Using Image Analytics and Natural Language Processing to Support Teachers in Promoting Active Learning in Computer Programming

This research investigates the use of image analytics and natural language processing (NLP) to support teachers in teaching computer programming in small classes. The project involves students creating videos of their assignments, which are then evaluated using image analytics and NLP techniques. The objectives include developing image analytics programs to detect correct diagrams, creating NLP programs to convert speech to text and compare explanations with model answers, and investigating the acceptance and impact of using student-created videos on student performance.

The methodology involves training students to use flowcharting tools, developing AI software to compare student submissions with teachers’ model answers, and collecting feedback through surveys and interviews. The research aims to explore the feasibility of using image analytics and NLP on a small dataset that represents a typical computer programming class.

Results indicate that using pre-trained models can improve the accuracy of AI software in image analytics and NLP. Survey and interview results show that students generally accept creating videos as a means of assessment. However, they do not prefer this method due to the extra work involved. The authors suggest further research in this area using different computer programming assignments to enhance the understanding and application of these technologies in educational settings.