Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Marjan Eggermont, University of Calgary

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #42654Let’s Get Physical: From Data Visualization to Data PhysicalizationDr. Marjan Eggermont, University of Calgary Marjan Eggermont is a Professor (Teaching), Associate Dean (Sustainability) and faculty member at the University of Calgary in the Mechanical and Manufacturing department of the Schulich School of Engineering. She co-founded and designs Zygote Quarterly, an online bio-inspired design journal (zqjournal.org). ©American Society for Engineering Education, 2024 Work in progress Let’s get physical: from data visualization to

Conference Session

DSA Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Aidan Kenny, Northeastern University; Andrew L Gillen, Northeastern University

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Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #41625Innovating Engineering Education Analysis through Creative Data VisualizationAidan Kenny, Northeastern UniversityDr. Andrew L Gillen, Northeastern University Andrew L. Gillen is an Assistant Teaching Professor at Northeastern University in the First Year Engineering Program and an affiliate faculty member to Civil and Environmental Engineering. He earned his Ph.D. in Engineering Education from Virginia Tech and B.S. in Civil Engineering from Northeastern University. ©American Society for Engineering Education, 2024 Innovating Engineering Education Analysis through

Conference Session

DSA Technical Session 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ben D Radhakrishnan, National University; James Jay Jaurez, National University; Nelson Altamirano, National University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #42783Application of Data Analysis and Visualization Tools for U.S. Renewable SolarEnergy Generation, Its Sustainability Benefits, and Teaching In EngineeringCurriculumMr. Ben D Radhakrishnan, National University Ben D Radhakrishnan is a Professor of Practice, currently a full time Faculty in the Department of Engineering, School of Technology and Engineering, National University, San Diego, California, USA. He is the Academic Program Director for MS Engineering Management program. He develops and teaches Engineering courses in different programs including engineering and business management schools. His research

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Amirreza Mehrabi, Purdue Engineering Education; Jason Morphew, Purdue University, West Lafayette

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Data Science & Analytics Constituent Committee (DSA)

(with machine learning and cognitive research). My background is in Industrial Engineering (B.Sc. at the Sharif University of Technology and ”Gold medal” of Industrial Engineering Olympiad (Iran-2021- the highest-level prize in Iran)). Now I am working as a researcher in the Erasmus project, which is funded by European Unions (1M $ European Union & 7 Iranian Universities) which focus on TEL and students as well as professors’ adoption of technology(modern Education technology). Moreover, I cooperated with Dr. Taheri to write the ”R application in Engineering statistics” (an attachment of his new book ”Engineering probability and statistics.”)Dr. Jason Morphew, Purdue University, West Lafayette Jason W. Morphew is

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Paula Francisca Larrondo, Queen's University; Brian M Frank P.Eng., Queen's University; Julian Ortiz, Queen's University

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Data Science & Analytics Constituent Committee (DSA)

an M.Sc. in Mining Engineering (Geostatistics) from the University of Alberta (Canada).Prof. Brian M Frank P.Eng., Queen’s University Brian Frank is the DuPont Canada Chair in Engineering Education Research and Development, and the Director of Program Development in the Faculty of Engineering and Applied Science at Queen’s University where he works on engineering curriculum development,Julian Ortiz, Queen’s University Dr. Ortiz is a Mining Engineer from Universidad de Chile and Ph.D. from University of Alberta. Currently, he is Professor and Mark Cutifani / Anglo American Chair in Mining Innovation at University of Exeter - Camborne School of Mines, in the United Kingdom, where he conducts research related to

Conference Session

DSA Technical Session 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Xiang Zhao, Alabama A&M University; Mebougna L. Drabo, Alabama A&M University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #41074Integrating Data Science into the Pipeline Building Toward a Diversified Workforcein Nuclear Energy and SecurityDr. Xiang Zhao, Alabama A&M University Dr. Xiang (Susie) Zhao, Professor in the Department of Electrical Engineering and Computer Science at the Alabama A&M University, has over 20 years of teaching experience in traditional on-campus settings or online format at several universities in US and aboard. Her teaching and research interests include programming languages, high performance algorithm design, data science, and evidence-based STEM teaching pedagogies. Her recent research work has been

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Duncan Davis, Northeastern University; Nicole Alexandra Batrouny, Northeastern Univeristy; Adetoun Yeaman, Northeastern University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #41739Unfettered ChatGPT Access in First-Year Engineering: Student Usage &PerceptionsDr. Duncan Davis, Northeastern University Duncan Davis is an Associate Teaching Professor in First Year Engineering. His research focuses on using gamification to convey course content in first year classes. He is particularly interested in using the construction of Escape Rooms to teach Engineering Principles.Dr. Nicole Alexandra Batrouny, Northeastern Univeristy Nicole Batrouny is an Assistant Teaching Professor in First Year Engineering at Northeastern University. Her engineering education research interests include the

Conference Session

DSA Technical Session 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ahmad Slim, The University of Arizona; Gregory L. Heileman, The University of Arizona; Husain Al Yusuf, The University of Arizona; Yiming Zhang, The University of Arizona; Asma Wasfi; Mohammad Hayajneh; Bisni Fahad Mon, United Arab Emirates University; Ameer Slim, University of New Mexico

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Paper ID #42646Enhancing Academic Pathways: A Data-Driven Approach to Reducing CurriculumComplexity and Improving Graduation Rates in Higher EducationDr. Ahmad Slim, The University of Arizona Dr. Ahmad Slim is a PostDoc researcher at the University of Arizona, where he specializes in educational data mining and machine learning. With a Ph.D. in Computer Engineering from the University of New Mexico, he leads initiatives to develop analytics solutions that support strategic decision-making in academic and administrative domains. His work includes the creation of predictive models and data visualization tools that aim to

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Safia Malallah, Kansas State University; Ejiro U Osiobe, Baker University; Zahraa Marafie, Kuwait University; Patricia Henriquez-Coronel; Lior Shamir, Kansas State University; Ella Lucille Carlson, Kansas State University; Joshua Levi Weese, Kansas State University

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Data Science & Analytics Constituent Committee (DSA)

, Baker University .Zahraa Marafie, Kuwait UniversityPatricia Henriquez-CoronelLior Shamir, Kansas State University Associate professor of computer science at Kansas State University.Ella Lucille Carlson, Kansas State UniversityJoshua Levi Weese, Kansas State University Dr. Josh Weese is a Teaching Assistant Professor at Kansas State University in the department of Computer Science. Dr. Weese joined K-State as faculty in the Fall of 2017. He has expertise in data science, software engineering, web technologies, computer science education research, and primary and secondary outreach programs. Dr. Weese has been a highly active member in advocating for computer science education in Kansas including PK-12 model standards

Conference Session

DSA Technical Session 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Emma Fox, Franklin W. Olin College of Engineering; Zachary del Rosario, Olin College of Engineering

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Data Science & Analytics Constituent Committee (DSA)

://onlinestatbook.com/[11] R. R. Sokal and F. J. Rohlf, Biometry: the principles and practice of statistics in biological research, 3rd ed. New York: W.H. Freeman, 1995.[12] D. Thunnissen, “Uncertainty Classification for the Design and Development of Complex Systems,” 2003.[13] B. M. Ayyub, Ed., Uncertainty modeling and analysis in civil engineering. Boca Raton: CRC Press, 1998.[14] A. A. diSessa, “Toward an Epistemology of Physics,” Cogn. Instr., vol. 10, no. 2–3, pp. 105–225, 1993.[15] A. diSessa, “A History of Conceptual Change Research: Threads and Fault Lines,” in The Cambridge handbook of: The learning sciences, Cambridge University Press, 2006, pp. 265–281.[16] A. J. Magana, “The role of frameworks in engineering education

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Emily Nutwell, The Ohio State University; Thomas Bihari, The Ohio State University; Thomas Metzger, The Ohio State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

. She holds a BS in mechanical engineering, MA in educational studies, and a PhD in Engineering Education where her research focuses on digital learning environments for the STEM workforce.Thomas Bihari, The Ohio State UniversityThomas Metzger, The Ohio State University ©American Society for Engineering Education, 2024 An Online Interdisciplinary Professional Master’s Program in Translational Data AnalyticsAbstractThis paper describes an interdisciplinary data analytics professional master’s program whichincludes courses from the disciplines of computer science, statistics, and design. The onlinecurriculum structure specifically addresses the needs of working professionals

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Duo Li, Shenyang Institute of Technology; Elizabeth Milonas, New York City College of Technology; Qiping Zhang, Long Island University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

College of Technology - City University of New York (CUNY). She currently teaches relational and non-relational databases and data science courses to undergraduate students. She holds a BA in Computer Science and English Literature from Fordham University, an MS in Information Systems from New York University, and a Ph.D. from Long Island University. Her research interests focus on three key areas: data science curriculum and ethics, retention of minority students in STEM degree programs, and organization and classification of big data.Dr. Qiping Zhang, Long Island University Dr. Qiping Zhang is an Associate Professor in the Palmer School of Library and Information Science at the C.W. Post Campus of Long Island

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Isil Anakok, Virginia Polytechnic Institute and State University; Kai Jun Chew, Embry-Riddle Aeronautical University, Daytona Beach; Holly M Matusovich, Virginia Polytechnic Institute and State University; Andrew Katz, Virginia Polytechnic Institute and State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

passionate about teaching and research, and he strives to produce knowledge that informs better teaching. His research intersects assessment and evaluation, motivation, and equity. His research goal is to promote engineering as a way to advance social justice causes.Dr. Holly M Matusovich, Virginia Polytechnic Institute and State University Dr. Holly Matusovich is the Associate Dean for Graduate and Professional Studies in the College of Engineering at Virginia Tech and a Professor in the Department of Engineering Education where she has also served in key leadership positions. Dr. Matusovich is recognized for her research and leadership related to graduate student mentoring and faculty development. She won the Hokie

Conference Session

DSA Technical Session 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Betul Bilgin, The University of Illinois at Chicago; Naomi Groza, The University of Illinois at Chicago

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

concepts, their recognition of data science's relevance in chemical engineering, and theirreadiness to engage with data science tools and techniques. Students, both seniors, juniors andsophomores in CHE, participated in the interviews. The study's findings reveal that, despitelimited exposure within their coursework, most students acknowledge the growing significanceof data science in the chemical engineering field. The majority express a keen interest inexpanding their knowledge of data science and are receptive to its integration into their academicand future career paths.Moreover, this research identifies barriers to the incorporation of data science in the CHEcurriculum, such as the need for additional resources and training for both students

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Tony Maricic, New York University Tandon School of Engineering; Nisha Ramanna, New York University Tandon School of Engineering; Alison Reed, New York University Tandon School of Engineering; Rui Li, New York University; Jack Yang, New York University Tandon School of Engineering

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

aims for teaching in teams,one of which is to improve students' collaborative abilities. Engineering expertise, as well aspedagogical goals such as greater learning and motivation, are under consideration whenbuilding an effective team pedagogy. CATME, and other platforms have long been used tofacilitate the process of monitoring team performance. The comprehensive data that the platformprovided has enabled faculty members to analyze the problems in detail. Also, it is very helpfulwhen documenting the team performance from year to year. At New York University, 700students are taking a fundamental engineering course on an annual basis. The students are askedto form project teams after the first two weeks and work on a semester-long project on a

Conference Session

DSA Technical Session 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ahmad Slim, The University of Arizona; Gregory L. Heileman, The University of Arizona; Melika Akbarsharifi, The University of Arizona; Kristina A Manasil, The University of Arizona; Ameer Slim, University of New Mexico

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #44170Causal Inference Networks: Unraveling the Complex Relationships BetweenCurriculum Complexity, Student Characteristics, and Performance in HigherEducationDr. Ahmad Slim, The University of Arizona Dr. Ahmad Slim is a PostDoc researcher at the University of Arizona, where he specializes in educational data mining and machine learning. With a Ph.D. in Computer Engineering from the University of New Mexico, he leads initiatives to develop analytics solutions that support strategic decision-making in academic and administrative domains. His work includes the creation of predictive models and data visualization

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Harpreet Auby, Tufts University; Namrata Shivagunde, University of Massachusetts, Lowell; Anna Rumshisky, University of Massachusetts, Lowell; Milo Koretsky, Tufts University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Paper ID #43642Using Machine Learning to Analyze Short-Answer Responses to ConceptuallyChallenging Chemical Engineering Thermodynamics QuestionsHarpreet Auby, Tufts University Harpreet is a graduate student in Chemical Engineering and STEM Education. He works with Dr. Milo Koretsky and helps study the role of learning assistants in the classroom as well as machine learning applications within educational research and evaluation. He is also involved in projects studying the uptake of the Concept Warehouse. His research interests include chemical engineering education, learning sciences, and social justice.Namrata

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Nicolas Leger, Florida International University; Maimuna Begum Kali, Florida International University; Stephanie Jill Lunn, Florida International University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

teaching scenarios while learners struggle to master each of thecomponent fields [8]. Non-computing engineers can benefit from understanding how to applydata science methodologies in their work, as it can provide them with valuable insights, improvedecision-making, and drive innovation in their respective domains. As technology progresses,data science practices [9] and computer-based tools [10] continue to expand and advance, theyhave become increasingly integrated into the engineering world. According to a study conductedby the McKinsey Global Institute (MGI) in 2011, the analysis of massive data sets will becomecrucial to competitiveness, productivity development, and innovation. They note that “inmanufacturing, integrating data from Research

Conference Session

DSA Technical Session 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Gregory L. Heileman, The University of Arizona; Yiming Zhang, The University of Arizona

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Electromagnetics Elective Physics I Lab Microprocessors Numberical Methods Elective Chemistry I Lab Physics II Lab Elective Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8 Figure 1: A dag representation of an electrical engineering curriculumdesign techniques are considered that involve optimizing the arrangement of course-level learningoutcomes within engineering curricula in ways that lead to improved student success outcomes.2

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Neha Kardam, University of Washington; Denise Wilson, University of Washington; Sep Makhsous, University of Washington

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

student feedback.Specifically, it assesses student preferences for Teaching Assistant (TA) support in engineeringcourses at a large public research university. This work complements existing research with anin-depth comparative analysis of NLP approaches to examining qualitative data within the realmof engineering education, utilizing survey data (training set = 1359, test set = 341) collected from2017 to 2022. The challenges and intricacies of multiple types of classification errors emergingfrom five NLP methods are highlighted: Latent Dirichlet Allocation (LDA), Non-NegativeMatrix Factorization (NMF), BERTopic, Latent Semantic Analysis (LSA), and PrincipalComponent Analysis (PCA). These results are compared with results from traditional

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Kristina A Manasil, The University of Arizona; Gregory L. Heileman, The University of Arizona; Bhavya Sharma, The University of Arizona; Ahmad Slim, The University of Arizona; Aryan Ajay Pathare, The University of Arizona; Husain Al Yusuf, The University of Arizona; Roxana Sharifi, The University of Arizona; Rohit Hemaraja, The University of Arizona; Melika Akbarsharifi, The University of Arizona

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

proficiency spans multiple programming languages and cloud computing, furthering her research into innovative educational technologies ©American Society for Engineering Education, 2024 Leveraging Cohort-Based Analytics for In-Depth Exploration of Student Progress in Engineering Programs Kristi Manasil,? Gregory L. Heileman,† Rohit Hemaraja,? Bhavya Sharma,‡ Ahmad Slim,† Melika Sharifi,† Roxana Sharifi,† and Husain Al Yusuf† {kmanasil, heileman, rohitheramaja, bhavyasharma, ahslim, akbarsharifi, roxanaa, halyusuf}@arizona.edu ? The School of Information

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Fengbo Ma, Northeastern University; Xuemin Jin, Northeastern University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

. Xuemin Jin is a teaching professor at the Department of Mechanical and Industrial Engineering at Northeastern University. He teaches two core courses for the Data Analytics Engineering Graduate Program, Data Management for Analytics and Data Mining in Engineering. His current research interests include emotion detection, remote sensing and atmospheric compensation. Before joining Northeastern University, Dr. Jin was a data scientist at State Street Corporation, a principal scientist at Spectral Sciences, Inc., a software engineer at eXcelon Corp, and a scientist at SerOptics, Inc. Dr. Jin received his Ph.D. in physics from University of Maryland at College Park. He was a postdoctoral at MIT and at TRIUMF Canada

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mehmet Ergezer, Wentworth Institute of Technology; Mark Mixer, Wentworth Institute of Technology; Weijie Pang, Wentworth Institute of Technology

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

and inclusivity in teaching cannot be overstated [33, 34].Numerous educators actively structure their courses and labs to enhance diversity, equity, and inclusion(DEI) throughout their teaching practices[35, 36]. Within the realm of Data Science, effective teachingmethodologies serve to ensure a comprehensive understanding of diverse perspectives, foster innovation,and equip students with the skills necessary to adeptly navigate real-world challenges within the complex,interdisciplinary landscape of this field, spanning across diverse industries and contexts [37]. To establishan inclusive educational atmosphere in our Data Science program and prepare students for the diverseworking environments of the future, we have undertaken numerous

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Karl D. Schubert FIET, University of Arkansas; Shantel Romer, University of Arkansas; Stephen R. Addison, IEEE Educational Activities; Tina D Moore; Laura J Berry, North Arkansas College; Jennifer Marie Fowler, Arkansas State University; Lee Shoultz, University of Arkansas; Christine C Davis

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Sciences or Humanities for Engineers II or Humanities Elective Elective Data Management &DASC 2103 Data Structures & Algorithms DVSC 2203 Database DASC 2203 Data Management & Data Base CIS 2203 Data Structures & Algorithm Role of Data Science in Today's DASC 1222 World Total 62 credits Total 62

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Neha Kardam, University of Washington; Denise Wilson, University of Washington

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

process to enhance the value of NLP in educationalresearch?Methodology Research Question (RQ2):How does data analysis involving both unsupervised learning methods compared with analysisusing only unsupervised methods?ParticipantsThe study involved a total of 1,857 participants, consisting of sophomores and juniors from fourdifferent engineering majors enrolled as undergraduates. The participants were surveyed betweenthe winter of 2017 and the spring of 2022. The study population was divided into two settings:traditional (in-person) prior to the COVID-19 pandemic and emergency remote teaching (ERT),which was conducted remotely during the pandemic. The gender distribution showed that 74.1%of the participants were male, 24.4% were female, and a

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Abdulrahman Alsharif, Virginia Polytechnic Institute and State University; Andrew Katz, Virginia Polytechnic Institute and State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #41721From Manual Coding to Machine Understanding: Students’ Feedback AnalysisMr. Abdulrahman Alsharif, Virginia Polytechnic Institute and State University Abdulrahman M. Alsharif is a research assistant for the Engineering Education Department and a PhD candidate at Virginia Tech.Dr. Andrew Katz, Virginia Polytechnic Institute and State University Andrew Katz is an assistant professor in the Department of Engineering Education at Virginia Tech. He leads the Improving Decisions in Engineering Education Agents and Systems (IDEEAS) Lab. ©American Society for Engineering Education, 2024From Manual

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Sami Khorbotly, Valparaiso University; Daniel White, Valparaiso University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

) method while [5]solved the problem using a Tabu search. All of these efforts attempted to find an acceptablesolution while optimizing all the parameters of the problem. A different approach was taken in [6],where the focus is on maximizing the faculty preference when it comes to the courses to teach andthe assigned time-blocks. Similarly, [7] focused on the optimization of the faculty assignment.However, their approach was to use the Depth-First Search algorithm which assigns one course ata time before moving to the next one. Our approach, using Linear Optimization takes a wholisticapproach to optimize the assignment of all the courses.As a student-first, undergraduate program, the College of Engineering at Valparaiso Universityprioritizes the

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Smitesh Bakrania, Rowan University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

LA. The combinationof the insights gained by addressing the two questions above are captured in this work. Theoutcomes have the potential to inform future implementation of LA in regular courses andimprove teaching effectiveness.MethodologyTo address the two research questions, this work was split into two stages. The first stage,Course Learning Analytics, involved applying LA to two existing courses and recognizing thepotential insights. For the second stage, Instructor Perspective Survey, the LA results were thenused to gather faculty perceptions on the value of LA to their courses. It was important to useexisting courses at Rowan University’s Mechanical Engineering program for LA to demonstrateits utility in making educational decisions

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Galen I. Papkov, Florida Gulf Coast University; Jiehong Liao, Florida Gulf Coast University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Millennium Scholars. Before joining FGCU, she was a visiting Assistant Professor of Biotechnology in the Division of Science and Technology at the United International College (UIC) in Zhuhai China. She has trained with ASCE’s Excellence in Civil Engineering Education (ExCEEd) initiative, been exploring and applying evidence-based strategies for instruction, and is a proponent of Learning Assistants (LAs). Her scholarship of teaching and learning interests are in motivation and mindset, teamwork and collaboration, and learning through failure and reflection. Her bioengineering research interests and collaborations are in the areas of biomaterials, cellular microenvironments, and tissue engineering and regenerative