Staff employees of the University of Michigan use a variety of information technologies (IT) to conduct committee work, administer finances, facilitate tenure review and candidate searches, and a host of other functions. This project is interested in investigating how and why staff members appropriate technologies, initially designed for teachning and learning, but are now being utilized by staff. Surveys, interviews, and other methodologies are used in the coure of this investigation. Selected survey results and instruments are available by clicking here.

This project team administered an online survey to U.S. undergraduate students to examine students' self-reported learning strategies, self-regulated learning, learning abilities, help seeking, responsibility, and team project based learning. The USE lab provided support and advice for survey development, administration and analysis. Data will be compared and contrasted with a similar survey previously administered to undergraduate students in South Korea. Furthermore, these researchers are also examining three internationally-used learning management systems and developing a coding rubric to rate and compare their capabilities for supporting self-regulated learning.

Our team will be examining how higher education networks of connectivity (both social and academic) among courses, students, and faculty relate to student engagement and learning outcomes. The study will employ a social network research methodology using surveys and learning analytics data from the university to identify existing relationships and patterns of interaction at various levels, such as student-student, student-instructor, and student-course.

This project seeks to help identify which courses, in what combinations, and in which order are optimal for different students within STEM learning communities. Utilizing educational data mining techniques, this project will create the data models for a Customized Course Advising (CCA) system with tools that help students understand and plan for their future course planning in concert with their academic advisors and coaches. Further, information about transfer credit from other academic institutions will be utilized to optimize the models and help inform student choices before taking such courses. Finally, CCA seeks to inform students and academic advisors about the transitional period from high school to college and how the length and content of that transition may need to be adjusted.

GradeCraft is a new kind of learning management system, specifically designed to support game-inspired courses. Built in Ruby on Rails, the application gives students an interactive space to check their course progress and make choices about their learning plan. GradeCraft also gives instructors access to exciting new learning analytics to gain insight into their students' progress. 

This project takes qualitative data collected from large institutional surveys and codes for content and meaning according to USE Lab-developed rubrics. In the first phase of the project, the research team analyzed broad trends in use of learning management systems through open-ended data from both students and instructors at a commuter and residential campus. Results suggest that the LMS has a positive impact on in-class time, mainly through facilitating logistics for instructors and providing students with access to course resources, and suggested improvements included improved website design and specific recommendations for instructors’ use. Currently, the project is in its second phase, focused on how instructor's use of learning management systems impacts the planning stage of college students' self-regulation. This project is intended to produce recommended objectives that students, instructors, institutions, and LMS developers should focus on for improving use of LMS in higher education.

The University Library has a vast network of services and resources, which provide substantial data that can support campus-wide learning analytics efforts. Included in these user-focused services and resources are in-class librarian instruction, print material circulation, electronic resource and database use, special collections use, technology consultations, and email-based reference. Analysis of this type of library data can further our understanding of teaching, student learning, and engagement across academic departments, contexts, and settings. Understanding potential correlations between library use and student success will enable the library to develop new services and target effective campus collaborations, enhance current services, and plan for future resource allocation to ensure the University Library continues to contribute to the academic success of our community and make the highest possible impact.

This project is investigating how early warning systems designed for academic advisors can inform and impact retention and success programs for students across the University of Michigan. Ultimately, advisors will be able to identify students who may be having academic difficulty, connect course performance with students’ study behaviors, and investigate students’ background in order to provide ongoing collaborative intervention and holistic support for student success. Our partners include the M-STEM Academies, the Comprehensive Studies Program, and Literature, Science, and the Arts.

In collaboration with several other USE Lab projects, this research project conducts experiments that serve to understand the motivations and related behavior of online survey respondents. The populations include instructors, students, and staff in higher education. Manipulations include subject headings, day of invitation, and motivations for survey participation.

Massive Open Online Communities (MOOCs) are seen as an opportunity to gain an education; to gain new skills to prepare for high-paying jobs; and to gain upward mobility without incurring the increasingly crushing debt that comes with a university degree. Although MOOCs are seen as one possible path toward upward mobility, few studies have examined whether and/or how the populations with the most to gain leverage these resources. Our proposed research will investigate if MOOCs can help economically disadvantaged populations build the skills necessary to find employment and to achieve upward mobility. We will conduct a preliminary investigation of the experience of learners motivated to take MOOCs because of their inability to afford higher education; our goal is to better understand whether and how MOOCs can be a pathway to employment for these populations. Specifically, we propose to:

1. conduct an analysis of current University of Michigan (UM) Coursera-based course data to determine the number of these students enrolled in our courses;
2. provide an evaluation of student activities and performance in those courses; and
3. initiate interviews with targeted students to better understand their experience.

Through these activities, we will begin to understand the ways in which the MOOC experiences may affect employment or potential employment for students who are not currently well served by more traditional forms of higher education.

Massive Open Online Courses (MOOCs) have the opportunity to significantly impact how students choose to engage with traditional institutions of higher education. This proposal seeks to shed light on the relationship MOOC students have with traditional degree-granting programs. In particular, we are interested in quantifying performance and patterns of usage of MOOC resources for those students who choose to enroll in or disenroll from traditional degree granting programs, or have previously graduated from traditional degree programs from the same institution offering the MOOC. We will do this through a data-driven investigation, looking at the natural groups of learners presented by historical usage data and customizing our survey instruments and methodologies as appropriate.

Beginning with the development of Sakai at the University of Michigan in 2003, the USE Lab has been highly engaged in investigating how and why instructors and students use information technologies (IT) in their courses and projects. We are particularly interested in wide-scale IT such as Learning Management Systems (LMS). As part of this investigation, the USE Lab also uses use log data from the LMS to compare actual use with survey attitudes and perceptions. This log data includes meaningful user actions in nearly 4,000 course sites each semester across both the large residential and smaller commuter campuses of the University of Michigan. Selected survey results and instruments are available by clicking here.

An interdisciplinary group of faculty, staff, and graduate students who are working together to deploy quality online learning objects in introductory courses at the University of Michigan. The research questions are based on the belief that effective use of online learning objects can increase student engagement and enhance student learning. Steven is assisting with the data collection and analysis as part of this project. More information is available by clicking here.

In partnership with the Division of Student Affairs and the University Library, the USE Lab is actively engaged in working with the MPortfolio pilot project to analyze existing data and assist in future data collection efforts. Additional information about the MPortfolio project is available by clicking here.

In partnership with the University of Michigan Library, this project is investigating how and why instructors and librarians use tools within learning management systems specifically designed as library resources. These tools include instant chat with librarians and linking to physical materials at the library. Faculty currently using these tools, non-usng faculty in high-use departments, and university librarians are the primary focus of this research.