LAB@SMART: A Digital Workflow for Continuous Formative Assessment in FPGA-Based Digital Systems Laboratories

The digital transformation of engineering education has created new opportunities to support laboratory learning through continuous formative assessment. However, digital technologies are frequently implemented as isolated tools rather than as components of a coherent pedagogical framework. This paper presents LAB@SMART, an integrated digital framework that supports the complete FPGA-based laboratory learning cycle by combining pre-laboratory preparation, in-class formative assessment, laboratory practice, post-laboratory consolidation, and continuous feedback within a unified workflow. The framework integrates complementary digital technologies with conventional laboratory teaching without requiring modifications to the curriculum or laboratory infrastructure. A pilot deployment was conducted during the second semester of the 2025/2026 academic year in a Digital Systems Laboratory course involving two laboratory groups (35 students). The evaluation combined academic performance, informal implementation observations, and an anonymous student questionnaire. Among students who attended the examinations, the pilot groups showed a slightly higher pass rate than the remaining course cohort while also obtaining a slightly higher average final grade. Students also reported positive perceptions of the integrated workflow, particularly regarding laboratory preparation, continuous monitoring of their learning progress, and formative feedback. Although the pilot was limited in duration and scale, the results provide preliminary evidence of the practical feasibility of the proposed framework and support its further evaluation in other engineering laboratory contexts.

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Published
2026-10-15
DOI
https://doi.org/10.3390/info17101029
Primary Topic
Experimental Learning in Engineering
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LAB@SMART: A Digital Workflow for Continuous Formative Assessment in FPGA-Based Digital Systems Laboratories

Iouliia Skliarova
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Experimental Learning in Engineering
article

LAB@SMART: A Digital Workflow for Continuous Formative Assessment in FPGA-Based Digital Systems Laboratories

Iouliia Skliarova
article en

Abstract

The digital transformation of engineering education has created new opportunities to support laboratory learning through continuous formative assessment. However, digital technologies are frequently implemented as isolated tools rather than as components of a coherent pedagogical framework. This paper presents LAB@SMART, an integrated digital framework that supports the complete FPGA-based laboratory learning cycle by combining pre-laboratory preparation, in-class formative assessment, laboratory practice, post-laboratory consolidation, and continuous feedback within a unified workflow. The framework integrates complementary digital technologies with conventional laboratory teaching without requiring modifications to the curriculum or laboratory infrastructure. A pilot deployment was conducted during the second semester of the 2025/2026 academic year in a Digital Systems Laboratory course involving two laboratory groups (35 students). The evaluation combined academic performance, informal implementation observations, and an anonymous student questionnaire. Among students who attended the examinations, the pilot groups showed a slightly higher pass rate than the remaining course cohort while also obtaining a slightly higher average final grade. Students also reported positive perceptions of the integrated workflow, particularly regarding laboratory preparation, continuous monitoring of their learning progress, and formative feedback. Although the pilot was limited in duration and scale, the results provide preliminary evidence of the practical feasibility of the proposed framework and support its further evaluation in other engineering laboratory contexts.

InformationVol. 17(10)
University of Aveiro (PT)
Universidade de Aveiro, Foundation for Science and Technology
Quality education, Industry, innovation and infrastructure
Openalex Percentile: Top 14%
Experimental Learning in Engineering
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