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Articles 1 - 5 of 5
Full-Text Articles in Education
Ieee Istas13- People As Sensors: The Social Implications Of Living In A Smart World, Alexander Hayes, Katina Michael, Nick Rheinberger
Ieee Istas13- People As Sensors: The Social Implications Of Living In A Smart World, Alexander Hayes, Katina Michael, Nick Rheinberger
Alexander Hayes Mr.
What is the technological trajectory of people wearing sensors? What are the benefits, risks and costs? What is the vibe going to be like at ISTAS13 with people like Marvin Minsky and Ray Kurzweil attending? What do you hope to gain from the meeting? Can we foresee a time that all glasses will be embedded with sensors? What are the implications? E.g. in the higher education sector? What about the gathering of evidence by law enforcement? What is point of eye?
The Impacts Of Societal Context On Student Motivation And Engagement, Jonathan Stolk
The Impacts Of Societal Context On Student Motivation And Engagement, Jonathan Stolk
Jonathan Stolk
Promoting a sense of societal connectedness is critical in today’s engineering educational environment. The NAE’s Grand Challenges for Engineering point to broad human concerns — sustainability, health, vulnerability, and joy of living — and human connectivity as the future of engineering problem solving. Engineering studies, however, are often presented in a completely decontextualized manner, with an emphasis on technical content that is free of any human meaning. As a result, students may have difficulty identifying either personal or societal value in their learning tasks. Through their course design, instructors can help students situate themselves and their engineering learning experiences within …
An Interactive Exploration Of Gender And Engineering: Unpacking The Experience, Debbie Chachra, Lynn Stein, Alisha Sarang-Sieminski, Caitrin Lynch, Yevgeniya Zastavker
An Interactive Exploration Of Gender And Engineering: Unpacking The Experience, Debbie Chachra, Lynn Stein, Alisha Sarang-Sieminski, Caitrin Lynch, Yevgeniya Zastavker
Alisha L. Sarang-Sieminski
The engineering student experience is understood to differ for male and female students; gendered interactions affect the development of academic and professional role confidence, as well as engineering identity. The purpose of this session is twofold. First, we aim to introduce participants to concepts of gender schemas, privilege, and identity using a range of interactive activities, including brainstorming and structured discussion. Second, we intend to share information about and obtain feedback on a Gender Discussion Exploration Kit, which the participants will be encouraged to review, use, and share at their home institutions.
Leveraging Machine-Learned Detectors Of Systematic Inquiry Behavior To Estimate And Predict Transfer Of Inquiry Skill, Ryan Baker, Michael Sao Pedro, Janice Gobert, Orlando Montalvo, Adam Nakama
Leveraging Machine-Learned Detectors Of Systematic Inquiry Behavior To Estimate And Predict Transfer Of Inquiry Skill, Ryan Baker, Michael Sao Pedro, Janice Gobert, Orlando Montalvo, Adam Nakama
Ryan S.J.d. Baker
We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine‐learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand‐coding log files, “text replay tagging”. This approach led to detectors that can automatically and accurately identify these inquiry skills under student‐level cross‐validation. The resulting detectors can be applied at run‐time to drive scaffolding intervention. …
Towards Automatically Detecting Whether Student Learning Is Shallow, Ryan Baker, Sujith Gowda, Albert Corbett, Lisa Rossi
Towards Automatically Detecting Whether Student Learning Is Shallow, Ryan Baker, Sujith Gowda, Albert Corbett, Lisa Rossi
Ryan S.J.d. Baker
Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning – learning that enables the student to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions: they may have no learning, or they may have shallow learning (learning that applies only to the current skill, and does not support transfer or PFL). Within this paper, we present automated detectors which identify shallow learners, who are likely to need …