Category: Research

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Designing Effective Online Professional Development for CS Teachers

We just published results from a 2-year study investigating how teachers teaching the AP CS Principles course for the first time used our online PD materials, PD4CS. Our results showed that the teaching and computing background of teachers had a significant impact on the teachers’ need for and use of online PD material. More specifically, novice CS teachers needed and used PD for developing their pedagogical content knowledge (PCK). Non-CS teachers needed and used PD materials emphasizing content knowledge. Experienced CS teachers believed they had little need for PD even though they were teaching a new course. Our study makes three recommendations for designing effective online PD for CS teachers: match PD to teachers’ background, align PD with the course curriculum, and use effective motivational design to enhance teacher engagement. You can find the full paper here

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Computer Science & Computational Thinking: Research and Practice

We have a new chapter in the Handbook of Information Technology in Primary and Secondary Education, in which we discuss how computer science efforts to increase the role of computing in schools gives us a unique opportunity to expand computing education research. Specifically, we have laid out directions for future research around computer science teacher development and factors that influence student learning in computer science. However, we argue that computer education researchers needs to move beyond experimental design as the standard for methodological rigor and value other theoretical perspectives and approaches. Here is a link to the chapter

Reference: Yadav, A., Sands, P., Good, J., & Lishinski, A. (2018). Computer science and computational thinking in the curriculum: Research and practice. In. J. Voogt, G. Knezek, R Christensen, & K-W Lai (Eds.). Handbook of Information Technology in Primary and Secondary Education. Springer International Handbooks of Education. Springer, Cham. DOI: 10.1007/978-3-319-53803-7_6-1

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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)

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Preservice teachers’ intention to teach media & information literacy

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.

 

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Walking the line between reality and fiction in online spaces

We have a publication in the Journal of Media Literacy Education to shed light on the critical need for media literacy given the recent focus on “fake news” and misinformation online. As digital stories through blogs, videos, or social networks have become the main form of communication, we argue that the line between facts and fiction can often become blurry. As a results students might find it difficult to distinguish between reality and fantasy in online spaces, which can have important consequences in their lives. Using contemporary examples from news stories, fanfiction, advertising, and radicalization, we outline the features, affordances, and real-life implications of digital stories. As a result, we provide recommendations for educators to create awareness and empower students about digital storytelling practices. Read more..

 

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Challenges of teaching computer science

In a study published in Computer Science Education, we examined teachers’ perspectives about teaching computer science, challenges they face in the classroom, and areas they believe are needed for them to be successful in the classroom. Teachers in our study highlighted a number of challenges beginning computer science teachers face and discussed professional development needs they feel would have helped them adapt to the classroom. Teachers reported that during the rst few years they them- selves did not either have adequate content knowledge or pedagogical knowledge to teach computer science. Teachers with a formal background in teaching often didn’t have the CS content knowledge needed to teach CS. On the other hand, teachers with industry experi- ence in programming did not have any teaching background to e ectively deliver CS lessons. Read more..

 

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Computer Science education reserach @ICER

Alex Lishinski, Jon Good, and I presented two papers at International Computing Educational Research Conference in Melbourne, Australia.

In the first paper “Learning to Program: Gender Differences and Interactive Effects of Students’ Motivation, Goals and Self-Efficacy on Performance“, Alex will present results from an empirical on examining CS1 students along dimensions of self-regulated learning, and exploring the relationships between these dimensions and course outcomes. 346 students in an introductory programming course completed measures to assess their self-efficacy, metacognitive self-regulation, intrinsic goal orientation, extrinsic goal orientation, performance on code writing assignments, and performance on multiple choice exams. A path analysis model was created, and the results of the model showed that the best predictor of student exam and project performance in CS1 was self-efficacy. Furthermore, the results of the study exhibited evidence for a reciprocal self-efficacy “feedback loop” in which student self-efficacy beliefs influence their course performance which later influences their self-efficacy beliefs which influence later performance. A correlation analysis also demonstrated interesting results by gender. While both genders experienced the self-efficacy feedback loop process, in which self-efficacy beliefs become more accurate, this process appears to happen more quickly with female students than with male students. This suggests that female students internalize performance feedback more quickly than do male students. These results are interesting because they show the importance of self-efficacy in the learning process and that self-efficacy is not a static student characteristic, but one that is continually changing, and in different ways for different groups of students.

In the second paper “Methodological Rigor and Theoretical Foundations of CS Education Research“, Jon will present a literature review of recent CS Education publications. The paper details the results of a literature review of papers from the journal Computer Science Education and the proceedings of the ICER conference from the years 2012-2015. The review focuses on two main elements of the research presented in these papers. First, papers were examined for the extent to which they made use of theoretical frameworks from outside of computer science education, in particular, from education, psychology, and other social sciences. Second, the papers were examined for the rigor of methodological approaches using a number of indicators. The study found that, compared to previous research, recent publications in computer science education are making greater use of theory from outside the CS ED field, but the particular methodological approaches being used are no more rigorous than in results from a decade earlier.

 

 

 

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NSF project to support engineering students’ design thinking

My colleague Ashlie Martini and I have received funding from National Science Foundation to use case studies to introduce design thinking to first-generation and underrepresented students to help them transition from classroom and lab-based learning to engineering capstone design and ultimately to their careers in engineering fields. Through this project, we will (a) develop a library of case studies that use the designing thinking to expand the engineering students’ view of problem formulation, and solution development, and (b) evaluate the effectiveness of case-based instruction in developing design thinking behavior for a diverse student population. Read more about the NSF award here at http://1.usa.gov/1J8seLC

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View from the ground: Student perspective on cases

We used qualitative interviews for an in-depth examination of student perceptions on the use of cases in a mechanical engineering course. Specifically, we examined what aspects of case studies students found to be beneficial and what aspects they found to be challenging.  The interviews produced a rich set of qualitative data, which suggested that students found cases to be beneficial with regards to allowing them to see real world application of course concepts. Students also reported some challenging aspects of learning from cases, such as frustrations with the ill-structured nature of cases and the inefficient use of class time when using cases. Read More..

 

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