IMPROVING NOVICE PROGRAMMING OUTCOMES IN UZBEKISTAN: A QUASI-EXPERIMENTAL STUDY OF THE IPRV INSTRUCTIONAL PROTOCOL

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Authors: Gurdeep Singh Virdee
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Abstract

Students entering higher education in Uzbekistan frequently begin programming courses with minimal prior exposure to formal computing concepts. In such contexts, conventional instruction—typically consisting of theoretical explanation followed by unstructured laboratory execution—often fails to support conceptual understanding among novice learners. This paper reports the classroom implementation and evaluation of the IPRV (Instruct–Predict–Run–Verify) pedagogical protocol, a structured instructional cycle designed to promote reasoning and reflection prior to code execution. The IPRV protocol was implemented over a four-month instructional period in undergraduate programming courses and evaluated using a quasi-experimental, non-equivalent groups design. Student performance in an intervention group taught using IPRV (n = 25) was compared with a control group receiving traditional instruction followed by laboratory execution (n = 28). Results indicate that student performance in the IPRV group was strongly associated with higher learning outcomes (Mann–Whitney U = 581.5, p < .001), with a large practical effect size (r = 0.57). Distributional and quartile analyses further suggest reduced performance disparity and improved outcomes among lower-performing students. While the instructional components of IPRV are individually well established, the contribution of this study lies in their mandatory sequencing, enforced prediction commitment, and empirical validation as a unified pedagogical protocol for novice programmers. The findings suggest that carefully constrained instructional design can meaningfully support introductory programming learning in contexts where students enter higher education with limited prior IT exposure. These results have immediate practical implications for curriculum developers and university instructors in Uzbekistan, offering a scalable, evidence-based strategy to enhance digital literacy and instructional quality in alignment with national education reforms.

Fields of Study & Subjects

Teaching and Learning Programming Innovative Teaching Methods Statistics Education and Methodologies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
28 January 2026