Workshop: Improve your Teaching & Student Learning with Research from the Classroom

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).


Journal Article: PerformanceVis: Visual Analytics of Student Performance Data from an Introductory Chemistry Course

Hyperlinked citation with an open acces link to full journal article:

Deng, H., Wang, X., Guo, Z., Decker, A., Duan, X., Wang, C., Ambrose, G., & Abbott, K.  (2019). PerformanceVis: Visual analytics of student performance data from an introductory chemistry course. Visual Informatics.

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.

Student performance, Item analysis, Grade prediction, Learning analytics, Knowledge discovery


VI Graphic 

Practitioner Report: Learning Analytics for Inclusive STEM Student Success

Duan, Xiaojing, Ambrose, G. Alex, Wang, Chaoli, Abbott, Kevin, Woodard, Victoria, Young, Kelley (2020) Learning Analytics for Inclusive STEM Student Success. Learning Analytics & Knowledge Conference. Practitioner Report and Poster. Frankfurt, Germany

ABSTRACT: The challenge was to identify and help underserved and underprepared students in an introductory chemistry course to be retained and thrive in the college of science or engineering while supporting the general population. In this paper, we describe our methods for identifying these students, evaluating the impact of a special treatment program that was provided to a subset of those students, discuss our efforts to help the general population, and evaluate the short- and long-term impacts. In particular, we discuss a data-informed framework for analyzing student and outcome variables.

Keywords: STEM Retention; Learning Visualization Dashboard; Inclusive Pedagogy; Learning Analytics

Click here for a current version of the practitioner report