PSY-FPX6110 goes deeper into specific learning theory frameworks than PSY-FPX6100 does — the course expects doctoral students to understand each theory's internal logic, its empirical foundations, and the specific instructional or design implications that follow from it. Assessments require students to move from "this theory says learning is X" to "therefore, an instructional environment designed on this theory would have these specific features, produce these specific outcomes, and fail in these specific ways." This guide explains what each assessment targets and how PSY-FPX6110 doctoral support helps you produce work at that analytical depth.
Course Overview
The course covers classical conditioning (Pavlov, Watson) and its applications, operant conditioning (Skinner, behavior modification), social learning theory (Bandura's observational learning, self-efficacy, self-regulation), cognitive learning theories (information processing, schema theory, metacognition), constructivism (Piaget, Vygotsky, problem-based learning), situated learning and communities of practice (Lave and Wenger), connectivism (Siemens — learning in networked digital environments), and the implications of each for instructional design, technology-mediated learning, and professional training environments. The course also addresses the evidence base for learning theory claims and controversies (e.g., learning styles as a debunked concept despite widespread belief).
Common Assessment Focus Areas
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1Learning Theory Deep Analysis
Provides a doctoral-level critical analysis of one or two learning theories — examining their foundational assumptions, the experimental or empirical evidence that supports them, the mechanisms by which learning is proposed to occur, and the conditions under which the theory's predictions fail or are limited. Not a summary, but a critical evaluation.
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2Theory-Driven Instructional Design Application
Applies a specific learning theory framework to design or analyze a learning environment — specifying how the theory's core constructs are instantiated in instructional decisions, assessment choices, and environmental features. Must demonstrate that the design follows from the theory, not just that both are good ideas.
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3Technology and Learning Theory
Examines how digital or technology-mediated learning environments interact with learning theory predictions — applying connectivism, cognitive load considerations for multimedia, or social learning in online communities to evaluate or design a technology-enhanced learning context. Must address the limitations of applying theories developed before the digital era.
How We Help With PSY-FPX6110
- Producing a genuine critical analysis in Assessment 1 — identifying what the theory cannot explain, not just what it proposes
- Designing the Assessment 2 learning environment so that specific design features are explicitly derived from theoretical constructs
- Applying connectivism and digital learning frameworks in Assessment 3 with appropriate acknowledgment of their limited empirical base
- Addressing learning theory controversies (learning styles myth, direct instruction vs. discovery learning debates) with scholarly evidence
- APA 7 doctoral-level writing integrating foundational learning theory texts with contemporary educational technology research
Common Challenges in This Course
Assessment 2's instructional design application is the most common failure point — students propose a learning environment and then separately explain the learning theory, without demonstrating that the design choices follow from the theory's specific claims. Doctoral rubrics look for explicit causal chains: "because this theory proposes that learning occurs via [mechanism], the instructional environment must [specific design feature] to activate that mechanism." Assessment 3 on technology and learning frequently over-relies on connectivism, which has significant theoretical critics and limited empirical support compared to cognitive load theory's application to multimedia design — the analysis should acknowledge this difference in evidence quality.
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PSY-FPX6110 FAQ
Doctoral-level engagement requires evaluating the evidence base for each theory — and connectivism (Siemens, 2005) has a much thinner empirical foundation than behaviorism, social learning theory, or cognitive load theory. The course expects you to apply connectivism to digital learning contexts while acknowledging its theoretical criticisms and limited empirical support.
Yes — learning styles (VAK, VARK) are addressed as an example of a widely believed but empirically debunked educational psychology concept. Doctoral students are expected to know that the "meshing hypothesis" (match teaching style to learning style) has failed empirical testing and to apply this understanding to discussions of personalized learning.
Bandura's framework — including observational learning, modeling, self-efficacy, and self-regulation — is applied to instructional design contexts: role model demonstrations in professional training, peer learning environments, self-efficacy as an instructional design target, and self-regulated learning as a skill to be taught explicitly rather than assumed.
Lave and Wenger's situated learning framework proposes that learning is inseparable from participation in a community of practice — newcomers learn by moving from legitimate peripheral participation toward full participation in an authentic community. This framework is applied to apprenticeship learning, workplace training design, and professional identity development in Assessment 2 and 3.