Cid
A Framework for the Cognitive Analysis of Composite Instructional Designs
Datos Bibliográficos
| ID | 7155803 |
|---|---|
| Autores | Katharina Loibl (0000-0002-1773-1913, University of Education Freiburg, autor de correspondencia), Timo Leuders (0000-0002-7621-7826, University of Education Freiburg), Inga Glogger-Frey (0000-0002-1409-2116, University of Erfurt), Nikol Rummel (0000-0002-3187-5534, Center for Advanced Internet Studies) |
| Año | 2025 |
| Volumen | 53 |
| Número | 6 |
| Páginas | 1485-1509 |
| Fecha de publicación | 2025-12-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Instructional Science (JOURNAL) |
| Identificadores de la revista | ISSN: 0020-4277 • E-ISSN: 1573-1952 |
| Editorial | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11251-024-09665-9 |
| OpenAlex | W4397045934 |
| Idioma | EN |
| Citas recibidas | 13 |
| Referencias citadas | 61 |
Instruction often spans multiple phases (e.g., phases of discovery learning, instructional explanations, practice) with different learning goals and different pedagogies. For any combination of multiple phases, we use the term composite instructional design (CID). To understand the mechanisms underlying composite instructional designs, we propose a framework that links three levels (knowledge, learning, instruction) across multiple phases: Its core element is the specification of learning mechanisms that explain how intermediate knowledge (i.e., the knowledge state between instructional phases) generated by the learning processes of one phase impacts the learning processes of a following phase. The CID framework serves as a basis for conducting research on composite instructional designs based on a cognitive analysis, which we exemplify by discussing existing research in light of the framework. We discuss how the CID framework supports understanding of the effects of composite instructional designs beyond the individual effects of the single phases through an analysis of effects on intermediate knowledge (i.e., the knowledge state resulting from a first instructional phase) and how it alters the learning processes initiated by the instructional design of a second phase. We also aim to illustrate how CID can help resolve contradictory findings of prior studies (e.g., studies that did or did not find beneficial effects of problem solving prior to instruction). Methodologically, we highlight the challenge of altering one learning mechanism at a time as experimental variations on the instructional design level often affect multiple learning processes across phases
Cognition · Cognitive psychology · Cognitive science · Composite number · Educational psychology · Instructional design · Mathematics education · Computer Science · Innovative Teaching and Learning Methods · Intelligent Tutoring Systems and Adaptive Learning · Psychology · Visual and Cognitive Learning Processes
Flipping a simulation before instruction can improve students' learning, interest and perceived competence
Fostering conceptual change by comparing correct and incorrect solutions – The role of individual misconceptions
Making the productive failure in PS-I more productive
Advances in research on composite instructional designs – investigating intermediate knowledge and preparation effects
Prior knowledge activation as preparation prior to instruction
Learning about multivariable causality with interactive simulations
Increasing contrasting cases during exploration or practice problems given before or after instruction
Comparing effectiveness of exploratory learning activities given before instruction
When is observing failure productive? Investigating the role of solution diversity in vicarious failure
Problem-solving before instruction for learning linear algebra in university mathematics
Problem-solving prior to instructional explanations when learning javelin throwing in primary school
Investigating the order of example-problem sequences when learning experimental design and graphing competencies
Can failure be made productive also in Bayesian reasoning? A conceptual replication study
The Knowledge‐Learning‐Instruction Framework
Towards a Theory of When and How Problem Solving Followed by Instruction Supports Learning
Rethinking the Boundaries of Cognitive Load Theory in Complex Learning
Inventing to Prepare for Future Learning
Let's talk evidence – The case for combining inquiry-based and direct instruction
Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load
Toward an Instructionally Oriented Theory of Example‐Based Learning
Practicing versus inventing with contrasting cases
Learning and transfer
The use of advance organizers in the learning and retention of meaningful verbal material.
Signaling text-picture relations in multimedia learning
Knowing what you don't know makes failure productive
Understanding and solving word arithmetic problems.
The Merits of Using Longitudinal Mediation
Expertise Reversal Effect and Its Implications for Learner-Tailored Instruction
Structure‐Mapping
Developing conceptual understanding and procedural skill in mathematics
Designing for Productive Failure
Do learning protocols support learning strategies and outcomes? The role of cognitive and metacognitive prompts
The mechanisms of analogical learning
Enriching problem-solving followed by instruction with explanatory accounts of emotions
Instructional sequences in science teaching
When Problem Solving Followed by Instruction Works
Effectiveness of invention tasks and explicit instruction in preparing intellectually gifted adolescents for learning
How preparation-for-learning with a worked versus an open inventing problem affect subsequent learning processes in pre-service teachers
The effects of activating prior topic and metacognitive knowledge on text comprehension scores
When and where do we apply what we learn
Do procedures for verbal reporting of thinking have to be reactive? A meta-analysis and recommendations for best reporting methods
| Obras citantes distintas | 13 |
|---|---|
| Citas por año | 13 |
| Intervalo de citas | 2025 - 2026 (2) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 12 |