Investigating the utility of personalized learning experiences for chemistry students through technological classroom integrations

Abstract

This dissertation comprises work developing and leveraging technology as a means to aid student learning as well as to provide access to course-specific information. As technology continues to advance, tools are being developed and adopted by instructors to help fill gaps in previous systems and further extend their usefulness in the classroom. Two technologies discussed here are intelligent tutoring systems and chatbots. Intelligent tutoring systems have been deployed in a variety of fields and follow along with student work to provide hints and feedback. Chatbots, over the last several years, have grown in popularity and can be seen in a variety of fields and applications. The usefulness of curated chatbots was specifically investigated here. The first chapter provides an overview of these technologies. The next several chapters will detail work involving the Open-Response Chemistry Cognitive Assistance (ORCCA) tutor. This dynamically scaffolded intelligent tutoring system is designed to aid students with quantitative exercises in the general chemistry classroom. An overview of the system and its use in the classroom are examined, along with a more detailed look into the extension of model tracing to allow for the solving of quantitative problems in a free-form fashion. In addition to the development of the system, a look into students’ use of the system and their thoughts when comparing it to other homework systems will also be discussed. Lastly, work describing the development and incorporation of a curated syllabus chatbot will be reported on, along with a conclusion chapter that discusses the overall conclusions from the dissertation as well as an outlook on future research relating to the work presented.