Location: Ballroom C+D+E
This is a roundtable in the Cyberlearning 2017 Roundtable session.
Replicating Peer-Led Team Learning in Cyberspace
Peer-Led Team Learning (PLTL) is a pedagogy that encourages students taking STEM classes to be more active in their own learning, thus improving their problem-solving skills, ability to work in teams, and success in introductory science courses. This talk will present a brief overview of the PLTL research before describing the development and evaluation of cPLTL, the online adaptation of PLTL. The presentation will conclude with the description of the mixed methods study that examined the transfer of a PLTL to an online workshop environment in a general chemistry course at a research university in the Midwest. The null hypothesis guiding the study was that no substantive differences would emerge between the two workshop settings. The final exam scores and discourse analysis support the null hypothesis and use of both face-to-face and synchronous online peer-led workshops in early science courses. More information about PLTL and cPLTL can be found at http: cpltl.iupui.edu/.
Project: Homepage, NSF Award #0941978 – Cyber PLTL (cPLTL): Development, Implementation, and Evaluation
Participation-based Student Performance Prediction Model through Interpretable Genetic Programming: Theory Informed Learning Analytics
Building an understandable student performance prediction model is a challenging task fraught with many confounding factors collected and measured. Traditionally, most prediction models are unable to provide teachers with actionable information due to the lack of semantic background for interpretation, which poses significant problems for model use as well as model evaluation. In this paper, we connect learning analytics, and theory to solve this problem using real data from a computer supported collaborative learning (CSCL) environment. Firstly, we operationalized activity theory to holistically quantify students’ participation in the CSCL course. As a result, 6 variables, Subject, Rules, Tools, Division of Labor, Community, Objects, are constructed. Secondly, an advanced modeling technique, Interpretable Genetic Programming (GP), is designed and coded to develop the performance prediction model. With an optimized paradigm and an easily applicable rule format, the results show that the proposed methodology outperforms traditional models in prediction rate, and in understandability.
Mobile City Science: Technology-Supported Collaborative Learning at Community Scale
In a new era of digital media and democracy, there is widespread concern that technologies have incapacitated us from learning and teaching across diverse communities and perspectives. While this notion may ring true in certain contexts, this paper describes a study, “Mobile City Science,” that designed a novel learning experience in which educators and young people used mobile and place-based technologies to document and analyze the diverse perspectives of community members living in rapidly changing urban areas. The objective of this work was to teach young people digital literacies associated with “city science,” an emerging interdisciplinary field that creates data-driven approaches to complicated community issues. Participants were videotaped as they collected and analyzed information about a specific neighborhood using wearable cameras, GPS devices, heart rate monitors, and a GIS software. Early findings show that Mobile City Science uses technology to engage people with diverse perspectives around a community scale problem.
Project: NSF Award #1645102 Mobile City Science: Youth Mapping Community Learning Opportunities