AERA 2020

The AERA 2020 conference has been canceled.

San Francisco, CA
April 17-21, 2020
Conference Website

The theme of the AERA 2020 conference is “The Power and Possibilities for the Public Good When Researchers and Organizational Stakeholders Collaborate.” This meeting offers a wide array of sessions that advance knowledge and connect to policy and practice.

Saturday, April 18

Augmented Visual Perception—Interpreting Thermal Sensation with Innovative Technology

Shannon Hsianghan-Huang Sung, Xudong Huang, Ji Shen, Changzhao Wang, Charles Xie, Yifang Zeng, Guanhua Chen

8:15 – 9:45 AM, Moscone Center, South Building, Exhibition Level, Moscone Hall A

Thermal sensation, which refers to how humans intuitively perceive the temperature of objects, may mislead us to draw false conclusions. Our team has developed a smartphone application (i.e., SmartIR) to equip learners with spatial-, translational-, and temporal-augmented visual perception (AVP), to “see” heat transfer via infrared thermal imaging. The prediction-observation-explanation cycle was adopted to design corresponding student activities using SmartIR. Drawing from research on multiple representations, we investigated whether students use these AVP features to interpret their thermal sensation. We found that students used a higher percentage of AVP during the observation and explanation phases, indicating that these tools increased different venues to interpret abstract concepts and thermal sensation. Educational implications are discussed.

Sunday, April 19

Understanding Temporal Dimension of Students’ Engagement in Engineering Design Learning

Wanli Xing, Yifang Zeng, Xudong Huang, Juan Zheng, Guanhua Chen, Charles Xie

12:25 – 1:55 PM, Marriott Marquis San Francisco, Fourth Level, Yerba Buena Salon

Using an explanatory mixed method design, the goal of this study was to investigate the temporal variation of students’ engagement when they are studying engineering design. A total of 111 9th grade high school students participated. Data mining techniques designed for education was implemented to group students into three clusters with distinct temporal patterns of engagement. Then, ANOVA and Turkey HSD tests were used to identify the relationship of each of these patterns to student performance. Our results showed that students who are gradually inactive in the process have the worst performance among three clusters, while there is non-significant difference of performance between the remaining two clusters.

Monday, April 20

The Interplay Between Self-Regulation and Students’ Engineering Design

Wanli Xing, Juan Zheng, Gaoxia Zhu, Charles Xie

8:15 – 9:45 AM, Moscone Center, South Building, Exhibition Level, Moscone Hall A

This study analyzed the engineering design behaviors of 108 ninth-grade participants from the United States using principal component analysis and cluster analysis and classified the students into four distinct types: competent, cognitive-oriented, reflective-oriented, and minimally self-regulated learners. Competent self-regulated learners perceived themselves as the most self-regulated learners and had the greatest learning gains, although they did not perform best in the task. Cognitive-oriented self-regulated learners perceived themselves as the least self-regulated learners although they were the second best in both the performance of the task and learning gains. In contrast, reflective learners had the best performance in the task. Minimally self-regulated learners did not perform well in the task and had the lowest learning gains.

Design of Next Generation Science Assessments: Measuring What Matters

James W. Pellegrino, Christopher J. Harris, Joseph S. Krajcik, Daniel Damelin

10:35 AM – 12:05 PM, Moscone Center, South Building, Level Three, Room 308

We overview our systematic, scalable, and equity-focused approach for designing assessment items that measure student proficiency with new science learning goals that integrate disciplinary core ideas and crosscutting concepts with scientific practices. The assessment tasks are intended for formative use within classroom instruction.

Across a series of studies using multiple research methods, we have assembled data indicating that our tasks are functioning as intended, minimize construct-irrelevant variance, and support teachers’ classroom practice. For example, classroom observations show that teachers use the assessments in a variety of different modes, spanning a range between formative and summative use. Student cognitive lab studies provided data on both task comprehensibility and issues of equity and construct-irrelevant variance. Task performance studies have provided data on item features (e.g., difficulty) that affect student performance and on the utility of our rubric design in affording partial credit scores based on the presence or absence of key knowledge targets in the student responses.

Tuesday, April 21

Connections of Earth and Sky with Augmented Reality (CEASAR)

Nathan Kimball, Robb Lindgren, Jina Kang, Emma M. Mercier, Brian Guerrero, James P. Planey, Christine Hart, Matt Lewandowski

8:15 – 9:45 AM, Moscone Center, West Building, Level Three, Room 3001

The Connections of Earth and Sky with Augmented Reality (CEASAR) project is using AR to immerse groups of learners in the 3D complex system of the motions of the Earth, moon, sun, and stars. AR facilitates face-to-face interaction so students and teachers with multiple devices can interact freely to contribute observations and measurements from unique vantages around holographic models.

The CEASAR AR platform consists of both Earth- and space-based models. Our environment is designed such that these models can be viewed, configured, related, captured, and discussed in a networked environment. Each user has a device on which they can share the same model from a different perspective or configure a view in a related model in pursuit of solving a problem. We have selected the Microsoft Hololens V2 as the optimal device for testing the CEASAR design given that it is head-mounted, hands-free, and able to detect gestures. Our environment is also accessible and connected to common devices such as tablets, phones, and laptops, all enabled with the same models created using the Unity development platform. Tools for collaboration include sharing gestures and attention across models, and sharing snapshots of individual perspectives.

We are conducting research in community college astronomy and physics classes and have involved the classroom instructors in the creation of open tasks that are appropriate to their curriculum. Numerous streams of data are generated to assess learning and collaboration. User events are logged, external video captures student action and speech, and video internal to the AR devices is recorded and analyzed.

Student Performance Prediction in Engineering Design

Wanli Xing, Bo Pei, Shan Li, Charles Xie

10:35 AM – 12:05 PM, Moscone Center, South Building, Exhibition Level, Moscone Hall A

Providing timely support to students’ learning of engineering design has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to identify struggling students. Specifically, to address the data sparsity and high dimensionality problems, a prediction workflow including a two-stage feature selection combined with an advanced supervised machine learning algorithm was designed and tested in a real engineering design class. Further, given that previous prediction research is usually implemented at arbitrary time points without consideration for the fluctuation of prediction model, this work proposed a brute force way to identify the best time point to run the prediction so that the performance is optimized and early enough.

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