Computer Science and Engineering Education Research
Computer Science and Engineering Education Research

Factors that influence learning to program

Cognitive, Affective, and Dispositional Components of Learning to Program

Alex Lishinski Dissertation 

Given the national importance being placed on computing education, such as the CSforAll initiative to expand computer science (CS) education to K-12 students, there is a need to better understand how students come to learn CS concepts. Prior research has suggested that successfully teaching programming is a difficult endeavor (Jenkins, 2002; Sleeman, 1986), as only about two thirds of introductory programming students at the undergraduate level pass their course (Bennedsen and Caspersen, 2007; Watson and Li, 2014). The existing research investigating how students learn to program can be divided into two strands: research that focuses on task demands, and research that focuses on student factors. The research on task demands has focused on what it is about the material that makes programming difficult to learn (e.g. Lopez et al., 2008; Rivers et al., 2016; Robins, 2010). The research on student factors has examined how factors like students’ background, demographics, academic aptitude, cognitive skills, and social and emotional factors influence learning outcomes in programming courses (Bergin and Reilly, 2005). Many individual factors have been examined repeatedly, such as previous programming experience, math ability, and spatial reasoning, whereas factors like personality traits and metacognitive self-regulation have been seldom examined. However, there is much that remains unknown about how various student factors influence learning to program.

Alex’s dissertation examined a large number student factors to determine how they influence success in introductory programming courses across the categories of cognitive, affective, and dispositional factors. The study examined four research questions in order to better understand the influence of student factors on outcomes in introductory programming. 1) How do different types of student factors (such as problem solving ability, and self-efficacy) interact and influence student outcomes? 2) Which of these different factors are the most predictive of student outcomes? 3) How do students’ emotional responses to programming projects influence their outcomes over time? 4) What is the impact of a self-evaluation intervention on students’ self-efficacy and outcomes?

The first question was answered using structural equation modeling, and the results of this analysis showed that the biggest predictor of students’ outcomes, looking at both programming project and exam scores, was problem solving ability. Furthermore, conscientiousness was found to have a significant impact on programming project scores. The second question was answered using multiple regression to show that indeed, all things considered, problem solving ability is the best predictor of student outcomes in programming. The third question was answered using structural equation modeling to examine the relationship between four discrete types of emotional reactions and programming project scores, finding that students’ confidence that they would do well had significant reciprocal effects on project scores over time. The fourth question examined the influence of a self-evaluation task on students’ self-efficacy and project scores, finding that the students’ in the self-evaluation group had significantly higher project scores during the intervention, controlling for prior project scores and self-efficacy.

Alex’s dissertation has important implications for CS education, particularly towards future research on the student factors that influence success, which may be key to addressing the CS participation gaps. Alex is currently working as a Researcher for the American Institutes for Research (AIR), and pursuing funding to continue his work on factors that influence success in programming.

Michigan State University College of Education Ph.D. Hooding Ceremony (From left: Richard Prawat, Dr! Alex Lishinski, Aman Yadav – Proud Advisor)