Mastery-based learning provides an opportunity for higher education to reimagine the purpose of assessment, which is one of the primary challenges faced by educators and students throughout the COVID-19 pandemic. This poster session will showcase formative assessment using a mastery-based grading approach in one college-level math course offered in dual mode to explore how different groups across the university work collaboratively to innovate teaching and learning with effective pedagogical practice to promote student success.
Hsu, Kuang-Chen, Mulholland, Brian Ambrose, G. Alex, Szekelyhidi, Sonja Mapes, Craker, Andrew (2021) “Flexible Assessment in Math During (and After) COVID” Association for Educational Communications and Technology (AECT) Conference.
On Oct 27th, 2021, a panel of faculty from Math, Chemistry and Neuroscience will share lessons learned when moving from a few high stakes assessments (exams) to more frequent low stakes assessments (quizzes) and in one case providing a mastery-based learning opportunity. Panelists will discuss why they made the shift, how it worked, and what they will do next. Ample time will be reserved for Q&A.
Sonja Mapes, Associate Professor of the Practice, Director of Undergraduate Studies, Department of Mathematics
Rachel Branco, Assistant Teaching Professor, Department of Chemistry & Biochemistry and Neuroscience & Behavior
Kelley Young, Assistant Teaching Professor, Department of Chemistry & Biochemistry
G. Alex Ambrose, Professor of the Practice, Director of Learning Research, ND Learning | Kaneb Center for Teaching Excellence
Young, Kelley, Mapes, Sonja, Branco, Rachel, Ambrose, G. Alex (2021) “Enhancing Learning with Low Stakes Assessments” Faculty Panel, ND Learning | Kaneb Center for Teaching Excellence. The University of Notre Dame.
It is no secret that large data analysis is fast becoming a necessary skill in all scientific fields, and that programming knowledge is required to carry out these analyses. However, we have identified that many science majors do not obtain training in coding within their standard curriculum, and thus perceive that these data analysis skills are out of their reach. In the Whelan Lab, we believe that it is easy and effective to include programming assignments in standard scientific undergraduate classes, which expose students to programming in the context of their familiar disciplines and allow them see how useful code can be in answering relevant scientific questions. With this inspiration, we created a web-based assignment targeted towards analytical chemistry students that walks them through increasingly involved tasks in R, a common programming language, culminating in the students writing their own code to solve interesting chemistry questions. The assignment website can be accessed here: https://weaversd.github.io/R_with_peptides_Project/.
In a pilot study, we found that the assignment was efficient, effective, and enjoyable, and that novice coders said that they planned to use R going forward in their own projects. See the ePoster below for the preliminary findings.
Click here to zoom into a larger view of the poster and access the hyperlinks on the ePoster
Cite this poster:
Weaver, Simon, Whelan, Rebecca (2021) “Introducing computational programing skills to an analytical chemistry class using discipline-focused problem solving in R” Midwestern Universities Analytical Chemistry Conference.
About the Researchers:
Simon Weaver is a graduate student studying genomics and proteomics in the Integrated Biomedical Sciences (IBMS) program at Notre Dame. His research focuses on improving the early detection of ovarian cancer.
Rebecca Whelan is an Associate Professor of Chemistry and Biochemistry. She has been teaching analytical chemistry in various forms for 15 years. Research efforts in the Whelan lab involve applying tools from bioanalytical chemistry–including microscale separations, affinity interactions, and mass spectrometry–to human health.
Note: The ND Learning Research Team assisted with the IRB application, research/evaluation design, assignment design consultation, survey construction, analysis and poster presentation. If you are interested in contributing to the scholarship of teaching and learning (SoTL) please contact us.
On June 3, 2021, ND Learning supported Teaching Professor Brian Mulholland’s presentation to the Board of Trustees. on “Enhancing Learning with Low-stakes Assessment & Mastery Grading in Math During COVID” that spotlighted sliver lining learning stories coming out of the Pandemic.
Mulholland, Brian, Szekelyhidi, Sonja Mapes Hsu, Kuang-Chen, Barry, Kevin, Ambrose, G. Alex (2021) “Enhancing Learning with Low-stakes Assessment & Mastery Grading in Math During COVID.” University of Notre Dame Board of Trustees Meeting. June 3, 2021.
Description: We present an inclusive learning analytics framework for identifying at-risk or rather “non-thriving” students in a large-enrollment introductory general chemistry course. With the overall goals of closing opportunity gaps, maximizing all students’ potential for course success, and increasing STEM retention rates, our study used a hybrid approach of combining predictive modeling and domain experts decision-making to identify underperforming students during the early part of the course. We recognize that different institutions will have different definitions of thriving and course structures, but the methods we used in our study provide scholar-practitioners with a set of tools that can be replicated and customized for STEM courses on their campus.
Ambrose, G. Alex, Goodrich, Victoria, Craker, Andrew, McWilliams, Leo (2021) “Using Disaggregation & Inclusive Curriculum Analytics to Identify Barriers, Measure Outcome Disparities, and Close Achievement Gaps.” Indiana University’s 3rd Annual Learning Analytics Summit: Data-informed Stories, Transformational Journeys: Indiana
To access and comment on the slides click here or click here to watch the 18 min recorded presentation
Abstract We present an inclusive learning analytics framework for identifying at-risk or rather “non-thriving” students in a large-enrollment introductory general chemistry course. With the overall goals of closing opportunity gaps, maximizing all students’ potential for course success, and increasing STEM retention rates, our study used a hybrid approach of combining predictive modeling and domain experts decision-making to identify underperforming students during the early part of the course. We recognize that different institutions will have different definitions of thriving and course structures, but the methods we used in our study provide scholar-practitioners with a set of tools that can be replicated and customized for STEM courses on their campus.
Descriptions Although identifying “at-risk” students has been a popular field of research for introductory science courses, our study expanded the current research in two areas: 1) broadening the search criteria to students who are likely non-thriving, not necessarily “at-risk” of failing the course; and 2) utilizing early and current course performance data instead of before-course characteristics. These two focus points allowed us to capture a more refined demographic of students, with the goal of helping all students to not just survive, but thrive in STEM programs.
Our study is grounded in these two research questions: (1) What are the best and earliest predictors of non-thriving learners early in the course? (2) What data-driven methods can we provide administrators and instructors to identify these students and help them improve their course performance?
To answer those research questions, we coupled the exploratory data analysis approach with decision-making by domain experts (course professors and coordinators, advisors, data scientists, and learning experts from the university’s teaching and learning center). This hybrid approach ensured campus context was taken into consideration when identifying non-thriving students. We used it to determine the potential grades cut-off for non-thriving triggers, and identify the best and earliest predictors of non-thriving performance. Our predictors were able to catch 6 out of the 6 students who dropped out of the course, and 19 out of the 33 non-thriving students. We plan to improve the accuracy of our predicting model and the effectiveness of our boosting strategies in the future iteration of our study.
References Bentley, A. B.; Gellene, G. I. A six-year study of the eﬀects of a remedial course in the chemistry curriculum. Journal of Chemical Education 2005, 82, 125–130.
Chan, J. Y.; Bauer, C. F. Identifying at-risk students in general chemistry via cluster analysis of aﬀective characteristics. Journal of Chemical Education 2014, 91, 1417–1425.
Daniel House, J. Noncognitive predictors of achievement in introductory college chemistry. Research in Higher Education 1995, 36, 473–490.
Hunter, N. W. A chemistry prep course that seems to work. 1976; https://pubs.acs.org/sharingguidelines.
Kennepohl, D.; Guay, M.; Thomas, V. Using an online, self-diagnostic test for introductory general chemistry at an open university. Journal of Chemical Education 2010, 87, 1273–1277.
Check here for the 12 min recording of the presentation
To access and comment on the slide deck, click here
Abstract: This session will share COVID dual-mode (live in-person and remote classrooms) technology-enhanced classroom, teaching, and learning experiences from the University of Notre Dame. Using survey data from over 2k students and about 30 instructors across 6 classrooms (small, medium, and large across all disciplines) we will share how our classroom upgrades performed during COVID and the implications for the future classroom post-COVID.
Research Question: RQ1 Evaluating Dual-Mode Classroom Design: How can we improve dual-mode (in-person + live remote) classrooms during COVID and for future semesters and optimize to increase flexibility?
RQ 2 Understanding Dual-Mode Experiences: How can the experience of instructors, in-person and remote learners be improved by changes to the classroom, and what are the implications for post-COVID?
Context: During the Fall 2020 Semester, the University of Notre Dame, like all universities, had to make adjustments to its classrooms and traditional models of teaching in order to accommodate learning in a world with COVID-19. When COVID hit in spring 2020, Notre Dame transitioned to completely online learning with no students on campus. For Fall 2020, Notre Dame adopted multiple modes of teaching for its classes, with some online, some in person, and some hybrid, with half the class attending in-person while the other half attended online. Every in-person class had the capacity to be dual-mode, with the professor and some students in-person while students who could not come to class attended live remotely. In order to allow for dual mode delivery, classrooms across campus were upgraded with new technology, including an extra computer monitor on the lectern for the instructor workstation, with new webcams and microphones. Six classrooms with 30 different courses from all major disciplines were studied. A total of 29 faculty and 1,215 students were surveyed with a small sample of interviews and observations.
Methods: We analyzed data from multiple methods including surveys, interviews, observations, and the Learning Space Rating Score.
Related Work: Staggs, Jessica, Ambrose, G. Alex (expected May 2021) “COVID-19’s Effects on Classrooms’ Learning Space Rating System Scores” [Upcoming Article and Infographic: http://bit.ly/lsrsCovidND]
Kaneb Center Postdoctoral and Graduate Associates in Learning Research
The ND Learning | Kaneb Center for Teaching Excellence seeks graduate students and postdocs to serve as Kaneb Center Postdoctoral and Graduate Associates (PGAs) in the Research and Assessment for Learning (ReAL) Lab for the 2020-2021 academic year. The PGA will become acquainted with the fundamental concepts and core practices of the Scholarship of Teaching and Learning (SoTL), an inquiry that examines the intersection of instruction and student learning across disciplines in higher education.
A potential PGA would work with a team of faculty on course assessment, program/grant evaluation, conference proposals/presentations, and support articles for publication. If hired you would participate in applied learning research and work toward submitting at least one co-authored article for publication, submitting a conference and/or grant proposal, and creating a culminating poster presentation or conducting faculty SoTL and assessment-related workshops. This position is an excellent opportunity to develop as a professional, a teaching scholar-practitioner. For more information see the ReAL Lab blog at: https://kaneb.nd.edu/real/.
Throughout the academic year, PGAs contribute an average of 5 hours per week, scheduled
according to availability, and receive pay of $20/hour. They attend weekly meetings with Dr. G. Alex Ambrose, Director of Learning Research at Notre Dame Learning | Kaneb Center.
Applicants should have completed one or more semesters of TAing or teaching, preferably at Notre Dame, before holding this position. Postdocs may be eligible; contact firstname.lastname@example.org for more information. Advisor and DGS approval will be required before hiring is finalized. Applicants must be in residence during the fall 2020 and spring 2021 semesters.
To apply, please submit the information below to email@example.com by 11:59pm, Wednesday April 27, 2020. Interviews will take place in late April and early May.
– Current year in graduate school & anticipated year of graduation
– Paragraph describing your interest in this position