Student Learning

Multiple professional fields of research are devoted to the study of how people (including students) learn, such as the learning sciences, educational psychology, sociology, and other educational research domains. How People Learn is a free foundational and accessible text on the topic. Below is a sampling of some important findings and theories concerning student learning.

Students are not a blank slate (“tabula rasa”). Students have pre-existing knowledge and misconceptions, which can support or hinder new learning if not acknowledged. See for example this video about Khan Academy and the Effectiveness of Science Videos. And learning does not happen passively by observation or like in The Matrix (“I know kung fu”). Effective learning is an active and collaborative process. Learning takes effort and action, like with exercise or learning a sport. There is some accuracy to the saying: “I hear and I forget. I see and I remember. I do and I understand.” One of the most heavily researched and established educational psychology findings, for example, is retrieval practice, which finds that students learn much more from actively trying to recall and apply information and “test” themselves compared to merely re-reading a chapter or watching an instructor lecture or watching someone else show how to solve a problem.

ICAP Framework

Michilene Chi summarized research on learning with the ICAP Framework, which identifies four types of student learning and engagement: Interactive -> Constructive -> Active -> Passive. Passive learning is inferior to active learning, which is inferior to constructive and interactive learning.

Passive learning involves passively observing or reading information:

We define the Passive mode of engagement as learners interacting with instruction physically by orienting towards or paying attention to instruction with no visible outputs produced. For example, paying attention and listening to a teacher’s lecture without taking notes is considered engaging in the Passive mode. Thus, the two overt indices of engaging in the Passive mode are paying attention and producing no visible outputs.

Active learning involves students actively engaging with material, such as interactive with a simulation, playing a game, doing a lab activity, or answering questions in a course.

Learners’ engagement with instructional materials is classified as Active when students physically manipulate some parts of the instructional materials, such as pointing to or gesturing at parts of what they are solving or reading (Alibali & DiRusso, 1999), pausing and rewinding parts of a videotape (Chi et al., 2008), underlining certain text sentences (Katayama et al., 2005), copying some problem solution steps (VanLehn et al., 2007), mixing certain chemical amounts in a hands-on laboratory (Yaron et al., 2010), choosing a justification from a menu of options (Conati & VanLehn, 2000), and bookmarking a page in a textbook. The visible outputs produced from manipulating can be identified as information already existing within the content materials.

Constructive learning involves individually creating new information.

ICAP defines the Constructive mode as having the characteristics of physically generating some external outputs and the external outputs contain additional information that goes beyond what was provided in the learning materials.

Examples include drawing a concept map (Biswas et al., 2005; Novak, 1990a; 1990b); taking notes in one’s own words (Trafton & Trickett, 2001); asking questions (Graesser & Person, 1994); comparing-and-contrasting cases; integrating two texts (Britt & Sommer, 2004), text and diagrams (Butcher, 2006), or across multimedia resources (Bodemer et al., 2004); inducing hypotheses and causal relations (Suthers & Hundhausen, 2003); drawing analogies (Chinn & Malhotra, 2002); generating predictions (Schauble et al., 1995); reflecting and monitoring one’s understanding or other self-regulatory activities (Azevedo et al., 2006); constructing timelines for historical phenomena (Dawson, 2004), and self-explaining (Chi et al., 1994).

Interactive learning expands on the idea of collaborative (or group) learning to include the stipulation that participants are collaboratively generating new information. Examples might include debates, collaborative projects, peer tutoring, critiquing/peer reviewing (and social annotation).

We define the Interactive mode as collaborations/interactions between students (such as through dialoging) and with instruction that meet two criterial indices: (1) two (or more) students are engaging in reciprocally co-generative behaviors; and (2) the outputs contain information that goes beyond the instructional materials and beyond what each partner contributes individually.

The Interactive mode requires a significant degree of turn-taking so that it allows each partner to incorporate her partner’s understanding of the domain into her own thinking and to make more frequent adjustments to her own mental model (Chi, 2000).

Lecture vs. Active and Collaborative Learning

Approximately 80% of STEM courses predominantly involve lecture. The median instructor lectures 89% of the time. Students are 1.5 times more likely to fail in lecture courses compared to active learning courses. Incorporating active learning into a course not only increases student success and achievement overall, but also decreases equity gaps. See more about active and collaborative learning techniques in a later section on evidence-based teaching strategies.

Rote Learning vs. Deep Learning and Understanding

Rote learning, also known as surface learning, involves lower-level cognitive activities such as memorizing or recalling information, including studying for or taking a standard test or exam. Approximately 85% of test questions in math involve rote learning and recall and memorization (“plug and chug”) and 92% of test questions in biology. An issue is that students tend to quickly forget much of the information they memorized not long after a test or lesson is over. At the end of an introductory psychology course, for example, students were asked what they remembered from the course, and they only recalled vivid instructional techniques like demonstrations and dramatic videos. Two years after an introductory psychology course was over, student performance on the final exam dropped 60%. See the assessment and evidence-based teaching sections below for alternative ideas for enhancing assessment and student learning and understanding.

This video clip from “Minds of Our Own” shows how even Harvard and MIT engineering grads on the day of graduation are often unable to make a bulb light, given a bulb, battery, and wire.

Rote learning is contrasted with higher-order thinking, deep learning, and understanding activities such as interpreting information, comparing and contrasting, debating, creating, and designing. Bloom’s taxonomy (figure below) shows these different types of learning in a pyramid diagram, but it is a misconception that this implies that students need to memorize information and facts before they can tackle higher order thinking and practices. In fact, according to research on productive failure and other areas, students actually learn better in the reverse situation. Give students problems or challenges or data for them to explore and potentially struggle with first, and afterward when they hear a lecture or see other information explaining or clarifying things, they’ll better understand and learn from the lecture, since they have common experiences and unanswered questions to connect the information to. Otherwise, if students hear a lecture or read text before some kind of motivating context or grounding activity, they’ll be less likely to learn, remember, or understand it, as they have no reason for learning it (other than memorizing for a test), and they will quickly forget much of what they learned soon after.

Transfer of Learning

Two related deep learning goals of instruction are to help students gain knowledge and skills that will A) transfer to the real world and their careers, and B) at least in the short term transfer their learning to future courses for which their current course is a prerequisite. Unfortunately, both goals are often not achieved, and they are indeed difficult to achieve, especially when only utilizing traditional teaching and assessment techniques. Chapter 3 of How People Learn goes into more depth with examples of transfer and learning, and later sections here on motivation and evidence-based teaching share strategies for contextualizing instruction for better transfer of learning, student engagement, and learning.

What students learn abstractly in a math course, for example, often does not transfer to a physics course the very next or even same semester, as well as future engineering or other courses.

Nurses report that their core anatomy and physiology course does not align with the skills and knowledge they need in the profession:

We investigated the relevance of prerequisite course content for students’ careers through semistructured interviews with practicing nurses regarding their undergraduate anatomy and physiology (A&P) courses. Nurses reported that A&P content does not align with the skills and knowledge needed in the nursing profession. Interviewees averaged 39% on a brief A&P assessment, suggesting A&P prerequisites failed to impart a high degree of long-term A&P knowledge among nurses. Further, practicing nurses perceived overcommitment to A&P content coverage as an exclusionary practice that eliminates capable individuals from the prenursing pathway.

Another issue related to transfer of learning is that you cannot teach some fundamental skill or literacy (like writing, math, communication, information literacy) in a single course or lesson and then expect students to have mastered it or be able to transfer what they may have learned to other contexts. Instead, these fundamental skills and literacies need to be reinforced throughout the curriculum, as exemplified in the Writing Across the Curriculum (WAC) approach and also in various interdisciplinary education initiatives. Other examples include incorporating discipline-specific, real world examples and cases in math and science courses or incorporating computational thinking or information literacy skills in humanities and social sciences and other courses.

Another common issue hurting learning and transfer is explored further in the course design section. The issue has been referred to as the “sin of coverage” – courses overly focus on content coverage vs. learning and understanding.

Traditional education focuses on teaching, not learning. It incorrectly assumes that for every ounce of teaching there is an ounce of learning by those who are taught.

A course design model known as backward design provides guidance on how to start from the goal of helping students understand big ideas and big questions and working backward from that to assessment and instructional activities.

Another set of techniques to help students transfer their knowledge and skills to other contexts is explored further in the metacognition section.

More resources on transfer of learning

• 6 Ways to Help Students Transfer Learning to New Contexts
• Can They Do It in the Real World? Designing for Transfer of Learning
• Retention And Transfer Of Learning From Math To Physics To Engineering
• Content Coverage as a Persistent Exclusionary Practice: Investigating Perspectives of Health Professionals on the Influence of Undergraduate Coursework
• Deliberate Erring Improves Far Transfer of Learning More Than Errorless Elaboration and Spotting and Correcting Others’ Errors
• Learning and transfer effects of embodied simulations targeting crosscutting concepts in science

Metacognition, Reflection, and Self-Regulated Learning

Other frameworks and learning theories offer strategies for improving student success and equity and well-being, including metacognition, reflection, and self-regulated learning.

Metacognition involves thinking about thinking, or reflecting on your learning. This might be facilitated by incorporating metacognitive prompts in homework or in other assessment activities that encourage students to reflect on their learning and what they know and are doing. See for example minute papers and exam wrappers. Having students write in journals or learning diaries through eportfolios, blogs, or assignments is another technique to foster metacognitive development through reflection. Teach Students How to Learn is a book by Saundra McGuire with strategies for helping students become more metacognitive, self-directed learners. See the supporting resources section for PowerPoints used for a lecture she gives to students about using metacognition to help students ace exams.

Self-regulated learning is a framework for teaching students how to be more metacognitive and adaptive in their learning approaches. Self-regulated learning is correlated with student achievement (Fong et al., 2023), and it is also a trainable skill, not a fixed or innate trait of students. In addition to several strategies detailed in the resources below, an introductory college success course that focuses on research-based strategies for teaching students how learning works and how to “learn-to-learn” can have tremendous positive effects on student success. One course improved graduation rates by 45%, and another course improved graduation rates by 30%.

Students in academic difficulty who took the “Learning and Motivation Strategies” course in their first quarter at Ohio State were about 45 percent more likely to graduate within six years than similar students who didn’t take the class. Average-ability students who took the course were also six times more likely to stay in college for a second year and had higher grade point averages than those who didn’t take the class.

More resources on self-regulated learning

• How to Guide Students to Self-Regulated Learning
• Academic Self-Regulation Interventions Can Promote Success for All
• Teaching students how to learn: Setting the stage for lifelong learning
• Self-Directed Learning Skills: Strategies to Support Student Learning in Online STEM Courses
• The effects of self-regulated learning training on community college students’ metacognition and achievement in developmental math courses
• Can a Brief, Digital Skill Training Intervention Help Undergraduates “Learn to Learn” and Improve Their STEM Achievement?
• Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis
• How Should I Study for the Exam? Self-Regulated Learning Strategies and Achievement in Introductory Biology
• A self-regulated learning analytics prediction-and-intervention design: Detecting and supporting struggling biology students
• The Self-Regulation for Learning Online (SRL-O) questionnaire and article
• Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review
• Beneficial for some or for everyone? Exploring the effects of an autonomy-supportive intervention in the real-life classroom
• Examining the relationships among self-regulated learning, homework timeliness, and course achievement
• The promotion of self-regulated learning in the classroom: a theoretical framework and an observation study
• Improving self-regulated learning and academic engagement: Evaluating a college learning to learn course
• Teaching Learning Strategies to Increase Success of First-Term College Students
• Academic underachievement and its motivational and self-regulated learning correlates: A meta-analytic review of 80 years of research

Faculty Mindset and Active Learning

Faculty beliefs about the nature of intelligence – whether it is fixed and innate or malleable – influence the rate at which they adopt active learning practices, and thus influence student success and equity in their courses. See a later section on Beliefs about Teaching and Learning for more resources on fostering a growth mindset in yourself and your students.

• Faculty Beliefs about Intelligence Are Related to the Adoption of Active-Learning Practices
• STEM faculty who believe ability is fixed have larger racial achievement gaps and inspire less student motivation in their classes

Barriers and Drivers to Adopting Active Learning

A recent study identified 18 barriers and 15 drivers for faculty adoption of active learning and other evidence-based teaching techniques. See the later sections on evidence-based teaching and student resistance for more information.

Another study identified factors such as class size, classroom setup, and teaching evaluations.

More Readings and Resources on Learning

• Applying the ICAP Framework to Improve Classroom Learning
• What really impacts the use of active learning in undergraduate STEM education?
• Free ebooks
  • How People Learn and How People Learn II
  • Reaching Students: What Research Says About Effective Instruction in Undergraduate Science and Engineering
  • 100 Ideas for Active Learning