We are mindful of the fact that it can be a challenging task to create a high quality assessment while providing rich and detailed personalised feedback. Grading is relatively simple when the student answer is 100% correct or 100% incorrect. What is not simple is to implement a fair and consistent grading scheme with meaningful feedback when the student answer is somewhere in between. This becomes an even more pressing matter for large cohorts.
We believe that assessment grading does not need to be a struggle - regardless of a few dozen students or a few thousands. With advanced automation and AI technology, we created SM Grader to free up your capacity as an educator without compromising the quality of grading so that you can focus on what matters most: creating a better learning experience for your students.
Our story starts with David, an accomplished mathematician and statistician, with many years of international educational experience and research behind him.
On joining a prestigious university in Melbourne, he faced challenges with his Course Experience Surveys. These surveys, conducted post-course, assess a teacher's effectiveness in explanation, motivation, and engaging students with course material, with a strong emphasis on providing constructive feedback. The feedback score notably impacts the overall assessment.
The crux of this narrative is David's initial foray into teaching Machine Learning in 2017, where he encountered a setback, receiving a Good Teaching Scale (GTS) score below 60%.
The Good Teaching Scale (GTS) not only influences opportunities and promotions but, if low enough, can trigger performance reviews. Despite challenges, David improved his GTS by 11.5% over three years, nearing 70%.
The COVID-19 pandemic then led to Melbourne facing one of the world's strictest lockdowns under Premier Dan Andrews, with 263 days of severe restrictions.
Driven by a desire to excel, not very happy with his GTS scores, and the pressures of lockdown, David leveraged his passion and deep technical expertise in software engineering, machine learning, and AI to address the issue most affecting his professional life: the assessment grading and feedback process.
This unique blend of skills, coupled with ample time and a home environment bustling with family activities, led to the creation of SM Grader. David's application of his invention to his Machine Learning course yielded significant improvements in grading speed, accuracy, and the provision of valuable feedback, enhancing students' learning experiences.
David's students embraced SM Grader, propelling his GTS to over 92%. This leap from 69.5% in 2020 to 92.6% in 2022 was attributed solely to the initial version of SM Grader, earning David recognition and awards within his faculty.
However, the journey didn't stop there. Enter Simon, who met David at a Data Science bootcamp. Simon played a pivotal role in helping David build SM Grader from a desktop solution to a comprehensive online application with a user-friendly interface.
Support from sponsors at David's university was crucial. During a 6+ month proof of concept phase, they tested SM Grader on numerous courses, some with over 600 students, providing feedback that helped refine the solution and gather testimonials and data to bolster confidence in its benefits.
The next step in the journey was with David inviting Geri to the SM Grader team. Their friendship began when David they worked in the Chief Data Office of Australia's national broadband network. Their collaboration on various projects laid the groundwork for their future partnership in SM Grader.
Maintaining their friendship over the years, when David approached Geri with an opportunity, she was ready for a new venture and eagerly took on the role as SM Grader's spokesperson.
Coming from a family of educators spanning K-12 to higher education, Geri quickly grasped SM Grader's potential impact. Her enthusiasm for technology that enables educators to prioritise teaching over tedious tasks aligns with her philosophy of working smarter, not harder, using tech as a catalyst.
David, Simon, Geri, and many supporters have fueled SM Grader's journey with their belief, dedication, and personal investments. This self-funded project is a testament to our commitment to making a meaningful difference in education.
Sharing our story, particularly David's candid performance insights, is not done lightly. It's shared with conviction in SM Grader's mission, recognising that many educators face similar challenges and that our solution significantly eases their burden.
SM Grader stands out by automating repetitive tasks, enabling educators to focus on teaching rather than grading hundreds of times over. Its AI learns individual grading styles to mimic educators' work, ensuring privacy and security as a standalone system without external data sharing.
The apprehension surrounding AI is understandable, yet SM Grader exemplifies AI's role not as a threat but as a tool to eliminate monotony from educational roles, reinforcing the promise of technology in enhancing teaching and learning experiences.
Geri Overberg, a professional with over three decades of expertise across technology in large corporates, holds a degree in Forest Science and Land Management from Melbourne University.
Beginning in New Zealand, Geri specialised in data and analytics before transitioning to a global role with a boutique software firm. Her journey continued across Health Insurance and Telecommunications sectors, where she honed her skills in Knowledge Management, Capability, and Change for over two decades. Geri's career trajectory then shifted to Agile Product and Portfolio Management, where she spearheaded initiatives in customer service, automation, data and analytics.
With a passion for leveraging technology to solve intricate problems and a track record of transforming vision into reality, Geri excels in delivering impactful solutions for organisations and end-users.
Dr. David Akman is an AI Expert and a former Senior Lecturer with an extensive track record of success and value. He is a doctoral degree holder in Applied Statistics from Johns Hopkins University (USA).
Dr. David has leveraged advanced analytics tools and techniques for addressing numerous complex real-world problems across a wide variety of business and industrial domains for more than two decades. His deep expertise in AI, machine learning and data science combined with his proficiency in programming and Cloud technologies empower him to design, develop, and lead impactful and value-driven data products and solutions.
Dr. David taught postgraduate machine learning & data analytics courses at Johns Hopkins University in the USA as well as at Royal Melbourne Institute of Technology (RMIT) University in Australia. He taught large cohorts (300/400+ students) from diverse backgrounds on a regular basis.
Simon is a well-established technology educator with a successful track record of innovative teaching throughout his entire career.
Simon worked at a wide range of schools, from grammar schools in UK to independent schools in Melbourne, Victoria.
After completing his Master of Education at Westminster University and a series of courses in data science at Clarusway and IBM, he created a brand-new approach in secondary school IT education in Australia and designed a series of courses for an innovative IT curriculum which helps students learn enough to be regarded as “Junior Data Scientists” upon completion of high school.
His passion, vision, and pioneering ideas in technology and data science education in general crossed his path with Dr. David, who advised him throughout his amazing journey for a new cloud-based auto-marking platform called SM Grader.