A SYSTEMS DYNAMICS MODEL FOR STUDENT RETENTION AND GRADUATION EFFICIENCY IN TECHNICAL INSTITUTIONS

Abstract

Technical and Vocational Education and Training (TVET) institutions in sub-Saharan Africa face a systemic challenge of high student dropout and low graduation efficiency, driven by interlocking dynamics of financial stress, inadequate academic support, misaligned employability expectations, and weak industry linkages. Traditional linear statistical models fail to capture the feedback-driven complexity of these interactions, which operate across multiple time scales and generate non-linear institutional behaviour. Objective: This study develops and validates a Systems Dynamics (SD) model to simulate the feedback interactions among dropout, retention, financial stress, academic support, and employability expectations in Kenyan TVET institutions, calibrated to empirical data from the TVET Authority (TVETA) and the Higher Education Loans Board (HELB), with the objective of identifying high-leverage policy interventions for improving graduation efficiency. Methods: A stock-and-flow SD model comprising five primary stocks (Enrolled Students, Retained Students, Dropout Students, Graduates, and Financial Stress Index) and twelve feedback loops — six reinforcing and six balancing — was developed using Forrester’s system dynamics methodology. Model parameters were estimated from TVETA Annual Returns data (2018–2023), HELB sustainability reports, and published empirical literature on TVET dropout determinants. Structural validity was confirmed through dimensional consistency checks, extreme-condition tests, and behaviour reproduction against the documented dropout decline from 20.77% (2018) to 2.94% (2023). Three policy scenarios — financial support expansion (Policy A), academic quality and employability linkage (Policy B), and combined intervention (Policy C) — were simulated over a 10-year horizon (2018–2028). Results: The calibrated model reproduced the observed dropout trajectory with a Mean Absolute Percentage Error (MAPE) of 3.8%, confirming structural validity. Sensitivity analysis identified HELB financial coverage expansion (PRCC = −0.84) and industry–TVET employment linkage (PRCC = +0.79 for graduation rate gain) as the dominant leverage points. Policy C (combined) simulations project dropout rates declining to 1.2% by 2028 and graduation rates reaching 87.4%, compared with 3.1% and 72.6% respectively under the baseline. The financial stress feedback loop, operating with a time delay of approximately 1.8 years, is identified as the primary driver of dropout reinforcement. Conclusion: SD modelling reveals that TVET student attrition is a non-linear, feedback-driven phenomenon resistant to isolated single-domain interventions. Combined financial support and employability-linked academic quality programmes generate synergistic gains exceeding the sum of individual policy effects, demonstrating the existence of a positive reinforcing loop between retention and institutional reputation that policymakers must deliberately activate.

Keywords: Systems dynamics, student retention, TVET, dropout modelling, feedback loops, graduation efficiency, Kenya.

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