Learning Sciences Foundations for Engineering Education: A Framework for Epistemological Articulation Across Paradigms

Abstract

Educational research explains learning through three major traditions—behaviorism, cognitivism, and the situative perspective—yet typically deploys them as rival options rather than complementary levels of analysis. This article argues that the fragmentation is not a matter of preference but an unresolved epistemological problem: the three paradigms operate on qualitatively distinct epistemic objects—observable behavior, the internal architecture of knowledge and its acquisition and transfer, and the structurally distributed conditions that determine what counts as knowing in a disciplinary community—and therefore generate questions, instruments, and validity criteria that are not mutually substitutable. On this basis, we develop a conceptual synthesis that does not unify the paradigms but specifies the conditions under which each—together with its contemporary extensions (Cognitive Load Theory, self-regulated learning, Self-Determination Theory, and the Cognitive Theory of Multimedia Learning)—is methodologically productive. We elaborate the framework in higher education, using engineering education as an analytically revealing case spanning technical complexity, expert reasoning, and participation in professional communities. It yields concrete implications for pedagogy and assessment: multi-level research and evaluation designs; participation and professional-identity indicators in assessment; the procedural–declarative asymmetry in technical learning; and the conditions under which assessing expert reasoning is valid—argumentative coherence, domain-expert assessors, and compatible group sizes. Applied to AI-enabled learning environments, the framework shows that the “learning” most current tools optimize is behavioral in kind rather than theory-neutral, and specifies the multi-level evidence their adoption in engineering programmes should require.