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.
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]
We present a data-driven framework for identifying at-risk, or rather “non-thriving,” students in a large-enrollment introductory general chemistry course. This predictive learning analytic methodology was used to identify underperforming students during the early part of the course, through a hybrid approach of statistical modeling and domain expert decision making.
Description:
Background & Lit Review
There has been a long history and large body of knowledge in pursuing our research question in post secondary chemistry education. For most of these studies, the objective was to decrease drop, fail, withdrawal (DFW) rates by classifying “at-risk” predictors and intervening before the identified students began college coursework. These predictors often took the form of cognitive characteristics, such as standardized test scores, university-made placement exams focusing on mathematical ability, prior conceptual chemistry knowledge and logical reasoning skills. (Spencer1996; Kennepohl2010; Wagner2002; Pickering1975; Bird2010; Ozsogomonyan1979). Another less objective area of inquiry was students’ affective experiences which included self-concept, attitude, and motivation (Xu2013; Chan2014; DanielHouse1995), and intervention typically involved remedial courses(Kilner2018, Walmsley1977, Bentley2005; Mason2001; Kogut1993), or preparatory courses and transition programs (Hunter1976; Krannich2977; Shields2012, Stone2018). Previous efforts at our university include identifying non-thriving students during a required first-year-experience (FYE) course (Syed2019), and an introductory engineering course (Bartolini2020).
Research Problem
Although identifying “at-risk” students has been a popular field of research for introductory science courses, we make the distinction between “thriving” and “surviving” because the students identified in the current study are not necessarily at risk of failing the course, but they are likely to withdraw from the course or from their STEM program. How do you identify and intervene students who are not thriving while it is early enough to make improvements in the course?
Research Question
What are the best and earliest predictors of non-thriving learners early in the course, and what data-driven methods can we provide administrators and instructors to identify these students?
Method
Our hybrid approach combined exploratory data analysis to determine potential cut off points for non-thriving triggers through visualized data sets, and supervised machine learning to identify and utilize significant predictive features of students’ course success. Objective quantitative data was coupled 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 modeling and visualization approach ensured campus context was taken into consideration when manipulating this largely data-driven approach. Our statistical analysis, suggested machine-learning models, and interactive visualizations of the multidimensional data sets are described in this section to show how we addressed our research question.
Step 1: Determine the non-thriving point for the final course grade.
1a. Collect historical performance data from the previous year.
1b. Visualize data into a grade distribution chart.
1c. Set the non-thriving point.
Step 2: Determine the best and earliest predictors of non-thriving students based on historical data.
2a. Collect historical performance data from the previous year and identify all non-thriving students.
2b. Model student data to identify the performance features most correlated to non-thriving performance.
2c. Visualize the data to determine the specific cut-off ranges.
Step 3: Replicate and improve the model early on during the current course.
3a. Export gradebook data for the current set of students at the data collection time point
3b. Filter the data to identify the students whose performance matched the predicted trigger for non thriving students from the previous year.
The earliest performance triggers for non-thriving grades ranked greatest to least from left to right.
Results & Conclusion
Student performance data was used to create learning analytics visuals to aid in discovering trends among non-thriving students, while domain experts made decisions about appropriate cut-off points to classify non-thriving performance. With the overall goals of closing opportunity gaps, maximizing all students’ potential for success in the course, and increasing STEM retention rates, we use student admissions and performance data visuals to ultimately create an environment that better supports all first semester chemistry students, and early on in the course.
Previous efforts to increase STEM retention rates have centered around identifying “at-risk” students before the course begins, based on admissions data. In this paper, we expanded the efforts of 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. These methods better prepared us to support all students based on their performance in class, not just their before-course attributes, many of which are inherently biased and cannot account for many contextual differences. Additionally, in our approach to organizing data into interactive visual representations, we made our methods accessible to all faculty and administrators so that context-driven decisions can be made for the course.
With a largely data-driven approach, we sought to answer the research question: What are the best and earliest predictors of non-thriving learners early in the course, and what tools can we provide administrators and instructors to identify these students? Through a K- Nearest Neighbor modeling approach with one semester of data, it was determined that the best performance predictors of non-thriving students was 2 or more below-average homework scores, and an exam 1 score below 81. However, using these exact cutoffs did not appear to be the best strategy for identifying students in the following semester. A method of iterative refinement was implemented to update the appropriate selection criteria and will continue to be used until our model is fined tuned.
As we collect data from more semesters, we plan to continue to use of this iterative refinement on our model to determine the best set of assignments and amount of time needed to accurately predict the non-thriving status of our students. The methods we have described here provide scholar-practitioners with a set of tools that can be replicated and customized for STEM courses on their campus. We recognize that different institutions will have different definitions of thriving, and course structures. We provide examples specific to our institution for context, but we encourage those that seek to utilize our method to customize this process to one that fits the specifics of their institution. This will entail using data-driven approaches to identify a thriving cutoff point, suggesting a cutoff date and number of assignments for identifying non-thriving students, and implementing best-practice intervention approaches to offer students non-thriving students a boost.
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.
Kilner, W. C. ConfChem Conference on Mathematics in Undergraduate Chemistry Instruction: The Chem-Math Project. Journal of Chemical Education 2018, 95, 1436–1437.
Kogut, L. S. A general chemistry course for science and engineering majors with marginal academic preparation. Journal of Chemical Education 1993, 70, 565–567.
Krannich, L. K.; Patick, D.; Pevear, J. A pre-general chemistry course for the under-prepared student. Journal of Chemical Education 1977, 54, 730–735.
Mason, D.; Verdel, E. Gateway to Success for At-Risk Students in a Large-Group Introductory Chemistry Class. Journal of Chemical Education 2001, 78, 252–255.
Ozsogomonyan, A.; Loftus, D. Predictors of general chemistry grades. Journal of Chemical Education 1979, 56, 173–175.
Pickering, M. Helping the high risk freshman chemist. Journal of Chemical Education 1975, 52, 512–514.
Shields, S. P.; Hogrebe, M. C.; Spees, W. M.; Handlin, L. B.; Noelken, G. P.; Riley, J. M.; Frey, R. F. A transition program for underprepared students in general chemistry: Diagnosis, implementation, and evaluation. Journal of Chemical Education 2012, 89, 995–1000.
Stone, K. L.; Shaner, S. E.; Fendrick, C. M. Improving the success of first term general chemistry students at a liberal arts institution. Education Sciences 2018, 8, 5.
Spencer, H. E. Mathematical SAT test scores and college chemistry grades. Journal of Chemical Education 1996, 73, 1150–1153.
Syed, M.; Duan, X.; Anggara, T.; Alex Ambrose, G.; Lanski, A.; Chawla, N. V. Integrated closed-loop learning analytics scheme in a first year experience course. ACM International Conference Proceeding Series. New York, New York, USA, 2019; pp 521–530.
Wagner, E. P.; Sasser, H.; DiBiase, W. J. Predicting students at risk in general chemistry using pre-semester assessments and demographic information. Journal of Chemical Education 2002, 79, 749.
Walmsley, F. A course for the underprepared chemistry student. Journal of Chemical Education 1977, 54, 314–315.
Xu, X.; Villafane, S. M.; Lewis, J. E. College students’ attitudes toward chemistry, conceptual knowledge and achievement: Structural equation model analysis. Chemistry Education Research and Practice 2013, 14, 188–200.
Citation: Serafini, Tiziana, , Rouamba, Guieswende, Ambroe, G. Alex (2021) “Translating Authentic Italian Text in a Technology-Enhanced Active Learning Classroom” Midwest Scholarship of Teaching & Learning (SoTL) Annual Conference. Virtual.
Abstract: Beyond textbook readings: Are you interested to see how learning and teaching transform in a state-of-the-art active learning classroom? Through this poster presentation, you will be able to learn how integrating reading strategies into instruction in an active learning classroom made beginners enthusiastic about reading an authentic book in Italian.
Context:
In the summer of 2017, the University of Notre Dame was awarded a $65K furniture grant from Steelcase Educations’s Active Learning Center. Notre Dame’s Office of Facilities Design and Operations contributed $50k in classroom renovation funding and the Office of Information Technologies $25K in technology funding to renovate Debartolo Hall 232 into a state-of-the-art prototype flexible classroom to maximize interactive learning.
Research Goal: Increase student confidence in reading authentic texts through instructional strategies in an online platform.
Research Questions: • RQ1: Does the teaching of reading strategies enable elementary-level learners to successfully understand and translate an authentic Italian text without external aids? • RQ2: How does technology support the development of reading skills in lower-level classes?
Lit Review: Over the last few years, the shift toward a communicative classroom has resulted in reading being relegated to “the wayside” (Aski, 2000, p. 495). Despite being “one of the most obvious source of authentic FL language,” (idem) reading is often shunned by teachers and learners alike. Teachers may find it challenging to expose their students to unfamiliar vocabulary and grammatical structures, and students, in turn, may feel frustrated at syntactical and lexical complexity. How to come out of this gridlock? Some researchers chose to focus on text types and reading skills (Lee and Musumeci, 1988, p. 174). They asked whether establishing a hierarchy of text types and a corresponding hierarchy of reading skills constitutes an accurate tool to assess reading performance. Others turned their attention to the learners themselves, and analyzed the relationship between L1 (skill proficiency in ) and L2 (language knowledge) in successful readers (Bernhardt and Kamil, 1995).
Another line of inquiry centers upon cognitive psychology, and examines the ways in which learners and teachers can tackle authentic texts by learning and teaching reading strategies. Carrell and Eisterhold argue that at an elementary level “low-proficiency readers are more word-bound and [..] for them meaning tends to break down at the word level.” (1983). Schulz confirms that low-proficiency readers “engage in word-by-word decoding and translation,” (1983) and are often unable to gain a general understanding of a text. For this reason, Aski proposes teaching elementary-level students those strategies that focus on global meaning (top-down strategies). In contrast to bottom-up strategies, which concentrate on discrete, and oftentimes too challenging linguistic elements, top-down strategies allow readers the possibility to make logical inferences about general aspects of a text. Readers can then use that knowledge as a starting point against which they can test comprehension accuracy.
Is it, however, viable and constructive to also teach bottom-up strategies at a lower level? Is it possible for low-proficiency readers to apply both bottom-up and top-down strategies to an authentic text that is well above their reading level?
Methodology A combination of quantitative and qualitative analysis.
Scholarly references Aski, Janice. “Effective Integration of Reading in the Communicative Italian (FL) Classroom,” Italica, 77, No. 4, (Winter 2000): 495-508.
Bernhardt, Elizabeth, and Michael Kamil (1995). “Interpreting Relationships between L1 and L2 Reading:Consolidating the Linguistic Threshold and the Linguistic Interdependence Hypotheses.” Applied Linguistics 16 (1995): 15-34.
Carrell, Patricia, and Joan Eisterhold, “Schema Theory and ESL Reading Pedagogy.” TESOL Quarterly 17(1983): 553-573.
Lee, James and Diane Musumeci, “On Hierarchies of Reading Skills and Text Types.” The Modern Language Journal 72 (1988):173-87.
Inclusive Curriculum Analytics for Undergraduate STEM Degrees: Using data to identify barriers, measure outcome disparities, and close achievement gaps
Abstract:
As formal credit earning opportunities grow, such as through credit by examination, it’s imperative that institutions understand how their advanced placement credit acceptance policies shape their students’ experiences on campus. While most schools have focused on how students with advanced credit perform in the follow on classes, fewer have focused on how these policies affect students without the same opportunities. This case study will answer: how do credit acceptance policies shape the student academic experience within one College of Engineering? The poster will focus on how one College of Engineering identified inequities through data driven study of students’ college performance as it relates to their credits earned prior to matriculation. It will provide a roadmap for other institutions to investigate their own student data as it pertains to current policies.
Background, Problem, & Evidence:
More and more students begin college having earned multiple college credits. As formal credit earning opportunities grow, such as through credit by examination, it is imperative that colleges and universities understand how their credit acceptance policies shape their students’ experiences on campus. While many studies have focused on program benefits such as additional schedule flexibility, less time to degree, and exposure to advanced topics, few have quantified the collateral impact of credit earning opportunities on the students that do not have credit when entering college. By not specifically quantifying and understanding this, it is easy to perpetuate or expand an achievement gap that started well before students enter college.
In this session, we will show how one College of Engineering used student performance data to identify and understand potential inequities in existing policy decisions. By accepting credit in required courses, in this case calculus, chemistry and physics, two groups were formed: (1) students that were ahead of the stated curriculum and (2) students that were executing the curriculum as published and expected. Looking at shared courses between these two tracks, such as physics or sophomore level engineering courses, exposed real and concerning disparities in grade performance from this policy. This session will present data from this study and describe a methodology for creating similar data analysis at other schools and within a wide range of programs.
Expanded Figures from the poster:
CoursePathVis is a visual analytical tool for exploring and analyzing students’ progress through a college curriculum using a Sankey diagram. We group students in multiple ways (by their AP courses, term courses, and a user-specified fun-nel course) to offer different perspectives on the underlying data. With these flexible grouping techniques and the funnel-augmented Sankey diagram, CoursePathVis helps us identify patterns or outliers that affect student success.”
Victoria Goodrich, Associate Teaching Professor, Chemical and Biomolecular Engineering
Leo McWilliams, Assistant Dean for Undergraduate Programs, College of Engineering
G. Alex Ambrose, Director of Learning Research, ND Learning | Kaneb Center for Teaching Excellence
Acknowledgements:
Andrew Craker, Pat Miller, Kevin Abbott, Kevin Barry, Alex Oxner, Augie Freda, Shelin Mathews, Ryan Snodgrass, Keith Mcindoo, Roberto Casarez, Joel Dosmann, Chaoli Wang, Brendan O’Handley, Michael Niemier, Morgan Ludwig and Samantha Allison
PerformanceVis is a visual analytics tool developed for analyzing and visualizing students’ chemistry course performance through the lens of time, homework and exams, and demographic and academic background. The Introduction to Chemical Principles course is a required course for all college of science and college of engineering programs at the university and is the second largest course on campus with approximately 1,000 freshmen taking the course.
This engaging tool includes four main views (overall exam grade pathway, detailed exam grade pathway, detailed exam item analysis, and overall exam & homework analysis) which are dynamically linked together for user interaction and exploration. PerformanceVis enables instructors to improve their course and assessment design by visualizing students’ perceived difficulty level and topic correlation between assignments and exams. It assists instructors and administrators in evaluating the impact of a special treatment program (cohort) by reviewing the performance of regular, control, and cohort students overall and by exam. The image below shows a screenshot of PerformanceVis with the right side of the image showing a view of the gender performance gap for those students who were not thriving. The left side of the image shows Exam 1 item analysis for each test question.
Ambrose, G. Alex, Hubert, Dan, Rouamba, Guieswende (2019) “Improve your Teaching Student Learning with Classroom Research.” Kaneb Center for Teaching Excellence Workshop, Notre Dame, IN.
Click here for the slide deck
Click here for the handout
Participants will:
Explore the landscape of Discipline-Based Research (DBR) and the Scholarship of Teaching and Learning (SoTL).
Brainstorm potential research goals, questions, and data for their own course.
Become familiar with applied learning research support services and resources (e.g. survey/rubric design, video observation, consent forms, and umbrella IRB).
Abstract:
We present PerformanceVis, a visual analytics tool for analyzing student admission and course performance data and investigating homework and exam question design. Targeting a university-wide introductory chemistry course with nearly 1000 student enrollment, we consider the requirements and needs of students, instructors, and administrators in the design of PerformanceVis. We study the correlation between question items from assignments and exams, employ machine learning techniques for student grade prediction, and develop an interface for interactive exploration of student course performance data. PerformanceVis includes four main views (overall exam grade pathway, detailed exam grade pathway, detailed exam item analysis, and overall exam & homework analysis) which are dynamically linked together for user interaction and exploration. We demonstrate the effectiveness of PerformanceVis through case studies along with an ad-hoc expert evaluation. Finally, we conclude this work by pointing out future work in this direction of learning analytics research.
