Initial Publication Date: March 16, 2017

Building Self-Efficacy

Content on this page is derived from participant presentations, discussions, and breakout groups at the 2016 Teaching Computation in the Sciences Using MATLAB workshop as well as The Affective Domain in the Classroom from On the Cutting Edge.

Self-efficacy is an individual's belief their own capacity to achieve a goal or outcome. Self-efficacious students have higher motivation, are more likely to challenge themselves, and are more likely to achieve their goals. Conversely, students who have low self-efficacy are less motivated, less likely to rise to challenges, and have low aspirations, which can negatively impact their academic performance.

Building students' self-efficacy with computation can help them more deeply understand the foundations of their scientific discipline and enhance their general STEM skills such as testing hypotheses, evaluating assumptions, making predictions, breaking problems into computable parts, understanding physical systems, developing models, processing data, and making visualizations. These skills are transferable between courses and disciplines and make students highly marketable for future STEM jobs. However, many students in STEM do not have a strong background in computation, or the important mathematics and programming foundation.

In order to improve these skills, students may need to develop comfort with computation and motivation to do the hard work. Faculty can build student self-efficacy using approaches such as: easing students into using MATLAB by being available for hands-on support, making the motivation for the assignment clear, and limiting the number of learning objectives.

Strategies for Building Self-Efficacy

Self-efficacy is built through successful experiences. Faculty can increase the likelihood of successful learning experiences and foster student self-efficacy by scaffolding assignments, using collaborative learning, and giving effective feedback. Adapted from Fencl & Scheel (2005) and Margolis & McCabe (2006).

Use focused, short-term, and achievable learning goals

Goals that are too difficult will discourage students while goals that are too easy may result in boredom or embarassment

Keep tasks relevant and interesting

Tying tasks to student interests or valuable skills can increase student motivation

Incorporate collaborative learning and assignments

Collaborative problem-solving, reciprocal teaching, and group discussion can contribute to student learning and confidence

Scaffold assignments to provide early support

Providing students with structure and context early on increases efficiency, clarifies purpose, and enhances individual responsibility

Give frequent and useful feedback

Avoid comparing students and instead give individualized feedback

Developing Computational Self-Efficacy with MATLAB

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Wendy Thomas (University of Washington) presented her keys to building comfort with MATLAB and the scientific computing skills that students may need to develop throughout their undergraduate program. Her tips, summarized below, align with best-practices that foster self-efficacy.

Download the presentation (PowerPoint 2007 (.pptx) 168kB Oct25 16)

Keys to Building Self-Efficacy with MATLAB

Provide software support:

Offer hands-on support in front of the computer (e.g. lab, office hours with laptops)
Use peer tutoring/workshops to help students
Take advantage of existing resources for getting started such as the MATLAB Onramp
Essay: Helping Students Tinker with the Black Box by Benjamin Bratton (Princeton University)

Build student motivation:

Illustrate how tasks can be easier to perform in MATLAB that in common alternatives (e.g. calculator, Excel), even at this stage of experience
Demonstrate how tasks are related to something of value such as course content or common experiences
Essay: The Dynamical Nature of Nature by Sean Bartz (Macalester College)
Essay: Teaching and Integrating Computation in Graduate Research by Michael Cardiff (University of Wisconsin-Madison)
Activity: Using MATLAB to understand distributions: Pokémon GO by Benjamin Bratton (Princeton University)

Use focused learning objectives:

Identify a limited set of computing concepts and MATLAB tools that are easy to learn at this stage of experience
Don't let students spend hours on something unrelated to the objectives; jump start their work by providing needed resources such as:

pseudo-code activity to help design an algorithm
sample commented code for a related problem
tutorial-like part 1 followed by an independent part 2

Activity: Signal processing and earthquake triggering by Jackie Caplan-Auerbach (Western Washington University)

Provide opportunities to practice through low stakes assignments:

Short, focused activities with limited or no grade impact provide students with low stress opportunities to practice and provide instructors with a chance to use formative assessment.
Activity: Introduction to Matlab: A First Glimpse at New Data by Carlye Peterson (University of California-Davis)

Resources for Developing Students' Self-Efficacy

References

Fencl, H., & Scheel, K. (2005). Engaging students. Journal of College Science Teaching, 35(1), 20.
Margolis, H., & McCabe, P. P. (2006). Improving self-efficacy and motivation: What to do, what to say. Intervention in School and Clinic, 41(4), 218-227.