Analysis of Student Self-Efficacy During an Integrated, Life-Centered Robotics Learning Experience

Principles	Description
Integration	Combines content from science, technology, engineering, and mathematics
Problem-centered learning	Focuses on using real-world problems to create the context for concept application
Inquiry-based learning	Questioning, discussing, interpreting, using, and attempting to understand the content
Design-based learning	Using engineering or technological design
Cooperative learning	Centers around teamwork and collaboration with others in small group

[i] *key principles for integrated STEM education, Üçgül & Altıok, 2021; Thibaut et al., 2018

Engineering Skills Self-Efficacy Instrument: Constructs Measured

Construct	Questions on the Instrument
General Self-Efficacy	I can master the content in the engineering-related courses I am taking this semester. I can master the content in even the most challenging engineering course. I can do a good job on almost all of my engineering coursework. I can learn the content taught in my engineering-related courses. I can earn a good grade in my engineering-related courses.
Experimental Self-Efficacy	I can perform experiments independently. I can analyze data resulting from experiments. I can orally communicate results of experiments. I can communicate results of experiments in written form.
Design Self-Efficacy	I can identify a design need. I can develop a design solution. I can evaluate a design. I can recognize changes needed for a design solution to work.
Tinkering Self-Efficacy	I can work with machines. I can build machines. I can assemble things. I can disassemble things.

[i] Engineering Skills Self-Efficacy Instrument (Jackson, 2018; Mamaril et al., 2016).

Engineering Self-Efficacy Evaluation Mean Scores

Engineering Self-Efficacy Constructs	Pre (SD)	Post (SD)	Gain
General Self-Efficacy	3.45 (1.27)	4.24 (1.01)	0.79
Experimental Self-Efficacy	3.88 (1.54)	5.32 (1.71)	1.44
Design Self-Efficacy	2.45 (1.16)	3.36 (0.99)	0.91
Tinkering Self-Efficacy	3.79 (1.35)	4.54 (1.23)	0.75

[i] Note. n = 30; *p < .05, two-tailed, paired; ^†Effect Size (Cohen’s d)

Pretest/Posttest Data Analysis Results

Engineering Self-Efficacy Constructs	Self-Efficacy Gain Score
Engineering Self-Efficacy Constructs	M	SD	t	p	^†ES
General	0.79	1.33	3.26	0.003*	1.33
Experimental	1.08	1.21	4.90	≤ 0.001*	1.22
Design	1.13	1.53	4.04	≤ 0.001*	1.53
Tinkering	0.75	1.72	2.39	0.024*	1.72

[i] Note. n = 30; *p < .05, two-tailed, paired; ^†Effect Size (Cohen’s d)

Kruskal-Wallis analysis for male and nonmale participants

Engineering Self-Efficacy Constructs	Group	Pretest (SD)	Posttest (SD)	Self-Efficacy Gain Score
Engineering Self-Efficacy Constructs	Group	Pretest (SD)	Posttest (SD)	M Rank	H	p
General	Male Nonmale	3.89 (1.39) 3.19 (1.15)	4.18 (1.04) 4.27 (1.02)	12.09 17.47	2.68	0.101
Experimental	Male Nonmale	4.06 (1.93) 3.77 (1.30)	5.48 (1.91) 5.23 (1.64)	15.32 15.61	0.008	0.930
Design	Male Nonmale	2.73 (1.15) 2.29 (1.16)	3.26 (0.87) 3.43 (1.07)	13.36 16.74	1.08	0.299
Tinkering	Male Nonmale	4.48 (0.97) 3.39 (1.39)	4.50 (1.10) 4.57 (1.33)	12.55 17.21	2.02	0.156

Kruskal-Wallis analysis for white and nonwhite participants

Engineering Self-Efficacy Constructs	Group	Pretest (SD)	Posttest (SD)	Self-Efficacy Gain Score
Engineering Self-Efficacy Constructs	Group	Pretest (SD)	Posttest (SD)	M Rank	H	p
General	Male Nonmale	3.45 (1.02) 3.44 (1.73)	4.31 (0.97) 4.10 (1.13)	15.88 14.75	0.112	0.738
Experimental	Male Nonmale	4.27 (1.14) 3.10 (1.96)	5.60 (1.43) 4.77 (2.15)	15.30 15.90	0.032	0.857
Design	Male Nonmale	2.63 (1.05) 2.10 (1.34)	3.45 (0.83) 3.18 (1.28)	15.63 15.25	0.013	0.910
Tinkering	Male Nonmale	3.83 (1.01) 3.73 (1.91)	4.81 (0.84) 4.00 (1.70)	16.95 12.60	1.68	0.195