Learning Analytics

SLAM: Case Of The Month: An Interactive Approach to Case Based Learning in Oral Pathology

Case Of The Month: An Interactive Approach to Case Based Learning in Oral Pathology, presented by Renee Ismail, 
U-M, School of Dentistry

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SLAM: The Texas Vision: A Cross-institutional Strategy for Access, Affordability, and Student Success

Presenter Steven Mintz, Executive Director, Institute for Transformational Learning at University of Texas System.

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SLAM: Learning Analytics Task Force: 2012-2015

Presenter Tim McKay, Learning Analytics Task Force: 2012-2015.

The UM Learning Analytics Task Force (LATF) was launched by then-Provost Phil Hanlon in May of 2012. During its three year term, LATF has worked to help the campus community use data to understand and improve teaching and learning. In this talk, I will review highlights of the rapid progress in this field at Michigan, provide a preview of the final report from LATF, and make some predictions about where learning analytics is headed, especially here at Michigan.

 

For more information about learning analytics at U-M and to view videos and slides from past SLAM presentations, click here.

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SLAM: Characterizing Instruction in Introductory Science Courses at Michigan State University

In this session, Becky Matz (CREATE for STEM Institute, Michigan State University) will characterize how patterns in teaching activities in introductory science classes differ by discipline at MSU and offer preliminary data on the teaching of scientific practices and phenomena in these classes. She will contextualize these results with course- and program-level data that highlight some of the challenges in teaching and taking introductory science classes.

For more information about learning analytics at U-M and to view videos and slides from past SLAM presentations, click here.

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SLAM: Two Years of Michigan MOOCs: What Analytics Tell us About Learning in these Environments

Presenters: Christopher Brooks and Stephanie Teasley, School of Information, U-M

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SLAM: Multi-Institutional Explorations of Grade Penalties

Two Presentations:

Multi-Institutional Explorations of Grade Penalties presented by
 Benjamin Koester, Physics, U-M

Case Of The Month: An Interactive Approach to Case Based Learning in Oral Pathology, presented by Renee Ismail, 
U-M, School of Dentistry

For more information about learning analytics at U-M and to view videos and slides from past SLAM presentations, click here.
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SLAM: Quantitative Insights on In-Class Creation and Sharing of Knowledge

From “think-pair-share” to group projects, it is well known that collaborative elements enhance active learning. We examined three university and community college classes using SKIES, a tablet-based technology that enables students and teachers to create and contribute knowledge together in real-time to an interconnected class tree visible to all. Instructors promoted joint knowledge creation in their classes in different ways, from encouraging students to participate via free-form backchannels to leading students through structured activities built into the lesson. Varying practices are related to how, when, where, and what students and teachers created together, with resultant positive effects on student learning. These results inform new approaches to learning analytics and data visualization relevant to instructors and students alike.

For more information about learning analytics at U-M and to view videos and slides from past SLAM presentations, click here.
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SLAM: Education without States

Throughout the twentieth century most educational data were produced and analyzed with government patronage, with the resulting knowledge defined as a public good for the improvement of citizens, workers and policies. Very recently, proprietary firms are producing huge new stores of educationally relevant data through digital media and are underwriting scientific investigation in the interest of improving privately owned educational products and services. This represents a major change in the ecology of educational knowledge production. I provide a synthetic description of this change and specify its implications for educational science, governance, business, and citizenship in the twenty-first century.
 
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SLAM: Tell Us How You Really Feel: Insights from Large-Scale Text Analysis of Student Survey Responses

We present initial results from a large-scale text mining analysis of student written responses to UM course evaluation questions, based on a dataset comprising millions of comments for thousands of courses over 10 semesters. Our goals are to (1) build on existing analysis of numeric ratings; (2) understand more about which specific positive and negative factors in the learning environment may distinguish courses at similar and different numeric rating levels across different evaluation questions; (3) look at how these positive or negative factors distinguish courses across other facets like sections within a larger course, school/college/department, or instructor demographic. Project contributors: Kevyn Collins-Thompson, Phyllis Ford, Adam Levick, Florence Lee, Mika LaVaque-Manty, Michael Spiegel, Lifan Yang. Supported by CRLT Winter 2014 Learning Analytics Fellows Program.
 
For more information about learning analytics at U-M and to view videos and slides from past SLAM presentations, click here.
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