid validity windows act as an invisible filter that disproportionately penalizes students with lower self-regulatory capital.

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The CAPIRE Curriculum Graph: Structural Feature Engineering for Curriculum-Constrained Student Modelling in Higher Education

Curricula in long-cycle programmes are usually recorded in institutional databases as linear lists of courses, yet in practice they operate as directed graphs of prerequisite relationships that constrain student progression through complex dependencies. This paper introduces the CAPIRE Curriculum Graph, a structural feature engineering layer embedded within the CAPIRE framework for student attrition prediction in Civil Engineering at Universidad Nacional de Tucuman, Argentina. We formalise the curriculum as a directed acyclic graph, compute course-level centrality metrics to identify bottleneck and backbone courses, and derive nine structural features at the student-semester level that capture how students navigate the prerequisite network over time. These features include backbone completion rate, bottleneck approval ratio, blocked credits due to incomplete prerequisites, and graph distance to graduation. We compare three model configurations - baseline CAPIRE, CAPIRE plus macro-context variables, and CAPIRE plus macro plus structural features - using Random Forest classifiers on 1,343 students across seven cohorts (2015-2021). While macro-context socioeconomic indicators fail to improve upon the baseline, structural curriculum features yield consistent gains in performance, with the best configuration achieving overall Accuracy of 86.66% and F1-score of 88.08% and improving Balanced Accuracy by 0.87 percentage points over a strong baseline. Ablation analysis further shows that all structural features contribute in a synergistic fashion rather than through a single dominant metric. By making curriculum structure an explicit object in the feature layer, this work extends CAPIRE from a multilevel leakage-aware framework to a curriculum-constrained prediction system that bridges network science, educational data mining, and institutional research.

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A Leakage-Aware Data Layer For Student Analytics: The Capire Framework For Multilevel Trajectory Modeling

Predictive models for student dropout, while often accurate, frequently rely on opportunistic feature sets and suffer from undocumented data leakage, limiting their explanatory power and institutional usefulness. This paper introduces a leakage-aware data layer for student trajectory analytics, which serves as the methodological foundation for the CAPIRE framework for multilevel modelling. We propose a feature engineering design that organizes predictors into four levels: N1 (personal and socio-economic attributes), N2 (entry moment and academic history), N3 (curricular friction and performance), and N4 (institutional and macro-context variables)As a core component, we formalize the Value of Observation Time (VOT) as a critical design parameter that rigorously separates observation windows from outcome horizons, preventing data leakage by construction. An illustrative application in a long-cycle engineering program (1,343 students, ~57% dropout) demonstrates that VOT-restricted multilevel features support robust archetype discovery. A UMAP + DBSCAN pipeline uncovers 13 trajectory archetypes, including profiles of "early structural crisis," "sustained friction," and "hidden vulnerability" (low friction but high dropout). Bootstrap and permutation tests confirm these archetypes are statistically robust and temporally stable. We argue that this approach transforms feature engineering from a technical step into a central methodological artifact. This data layer serves as a disciplined bridge between retention theory, early-warning systems, and the future implementation of causal inference and agent-based modelling (ABM) within the CAPIRE program.

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