We recognize that opportunities for continued professional development are important to our membership. As such, SERA will host various webinars about topics of interest to the membership. More information about these webinars can be found in the current newsletter.
Please note: The views expressed in these webinars are those of the presenters and do not necessarily represent the views, opinions, or positions of SERA.
Upcoming Webinars
SERA will host three webinars this spring and summer to continue scholarship opportunities beyond our yearly conference. Upcoming webinars are listed below. Webinars are free to SERA members who register (Webinar Zoom link and Passcode sent upon registration). We hope you are
able to attend.
Please join us for this free webinar on Complexity Informed Analysis of Large and Multi-level Data Sets: Methods and Practical Insights. Monday, August 15th at noon Eastern. Featuring presentations by Dr. Jonathan Hilpert, UNLV and Dr. Don Gilstrap, UA.
Register here: https://uncp.webex.com/uncp/j.php?RGID=ra36fc4b1e1b93b877d8b1b03a70735c5
Dr. Hilpert’s talk is entitled: Network-based, interaction dominant analysis for Learning Management System events
One challenge for learning analytics is to identify scalable metrics that are useful to practitioners and stakeholders. A network-based, interaction dominant approach derived from complex systems thinking was used to analyze learning management system (LMS) events derived from log data. The network metrics were interpreted from an engagement perspective that is intuitive to instructors and aligned with educational psychological theory. Data drawn from one in-person (n = 226; events = 257k) and one online (n = 364; events = 496k) semester and submitted to regression models predicted an average of 28 percent of variance in students’ grades. Our interpretation suggested three types of domain general engagement variables can be derived from native LMS logs – breadth, depth, and patterned – which can be used to predict STEM performance beyond frequency counts. The results indicated that operationalizing LMS engagement from a complexity perspective can increase the effectiveness of models that rely upon a component dominant perspective commonly applied to many prediction models.
Dr. Gilstrap’s talk is entitled: Complexity Methods to Gauge Student Persistence in Higher Education
A benefit of complex systems theory is that it takes into account anomalies that emerge in linear models. This presentation is intended to focus on my current research, which includes 1) using network analysis to explore issues surrounding persistence among at-risk students in higher education and 2) using inverse Bayesian inference as a framework for studying dissipative structures. This presentation includes the use of network analysis and Multiple Linear Regression (MLR) in a complimentary manner based on data collected from a population of enrolled students at an urban serving university over several years (P=35,239). Using multicollinearity analysis from the MLR model, variables interacting at different dimensions were then analyzed through network analysis to show network linkages between interacting variables. Analysis of the data shows that, while variables may not be found to be significant in MLR models where anomalies are ruled out, network analysis takes these anomalies into account and further reveals complex layers of interactions between and among variables. Given time available during the presentation, the use of inverse Bayesian inference will also be explored as a complimentary method to Prigogine’s dissipative structures theory.
Discovering Methodologies (August 5, 2021)
Registration Link: https://www.signupgenius.com/go/409084EAFAB2EA0FC1-sera2
Links to previously recorded webinars
Submitting Proposals (Access Password: 37j7r*2C)
Preparing Grant Applications for the Institute of Education Sciences: A Workshop (slides)
February 14, 2018
Publishing in Professional Journals: Questions and Dialogue (slides)
November 9, 2017
An Introduction to Stata (slides)
October 3, 2017
Experimental Research: Some Tips for Success (slides)
April 25, 2017
Matching Methods to Improve Quasi-Experimental Design (syntax)
March 28, 2017
Statistical Simulation: A Hands-On Approach to Learning and Playing with Statistics in R (slides)
November 17, 2016
The ‘Why’ and ‘How’ of Analyzing Data with a Nested Structure via Hierarchical Linear Modeling (HLM)
October 25, 2016
Qualitative Research: A Refresher for Scholars and Practitioners (slides)
April 25, 2016
An Introduction to NCES Data (slides)
March 29, 2016
