Abstract:

Asynchronous online learning has become a key tool for the continuous training of professionals in highly specialised sectors such as the water industry. In this context, ensuring the effective attainment of the learning outcomes defined for each training activity is of paramount importance, both for the quality of the educational process and for its professional applicability. However, the diversity of assessment strategies raises questions regarding their coherence and their ability to reliably measure the level of competence achieved.

This study analyses the coherence of the assessment system implemented across 23 courses within the Eduaqua training framework, aimed at engineers and technical professionals in the water sector. These courses are grouped into three typologies: software-oriented courses, theoretical-conceptual courses, and theoretical-practical courses involving problem-solving. The assessment system combines checkpoints, unit tests, final tests, and practical exercises, each with different weightings. Checkpoints, embedded within the content delivery, are self-learning activities with immediate feedback that require learners to reach the correct answer through iteration, whereas tests are based on multiple-choice questions designed for quicker resolution.

The study methodology is based on the analysis of assessment results obtained over the past five years, considering a sample of more than 1,000 students. Results are examined comparatively according to both task type and course typology.

Findings reveal systematic discrepancies between assessment tasks: scores in checkpoints are consistently lower than those obtained in tests, despite assessing equivalent content. This behaviour suggests that tasks incorporating immediate feedback, designed to foster active learning, entail higher cognitive demand and are more closely aligned with deep learning processes, whereas tests may be capturing more superficial learning.

Based on these findings, the study explores the relationship between assessment design, deep learning, and student self-regulation, highlighting a potential misalignment between the relative weight of assessment tasks and their actual capacity to promote and measure deep learning. As its main contribution, the paper proposes a framework for adjusting the weighting of assessment tasks according to course typology, aimed at reinforcing activities that promote deep learning and self-regulation. This approach enhances the validity of assessment and more effectively aligns performance evidence with learning outcomes in specialised online training, particularly in the water sector.

Keywords:

Asynchronous online learning, Assessment coherence, Deep learning, Self-regulated learning, Water sector training.