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1. WIP: How do students respond to active learning? A coding guide for a systematic review of the literature

   Crockett, Caroline ; Nguyen, Kevin A. ; Shekhar, Prateek ; DeMonbrun, R. Matthew ; Tharayil, Sneha ; Rosenberg, Robyn ; Waters, Cindy K. ; Borrego, Maura ; Finelli, Cynthia J. ( June 2018 , Proceedings of 2018 ASEE Annual Conference & Exposition)

   This work in progress paper presents an example of conducting a systematic literature review (SLR) to understand students’ affective response to active learning practices, and it focuses on the development and testing of a coding form for analyzing the literature. Specifically, the full paper seeks to answer: (1) what affective responses do instructors measure, (2) what evidence is used to study those responses, and (3) how are course features connected with student response. We conducted database searches with carefully-defined search queries which resulted in 2,365 abstracts from 1990 to 2015. Each abstract was screened by two researchers based on meeting inclusion criteria, with an adjudication round in the case of disagreement. We used RefWorks, an online citation management program, to track abstracts during this process. We identified over 480 abstracts which satisfied our criteria. Following abstract screening, we developed and tested a manuscript coding guide to capture the salient characteristics of each paper. We created an initial coding form by determining what paper topics would address our research questions and reviewing the literature to determine the most frequent response categories. We then piloted and tested the reliability of the form over three rounds of independent pair-coding, with each round resultingmore »in clarifications to the form and mutual agreement on terms’ meanings. This process of developing a manuscript coding guide demonstrates how to use free online tools, such as Google Forms and Google Sheets, to inexpensively manage a large SLR team with significant turnover. Currently, we are in the process of applying the coding guide to the full texts. When complete, the resulting data will be synthesized by creating and testing relationships between variables, using each primary source as a case study to support or refute the hypothesized relationship.« less
2. Mitigating Ceiling Effects in a Longitudinal Study of Doctoral Engineering Student Stress and Persistence

   https://doi.org/10.28945/5118  

   Bahnson, Matthew ; Sallai, Gabriella ; Jwa, Kyeonghun ; Berdanier, Catherine ( January 2023 , International Journal of Doctoral Studies)

   Aim/Purpose: The research reported here aims to demonstrate a method by which novel applications of qualitative data in quantitative research can resolve ceiling effect tensions for educational and psychological research.Background: Self-report surveys and scales are essential to graduate education and social science research. Ceiling effects reflect the clustering of responses at the highest response categories resulting in non-linearity, a lack of variability which inhibits and distorts statistical analyses. Ceiling effects in stress reported by students can negatively impact the accuracy and utility of the resulting data.Methodology: A longitudinal sample example from graduate engineering students’ stress, open-ended critical events, and their early departure from doctoral study considerations demonstrate the utility and improved accuracy of adjusted stress measures to include open-ended critical event responses. Descriptive statistics are used to describe the ceiling effects in stress data and adjusted stress data. The longitudinal stress ratings were used to predict departure considerations in multilevel modeling ANCOVA analyses and demonstrate improved model predictiveness.Contribution: Combining qualitative data from open-ended responses with quantitative survey responses provides an opportunity to reduce ceiling effects and improve model performance in predicting graduate student persistence. Here, we present a method for adjusting stress scale responses by incorporating coded critical events basedmore »on the Taxonomy of Life Events, the application of this method in the analysis of stress responses in a longitudinal data set, and potential applications.Findings: The resulting process more effectively represents the doctoral student experience within statistical analyses. Stress and major life events significantly impact engineering doctoral students’ departure considerations.Recommendations for Practitioners: Graduate educators should be aware of students’ life events and assist students in managing graduate school expectations while maintaining progress toward their degree. Recommendation for Researchers: Integrating coded open-ended qualitative data into statistical models can increase the accuracy and representation of the lived student experience. The new approach improves the accuracy and presentation of students’ lived experiences by incorporating qualitative data into longitudinal analyses. The improvement assists researchers in correcting data with ceiling effects for use in longitudinal analyses.Impact on Society: The method described here provides a framework to systematically include open-ended qualitative data in which ceiling effects are present.Future Research: Future research should validate the coding process in similar samples and in samples of doctoral students in different fields and master’s students.« less
3. Cogenerative Dialogues and Development of the Culturally Relevant Pedagogical Guidelines for Computational Thinking and Computer Science.

   Sirrakos, G. ( January 2023 , American Association for the Advancement of Curriculum Studies Annual Meeting)

   In this proposal, we will share some initial findings about how teacher and student engagement in cogenerative dialogues influenced the development of the Culturally Relevant Pedagogical Guidelines for Computational Thinking and Computer Science (CRPG-CSCT). The CRPG-CSCT’s purpose is to provide computer science teachers with tools to enhance their instruction by accurately reflecting students’ diverse cultural resources in the classroom. Additionally, the CRPG-CSCT will provide guidance to non-computer science teachers on how to facilitate the integration of computational thinking skills to a broad spectrum of classes in the arts, humanities, sciences, social sciences, and mathematics. Our initial findings shared here are part of a larger NSF-funded research project (Award No. 2122367) which aims to better understand the barriers to entry and challenges for success faced by underrepresented secondary school students in computer science, through direct engagement with the students themselves. Throughout the 2022-23 academic year, the researchers have been working with a small team of secondary school teachers, students, and instructional designers, as well as university faculty in computer science, secondary education, and sociology to develop the CRPG-CSCT. The CRPG-CSCT is rooted in the tenets of culturally relevant pedagogy (Ladson-Billings, 1995) and borrows from Muhammad’s (2020) work in Cultivating Genius: Anmore »Equity Framework for Culturally and Historically Responsive Literacy. The CRPG-CCT is being developed over six day-long workshops held throughout the academic year. At the time of this submission, five of the six workshops had been completed. Each workshop utilized cogenerative dialogues (cogens) as the primary tool for organizing and sustaining participants’ engagement. Through cogens, participants more deeply learn about students’ cultural capital and the value of utilizing that capital within the classroom (Roth, Lawless, & Tobin, 2000). The success of cogens relies on following specific protocols (Emdin, 2016), such as listening attentively, ensuring there are equal opportunities for all participants to share, and affirming the experiences of other participants. The goal of a cogen is to reach a collective decision, based on the dialogue, that will positively impact students by explicitly addressing barriers to their engagement in the classroom. During each workshop, one member of the research team and one undergraduate research assistant observed the interactions among cogen participants and documented these in the form of ethnographic field notes. Another undergraduate research assistant took detailed notes during the workshop to record the content of small and large group discussions, presentations, and questions/responses throughout the workshops. A grounded theory approach was used to analyze the field notes. Additionally, at the conclusion of each workshop, participants completed a Cogen Feedback Survey (CFS) to gather additional information. The CFS were analyzed through open thematic coding, memos, and code frequencies. Our preliminary results demonstrate high levels of engagement from teacher and student participants during the workshops. Students identified that the cogen structure allowed them to participate comfortably, openly, and honestly. Further, students described feeling valued and heard. Students’ ideas and experiences were frequently affirmed, which served as an important step toward dismantling traditional teacher-student boundaries that might otherwise prevent them from sharing freely. Another result from the use of cogens was the shared experience of participants comprehending views from the other group’s perspective in the classroom. Students appreciated the opportunity to learn from teachers about their struggles in keeping students engaged. Teachers appreciated the opportunity to better understand students’ schooling experiences and how these may affirm or deny aspects of their identity. Finally, all participants shared meaningful suggestions and strategies for future workshops and for the collective betterment of the group. Initial findings shared here are important for several reasons. First, our findings suggest that cogens are an effective approach for fostering participants’ commitment to creating the conditions for students’ success in the classroom. Within the context of the workshops, cogens provided teachers, students, and faculty with opportunities to engage in authentic conversations for addressing the recruitment and retention problems in computer science for underrepresented students. These conversations often resulted in the development of tangible pedagogical approaches, examples, metaphors, and other strategies to directly address the recruitment and retention of underrepresented students in computer science. Finally, while we are still developing the CRPG-CSCT, cogens provided us with the opportunity to ensure the voices of teachers and students are well represented in and central to the document.« less
4. Comparing students’ solutions to an open-ended problem in an introductory programming course with and without explicit modeling interventions

   https://doi.org/10.18260/1-2--34311  

   Rodgers, Kelsey J. ; Verleger, Matthew A. ; Marbouti, Farshid ( June 2020 , ASEE Annual Conference proceedings)

   Engineers must understand how to build, apply, and adapt various types of models in order to be successful. Throughout undergraduate engineering education, modeling is fundamental for many core concepts, though it is rarely explicitly taught. There are many benefits to explicitly teaching modeling, particularly in the first years of an engineering program. The research questions that drove this study are: (1) How do students’ solutions to a complex, open-ended problem (both written and coded solutions) develop over the course of multiple submissions? and (2) How do these developments compare across groups of students that did and did not participate in a course centered around modeling?. Students’ solutions to an open-ended problem across multiple sections of an introductory programming course were explored. These sections were all divided across two groups: (1) experimental group - these sections discussed and utilized mathematical and computational models explicitly throughout the course, and (2) comparison group - these sections focused on developing algorithms and writing code with a more traditional approach. All sections required students to complete a common open-ended problem that consisted of two versions of the problem (the first version with smaller data set and the other a larger data set). Each version hadmore »two submissions – (1) a mathematical model or algorithm (i.e. students’ written solution potentially with tables and figures) and (2) a computational model or program (i.e. students’ MATLAB code). The students’ solutions were graded by student graders after completing two required training sessions that consisted of assessing multiple sample student solutions using the rubrics to ensure consistency across grading. The resulting assessments of students’ works based on the rubrics were analyzed to identify patterns students’ submissions and comparisons across sections. The results identified differences existing in the mathematical and computational model development between students from the experimental and comparison groups. The students in the experimental group were able to better address the complexity of the problem. Most groups demonstrated similar levels and types of change across the submissions for the other dimensions related to the purpose of model components, addressing the users’ anticipated needs, and communicating their solutions. These findings help inform other researchers and instructors how to help students develop mathematical and computational modeling skills, especially in a programming course. This work is part of a larger NSF study about the impact of varying levels of modeling interventions related to different types of models on students’ awareness of different types of models and their applications, as well as their ability to apply and develop different types of models.« less
5. Comparing Students’ Solutions to an Open-ended Problem in an Introductory Programming Course with and without Explicit Modeling Interventions

   Rodgers, K ; Verleger, M ; Marbouti, F ( January 2020 , ASEE Annual Conference proceedings)

   Engineers must understand how to build, apply, and adapt various types of models in order to be successful. Throughout undergraduate engineering education, modeling is fundamental for many core concepts, though it is rarely explicitly taught. There are many benefits to explicitly teaching modeling, particularly in the first years of an engineering program. The research questions that drove this study are: (1) How do students’ solutions to a complex, open-ended problem (both written and coded solutions) develop over the course of multiple submissions? and (2) How do these developments compare across groups of students that did and did not participate in a course centered around modeling?. Students’ solutions to an open-ended problem across multiple sections of an introductory programming course were explored. These sections were all divided across two groups: (1) experimental group - these sections discussed and utilized mathematical and computational models explicitly throughout the course, and (2) comparison group - these sections focused on developing algorithms and writing code with a more traditional approach. All sections required students to complete a common open-ended problem that consisted of two versions of the problem (the first version with smaller data set and the other a larger data set). Each version hadmore »two submissions – (1) a mathematical model or algorithm (i.e. students’ written solution potentially with tables and figures) and (2) a computational model or program (i.e. students’ MATLAB code). The students’ solutions were graded by student graders after completing two required training sessions that consisted of assessing multiple sample student solutions using the rubrics to ensure consistency across grading. The resulting assessments of students’ works based on the rubrics were analyzed to identify patterns students’ submissions and comparisons across sections. The results identified differences existing in the mathematical and computational model development between students from the experimental and comparison groups. The students in the experimental group were able to better address the complexity of the problem. Most groups demonstrated similar levels and types of change across the submissions for the other dimensions related to the purpose of model components, addressing the users’ anticipated needs, and communicating their solutions. These findings help inform other researchers and instructors how to help students develop mathematical and computational modeling skills, especially in a programming course. This work is part of a larger NSF study about the impact of varying levels of modeling interventions related to different types of models on students’ awareness of different types of models and their applications, as well as their ability to apply and develop different types of models.« less