Introduction

This project aims at delivering a complete framework of research-informed strategies for improved teaching practices and enhanced student learning to streamline assessment in a meaningful and seamless way, and make its benefits accessible to all who wish to have their instruction informed by rigor, research, data and equity.

We use association analysis and advanced data mining techniques to identify┬ámisconceptions, instructional issues and hidden relations between students’ mistakes. This gives us a scientific pointer for areas of intervention. For example, informing instructors of hidden misconceptions or creating targeted intervention can lead to more informed course adjustments and eventually to better student understanding and experience.

Our framework is a cycle of assessment, reflection and improvement that encompasses the outcomes of instructor teaching and student learning. This framework, called Reflection and Support is based on three main pillars:

      1. A new approach to understating student work: using conceptual questions, data analysis with methods from machine learning, new discovery techniques and precise interpretation of the  data.
      2. A new approach to delivering assessment and intervention: using the OER online platform WeBWorK to deliver the intervention and assessment and for easy data collection and minimal instructor responsibility. This approach for data collection involves minimal instructor role, suggesting a solution to this common problem in assessment efforts. In addition, due to the nature of our proposed data collection system, analysis and mining; reassessment is facilitated, allowing for a continuous assessment cycle.
      3. A new approach to designing intervention/improvement plans: using systematic redesign of curriculum and strategic mediation based on scientific evidence provided by the data, and more importantly, based on strategies that increase participation and support the success of undergraduate students in STEM learning.

Assessment of student work is at the heart of teaching, and assessment of courses and programs is central to the wellbeing of a college. We are approaching this central issue with the rigor of data mining.