Data Analytics for Interactive Virtual Laboratories

Jessie Keeler; Thomas W Ekstedt; Ying Cao; Milo Koretsky

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Conference: 2016 ASEE Annual Conference & Exposition
Location: New Orleans, Louisiana
Publication Date: June 26, 2016
Start Date: June 26, 2016
End Date: June 29, 2016
ISBN: 978-0-692-68565-5
ISSN: 2153-5965
Conference Session: Virtual and Web Learning in Chemical Engineering
Tagged Division: Chemical Engineering
Page Count: 17
DOI: 10.18260/p.26638
Permanent URL: https://peer.asee.org/26638
Download Count: 665

Paper Authors

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Jessie Keeler Oregon State University

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Jessie Keeler is a graduate student in the School of Chemical, Biological, and Environmental Engineering
at Oregon State University. She received her B.E. from the Youngstown State University in chemical engineering
and is pursuing her M.S. also in chemical engineering with an emphasis on engineering education.

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Thomas W Ekstedt Oregon State University

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Thomas Ekstedt is a software developer in the School of Chemical, Biological and Environmental Engineering at Oregon State University. He is involved in the development of technology-based educational systems, particularly in the areas of concept-based instruction and interactive simulation of physical phenomena.

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Ying Cao Oregon State University

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Milo Koretsky Oregon State University

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Milo Koretsky is a Professor of Chemical Engineering at Oregon State University. He received his B.S. and M.S. degrees from UC San Diego and his Ph.D. from UC Berkeley, all in Chemical Engineering. He currently has research activity in areas related engineering education and is interested in integrating technology into effective educational practices and in promoting the use of higher-level cognitive skills in engineering problem solving. His research interests particularly focus on what prevents students from being able to integrate and extend the knowledge developed in specific courses in the core curriculum to the more complex, authentic problems and projects they face as professionals. Dr. Koretsky is one of the founding members of the Center for Lifelong STEM Education Research at OSU.

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Abstract

Data Analytics for Interactive Virtual Laboratories

Previously we have reported on the development of a set of Interactive Virtual Laboratories (IVLs) that help students understand key concepts in thermodynamics. These laboratories guide students through approximately twenty frames using an inquiry-based “predict, observe, explain” pedagogy. Use of IVLs in class provides a copious amount of data from every student participant. This paper reports initial processes and strategies to make student thinking and learning visible to instructors and researchers through the use of data analytics. One outcome from this work is an automatic grading system to provide instructors detailed assessment data from the IVLs.

We analyze data from the implementation of three IVLs in a junior level thermodynamics course at a public university. Two hundred and forty seven students majoring in chemical, biological, or environmental engineering participated in the study. Amongst the participants, there were 12,350 total answers collected. Data gathered are used to develop an algorithm to help assess student responses, and this capability will be integrated into a web-based tool and available to faculty. As part of the process, each question within the IIVLs is coded in relation to student engagement. Questions are coded as procedural, conceptual, procedural, and reflection. Different question types are weighed differently in the assessment algorithm. For example, a question coded as “procedural” might involve interpreting a numerical value from a graph; while a question coded as “prediction” requires students to recall previous problems and predict how their answers will change due to a change in the system. Thus, a question coded as “prediction” might be worth more than a question coded as “procedural.” Using cluster analysis, we explore relations between how sets of students answer different type of questions. This analysis can provide information on what type of thinking the student is struggling with, and is the first step towards automated adaptive instruction.

Finally, we have audio-recorded 17 students as they completed the IVLs. The audio data provide a connection between reasoning employed by students and their submitted answers. These data will be used to confirm the coding scheme and verify the analytics’ prediction of where the students struggle.

Citation
Format

Keeler, J., & Ekstedt, T. W., & Cao, Y., & Koretsky, M. (2016, June), Data Analytics for Interactive Virtual Laboratories Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26638

TY - CPAPER
AB - Data Analytics for Interactive Virtual Laboratories

Previously we have reported on the development of a set of Interactive Virtual Laboratories (IVLs) that help students understand key concepts in thermodynamics. These laboratories guide students through approximately twenty frames using an inquiry-based “predict, observe, explain” pedagogy. Use of IVLs in class provides a copious amount of data from every student participant. This paper reports initial processes and strategies to make student thinking and learning visible to instructors and researchers through the use of data analytics. One outcome from this work is an automatic grading system to provide instructors detailed assessment data from the IVLs.

We analyze data from the implementation of three IVLs in a junior level thermodynamics course at a public university. Two hundred and forty seven students majoring in chemical, biological, or environmental engineering participated in the study. Amongst the participants, there were 12,350 total answers collected. Data gathered are used to develop an algorithm to help assess student responses, and this capability will be integrated into a web-based tool and available to faculty. As part of the process, each question within the IIVLs is coded in relation to student engagement. Questions are coded as procedural, conceptual, procedural, and reflection. Different question types are weighed differently in the assessment algorithm. For example, a question coded as “procedural” might involve interpreting a numerical value from a graph; while a question coded as “prediction” requires students to recall previous problems and predict how their answers will change due to a change in the system. Thus, a question coded as “prediction” might be worth more than a question coded as “procedural.” Using cluster analysis, we explore relations between how sets of students answer different type of questions. This analysis can provide information on what type of thinking the student is struggling with, and is the first step towards automated adaptive instruction.

Finally, we have audio-recorded 17 students as they completed the IVLs. The audio data provide a connection between reasoning employed by students and their submitted answers. These data will be used to confirm the coding scheme and verify the analytics’ prediction of where the students struggle.

AU - Jessie Keeler
AU - Thomas W Ekstedt
AU - Ying Cao
AU - Milo Koretsky
CY - New Orleans, Louisiana
DA - 2016/06/26
PB - ASEE Conferences
TI - Data Analytics for Interactive Virtual Laboratories
UR - https://peer.asee.org/26638
DO - 10.18260/p.26638
ER -