Machine Learning (ML) is a rapidly growing field within Artificial Intelligence and one of the most prominent areas of technological study, which is particularly challenging for new learners as it requires a strong grasp of abstract algorithmic structures along with rigorous mathematical reasoning. Despite this, traditional instructional approaches often fail to support deep conceptual understanding, particularly for foundational models such as Decision Trees. This study examines whether integrating multiple instructional representations (including textual explanations, visualizations, analogies, videos, and interactive simulations) enhances student learning outcomes compared to traditional text-only materials.

To examine the lack of empirical evidence on multi-representational teaching for Decision Trees in ML education, a mixed-methods pilot experiment was employed with 10 participants was employed, comparing a multi-representation tutorial group to a text-only group. After a pre-test on mathematical and logical reasoning, participants completed a structured learning phase and a post-test measuring conceptual understanding, problem-solving, and transfer. Semi-structured interviews were also conducted to capture learner experiences.

Quantitative analysis included independent group comparisons using descriptive statistics, and inferential tests. Qualitative data were analyzed with inductive and deductive thematic analysis.

This study provides preliminary evidence that multi-representational instruction may improve problem-solving performance in Decision Tree learning. However, due to the small sample size and lack of statistical significance on two of three outcomes, these findings should be interpreted cautiously and require replication with larger samples.

The study contributes a structured evaluation framework for multi-representational ML education and provides evidence supporting the potential benefits of interactive instructional design in teaching Decision Trees. ... Read more