Month: January 2018

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)

Preservice teachers’ intention to teach media & information literacy in their future classroom: An application of theory of planned behavior.

Sarah Gretter Dissertation

Today’s students are exposed to unfiltered media messages on a daily basis. International organizations such as UNESCO, and educational reforms within the United States like the Next Generation Science Standards, are increasingly placing emphasis on enhancing students’ critical thinking abilities by making evaluative judgments of mediatized information. As a consequence, educators need to embed Media & Information Literacy (MIL) skills in their classrooms to teach students how to assess the factual and social pertinence of digital information in their everyday lives. Yet, there are no existing policies or regulations to ensure basic MIL education in U.S. teacher education programs (Tiede et al., 2015).

Sarah’s dissertation asked: How can we support the implementation of MIL in K-12 education by understanding what factors play a role in preservice teachers’ intention to teach MIL in their classroom? One way to address this issue is through the Theory of Planned Behavior (TPB), a framework that explains individuals’ intention to perform a specific behavior based on three factors: attitudes (i.e., whether the person is in favor of doing it), subjective norms (i.e., how much social pressure the person feels to do it), and perceived behavioral control (i.e., whether the person feels in control of the behavior in question) (Ajzen, 1991). As such, he dissertation was a multi-phase study looking at preservice teachers’ intention to teach Media & Information Literacy in their future classroom. Each of the three studies answered a specific question: 1) What do preservice teachers think about teaching MIL? 2) What predicts preservice teachers’ intention to teach MIL? and 3) How can we support preservice teachers’ intention to teach MIL?

The first paper in her dissertation reported on an elicitation study conducted with focus groups of preservice teachers to understand the factors that would either impede or facilitate the teaching of MIL in their future classroom. The elicited list of factors provided the basis for designing a TPB survey. The second paper described the design, validation, and results of a survey based on these factors. The results showed that preservice teachers had a high intention to teach MIL in their future classroom, as were their attitudes–a significant predictor of their intention to teach MIL–towards MIL. However, the results also showed that their subjective norms and perceived behavioral control were lower. These results thus provided a basis to design an MIL module that would help support their positive intention. The third paper reported on this online module through reflective exercises designed around the results gathered in the aforementioned survey, and results showed that the majority of participants found a positive effect of the module on their intention to teach MIL in their future classroom.

Sarah’s dissertation has important implications for educational research, practice, and policy. She is continuing her work on MIL in her current position as senior learning designer at Michigan State University Hub for Innovation in Learning and Technology. For more information or to contact her, follow her Twitter handle @SarahGretter, or visit her website.

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

 

References

Ajzen, I. (1991). Theory of planned behavior. Organizational behavior and human decision processes, 50, 179-211.

Tiede, J., Grafe, S., & Hobbs, R. (2015). Pedagogical Media Competencies of Preservice Teachers in Germany and the United States: A Comparative Analysis of Theory and Practice. Peabody Journal of Education, 90(4), 533-545.