Research-based learning findings

Here we summarize several researched-based findings that have been shown to facilitate student learning and are useful for in-class and online learning. Click on each finding to find out more about each each, including implications for teaching, examples of their usage at MIT and references.

Retrieval practice (the “testing effect”): The ability to recall and remember knowledge increases if it is periodically retrieved [1].

Spaced and interleaved practice: Spacing out repetition is more effective for learning than is studying an idea over a single period (even if it is longer than each of the many repetitions) [2]

Worked and faded examples: When non-experts are learning new concepts, it is more effective for them to study solutions to solved problems than to attempt solving problems [3].

Active learning: Instruction that requires students to engage cognitively and meaningfully with content results in better learning than instruction where students are solely exposed to information passively [4].

Pre-/Post-testing: Assessing understanding of the most important concepts and tasks in a course at the beginning and end of the course allows instructors to determine what students know when they start the course and how much they learned in the course [5].

Instructional frameworks

The following instructional frameworks incorporate many of the research-based learning findings mentioned above.

First principles of instruction: David Merrill synthesized the key principles common among leading instructional design frameworks [6]. Namely, learning is promoted when:

  1. learners solve tasks; problems that integrate multiple pieces of skills and knowledge
  2. existing knowledge is activated as a foundation for new knowledge
  3. new knowledge is demonstrated to the learner,
  4. new knowledge is applied by the learner with feedback, and
  5. new knowledge is integrated into the learner’s world, providing a foundation for the next cycle of learning
Schematic of 4C/ID model from Ref. [7]

Four-component instructional design (4C/ID): The 4C/ID model is a specific framework, consistent with David Merrill’s principles (see ‘First principles of instruction’), and based on research on the limitations of working memory (cognitive load theory) [7]. Instructional blueprints are described using four basic components:

  1. tasks
  2. supportive information
  3. procedural information, and
  4. part-task practice



For questions on research findings and/or instructional frameworks, please contact the Residential Education team in the Office of Digital Learning:

Lourdes Alemán, Program Coordinator for Curriculum Innovation:
Sheryl Barnes, Program Manager, Digital Learning in Residential Education:

Additional resources for teaching and learning are available at MIT's Teaching and Learning Lab.


[1] Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20–27. DOI

[2] Roediger, H. L., & Pyc, M. A. (2012). Inexpensive techniques to improve education: Applying cognitive psychology to enhance educational practice. Journal of Applied Research in Memory and Cognition, 1(4), 242–248. DOI

[3] Renkl, A. (2014). Learning from worked examples: How to prepare students for meaningful problem solving. In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.). Applying science of learning in education: Infusing psychological science into the curriculum. HTTP (downloaded PDF available)

[4] Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. DOI

[5] Adams, W. K., & Wieman, C. E. (2011). Development and validation of instruments to measure learning of expert‐like thinking. International Journal of Science Education, 33(9), 1289–1312. DOI

[6] Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59. DOI

[7] van Merrienboer, J. J. G., Clark, R. E., & De Croock, M. B. M. (2002). Blueprints for complex learning: The 4C/ID model. Educational Technology Research and Development, 50(2), 39–64. DOI

In addition to these resources, we have found the following teaching-and-learning frameworks extremely valuable:

[8] Krathwohl, D. R. A revision of Bloom's taxonomy: An overview (2002). Theory Into Practice, 41(4), 212-218. This article reviews the framework of the original Taxonomy of Educational Objectives, a scheme for classifying educational goals, objectives, and standards, describing how the revised Taxonomy differs from the original. DOI

[9] Chi, Michelene TH, and Ruth Wylie. The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes (2014). Educational Psychologist 49(4), 219-243. This article proposes that the engagement behaviors can belong to one of four modes (from most to least active): Interactive, Constructive, Active, and Passive (ICAP). The ICAP hypothesis predicts that the students learn more as they engage with learning materials from passive to active to constructive to interactive mode. PDFDOI

Additional resources:

[10] Tom Bennett (2013). Teacher proof: why research in education doesn't always mean what it claims, and what you can do about it. Routledge.

[11] Richard E. Mayer, ed. (2014). The Cambridge handbook of multimedia learning.  2nd edition.  Cambridge University Press.

[12] M. David Merrill (2013). First principles of instruction: Identifying and designing effective, efficient and engaging instruction. Pfeiffer.

[13] Jeroen J. G. van Merriënboer and Paul A. Kirschner (2012). Ten steps to complex learning. Routledge.