An affective and Web 3.0-based learning environment for a programming language

We present a Web-based environment for learning Java programming that aims to provide adapted and individualized programming instruction to students by using modern learning technologies as a recommender and mining system, an affect recognizer, a sentiment analyzer, and an authoring tool. All these components interact in real time to provide an educational setting where the student learn to develop Java programs. The recommender system is an E-Learning 3.0 software component that recommends new exercises to a student based on the actions (ratings) of previous learners. The affect recognizer analyze pictures of the student to recognize learning-centered emotions (frustration, boredom, engagement, and excitement) that are used to provide personalized instruction. Sentiment text analysis determines the quality of the programming exercises based on the opinions of the students. The authoring tool is used to create new exercises with no programming work. We conducted two evaluations: one evaluation used the Technology Acceptance Model to assess the impact of our software tool on student behavior. The second evaluation calculated the student's t-test to assess the learning gain after a student used the tool. The results of the evaluations show the students perceived enjoyment and are willing to use the tool. The study also show that students using the tool have a greater learning gain than those who learn using a traditional method.