BlueFix : using crowd-sourced feedback to support programming students in error diagnosis and repair.

Feedback is regarded as one of the most important influences on student learning and motivation. But standard compiler feedback is designed for experts - not novice programming students, who can find it difficult to interpret and understand. In this paper we present BlueFix, an online tool currently integrated into the BlueJ IDE which is designed to assist programming students with error diagnosis and repair. Unlike existing approaches, BlueFix proposes a feedback algorithm based upon frameworks combined from the HCI and Pedagogical domains, which can provide different students with dynamic levels of support based upon their compilation behaviour. An evaluation revealed that students' viewed our tool positively and that our methodology could identify appropriate fixes for uncompilable source code with a significantly higher rate of speed and precision over related techniques in the literature

Exploring Eye Tracking to Increase Bandwidth in User Modeling

Abstract. The accuracy of a user model usually depends on the amount and quality of information available on the user’s states of interest. An eye-tracker provides data detailing where a user is looking during interaction with the system. In this paper we present a study to explore how this information can improve the performance of a model designed to assess the user’s tendency to engage in a meta-cognitive behavior known as self-explanation.

Scaffolding self-explanation to improve learning in exploratory learning environments

Abstract. Successful learning though exploration in open learning environments has been shown to depend on whether students possess the necessary meta-cognitive skills, including systematic exploration, hypothesis generation and hypothesis testing. We argue that an additional meta-cognitive skill crucial for effective learning through exploration is self-explanation: spontaneously explaining to oneself available instructional material in terms of the underlying domain knowledge. In this paper, we describe how we have expanded the student model of ACE, an open learning environment for mathematical functions, to track a learner’s self-explanation behaviour and how we use this model to improve the effectiveness of a student’s exploration.

Towards providing notifications to enhance teacher's awareness in the classroom

Students often need prompt feedback to make the best from the learning activities. Within classrooms, being aware of students' achievements and weaknesses can help teachers decide how to time feedback. However, they usually cannot easily assess student's progress. We present an approach to generate automated notifications that can enhance teacher's awareness in runtime. This paper formulates the theoretical framing and describes the technological infrastructure of a system that can help teachers orchestrate learning activities and monitor small groups in a multi-tabletop classroom. We define the design guidelines underpinning our system, which include: i) generating notifications from teacher-designed or AI-based sources; ii) enhancing teacher's awareness in the orchestration loop; iii) presenting both positive and negative notifications; iv) allowing teachers to tune the system; and v) providing a private teacher's user interface. Our approach aims to guide research on ways to generate notifications that can help teachers drive their attention and provide relevant feedback for small group learning activities in the classroom. © 2014 Springer International Publishing Switzerland