Shaping the embedding of reflection in engineering education

50th Annual Conference in September 2022 2293 Volker KraftTeaching analytics skills in Engineering: A hands-on introduction using JMP - 10.5821/conference-9788412322262.391 doi:10.5821/conference-9788412322262.391 TEACHING ANALYTICS SKILLS IN ENGINEERING: A HANDS-ON INTRODUCTION USING JMP V. Kraft 1 JMP Statistical Discovery Heidelberg, Germany Conference Key Areas: engineering skills; student engagement Keywords: problem solving, data skills, employability ABSTRACT Engineering curricula often require students to learn a range of analytics skills, which are critical for all practitioners who want to learn from data. With the right software, learning these skills can be hands-on and engaging, allowing students to explore and analyze realistic data without struggling with a clunky or tedious statistics tool. JMP is interactive and powerful point-and-click software for solving real-world engineering problems. It is ideal for engaging, hands-on teaching of relevant data skills in engineering and is also used by scientists and engineers at leading companies across the globe. While the fundamental skills addressed in this session include understanding variation and uncertainty, we will also look at applications like data modeling, designing experiments and quality management – all from a student’s perspective. This interactive session will demonstrate how JMP can help to engage students’ curiosity and teach engineering data skills which are most in-demand in industry today. We’ll guide you through a series of brief demonstrations, so that you can directly experience the difference JMP can make for your course. Participants will receive a free trial license before the workshop, and the presenters will provide sample data and lead you through several hands-on examples in JMP. We will also discuss best practices and share resources to support integration into engineering courses. 1 Corresponding Author V. Kraft [email protected] 50th Annual Conference in September 2022 2294 1 TEACHING WITH REAL CASES This short paper summarizes the key ideas from the practical workshop by JMP and provides the links to all free resources shared with the audience. 1.1 Why teaching with real cases? Why should you consider using real problems – typically shared as case studies – in teaching data analytics? There are many good arguments, which include 1. Real cases help to teach the analytical skills which will be relevant for students in a future workforce (refer to customer stories [1] for examples who shared or inspired the cases discussed here) 2. Storytelling by real cases can boost the engagement of students (see [2] for a demo of visual storytelling) 3. Instead of applying a single method, students take a journey of statistical discoveries (the JMP analytical workflow [3] shows a “landscape to travel from data to insights”). We will also discuss that real cases perfectly support the GAISE standard in statistics education [4], especially the first recommendation on teaching statistical thinking. Each case is an example for a "problem-solving and decision-making process" and requires multivariable thinking: Fig. 1. GAISE I recommendation about teaching statistical thinking [4] In addition to this guideline, other revised recommendations like “3. Integrate real data with a context and purpose” and “4. Foster active learning” support the key message of this workshop. 1.2 Access to real cases The workshop will suggest the JMP Case Study Library [5] as an open source providing free access to more than 50 case studies. Each case comes with a background story, a problemand task description and one or more datasets, a stepby-step solution with illustrations using JMP tools and summaries about lessons learned from a business, statistician’s and JMP user’s perspective. Solutions to optional exercises are shared with authorized instructors. 50th Annual Conference in September 2022 2295 Fig. 2. Examples from the JMP Case Study Library published at jmp.com/cases Most cases have been developed by practitioners in industry. They can be chosen from a list based on field and subject, covered key concepts and level of complexity. 2 LIVE DEMO A live demo will present the solution of case #52 on process optimization in biotech from LONZA [5] (Fig. 3), showing sample content and style of a JMP case study. The demo will also provide a practical example about applying statistical thinking, combining JMP capabilities for statistical discoveries to solve a real-world problem. Alternatively, readers of this paper can watch a recorded demo [6] presented by Andreas Trautmann from LONZA, who is also a co-author of the case study. 3 CASES IN THE CLASSROOM There are several options to use our cases studies in teaching: While educators can use case studies for in-class demonstrations, the most effective use are assignments to students as homework or group projects. The cases can stimulate discussions about which steps to take or comparisons of alternative solutions. Students can also be asked to present the learning outcome to “other decision makers”, or to explain why and how certain methods have been applied. 4 MORE TEACHING RESOURCES AND GETTING STARTED Case studies are just one kind of teaching resources provided by JMP’s Global Academic Program. During the workshop, additional resources will be shared and 50th Annual Conference in September 2022 2296 Fig. 3. Extract from case study #52 shared by LONZA discussed, including the JMP Leaning Library and the Statistical Thinking online course (see [7] for “Introductory Engineering Statistics” course material). Workshop participants and readers are welcome to contact the author for a personal discussion of JMP academic resources and capabilities. REFERENCES [1] JMP Customer Stories (2022), www.JMP.com/success. [2] King, C. (2021). A Dark Tale: Visual Storytelling With JMP. Discovery Summit Europe 2021. https://community.jmp.com/t5/Discovery-Summit-Europe2021/A-Dark-Tale-Visual-Storytelling-With-JMP-2021-EU-30MP-792/tap/349276 [3] [4 ] JMP Analytical Workflow (2022), http://www.jmp.com/workflow. American Statistical Association (2016). Guidelines for Assessment and Instruction in Statistics Education (GAISE) in Statistics Education (GAISE) College Report College Report2016. https://www.amstat.org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf. [5] JMP Case Study Library (2022), www.jmp.com/cases. [6] Trautmann, A. (2020). Industrial Biotech Case Study: Customized DOE for Process Optimization in Small-Scale Bioreactors. Discovery Summit Europe 2020. https://community.jmp.com/t5/Discovery-Summit-Munich2020/Industrial-Biotech-Case-Study-Customized-DOE-for-Process/tap/243896 [7] JMP Course Material (2022), https://www.jmp.com/en_gb/academic/coursematerials/engineering-stats.html.