According to the Federal Government, workers with an education in science, technology, engineering and mathematics (STEM) fields are deemed to be “critically important for our current and future productivity”. It has already allocated over $64m in funding for early learning and school STEM initiatives, a move designed to encourage further study in these fields. By Stephane Marouani.

In the coming decade, we will see a change in STEM careers and, consequently, the ways in which graduates will be prepared for them will need an overhaul. Based on an NAE survey of global technology leaders and scholars, STEM careers of the future will be faced with solving four “grand challenges” comprising major technology issues in the areas of sustainability, health, security, and the joy of living.

What are some of the areas that teachers, students and parents should track to help create a workforce that is ready to take on the challenges of the coming decade? Below are five of the major trends that we will see affect how university students and independent learners are taught to meet the challenges of the decade ahead.

  1. Authentic learning is bolstered by authentic assessment: We are seeing the rise of authentic assessment, with teachers moving their attention to assessing and supporting students individually as students engage in authentic learning practices. The ability to accurately track students’ progress is helped by interactive tools and technology that increase student engagement to improve learning outcomes. Professors are starting to explore and integrate tools into their curriculum that allow them to provide students with immediate feedback and automatically grade student work to help assess individual student performance.
  2. Shift from “Learning to Code” to “Coding to Learn”: Computational thinking enables solving problems, designing systems, and understanding human behaviour analytically. Bringing computational thinking to the classroom will help create professionals who can break down large and complex problems into a series of small, manageable problems that can often be solved algorithmically. This approach enables scientists and engineers to solve extremely complex problems with large data sets effectively and efficiently.
  3. Tools enabling global education collaboration: Over the last decade, we have seen more students learning and collaborating online using workflows and technologies that are used in industry. Looking ahead, we will continue to see a growing number of countries moving to technology and internet supported learning, as they shift from paper and blackboard-based teaching and learning. Students from different geographies who are learning on online platforms, are also enabled to address real-world local and global problems through collaboration with peers from all over the world. Teachers can engage with global communities, and have access to common, cloud-based storage locations like GitHub. Teachers from around the globe are now accessing, sharing and versioning code gathered from the global community to create real and effective teaching strategies and course work.
  4. Growing demand for “Bi-Lingual” engineers = computer science + X expertise: Developments in techniques for data-informed inference and decision-making have blended artificial intelligence (AI) and data science from the fields of statistics and computer science with other domain expertise. Academia is responding to such needs for individuals with both domain and AI expertise by creating programs that educate “bi-lingual” engineers and scientists. For example, individuals in fields of chemistry and signal processing who also possess modern computing skills like AI. Since these areas are also seeing rapid growth in industry, universities and industries are partnering to provide such “bi-lingual” educational opportunities. Students who are equipped with both domain and computing knowledge, will have the advantage of knowing how to use such tools and techniques and, more importantly, when to and when not to apply them.
  5. Growth of self-paced and personalised learning: For motivated independent learners, there are multiple ways to build and brush up on skills using self-paced online courses and certifications available from MOOC providers like Coursera and EdX. In addition to building knowledge, these courses demystify AI and allow engineers to see AI as an extension of their toolkit to solve problems and be innovative. As students, it is easy to become overwhelmed by buzzwords like AI. However, such courses give students opportunities to focus on how they can gain competencies in actual techniques that give them a professional edge such as data analytics and reinforcement learning.

Today, there is growing acknowledgement that we need to change the way we teach and learn if we want to improve the quality of life for the next generation. To do this effectively, we need STEM students to be comfortable and skilful in collaborating and working within multi-disciplinary environments. It has become even more imperative today to engage in lifelong learning and stay current with new concepts, systems, and approaches while in school and beyond. To help facilitate this, we are seeing a seismic shift from traditional approaches of teaching and evaluating students’ grasp of curriculum, to understanding each individual student’s readiness to use and apply tools and technology, to solve real-world challenges.

In 2020 and beyond, we will see industry and academia develop deeper relationships, where industry will support instructors as they teach and mentor the next generation of STEM students prepared to tackle the grand challenges that await them.

Stephane Marouani is the ANZ Country Manager at MathWorks