Intuitive interaction with complex artefacts

This thesis examines the role of intuition in the way that people operate unfamiliar devices, and the importance of this for designers. Intuition is a type of cognitive processing that is often　non-conscious and utilises stored experiential knowledge. Intuitive interaction involves the use of knowledge gained from other products and/or experiences. Therefore, products that people use intuitively are those with features they have encountered before. This position has been supported by two initial experimental studies, which revealed that prior exposure to products employing similar features helped participants to complete set tasks more quickly and intuitively, and that familiar　features were intuitively used more often than unfamiliar ones. Participants who had a higher level of familiarity with similar technologies were able to use significantly more of the　features intuitively the first time they　encountered them, and were significantly quicker at doing the tasks. Those who were less familiar with relevant technologies required more　assistance. A third experiment was designed to test four different interface designs on a remote control in order to establish which of two variables - a feature's appearance or its location - was more important in making a design intuitive to use. As with the previous experiments, the findings of Experiment 3 suggested that performance is　affected by a person's level of familiarity with similar technologies. Appearance (shape, size and labelling of buttons) seems to be the variable that most affects time spent on a task and intuitive uses. This suggests that the cues that people store in memory about a product's features depend on how the features look, rather than where on the product they are placed. Three principles of intuitive interaction have been developed. A conceptual tool has also been devised to guide designers in their planning for intuitive interaction. Designers can work with these in order to make interfaces intuitive to use, and thus help users to adapt more easily to new products and product types

Understanding Conceptual Transfer for Students Learning New Programming Languages

Prior research has shown that students face transition challenges between programming languages (PL) over the course of their education. We could not find research attempting to devise a model that describes the transition process and how students' learning of programming concepts is affected during the shift. In this paper, we propose a model to describe PL transfer for relative novices. In the model, during initial stages of learning a new language, students will engage in learning three categories of concepts, True Carryover Concepts, False Carryover Concepts, or Abstract True Carryover Concepts; during the transition, learners automatically effect a transfer of semantics between languages based on syntax matching. In order to find support for the model, we conducted two empirical studies. Study 1 investigated near-novice undergraduate students transitioning from procedural Python to object-oriented Java while Study 2 investigated near-novice postgraduate students doing a transfer from object-oriented Java to procedural Python. Results for both studies indicate that students had little or no difficulty with transitioning on TCC due to positive semantic transfer based on syntax similarities while they had the most difficulty transitioning on FCC due to negative semantic transfer. Students had little or no semantic transfer on ATCC due to differences in syntax between the languages. We suggest ways in which the model can inform pedagogy on how to ease the transition process