Premiere: Tuesday 15 June 2021 starting at 3pmBST/10amEDT
Curated by UCL ChangeMakers Student Partners, June Hong (Y1) and Xuyi Wang (Y3)
COVID-19 has presented an opportunity for educators to re-think about the way teaching is conducted. Beyond the tips and advice of how to successfully deliver online classes, today’s theme will show how technology has been employed to make classes more interactive. For example, through interactive graphs, simulations and projects that enhance students’ learning outcomes and their prospects in the labour market.
Emergence of Tech-Econ; Teaching Machine Learning and Big-Data Skills to Economics Students
Nazanin Khazra, University of Toronto
Premieres: 3pmBST/10amEDT
Abstract:
Academia and industry increasingly demand big-data skills such as working with satellite data, generating and scraping data, visualizing data, and applying machine learning (ML) methods to economic questions. This paper guides adjusting our curriculum and effectively teaching these skills to undergraduate and graduate students. One potential reason academia has not yet adjusted to this demand is that few models are on such courses in economics to draw upon. The main challenge in designing such courses is to focus on economic applications while teaching the required coding skills. I summarize the existing resources and their use in teaching tech-econ and discuss how we can overcome this challenge by properly integrating economic applications and coding skills and selecting the right material. I discuss methods to develop a team spirit, teach students with different backgrounds, and train teaching assistants. I have taught big-data and ML courses at the University of Toronto and the University of Illinois. Students worked with real-world datasets and produced research papers that showcased their economic intuition and coding skills in these courses. My experience requiring active work on a research paper motivates students to have an ownership approach to the assessments and is essential for their success in a tech-econ course. Students learn and benefit the most from such courses by working on their research projects. Student feedback I received has repeatedly included comments like “I got the job because of the course project” or “the course project was a hit on the market.” The intrinsic motivation created by the substantial demand for economists with data science skills and the potential for meaningful research output makes big data and ML courses exciting to teach.
Using Interactive Graphs to Teach Industrial Organisation
Flavio Toxvaerd, University of Cambridge
Premieres: 3:30pmBST/10:30amEDT
Abstract:
In this presentation, I will share my experience in designing and delivering two courses in Industrial Organisation (one graduate and another undergraduate) at the University of Cambridge. For these courses, I developed a suite of fully dynamic Interactive Graphs in the software package Mathematica. For each model covered during the lectures, I programmed an illustration of the main concepts and used them to supplement the mathematical analysis. Rather than relying on the usual static illustrations common in such courses and pointing at different points on the graphs, the developed graphs allowed me to change parameters and curves in real time to show the workings of the models in a clear and engaging manner.
The graphs allow me to study the relationship between multiple moving parts of a model through interlinked graphs which all change when the controls are manipulated. This is particularly useful for more complex models which are usually solved in stages. The materials have been used the past three years and have been extremely well received by the students. In addition to using the materials during lectures, I have made them available to students for the purpose of self-study, complete with instructions for “guided experimentation” designed to reinforce the learning during the lectures.
During the presentation, I will discuss the paedagocial thinking behind the construction of the Interactive Graphs and show how I integrate them into the teaching. I will show specific examples drawn from my lectures that highlight how these graphs improve on the standard approach based on either static graphs of whiteboard sketches.
Link to interactive graphs: https://sites.google.com/site/toxvaerd11/home/interactive-graphs-for-io
Teaching COVID-19 with simulations
Eileen Tipoe, University of Oxford/CORE Economics
Premieres: 4pmBST/11amEDT
Abstract:
I will discuss the pedagogical benefits of using interactive simulations to teach complex concepts, with specific examples of how I used this approach to design learning materials related to COVID-19.
The switch to online teaching due to the COVID-19 pandemic provided an opportunity to rethink the way that economic models are taught and applied to contemporary issues, and ways to make online learning more engaging. Incorporating interactivity via simulations and simulation-based learning activities gives students greater agency over their learning, which can make learning more effective and meaningful compared to passively receiving course material.
I designed an interactive simulation where students could visualise how a virus spreads across a population over time, and its associated effects on the economy and income inequality. Students can change factors that affect the spread of the virus, including the population size and probability of infection, and compare the outcomes under two different scenarios: no virus mitigation measures, and various degrees of social distancing.
To accompany this simulation, I created a short online module that explains the underlying model. This module was aimed at students with little or no prior background in economics or mathematics, including students with a general interest in understanding how policies affect COVID-19 transmission and the economy.
The module and simulation were published on two widely used learning platforms: LabXchange (Harvard University’s online community learning platform: https://tinyco.re/8796544) and CORE Economics (https://www.core-econ.org/covid-simulation/). My presentation will highlight the various ways that economics educators can incorporate these materials in their teaching, including self-learning and blended learning approaches.
Background information on the teaching resource: https://www.core-econ.org/project/explore-covid-19-transmission-using-networks/
Best Practice Tips for Online Teaching in Business and Economics in times of COVID-19
Tamara Pianos, ZBW – Leibniz Information Centre for Economics
Doreen Siegfried, ZBW – Leibniz Information Centre for Economics
Premieres: 4:15pmBST/11:15amEDT
Abstract:
What can teachers do to keep in touch with their students and to make digital lectures and classes more enjoyable for themselves and students? We collected 10 recommendations from university teachers that German students said were doing great in a digital teaching environment.
Our motivation for asking students and profs
In our search portal EconBiz we provide material for students and early career researchers on research skills. During the first lockdown we were asked if we could provide material for online teaching as well, so we looked out for some best practice experiences.
Best practices in teaching
German student council representatives in Business and Economics gave us the names of lecturers who inspired their students with good digital teaching. We then asked these lecturers and professors from different institutions for their experiences and recommendations. We did two online panels on digital teaching (videos in German Panel I, Panel II). In addition, we conducted a number of telephone interviews.
Here are the collected tips and tricks on the topic of good digital teaching. The tips encompass:
- Tip 1 – The teaching concept is more important than the tools
- Tip 2 – Plan the first event with precision and foresight
- Tip 3 – If you digitize your learning content do it in modules rather than one piece
- Tip 4 – Activate students
- Tip 5 – Work with motivations
- Tip 6 – Nudging
- Tip 7 – Clarity and reliability
- Tip 8 – Separation into synchronous and asynchronous
- Tip 9 – Reuse ready-made teaching materials
- Tip 10 – Individualize learning
A description of our findings plus the results of a survey on tools used in teaching can be found in a (German) blogpost.
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