Various methods of algorithm evaluation generate feedback on students’ progress. Feedback is an important aspect of effective teaching and learning, and therefore, adequate attention to generating it is essential. Although various methods of algorithm evaluation are discussed in the literature, not much is available on algorithm evaluation methodologies that yield feedback in the form of learning transitions from one algorithm to another. Quantitative methods of evaluation, like the allocation of marks to the sections of the algorithms, are statistical and do not fully generate a clear view of learning transitions. In this paper, we present a SOLO-adapted evaluation methodology (based on the SOLO taxonomy) that can generate meaningful feedback on learning transitions (learning progression) between algorithms. The SOLO-adapted evaluation was tested on actual introductory programming students’ algorithms at a university. In the experiment, the feedback (learning transitions) generated by the SOLO-adapted evaluation was used to inform pedagogical intervention. The SOLO-adapted evaluation can show learning transitions as either an improvement, intermediate improvement, stagnant learning, deteriorating learning, intermediate deterioration of learning or already-know.